--- _id: '10045' abstract: - lang: eng text: "Given a fixed finite metric space (V,μ), the {\\em minimum 0-extension problem}, denoted as 0-Ext[μ], is equivalent to the following optimization problem: minimize function of the form minx∈Vn∑ifi(xi)+∑ijcijμ(xi,xj) where cij,cvi are given nonnegative costs and fi:V→R are functions given by fi(xi)=∑v∈Vcviμ(xi,v). The computational complexity of 0-Ext[μ] has been recently established by Karzanov and by Hirai: if metric μ is {\\em orientable modular} then 0-Ext[μ] can be solved in polynomial time, otherwise 0-Ext[μ] is NP-hard. To prove the tractability part, Hirai developed a theory of discrete convex functions on orientable modular graphs generalizing several known classes of functions in discrete convex analysis, such as L♮-convex functions. We consider a more general version of the problem in which unary functions fi(xi) can additionally have terms of the form cuv;iμ(xi,{u,v}) for {u,v}∈F, where set F⊆(V2) is fixed. We extend the complexity classification above by providing an explicit condition on (μ,F) for the problem to be tractable. In order to prove the tractability part, we generalize Hirai's theory and define a larger class of discrete convex functions. It covers, in particular, another well-known class of functions, namely submodular functions on an integer lattice. Finally, we improve the complexity of Hirai's algorithm for solving 0-Ext on orientable modular graphs.\r\n" acknowledgement: We thank the anonymous reviewers for their careful reading of our manuscript and their many insightful comments and suggestions. Open access funding provided by Institute of Science and Technology (IST Austria). article_number: '2109.10203' article_processing_charge: Yes (via OA deal) article_type: original author: - first_name: Martin full_name: Dvorak, Martin id: 40ED02A8-C8B4-11E9-A9C0-453BE6697425 last_name: Dvorak orcid: 0000-0001-5293-214X - first_name: Vladimir full_name: Kolmogorov, Vladimir id: 3D50B0BA-F248-11E8-B48F-1D18A9856A87 last_name: Kolmogorov citation: ama: Dvorak M, Kolmogorov V. Generalized minimum 0-extension problem and discrete convexity. Mathematical Programming. 2024. doi:10.1007/s10107-024-02064-5 apa: Dvorak, M., & Kolmogorov, V. (2024). Generalized minimum 0-extension problem and discrete convexity. Mathematical Programming. Springer Nature. https://doi.org/10.1007/s10107-024-02064-5 chicago: Dvorak, Martin, and Vladimir Kolmogorov. “Generalized Minimum 0-Extension Problem and Discrete Convexity.” Mathematical Programming. Springer Nature, 2024. https://doi.org/10.1007/s10107-024-02064-5. ieee: M. Dvorak and V. Kolmogorov, “Generalized minimum 0-extension problem and discrete convexity,” Mathematical Programming. Springer Nature, 2024. ista: Dvorak M, Kolmogorov V. 2024. Generalized minimum 0-extension problem and discrete convexity. Mathematical Programming., 2109.10203. mla: Dvorak, Martin, and Vladimir Kolmogorov. “Generalized Minimum 0-Extension Problem and Discrete Convexity.” Mathematical Programming, 2109.10203, Springer Nature, 2024, doi:10.1007/s10107-024-02064-5. short: M. Dvorak, V. Kolmogorov, Mathematical Programming (2024). date_created: 2021-09-27T10:48:23Z date_published: 2024-03-07T00:00:00Z date_updated: 2024-03-19T08:20:31Z day: '07' ddc: - '004' department: - _id: GradSch - _id: VlKo doi: 10.1007/s10107-024-02064-5 external_id: arxiv: - '2109.10203' file: - access_level: open_access checksum: e7e83065f7bc18b9c188bf93b5ca5db6 content_type: application/pdf creator: mdvorak date_created: 2021-09-27T10:54:51Z date_updated: 2021-09-27T10:54:51Z file_id: '10046' file_name: Generalized-0-Ext.pdf file_size: 603672 relation: main_file success: 1 file_date_updated: 2021-09-27T10:54:51Z has_accepted_license: '1' keyword: - minimum 0-extension problem - metric labeling problem - discrete metric spaces - metric extensions - computational complexity - valued constraint satisfaction problems - discrete convex analysis - L-convex functions language: - iso: eng license: https://creativecommons.org/licenses/by/4.0/ month: '03' oa: 1 oa_version: Preprint publication: Mathematical Programming publication_identifier: eissn: - 1436-4646 issn: - 0025-5610 publication_status: epub_ahead publisher: Springer Nature quality_controlled: '1' scopus_import: '1' status: public title: Generalized minimum 0-extension problem and discrete convexity tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2024' ... --- _id: '14084' abstract: - lang: eng text: "A central problem in computational statistics is to convert a procedure for sampling combinatorial objects into a procedure for counting those objects, and vice versa. We will consider sampling problems which come from Gibbs distributions, which are families of probability distributions over a discrete space Ω with probability mass function of the form μ^Ω_β(ω) ∝ e^{β H(ω)} for β in an interval [β_min, β_max] and H(ω) ∈ {0} ∪ [1, n].\r\nThe partition function is the normalization factor Z(β) = ∑_{ω ∈ Ω} e^{β H(ω)}, and the log partition ratio is defined as q = (log Z(β_max))/Z(β_min)\r\nWe develop a number of algorithms to estimate the counts c_x using roughly Õ(q/ε²) samples for general Gibbs distributions and Õ(n²/ε²) samples for integer-valued distributions (ignoring some second-order terms and parameters), We show this is optimal up to logarithmic factors. We illustrate with improved algorithms for counting connected subgraphs and perfect matchings in a graph." acknowledgement: We thank Heng Guo for helpful explanations of algorithms for sampling connected subgraphs and matchings, Maksym Serbyn for bringing to our attention the Wang-Landau algorithm and its use in physics. alternative_title: - LIPIcs article_number: '72' article_processing_charge: Yes author: - first_name: David G. full_name: Harris, David G. last_name: Harris - first_name: Vladimir full_name: Kolmogorov, Vladimir id: 3D50B0BA-F248-11E8-B48F-1D18A9856A87 last_name: Kolmogorov citation: ama: 'Harris DG, Kolmogorov V. Parameter estimation for Gibbs distributions. In: 50th International Colloquium on Automata, Languages, and Programming. Vol 261. Schloss Dagstuhl - Leibniz-Zentrum für Informatik; 2023. doi:10.4230/LIPIcs.ICALP.2023.72' apa: 'Harris, D. G., & Kolmogorov, V. (2023). Parameter estimation for Gibbs distributions. In 50th International Colloquium on Automata, Languages, and Programming (Vol. 261). Paderborn, Germany: Schloss Dagstuhl - Leibniz-Zentrum für Informatik. https://doi.org/10.4230/LIPIcs.ICALP.2023.72' chicago: Harris, David G., and Vladimir Kolmogorov. “Parameter Estimation for Gibbs Distributions.” In 50th International Colloquium on Automata, Languages, and Programming, Vol. 261. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2023. https://doi.org/10.4230/LIPIcs.ICALP.2023.72. ieee: D. G. Harris and V. Kolmogorov, “Parameter estimation for Gibbs distributions,” in 50th International Colloquium on Automata, Languages, and Programming, Paderborn, Germany, 2023, vol. 261. ista: 'Harris DG, Kolmogorov V. 2023. Parameter estimation for Gibbs distributions. 50th International Colloquium on Automata, Languages, and Programming. ICALP: International Colloquium on Automata, Languages, and Programming, LIPIcs, vol. 261, 72.' mla: Harris, David G., and Vladimir Kolmogorov. “Parameter Estimation for Gibbs Distributions.” 50th International Colloquium on Automata, Languages, and Programming, vol. 261, 72, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2023, doi:10.4230/LIPIcs.ICALP.2023.72. short: D.G. Harris, V. Kolmogorov, in:, 50th International Colloquium on Automata, Languages, and Programming, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2023. conference: end_date: 2023-07-14 location: Paderborn, Germany name: 'ICALP: International Colloquium on Automata, Languages, and Programming' start_date: 2023-07-10 date_created: 2023-08-20T22:01:14Z date_published: 2023-07-01T00:00:00Z date_updated: 2023-08-21T06:49:11Z day: '01' ddc: - '000' - '510' department: - _id: VlKo doi: 10.4230/LIPIcs.ICALP.2023.72 external_id: arxiv: - '2007.10824' file: - access_level: open_access checksum: 6dee0684245bb1c524b9c955db1e933d content_type: application/pdf creator: dernst date_created: 2023-08-21T06:45:16Z date_updated: 2023-08-21T06:45:16Z file_id: '14088' file_name: 2023_LIPIcsICALP_Harris.pdf file_size: 917791 relation: main_file success: 1 file_date_updated: 2023-08-21T06:45:16Z has_accepted_license: '1' intvolume: ' 261' language: - iso: eng month: '07' oa: 1 oa_version: Published Version publication: 50th International Colloquium on Automata, Languages, and Programming publication_identifier: isbn: - '9783959772785' issn: - 1868-8969 publication_status: published publisher: Schloss Dagstuhl - Leibniz-Zentrum für Informatik quality_controlled: '1' scopus_import: '1' status: public title: Parameter estimation for Gibbs distributions tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 261 year: '2023' ... --- _id: '13120' abstract: - lang: eng text: 'We formalized general (i.e., type-0) grammars using the Lean 3 proof assistant. We defined basic notions of rewrite rules and of words derived by a grammar, and used grammars to show closure of the class of type-0 languages under four operations: union, reversal, concatenation, and the Kleene star. The literature mostly focuses on Turing machine arguments, which are possibly more difficult to formalize. For the Kleene star, we could not follow the literature and came up with our own grammar-based construction.' acknowledgement: "Jasmin Blanchette: This research has received funding from the Netherlands Organization\r\nfor Scientific Research (NWO) under the Vidi program (project No. 016.Vidi.189.037, Lean Forward).\r\n__\r\nWe thank Vladimir Kolmogorov for making this collaboration possible. We\r\nthank Václav Končický for discussing ideas about the Kleene star construction. We thank Patrick Johnson, Floris van Doorn, and Damiano Testa for their small yet very valuable contributions to our code. We thank Eric Wieser for simplifying one of our proofs. We thank Mark Summerfield for suggesting textual improvements. We thank the anonymous reviewers for very helpful comments. Finally, we thank the Lean community for helping us with various technical issues and answering many questions. " alternative_title: - LIPIcs article_number: '15' article_processing_charge: No author: - first_name: Martin full_name: Dvorak, Martin id: 40ED02A8-C8B4-11E9-A9C0-453BE6697425 last_name: Dvorak orcid: 0000-0001-5293-214X - first_name: Jasmin full_name: Blanchette, Jasmin last_name: Blanchette citation: ama: 'Dvorak M, Blanchette J. Closure properties of general grammars - formally verified. In: 14th International Conference on Interactive Theorem Proving. Vol 268. Schloss Dagstuhl - Leibniz-Zentrum für Informatik; 2023. doi:10.4230/LIPIcs.ITP.2023.15' apa: 'Dvorak, M., & Blanchette, J. (2023). Closure properties of general grammars - formally verified. In 14th International Conference on Interactive Theorem Proving (Vol. 268). Bialystok, Poland: Schloss Dagstuhl - Leibniz-Zentrum für Informatik. https://doi.org/10.4230/LIPIcs.ITP.2023.15' chicago: Dvorak, Martin, and Jasmin Blanchette. “Closure Properties of General Grammars - Formally Verified.” In 14th International Conference on Interactive Theorem Proving, Vol. 268. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2023. https://doi.org/10.4230/LIPIcs.ITP.2023.15. ieee: M. Dvorak and J. Blanchette, “Closure properties of general grammars - formally verified,” in 14th International Conference on Interactive Theorem Proving, Bialystok, Poland, 2023, vol. 268. ista: 'Dvorak M, Blanchette J. 2023. Closure properties of general grammars - formally verified. 14th International Conference on Interactive Theorem Proving. ITP: International Conference on Interactive Theorem Proving, LIPIcs, vol. 268, 15.' mla: Dvorak, Martin, and Jasmin Blanchette. “Closure Properties of General Grammars - Formally Verified.” 14th International Conference on Interactive Theorem Proving, vol. 268, 15, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2023, doi:10.4230/LIPIcs.ITP.2023.15. short: M. Dvorak, J. Blanchette, in:, 14th International Conference on Interactive Theorem Proving, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2023. conference: end_date: 2023-08-04 location: Bialystok, Poland name: 'ITP: International Conference on Interactive Theorem Proving' start_date: 2023-07-31 date_created: 2023-06-05T07:29:05Z date_published: 2023-07-27T00:00:00Z date_updated: 2023-09-25T11:04:29Z day: '27' ddc: - '000' department: - _id: GradSch - _id: VlKo doi: 10.4230/LIPIcs.ITP.2023.15 external_id: arxiv: - '2302.06420' file: - access_level: open_access checksum: 773a0197f05b67feaa6cb1e17ec3642d content_type: application/pdf creator: dernst date_created: 2023-08-07T11:55:43Z date_updated: 2023-08-07T11:55:43Z file_id: '13982' file_name: 2023_LIPIcS_Dvorak.pdf file_size: 715976 relation: main_file success: 1 file_date_updated: 2023-08-07T11:55:43Z has_accepted_license: '1' intvolume: ' 268' language: - iso: eng month: '07' oa: 1 oa_version: Published Version publication: 14th International Conference on Interactive Theorem Proving publication_identifier: eissn: - 1868-8969 isbn: - '9783959772846' publication_status: published publisher: Schloss Dagstuhl - Leibniz-Zentrum für Informatik quality_controlled: '1' related_material: link: - relation: software url: https://github.com/madvorak/grammars/tree/publish scopus_import: '1' status: public title: Closure properties of general grammars - formally verified tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 268 year: '2023' ... --- _id: '14448' abstract: - lang: eng text: We consider the problem of solving LP relaxations of MAP-MRF inference problems, and in particular the method proposed recently in [16], [35]. As a key computational subroutine, it uses a variant of the Frank-Wolfe (FW) method to minimize a smooth convex function over a combinatorial polytope. We propose an efficient implementation of this subroutine based on in-face Frank-Wolfe directions, introduced in [4] in a different context. More generally, we define an abstract data structure for a combinatorial subproblem that enables in-face FW directions, and describe its specialization for tree-structured MAP-MRF inference subproblems. Experimental results indicate that the resulting method is the current state-of-art LP solver for some classes of problems. Our code is available at pub.ist.ac.at/~vnk/papers/IN-FACE-FW.html. article_processing_charge: No author: - first_name: Vladimir full_name: Kolmogorov, Vladimir id: 3D50B0BA-F248-11E8-B48F-1D18A9856A87 last_name: Kolmogorov citation: ama: 'Kolmogorov V. Solving relaxations of MAP-MRF problems: Combinatorial in-face Frank-Wolfe directions. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Vol 2023. IEEE; 2023:11980-11989. doi:10.1109/CVPR52729.2023.01153' apa: 'Kolmogorov, V. (2023). Solving relaxations of MAP-MRF problems: Combinatorial in-face Frank-Wolfe directions. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Vol. 2023, pp. 11980–11989). Vancouver, Canada: IEEE. https://doi.org/10.1109/CVPR52729.2023.01153' chicago: 'Kolmogorov, Vladimir. “Solving Relaxations of MAP-MRF Problems: Combinatorial in-Face Frank-Wolfe Directions.” In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2023:11980–89. IEEE, 2023. https://doi.org/10.1109/CVPR52729.2023.01153.' ieee: 'V. Kolmogorov, “Solving relaxations of MAP-MRF problems: Combinatorial in-face Frank-Wolfe directions,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vancouver, Canada, 2023, vol. 2023, pp. 11980–11989.' ista: 'Kolmogorov V. 2023. Solving relaxations of MAP-MRF problems: Combinatorial in-face Frank-Wolfe directions. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR: Conference on Computer Vision and Pattern Recognition vol. 2023, 11980–11989.' mla: 'Kolmogorov, Vladimir. “Solving Relaxations of MAP-MRF Problems: Combinatorial in-Face Frank-Wolfe Directions.” Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2023, IEEE, 2023, pp. 11980–89, doi:10.1109/CVPR52729.2023.01153.' short: V. Kolmogorov, in:, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE, 2023, pp. 11980–11989. conference: end_date: 2023-06-24 location: Vancouver, Canada name: 'CVPR: Conference on Computer Vision and Pattern Recognition' start_date: 2023-06-17 date_created: 2023-10-22T22:01:16Z date_published: 2023-08-22T00:00:00Z date_updated: 2023-10-31T12:01:24Z day: '22' department: - _id: VlKo doi: 10.1109/CVPR52729.2023.01153 external_id: arxiv: - '2010.09567' intvolume: ' 2023' language: - iso: eng main_file_link: - open_access: '1' url: ' https://doi.org/10.48550/arXiv.2010.09567' month: '08' oa: 1 oa_version: Preprint page: 11980-11989 publication: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition publication_identifier: isbn: - '9798350301298' issn: - 1063-6919 publication_status: published publisher: IEEE quality_controlled: '1' scopus_import: '1' status: public title: 'Solving relaxations of MAP-MRF problems: Combinatorial in-face Frank-Wolfe directions' type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 2023 year: '2023' ... --- _id: '10737' abstract: - lang: eng text: We consider two models for the sequence labeling (tagging) problem. The first one is a Pattern-Based Conditional Random Field (PB), in which the energy of a string (chain labeling) x=x1⁢…⁢xn∈Dn is a sum of terms over intervals [i,j] where each term is non-zero only if the substring xi⁢…⁢xj equals a prespecified word w∈Λ. The second model is a Weighted Context-Free Grammar (WCFG) frequently used for natural language processing. PB and WCFG encode local and non-local interactions respectively, and thus can be viewed as complementary. We propose a Grammatical Pattern-Based CRF model (GPB) that combines the two in a natural way. We argue that it has certain advantages over existing approaches such as the Hybrid model of Benedí and Sanchez that combines N-grams and WCFGs. The focus of this paper is to analyze the complexity of inference tasks in a GPB such as computing MAP. We present a polynomial-time algorithm for general GPBs and a faster version for a special case that we call Interaction Grammars. article_processing_charge: No article_type: original author: - first_name: Rustem full_name: Takhanov, Rustem id: 2CCAC26C-F248-11E8-B48F-1D18A9856A87 last_name: Takhanov - first_name: Vladimir full_name: Kolmogorov, Vladimir id: 3D50B0BA-F248-11E8-B48F-1D18A9856A87 last_name: Kolmogorov citation: ama: Takhanov R, Kolmogorov V. Combining pattern-based CRFs and weighted context-free grammars. Intelligent Data Analysis. 2022;26(1):257-272. doi:10.3233/IDA-205623 apa: Takhanov, R., & Kolmogorov, V. (2022). Combining pattern-based CRFs and weighted context-free grammars. Intelligent Data Analysis. IOS Press. https://doi.org/10.3233/IDA-205623 chicago: Takhanov, Rustem, and Vladimir Kolmogorov. “Combining Pattern-Based CRFs and Weighted Context-Free Grammars.” Intelligent Data Analysis. IOS Press, 2022. https://doi.org/10.3233/IDA-205623. ieee: R. Takhanov and V. Kolmogorov, “Combining pattern-based CRFs and weighted context-free grammars,” Intelligent Data Analysis, vol. 26, no. 1. IOS Press, pp. 257–272, 2022. ista: Takhanov R, Kolmogorov V. 2022. Combining pattern-based CRFs and weighted context-free grammars. Intelligent Data Analysis. 26(1), 257–272. mla: Takhanov, Rustem, and Vladimir Kolmogorov. “Combining Pattern-Based CRFs and Weighted Context-Free Grammars.” Intelligent Data Analysis, vol. 26, no. 1, IOS Press, 2022, pp. 257–72, doi:10.3233/IDA-205623. short: R. Takhanov, V. Kolmogorov, Intelligent Data Analysis 26 (2022) 257–272. date_created: 2022-02-06T23:01:32Z date_published: 2022-01-14T00:00:00Z date_updated: 2023-08-02T14:09:41Z day: '14' department: - _id: VlKo doi: 10.3233/IDA-205623 external_id: arxiv: - '1404.5475' isi: - '000749997700015' intvolume: ' 26' isi: 1 issue: '1' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/1404.5475 month: '01' oa: 1 oa_version: Preprint page: 257-272 publication: Intelligent Data Analysis publication_identifier: eissn: - 1571-4128 issn: - 1088-467X publication_status: published publisher: IOS Press quality_controlled: '1' scopus_import: '1' status: public title: Combining pattern-based CRFs and weighted context-free grammars type: journal_article user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8 volume: 26 year: '2022' ... --- _id: '7577' abstract: - lang: eng text: Weak convergence of inertial iterative method for solving variational inequalities is the focus of this paper. The cost function is assumed to be non-Lipschitz and monotone. We propose a projection-type method with inertial terms and give weak convergence analysis under appropriate conditions. Some test results are performed and compared with relevant methods in the literature to show the efficiency and advantages given by our proposed methods. acknowledgement: The project of the first author has received funding from the European Research Council (ERC) under the European Union's Seventh Framework Program (FP7 - 2007-2013) (Grant agreement No. 616160). article_processing_charge: No article_type: original author: - first_name: Yekini full_name: Shehu, Yekini id: 3FC7CB58-F248-11E8-B48F-1D18A9856A87 last_name: Shehu orcid: 0000-0001-9224-7139 - first_name: Olaniyi S. full_name: Iyiola, Olaniyi S. last_name: Iyiola citation: ama: Shehu Y, Iyiola OS. Weak convergence for variational inequalities with inertial-type method. Applicable Analysis. 2022;101(1):192-216. doi:10.1080/00036811.2020.1736287 apa: Shehu, Y., & Iyiola, O. S. (2022). Weak convergence for variational inequalities with inertial-type method. Applicable Analysis. Taylor & Francis. https://doi.org/10.1080/00036811.2020.1736287 chicago: Shehu, Yekini, and Olaniyi S. Iyiola. “Weak Convergence for Variational Inequalities with Inertial-Type Method.” Applicable Analysis. Taylor & Francis, 2022. https://doi.org/10.1080/00036811.2020.1736287. ieee: Y. Shehu and O. S. Iyiola, “Weak convergence for variational inequalities with inertial-type method,” Applicable Analysis, vol. 101, no. 1. Taylor & Francis, pp. 192–216, 2022. ista: Shehu Y, Iyiola OS. 2022. Weak convergence for variational inequalities with inertial-type method. Applicable Analysis. 101(1), 192–216. mla: Shehu, Yekini, and Olaniyi S. Iyiola. “Weak Convergence for Variational Inequalities with Inertial-Type Method.” Applicable Analysis, vol. 101, no. 1, Taylor & Francis, 2022, pp. 192–216, doi:10.1080/00036811.2020.1736287. short: Y. Shehu, O.S. Iyiola, Applicable Analysis 101 (2022) 192–216. date_created: 2020-03-09T07:06:52Z date_published: 2022-01-01T00:00:00Z date_updated: 2024-03-05T14:01:52Z day: '01' ddc: - '510' - '515' - '518' department: - _id: VlKo doi: 10.1080/00036811.2020.1736287 ec_funded: 1 external_id: arxiv: - '2101.08057' isi: - '000518364100001' file: - access_level: open_access checksum: 869efe8cb09505dfa6012f67d20db63d content_type: application/pdf creator: dernst date_created: 2020-10-12T10:42:54Z date_updated: 2021-03-16T23:30:06Z embargo: 2021-03-15 file_id: '8648' file_name: 2020_ApplicAnalysis_Shehu.pdf file_size: 4282586 relation: main_file file_date_updated: 2021-03-16T23:30:06Z has_accepted_license: '1' intvolume: ' 101' isi: 1 issue: '1' language: - iso: eng month: '01' oa: 1 oa_version: Submitted Version page: 192-216 project: - _id: 25FBA906-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '616160' name: 'Discrete Optimization in Computer Vision: Theory and Practice' publication: Applicable Analysis publication_identifier: eissn: - 1563-504X issn: - 0003-6811 publication_status: published publisher: Taylor & Francis quality_controlled: '1' scopus_import: '1' status: public title: Weak convergence for variational inequalities with inertial-type method type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 101 year: '2022' ... --- _id: '10072' abstract: - lang: eng text: The Lovász Local Lemma (LLL) is a powerful tool in probabilistic combinatorics which can be used to establish the existence of objects that satisfy certain properties. The breakthrough paper of Moser and Tardos and follow-up works revealed that the LLL has intimate connections with a class of stochastic local search algorithms for finding such desirable objects. In particular, it can be seen as a sufficient condition for this type of algorithms to converge fast. Besides conditions for existence of and fast convergence to desirable objects, one may naturally ask further questions regarding properties of these algorithms. For instance, "are they parallelizable?", "how many solutions can they output?", "what is the expected "weight" of a solution?", etc. These questions and more have been answered for a class of LLL-inspired algorithms called commutative. In this paper we introduce a new, very natural and more general notion of commutativity (essentially matrix commutativity) which allows us to show a number of new refined properties of LLL-inspired local search algorithms with significantly simpler proofs. acknowledgement: "Fotis Iliopoulos: This material is based upon work directly supported by the IAS Fund for Math and indirectly supported by the National Science Foundation Grant No. CCF-1900460. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. This work is also supported by the National Science Foundation Grant No. CCF-1815328.\r\nVladimir Kolmogorov: Supported by the European Research Council under the European Unions Seventh Framework Programme (FP7/2007-2013)/ERC grant agreement no 616160." alternative_title: - LIPIcs article_number: '31' article_processing_charge: Yes author: - first_name: David G. full_name: Harris, David G. last_name: Harris - first_name: Fotis full_name: Iliopoulos, Fotis last_name: Iliopoulos - first_name: Vladimir full_name: Kolmogorov, Vladimir id: 3D50B0BA-F248-11E8-B48F-1D18A9856A87 last_name: Kolmogorov citation: ama: 'Harris DG, Iliopoulos F, Kolmogorov V. A new notion of commutativity for the algorithmic Lovász Local Lemma. In: Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques. Vol 207. Schloss Dagstuhl - Leibniz Zentrum für Informatik; 2021. doi:10.4230/LIPIcs.APPROX/RANDOM.2021.31' apa: 'Harris, D. G., Iliopoulos, F., & Kolmogorov, V. (2021). A new notion of commutativity for the algorithmic Lovász Local Lemma. In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (Vol. 207). Virtual: Schloss Dagstuhl - Leibniz Zentrum für Informatik. https://doi.org/10.4230/LIPIcs.APPROX/RANDOM.2021.31' chicago: Harris, David G., Fotis Iliopoulos, and Vladimir Kolmogorov. “A New Notion of Commutativity for the Algorithmic Lovász Local Lemma.” In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques, Vol. 207. Schloss Dagstuhl - Leibniz Zentrum für Informatik, 2021. https://doi.org/10.4230/LIPIcs.APPROX/RANDOM.2021.31. ieee: D. G. Harris, F. Iliopoulos, and V. Kolmogorov, “A new notion of commutativity for the algorithmic Lovász Local Lemma,” in Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques, Virtual, 2021, vol. 207. ista: 'Harris DG, Iliopoulos F, Kolmogorov V. 2021. A new notion of commutativity for the algorithmic Lovász Local Lemma. Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques. APPROX/RANDOM: Approximation Algorithms for Combinatorial Optimization Problems/ Randomization and Computation, LIPIcs, vol. 207, 31.' mla: Harris, David G., et al. “A New Notion of Commutativity for the Algorithmic Lovász Local Lemma.” Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques, vol. 207, 31, Schloss Dagstuhl - Leibniz Zentrum für Informatik, 2021, doi:10.4230/LIPIcs.APPROX/RANDOM.2021.31. short: D.G. Harris, F. Iliopoulos, V. Kolmogorov, in:, Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques, Schloss Dagstuhl - Leibniz Zentrum für Informatik, 2021. conference: end_date: 2021-08-18 location: Virtual name: 'APPROX/RANDOM: Approximation Algorithms for Combinatorial Optimization Problems/ Randomization and Computation' start_date: 2021-08-16 date_created: 2021-10-03T22:01:22Z date_published: 2021-09-15T00:00:00Z date_updated: 2022-03-18T10:08:25Z day: '15' ddc: - '000' department: - _id: VlKo doi: 10.4230/LIPIcs.APPROX/RANDOM.2021.31 ec_funded: 1 external_id: arxiv: - '2008.05569' file: - access_level: open_access checksum: 9d2544d53aa5b01565c6891d97a4d765 content_type: application/pdf creator: cchlebak date_created: 2021-10-06T13:51:54Z date_updated: 2021-10-06T13:51:54Z file_id: '10098' file_name: 2021_LIPIcs_Harris.pdf file_size: 804472 relation: main_file success: 1 file_date_updated: 2021-10-06T13:51:54Z has_accepted_license: '1' intvolume: ' 207' language: - iso: eng month: '09' oa: 1 oa_version: Published Version project: - _id: 25FBA906-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '616160' name: 'Discrete Optimization in Computer Vision: Theory and Practice' publication: Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques publication_identifier: isbn: - 978-3-9597-7207-5 issn: - 1868-8969 publication_status: published publisher: Schloss Dagstuhl - Leibniz Zentrum für Informatik quality_controlled: '1' scopus_import: '1' status: public title: A new notion of commutativity for the algorithmic Lovász Local Lemma tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 207 year: '2021' ... --- _id: '10552' abstract: - lang: eng text: We study a class of convex-concave saddle-point problems of the form minxmaxy⟨Kx,y⟩+fP(x)−h∗(y) where K is a linear operator, fP is the sum of a convex function f with a Lipschitz-continuous gradient and the indicator function of a bounded convex polytope P, and h∗ is a convex (possibly nonsmooth) function. Such problem arises, for example, as a Lagrangian relaxation of various discrete optimization problems. Our main assumptions are the existence of an efficient linear minimization oracle (lmo) for fP and an efficient proximal map for h∗ which motivate the solution via a blend of proximal primal-dual algorithms and Frank-Wolfe algorithms. In case h∗ is the indicator function of a linear constraint and function f is quadratic, we show a O(1/n2) convergence rate on the dual objective, requiring O(nlogn) calls of lmo. If the problem comes from the constrained optimization problem minx∈Rd{fP(x)|Ax−b=0} then we additionally get bound O(1/n2) both on the primal gap and on the infeasibility gap. In the most general case, we show a O(1/n) convergence rate of the primal-dual gap again requiring O(nlogn) calls of lmo. To the best of our knowledge, this improves on the known convergence rates for the considered class of saddle-point problems. We show applications to labeling problems frequently appearing in machine learning and computer vision. acknowledgement: Vladimir Kolmogorov was supported by the European Research Council under the European Unions Seventh Framework Programme (FP7/2007-2013)/ERC grant agreement no 616160. Thomas Pock acknowledges support by an ERC grant HOMOVIS, no 640156. article_processing_charge: No author: - first_name: Vladimir full_name: Kolmogorov, Vladimir id: 3D50B0BA-F248-11E8-B48F-1D18A9856A87 last_name: Kolmogorov - first_name: Thomas full_name: Pock, Thomas last_name: Pock citation: ama: 'Kolmogorov V, Pock T. One-sided Frank-Wolfe algorithms for saddle problems. In: 38th International Conference on Machine Learning. ; 2021.' apa: Kolmogorov, V., & Pock, T. (2021). One-sided Frank-Wolfe algorithms for saddle problems. In 38th International Conference on Machine Learning. Virtual. chicago: Kolmogorov, Vladimir, and Thomas Pock. “One-Sided Frank-Wolfe Algorithms for Saddle Problems.” In 38th International Conference on Machine Learning, 2021. ieee: V. Kolmogorov and T. Pock, “One-sided Frank-Wolfe algorithms for saddle problems,” in 38th International Conference on Machine Learning, Virtual, 2021. ista: 'Kolmogorov V, Pock T. 2021. One-sided Frank-Wolfe algorithms for saddle problems. 38th International Conference on Machine Learning. ICML: International Conference on Machine Learning.' mla: Kolmogorov, Vladimir, and Thomas Pock. “One-Sided Frank-Wolfe Algorithms for Saddle Problems.” 38th International Conference on Machine Learning, 2021. short: V. Kolmogorov, T. Pock, in:, 38th International Conference on Machine Learning, 2021. conference: end_date: 2021-07-24 location: Virtual name: 'ICML: International Conference on Machine Learning' start_date: 2021-07-18 date_created: 2021-12-16T12:41:20Z date_published: 2021-07-01T00:00:00Z date_updated: 2021-12-17T09:06:46Z day: '01' department: - _id: VlKo ec_funded: 1 external_id: arxiv: - '2101.12617' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/2101.12617 month: '07' oa: 1 oa_version: Preprint project: - _id: 25FBA906-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '616160' name: 'Discrete Optimization in Computer Vision: Theory and Practice' publication: 38th International Conference on Machine Learning publication_status: published quality_controlled: '1' status: public title: One-sided Frank-Wolfe algorithms for saddle problems type: conference user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9 year: '2021' ... --- _id: '9592' abstract: - lang: eng text: The convex grabbing game is a game where two players, Alice and Bob, alternate taking extremal points from the convex hull of a point set on the plane. Rational weights are given to the points. The goal of each player is to maximize the total weight over all points that they obtain. We restrict the setting to the case of binary weights. We show a construction of an arbitrarily large odd-sized point set that allows Bob to obtain almost 3/4 of the total weight. This construction answers a question asked by Matsumoto, Nakamigawa, and Sakuma in [Graphs and Combinatorics, 36/1 (2020)]. We also present an arbitrarily large even-sized point set where Bob can obtain the entirety of the total weight. Finally, we discuss conjectures about optimum moves in the convex grabbing game for both players in general. article_processing_charge: No author: - first_name: Martin full_name: Dvorak, Martin id: 40ED02A8-C8B4-11E9-A9C0-453BE6697425 last_name: Dvorak orcid: 0000-0001-5293-214X - first_name: Sara full_name: Nicholson, Sara last_name: Nicholson citation: ama: 'Dvorak M, Nicholson S. Massively winning configurations in the convex grabbing game on the plane. In: Proceedings of the 33rd Canadian Conference on Computational Geometry.' apa: Dvorak, M., & Nicholson, S. (n.d.). Massively winning configurations in the convex grabbing game on the plane. In Proceedings of the 33rd Canadian Conference on Computational Geometry. Halifax, NS, Canada. chicago: Dvorak, Martin, and Sara Nicholson. “Massively Winning Configurations in the Convex Grabbing Game on the Plane.” In Proceedings of the 33rd Canadian Conference on Computational Geometry, n.d. ieee: M. Dvorak and S. Nicholson, “Massively winning configurations in the convex grabbing game on the plane,” in Proceedings of the 33rd Canadian Conference on Computational Geometry, Halifax, NS, Canada. ista: 'Dvorak M, Nicholson S. Massively winning configurations in the convex grabbing game on the plane. Proceedings of the 33rd Canadian Conference on Computational Geometry. CCCG: Canadian Conference on Computational Geometry.' mla: Dvorak, Martin, and Sara Nicholson. “Massively Winning Configurations in the Convex Grabbing Game on the Plane.” Proceedings of the 33rd Canadian Conference on Computational Geometry. short: M. Dvorak, S. Nicholson, in:, Proceedings of the 33rd Canadian Conference on Computational Geometry, n.d. conference: end_date: 2021-08-12 location: Halifax, NS, Canada name: 'CCCG: Canadian Conference on Computational Geometry' start_date: 2021-08-10 date_created: 2021-06-22T15:57:11Z date_published: 2021-06-29T00:00:00Z date_updated: 2021-08-12T10:57:39Z day: '29' ddc: - '516' department: - _id: GradSch - _id: VlKo external_id: arxiv: - '2106.11247' file: - access_level: open_access checksum: 45accb1de9b7e0e4bb2fbfe5fd3e6239 content_type: application/pdf creator: mdvorak date_created: 2021-06-28T20:23:13Z date_updated: 2021-06-28T20:23:13Z file_id: '9616' file_name: Convex-Grabbing-Game_CCCG_proc_version.pdf file_size: 381306 relation: main_file success: 1 - access_level: open_access checksum: 9199cf18c65658553487458cc24d0ab2 content_type: application/pdf creator: kschuh date_created: 2021-08-12T10:57:21Z date_updated: 2021-08-12T10:57:21Z file_id: '9902' file_name: Convex-Grabbing-Game_FULL-VERSION.pdf file_size: 403645 relation: main_file success: 1 file_date_updated: 2021-08-12T10:57:21Z has_accepted_license: '1' keyword: - convex grabbing game - graph grabbing game - combinatorial game - convex geometry language: - iso: eng license: https://creativecommons.org/licenses/by-nd/4.0/ month: '06' oa: 1 oa_version: Submitted Version publication: Proceedings of the 33rd Canadian Conference on Computational Geometry publication_status: accepted quality_controlled: '1' status: public title: Massively winning configurations in the convex grabbing game on the plane tmp: image: /image/cc_by_nd.png legal_code_url: https://creativecommons.org/licenses/by-nd/4.0/legalcode name: Creative Commons Attribution-NoDerivatives 4.0 International (CC BY-ND 4.0) short: CC BY-ND (4.0) type: conference user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87 year: '2021' ... --- _id: '9469' abstract: - lang: eng text: In this paper, we consider reflected three-operator splitting methods for monotone inclusion problems in real Hilbert spaces. To do this, we first obtain weak convergence analysis and nonasymptotic O(1/n) convergence rate of the reflected Krasnosel'skiĭ-Mann iteration for finding a fixed point of nonexpansive mapping in real Hilbert spaces under some seemingly easy to implement conditions on the iterative parameters. We then apply our results to three-operator splitting for the monotone inclusion problem and consequently obtain the corresponding convergence analysis. Furthermore, we derive reflected primal-dual algorithms for highly structured monotone inclusion problems. Some numerical implementations are drawn from splitting methods to support the theoretical analysis. acknowledgement: The authors are grateful to the anonymous referees and the handling Editor for their insightful comments which have improved the earlier version of the manuscript greatly. The second author is grateful to the University of Hafr Al Batin. The last author has received funding from the European Research Council (ERC) under the European Union's Seventh Framework Program (FP7-2007-2013) (Grant agreement No. 616160). article_processing_charge: No article_type: original author: - first_name: Olaniyi S. full_name: Iyiola, Olaniyi S. last_name: Iyiola - first_name: Cyril D. full_name: Enyi, Cyril D. last_name: Enyi - first_name: Yekini full_name: Shehu, Yekini id: 3FC7CB58-F248-11E8-B48F-1D18A9856A87 last_name: Shehu orcid: 0000-0001-9224-7139 citation: ama: Iyiola OS, Enyi CD, Shehu Y. Reflected three-operator splitting method for monotone inclusion problem. Optimization Methods and Software. 2021. doi:10.1080/10556788.2021.1924715 apa: Iyiola, O. S., Enyi, C. D., & Shehu, Y. (2021). Reflected three-operator splitting method for monotone inclusion problem. Optimization Methods and Software. Taylor and Francis. https://doi.org/10.1080/10556788.2021.1924715 chicago: Iyiola, Olaniyi S., Cyril D. Enyi, and Yekini Shehu. “Reflected Three-Operator Splitting Method for Monotone Inclusion Problem.” Optimization Methods and Software. Taylor and Francis, 2021. https://doi.org/10.1080/10556788.2021.1924715. ieee: O. S. Iyiola, C. D. Enyi, and Y. Shehu, “Reflected three-operator splitting method for monotone inclusion problem,” Optimization Methods and Software. Taylor and Francis, 2021. ista: Iyiola OS, Enyi CD, Shehu Y. 2021. Reflected three-operator splitting method for monotone inclusion problem. Optimization Methods and Software. mla: Iyiola, Olaniyi S., et al. “Reflected Three-Operator Splitting Method for Monotone Inclusion Problem.” Optimization Methods and Software, Taylor and Francis, 2021, doi:10.1080/10556788.2021.1924715. short: O.S. Iyiola, C.D. Enyi, Y. Shehu, Optimization Methods and Software (2021). date_created: 2021-06-06T22:01:30Z date_published: 2021-05-12T00:00:00Z date_updated: 2023-08-08T13:57:43Z day: '12' department: - _id: VlKo doi: 10.1080/10556788.2021.1924715 ec_funded: 1 external_id: isi: - '000650507600001' isi: 1 language: - iso: eng month: '05' oa_version: None project: - _id: 25FBA906-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '616160' name: 'Discrete Optimization in Computer Vision: Theory and Practice' publication: Optimization Methods and Software publication_identifier: eissn: - 1029-4937 issn: - 1055-6788 publication_status: published publisher: Taylor and Francis quality_controlled: '1' scopus_import: '1' status: public title: Reflected three-operator splitting method for monotone inclusion problem type: journal_article user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8 year: '2021' ... --- _id: '9234' abstract: - lang: eng text: In this paper, we present two new inertial projection-type methods for solving multivalued variational inequality problems in finite-dimensional spaces. We establish the convergence of the sequence generated by these methods when the multivalued mapping associated with the problem is only required to be locally bounded without any monotonicity assumption. Furthermore, the inertial techniques that we employ in this paper are quite different from the ones used in most papers. Moreover, based on the weaker assumptions on the inertial factor in our methods, we derive several special cases of our methods. Finally, we present some experimental results to illustrate the profits that we gain by introducing the inertial extrapolation steps. acknowledgement: 'The authors sincerely thank the Editor-in-Chief and anonymous referees for their careful reading, constructive comments and fruitful suggestions that help improve the manuscript. The research of the first author is supported by the National Research Foundation (NRF) South Africa (S& F-DSI/NRF Free Standing Postdoctoral Fellowship; Grant Number: 120784). The first author also acknowledges the financial support from DSI/NRF, South Africa Center of Excellence in Mathematical and Statistical Sciences (CoE-MaSS) Postdoctoral Fellowship. The second author has received funding from the European Research Council (ERC) under the European Union’s Seventh Framework Program (FP7 - 2007-2013) (Grant agreement No. 616160). Open Access funding provided by Institute of Science and Technology (IST Austria).' article_processing_charge: Yes (via OA deal) article_type: original author: - first_name: Chinedu full_name: Izuchukwu, Chinedu last_name: Izuchukwu - first_name: Yekini full_name: Shehu, Yekini id: 3FC7CB58-F248-11E8-B48F-1D18A9856A87 last_name: Shehu orcid: 0000-0001-9224-7139 citation: ama: Izuchukwu C, Shehu Y. New inertial projection methods for solving multivalued variational inequality problems beyond monotonicity. Networks and Spatial Economics. 2021;21(2):291-323. doi:10.1007/s11067-021-09517-w apa: Izuchukwu, C., & Shehu, Y. (2021). New inertial projection methods for solving multivalued variational inequality problems beyond monotonicity. Networks and Spatial Economics. Springer Nature. https://doi.org/10.1007/s11067-021-09517-w chicago: Izuchukwu, Chinedu, and Yekini Shehu. “New Inertial Projection Methods for Solving Multivalued Variational Inequality Problems beyond Monotonicity.” Networks and Spatial Economics. Springer Nature, 2021. https://doi.org/10.1007/s11067-021-09517-w. ieee: C. Izuchukwu and Y. Shehu, “New inertial projection methods for solving multivalued variational inequality problems beyond monotonicity,” Networks and Spatial Economics, vol. 21, no. 2. Springer Nature, pp. 291–323, 2021. ista: Izuchukwu C, Shehu Y. 2021. New inertial projection methods for solving multivalued variational inequality problems beyond monotonicity. Networks and Spatial Economics. 21(2), 291–323. mla: Izuchukwu, Chinedu, and Yekini Shehu. “New Inertial Projection Methods for Solving Multivalued Variational Inequality Problems beyond Monotonicity.” Networks and Spatial Economics, vol. 21, no. 2, Springer Nature, 2021, pp. 291–323, doi:10.1007/s11067-021-09517-w. short: C. Izuchukwu, Y. Shehu, Networks and Spatial Economics 21 (2021) 291–323. date_created: 2021-03-10T12:18:47Z date_published: 2021-06-01T00:00:00Z date_updated: 2023-09-05T15:32:32Z day: '01' ddc: - '510' department: - _id: VlKo doi: 10.1007/s11067-021-09517-w ec_funded: 1 external_id: isi: - '000625002100001' file: - access_level: open_access checksum: 22b4253a2e5da843622a2df713784b4c content_type: application/pdf creator: kschuh date_created: 2021-08-11T12:44:16Z date_updated: 2021-08-11T12:44:16Z file_id: '9884' file_name: 2021_NetworksSpatialEconomics_Shehu.pdf file_size: 834964 relation: main_file success: 1 file_date_updated: 2021-08-11T12:44:16Z has_accepted_license: '1' intvolume: ' 21' isi: 1 issue: '2' keyword: - Computer Networks and Communications - Software - Artificial Intelligence language: - iso: eng month: '06' oa: 1 oa_version: Published Version page: 291-323 project: - _id: 25FBA906-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '616160' name: 'Discrete Optimization in Computer Vision: Theory and Practice' - _id: B67AFEDC-15C9-11EA-A837-991A96BB2854 name: IST Austria Open Access Fund publication: Networks and Spatial Economics publication_identifier: eissn: - 1572-9427 issn: - 1566-113X publication_status: published publisher: Springer Nature quality_controlled: '1' scopus_import: '1' status: public title: New inertial projection methods for solving multivalued variational inequality problems beyond monotonicity tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1 volume: 21 year: '2021' ... --- _id: '9227' abstract: - lang: eng text: In the multiway cut problem we are given a weighted undirected graph G=(V,E) and a set T⊆V of k terminals. The goal is to find a minimum weight set of edges E′⊆E with the property that by removing E′ from G all the terminals become disconnected. In this paper we present a simple local search approximation algorithm for the multiway cut problem with approximation ratio 2−2k . We present an experimental evaluation of the performance of our local search algorithm and show that it greatly outperforms the isolation heuristic of Dalhaus et al. and it has similar performance as the much more complex algorithms of Calinescu et al., Sharma and Vondrak, and Buchbinder et al. which have the currently best known approximation ratios for this problem. alternative_title: - LNCS article_processing_charge: No author: - first_name: Andrew full_name: Bloch-Hansen, Andrew last_name: Bloch-Hansen - first_name: Nasim full_name: Samei, Nasim id: C1531CAE-36E9-11EA-845F-33AA3DDC885E last_name: Samei - first_name: Roberto full_name: Solis-Oba, Roberto last_name: Solis-Oba citation: ama: 'Bloch-Hansen A, Samei N, Solis-Oba R. Experimental evaluation of a local search approximation algorithm for the multiway cut problem. In: Conference on Algorithms and Discrete Applied Mathematics. Vol 12601. Springer Nature; 2021:346-358. doi:10.1007/978-3-030-67899-9_28' apa: 'Bloch-Hansen, A., Samei, N., & Solis-Oba, R. (2021). Experimental evaluation of a local search approximation algorithm for the multiway cut problem. In Conference on Algorithms and Discrete Applied Mathematics (Vol. 12601, pp. 346–358). Rupnagar, India: Springer Nature. https://doi.org/10.1007/978-3-030-67899-9_28' chicago: Bloch-Hansen, Andrew, Nasim Samei, and Roberto Solis-Oba. “Experimental Evaluation of a Local Search Approximation Algorithm for the Multiway Cut Problem.” In Conference on Algorithms and Discrete Applied Mathematics, 12601:346–58. Springer Nature, 2021. https://doi.org/10.1007/978-3-030-67899-9_28. ieee: A. Bloch-Hansen, N. Samei, and R. Solis-Oba, “Experimental evaluation of a local search approximation algorithm for the multiway cut problem,” in Conference on Algorithms and Discrete Applied Mathematics, Rupnagar, India, 2021, vol. 12601, pp. 346–358. ista: 'Bloch-Hansen A, Samei N, Solis-Oba R. 2021. Experimental evaluation of a local search approximation algorithm for the multiway cut problem. Conference on Algorithms and Discrete Applied Mathematics. CALDAM: Conference on Algorithms and Discrete Applied Mathematics, LNCS, vol. 12601, 346–358.' mla: Bloch-Hansen, Andrew, et al. “Experimental Evaluation of a Local Search Approximation Algorithm for the Multiway Cut Problem.” Conference on Algorithms and Discrete Applied Mathematics, vol. 12601, Springer Nature, 2021, pp. 346–58, doi:10.1007/978-3-030-67899-9_28. short: A. Bloch-Hansen, N. Samei, R. Solis-Oba, in:, Conference on Algorithms and Discrete Applied Mathematics, Springer Nature, 2021, pp. 346–358. conference: end_date: 2021-02-13 location: Rupnagar, India name: 'CALDAM: Conference on Algorithms and Discrete Applied Mathematics' start_date: 2021-02-11 date_created: 2021-03-07T23:01:25Z date_published: 2021-01-28T00:00:00Z date_updated: 2023-10-10T09:29:08Z day: '28' department: - _id: VlKo doi: 10.1007/978-3-030-67899-9_28 intvolume: ' 12601' language: - iso: eng month: '01' oa_version: None page: 346-358 publication: Conference on Algorithms and Discrete Applied Mathematics publication_identifier: eissn: - 1611-3349 isbn: - '9783030678982' issn: - 0302-9743 publication_status: published publisher: Springer Nature quality_controlled: '1' scopus_import: '1' status: public title: Experimental evaluation of a local search approximation algorithm for the multiway cut problem type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 12601 year: '2021' ... --- _id: '8817' abstract: - lang: eng text: The paper introduces an inertial extragradient subgradient method with self-adaptive step sizes for solving equilibrium problems in real Hilbert spaces. Weak convergence of the proposed method is obtained under the condition that the bifunction is pseudomonotone and Lipchitz continuous. Linear convergence is also given when the bifunction is strongly pseudomonotone and Lipchitz continuous. Numerical implementations and comparisons with other related inertial methods are given using test problems including a real-world application to Nash–Cournot oligopolistic electricity market equilibrium model. acknowledgement: The authors are grateful to the two referees and the Associate Editor for their comments and suggestions which have improved the earlier version of the paper greatly. The project of Yekini Shehu has received funding from the European Research Council (ERC) under the European Union’s Seventh Framework Program (FP7 - 2007-2013) (Grant agreement No. 616160). article_processing_charge: No article_type: original author: - first_name: Yekini full_name: Shehu, Yekini id: 3FC7CB58-F248-11E8-B48F-1D18A9856A87 last_name: Shehu orcid: 0000-0001-9224-7139 - first_name: Olaniyi S. full_name: Iyiola, Olaniyi S. last_name: Iyiola - first_name: Duong Viet full_name: Thong, Duong Viet last_name: Thong - first_name: Nguyen Thi Cam full_name: Van, Nguyen Thi Cam last_name: Van citation: ama: Shehu Y, Iyiola OS, Thong DV, Van NTC. An inertial subgradient extragradient algorithm extended to pseudomonotone equilibrium problems. Mathematical Methods of Operations Research. 2021;93(2):213-242. doi:10.1007/s00186-020-00730-w apa: Shehu, Y., Iyiola, O. S., Thong, D. V., & Van, N. T. C. (2021). An inertial subgradient extragradient algorithm extended to pseudomonotone equilibrium problems. Mathematical Methods of Operations Research. Springer Nature. https://doi.org/10.1007/s00186-020-00730-w chicago: Shehu, Yekini, Olaniyi S. Iyiola, Duong Viet Thong, and Nguyen Thi Cam Van. “An Inertial Subgradient Extragradient Algorithm Extended to Pseudomonotone Equilibrium Problems.” Mathematical Methods of Operations Research. Springer Nature, 2021. https://doi.org/10.1007/s00186-020-00730-w. ieee: Y. Shehu, O. S. Iyiola, D. V. Thong, and N. T. C. Van, “An inertial subgradient extragradient algorithm extended to pseudomonotone equilibrium problems,” Mathematical Methods of Operations Research, vol. 93, no. 2. Springer Nature, pp. 213–242, 2021. ista: Shehu Y, Iyiola OS, Thong DV, Van NTC. 2021. An inertial subgradient extragradient algorithm extended to pseudomonotone equilibrium problems. Mathematical Methods of Operations Research. 93(2), 213–242. mla: Shehu, Yekini, et al. “An Inertial Subgradient Extragradient Algorithm Extended to Pseudomonotone Equilibrium Problems.” Mathematical Methods of Operations Research, vol. 93, no. 2, Springer Nature, 2021, pp. 213–42, doi:10.1007/s00186-020-00730-w. short: Y. Shehu, O.S. Iyiola, D.V. Thong, N.T.C. Van, Mathematical Methods of Operations Research 93 (2021) 213–242. date_created: 2020-11-29T23:01:18Z date_published: 2021-04-01T00:00:00Z date_updated: 2023-10-10T09:30:23Z day: '01' department: - _id: VlKo doi: 10.1007/s00186-020-00730-w ec_funded: 1 external_id: isi: - '000590497300001' intvolume: ' 93' isi: 1 issue: '2' language: - iso: eng month: '04' oa_version: None page: 213-242 project: - _id: 25FBA906-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '616160' name: 'Discrete Optimization in Computer Vision: Theory and Practice' publication: Mathematical Methods of Operations Research publication_identifier: eissn: - 1432-5217 issn: - 1432-2994 publication_status: published publisher: Springer Nature quality_controlled: '1' scopus_import: '1' status: public title: An inertial subgradient extragradient algorithm extended to pseudomonotone equilibrium problems type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 93 year: '2021' ... --- _id: '9315' abstract: - lang: eng text: We consider inertial iteration methods for Fermat–Weber location problem and primal–dual three-operator splitting in real Hilbert spaces. To do these, we first obtain weak convergence analysis and nonasymptotic O(1/n) convergence rate of the inertial Krasnoselskii–Mann iteration for fixed point of nonexpansive operators in infinite dimensional real Hilbert spaces under some seemingly easy to implement conditions on the iterative parameters. One of our contributions is that the convergence analysis and rate of convergence results are obtained using conditions which appear not complicated and restrictive as assumed in other previous related results in the literature. We then show that Fermat–Weber location problem and primal–dual three-operator splitting are special cases of fixed point problem of nonexpansive mapping and consequently obtain the convergence analysis of inertial iteration methods for Fermat–Weber location problem and primal–dual three-operator splitting in real Hilbert spaces. Some numerical implementations are drawn from primal–dual three-operator splitting to support the theoretical analysis. acknowledgement: The research of this author is supported by the Postdoctoral Fellowship from Institute of Science and Technology (IST), Austria. article_number: '75' article_processing_charge: No article_type: original author: - first_name: Olaniyi S. full_name: Iyiola, Olaniyi S. last_name: Iyiola - first_name: Yekini full_name: Shehu, Yekini id: 3FC7CB58-F248-11E8-B48F-1D18A9856A87 last_name: Shehu orcid: 0000-0001-9224-7139 citation: ama: Iyiola OS, Shehu Y. New convergence results for inertial Krasnoselskii–Mann iterations in Hilbert spaces with applications. Results in Mathematics. 2021;76(2). doi:10.1007/s00025-021-01381-x apa: Iyiola, O. S., & Shehu, Y. (2021). New convergence results for inertial Krasnoselskii–Mann iterations in Hilbert spaces with applications. Results in Mathematics. Springer Nature. https://doi.org/10.1007/s00025-021-01381-x chicago: Iyiola, Olaniyi S., and Yekini Shehu. “New Convergence Results for Inertial Krasnoselskii–Mann Iterations in Hilbert Spaces with Applications.” Results in Mathematics. Springer Nature, 2021. https://doi.org/10.1007/s00025-021-01381-x. ieee: O. S. Iyiola and Y. Shehu, “New convergence results for inertial Krasnoselskii–Mann iterations in Hilbert spaces with applications,” Results in Mathematics, vol. 76, no. 2. Springer Nature, 2021. ista: Iyiola OS, Shehu Y. 2021. New convergence results for inertial Krasnoselskii–Mann iterations in Hilbert spaces with applications. Results in Mathematics. 76(2), 75. mla: Iyiola, Olaniyi S., and Yekini Shehu. “New Convergence Results for Inertial Krasnoselskii–Mann Iterations in Hilbert Spaces with Applications.” Results in Mathematics, vol. 76, no. 2, 75, Springer Nature, 2021, doi:10.1007/s00025-021-01381-x. short: O.S. Iyiola, Y. Shehu, Results in Mathematics 76 (2021). date_created: 2021-04-11T22:01:14Z date_published: 2021-03-25T00:00:00Z date_updated: 2023-10-10T09:47:33Z day: '25' department: - _id: VlKo doi: 10.1007/s00025-021-01381-x external_id: isi: - '000632917700001' intvolume: ' 76' isi: 1 issue: '2' language: - iso: eng month: '03' oa_version: None publication: Results in Mathematics publication_identifier: eissn: - 1420-9012 issn: - 1422-6383 publication_status: published publisher: Springer Nature quality_controlled: '1' scopus_import: '1' status: public title: New convergence results for inertial Krasnoselskii–Mann iterations in Hilbert spaces with applications type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 76 year: '2021' ... --- _id: '9365' abstract: - lang: eng text: In this paper, we propose a new iterative method with alternated inertial step for solving split common null point problem in real Hilbert spaces. We obtain weak convergence of the proposed iterative algorithm. Furthermore, we introduce the notion of bounded linear regularity property for the split common null point problem and obtain the linear convergence property for the new algorithm under some mild assumptions. Finally, we provide some numerical examples to demonstrate the performance and efficiency of the proposed method. acknowledgement: The second author has received funding from the European Research Council (ERC) under the European Union's Seventh Framework Program (FP7-2007-2013) (Grant agreement No. 616160). article_processing_charge: No article_type: original author: - first_name: Ferdinard U. full_name: Ogbuisi, Ferdinard U. last_name: Ogbuisi - first_name: Yekini full_name: Shehu, Yekini id: 3FC7CB58-F248-11E8-B48F-1D18A9856A87 last_name: Shehu orcid: 0000-0001-9224-7139 - first_name: Jen Chih full_name: Yao, Jen Chih last_name: Yao citation: ama: Ogbuisi FU, Shehu Y, Yao JC. Convergence analysis of new inertial method for the split common null point problem. Optimization. 2021. doi:10.1080/02331934.2021.1914035 apa: Ogbuisi, F. U., Shehu, Y., & Yao, J. C. (2021). Convergence analysis of new inertial method for the split common null point problem. Optimization. Taylor and Francis. https://doi.org/10.1080/02331934.2021.1914035 chicago: Ogbuisi, Ferdinard U., Yekini Shehu, and Jen Chih Yao. “Convergence Analysis of New Inertial Method for the Split Common Null Point Problem.” Optimization. Taylor and Francis, 2021. https://doi.org/10.1080/02331934.2021.1914035. ieee: F. U. Ogbuisi, Y. Shehu, and J. C. Yao, “Convergence analysis of new inertial method for the split common null point problem,” Optimization. Taylor and Francis, 2021. ista: Ogbuisi FU, Shehu Y, Yao JC. 2021. Convergence analysis of new inertial method for the split common null point problem. Optimization. mla: Ogbuisi, Ferdinard U., et al. “Convergence Analysis of New Inertial Method for the Split Common Null Point Problem.” Optimization, Taylor and Francis, 2021, doi:10.1080/02331934.2021.1914035. short: F.U. Ogbuisi, Y. Shehu, J.C. Yao, Optimization (2021). date_created: 2021-05-02T22:01:29Z date_published: 2021-04-14T00:00:00Z date_updated: 2023-10-10T09:48:41Z day: '14' department: - _id: VlKo doi: 10.1080/02331934.2021.1914035 ec_funded: 1 external_id: isi: - '000640109300001' isi: 1 language: - iso: eng month: '04' oa_version: None project: - _id: 25FBA906-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '616160' name: 'Discrete Optimization in Computer Vision: Theory and Practice' publication: Optimization publication_identifier: eissn: - 1029-4945 issn: - 0233-1934 publication_status: published publisher: Taylor and Francis quality_controlled: '1' scopus_import: '1' status: public title: Convergence analysis of new inertial method for the split common null point problem type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2021' ... --- _id: '8196' abstract: - lang: eng text: This paper aims to obtain a strong convergence result for a Douglas–Rachford splitting method with inertial extrapolation step for finding a zero of the sum of two set-valued maximal monotone operators without any further assumption of uniform monotonicity on any of the involved maximal monotone operators. Furthermore, our proposed method is easy to implement and the inertial factor in our proposed method is a natural choice. Our method of proof is of independent interest. Finally, some numerical implementations are given to confirm the theoretical analysis. acknowledgement: Open access funding provided by Institute of Science and Technology (IST Austria). The project of Yekini Shehu has received funding from the European Research Council (ERC) under the European Union’s Seventh Framework Program (FP7—2007–2013) (Grant Agreement No. 616160). The authors are grateful to the anonymous referees and the handling Editor for their comments and suggestions which have improved the earlier version of the manuscript greatly. article_processing_charge: Yes (via OA deal) article_type: original author: - first_name: Yekini full_name: Shehu, Yekini id: 3FC7CB58-F248-11E8-B48F-1D18A9856A87 last_name: Shehu orcid: 0000-0001-9224-7139 - first_name: Qiao-Li full_name: Dong, Qiao-Li last_name: Dong - first_name: Lu-Lu full_name: Liu, Lu-Lu last_name: Liu - first_name: Jen-Chih full_name: Yao, Jen-Chih last_name: Yao citation: ama: Shehu Y, Dong Q-L, Liu L-L, Yao J-C. New strong convergence method for the sum of two maximal monotone operators. Optimization and Engineering. 2021;22:2627-2653. doi:10.1007/s11081-020-09544-5 apa: Shehu, Y., Dong, Q.-L., Liu, L.-L., & Yao, J.-C. (2021). New strong convergence method for the sum of two maximal monotone operators. Optimization and Engineering. Springer Nature. https://doi.org/10.1007/s11081-020-09544-5 chicago: Shehu, Yekini, Qiao-Li Dong, Lu-Lu Liu, and Jen-Chih Yao. “New Strong Convergence Method for the Sum of Two Maximal Monotone Operators.” Optimization and Engineering. Springer Nature, 2021. https://doi.org/10.1007/s11081-020-09544-5. ieee: Y. Shehu, Q.-L. Dong, L.-L. Liu, and J.-C. Yao, “New strong convergence method for the sum of two maximal monotone operators,” Optimization and Engineering, vol. 22. Springer Nature, pp. 2627–2653, 2021. ista: Shehu Y, Dong Q-L, Liu L-L, Yao J-C. 2021. New strong convergence method for the sum of two maximal monotone operators. Optimization and Engineering. 22, 2627–2653. mla: Shehu, Yekini, et al. “New Strong Convergence Method for the Sum of Two Maximal Monotone Operators.” Optimization and Engineering, vol. 22, Springer Nature, 2021, pp. 2627–53, doi:10.1007/s11081-020-09544-5. short: Y. Shehu, Q.-L. Dong, L.-L. Liu, J.-C. Yao, Optimization and Engineering 22 (2021) 2627–2653. date_created: 2020-08-03T14:29:57Z date_published: 2021-02-25T00:00:00Z date_updated: 2024-03-07T14:39:29Z day: '25' ddc: - '510' department: - _id: VlKo doi: 10.1007/s11081-020-09544-5 ec_funded: 1 external_id: isi: - '000559345400001' file: - access_level: open_access content_type: application/pdf creator: dernst date_created: 2020-08-03T15:24:39Z date_updated: 2020-08-03T15:24:39Z file_id: '8197' file_name: 2020_OptimizationEngineering_Shehu.pdf file_size: 2137860 relation: main_file success: 1 file_date_updated: 2020-08-03T15:24:39Z has_accepted_license: '1' intvolume: ' 22' isi: 1 language: - iso: eng month: '02' oa: 1 oa_version: Published Version page: 2627-2653 project: - _id: B67AFEDC-15C9-11EA-A837-991A96BB2854 name: IST Austria Open Access Fund - _id: 25FBA906-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '616160' name: 'Discrete Optimization in Computer Vision: Theory and Practice' publication: Optimization and Engineering publication_identifier: eissn: - 1573-2924 issn: - 1389-4420 publication_status: published publisher: Springer Nature quality_controlled: '1' scopus_import: '1' status: public title: New strong convergence method for the sum of two maximal monotone operators tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87 volume: 22 year: '2021' ... --- _id: '7925' abstract: - lang: eng text: In this paper, we introduce a relaxed CQ method with alternated inertial step for solving split feasibility problems. We give convergence of the sequence generated by our method under some suitable assumptions. Some numerical implementations from sparse signal and image deblurring are reported to show the efficiency of our method. acknowledgement: Open access funding provided by Institute of Science and Technology (IST Austria). The authors are grateful to the referees for their insightful comments which have improved the earlier version of the manuscript greatly. The first author has received funding from the European Research Council (ERC) under the European Union’s Seventh Framework Program (FP7-2007-2013) (Grant agreement No. 616160). article_processing_charge: Yes (via OA deal) article_type: original author: - first_name: Yekini full_name: Shehu, Yekini id: 3FC7CB58-F248-11E8-B48F-1D18A9856A87 last_name: Shehu orcid: 0000-0001-9224-7139 - first_name: Aviv full_name: Gibali, Aviv last_name: Gibali citation: ama: Shehu Y, Gibali A. New inertial relaxed method for solving split feasibilities. Optimization Letters. 2021;15:2109-2126. doi:10.1007/s11590-020-01603-1 apa: Shehu, Y., & Gibali, A. (2021). New inertial relaxed method for solving split feasibilities. Optimization Letters. Springer Nature. https://doi.org/10.1007/s11590-020-01603-1 chicago: Shehu, Yekini, and Aviv Gibali. “New Inertial Relaxed Method for Solving Split Feasibilities.” Optimization Letters. Springer Nature, 2021. https://doi.org/10.1007/s11590-020-01603-1. ieee: Y. Shehu and A. Gibali, “New inertial relaxed method for solving split feasibilities,” Optimization Letters, vol. 15. Springer Nature, pp. 2109–2126, 2021. ista: Shehu Y, Gibali A. 2021. New inertial relaxed method for solving split feasibilities. Optimization Letters. 15, 2109–2126. mla: Shehu, Yekini, and Aviv Gibali. “New Inertial Relaxed Method for Solving Split Feasibilities.” Optimization Letters, vol. 15, Springer Nature, 2021, pp. 2109–26, doi:10.1007/s11590-020-01603-1. short: Y. Shehu, A. Gibali, Optimization Letters 15 (2021) 2109–2126. date_created: 2020-06-04T11:28:33Z date_published: 2021-09-01T00:00:00Z date_updated: 2024-03-07T15:00:43Z day: '01' ddc: - '510' department: - _id: VlKo doi: 10.1007/s11590-020-01603-1 ec_funded: 1 external_id: isi: - '000537342300001' file: - access_level: open_access checksum: 63c5f31cd04626152a19f97a2476281b content_type: application/pdf creator: kschuh date_created: 2024-03-07T14:58:51Z date_updated: 2024-03-07T14:58:51Z file_id: '15089' file_name: 2021_OptimizationLetters_Shehu.pdf file_size: 2148882 relation: main_file success: 1 file_date_updated: 2024-03-07T14:58:51Z has_accepted_license: '1' intvolume: ' 15' isi: 1 language: - iso: eng month: '09' oa: 1 oa_version: Published Version page: 2109-2126 project: - _id: 25FBA906-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '616160' name: 'Discrete Optimization in Computer Vision: Theory and Practice' - _id: B67AFEDC-15C9-11EA-A837-991A96BB2854 name: IST Austria Open Access Fund publication: Optimization Letters publication_identifier: eissn: - 1862-4480 issn: - 1862-4472 publication_status: published publisher: Springer Nature quality_controlled: '1' scopus_import: '1' status: public title: New inertial relaxed method for solving split feasibilities tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87 volume: 15 year: '2021' ... --- _id: '6593' abstract: - lang: eng text: 'We consider the monotone variational inequality problem in a Hilbert space and describe a projection-type method with inertial terms under the following properties: (a) The method generates a strongly convergent iteration sequence; (b) The method requires, at each iteration, only one projection onto the feasible set and two evaluations of the operator; (c) The method is designed for variational inequality for which the underline operator is monotone and uniformly continuous; (d) The method includes an inertial term. The latter is also shown to speed up the convergence in our numerical results. A comparison with some related methods is given and indicates that the new method is promising.' acknowledgement: The research of this author is supported by the ERC grant at the IST. article_processing_charge: No article_type: original author: - first_name: Yekini full_name: Shehu, Yekini id: 3FC7CB58-F248-11E8-B48F-1D18A9856A87 last_name: Shehu orcid: 0000-0001-9224-7139 - first_name: Xiao-Huan full_name: Li, Xiao-Huan last_name: Li - first_name: Qiao-Li full_name: Dong, Qiao-Li last_name: Dong citation: ama: Shehu Y, Li X-H, Dong Q-L. An efficient projection-type method for monotone variational inequalities in Hilbert spaces. Numerical Algorithms. 2020;84:365-388. doi:10.1007/s11075-019-00758-y apa: Shehu, Y., Li, X.-H., & Dong, Q.-L. (2020). An efficient projection-type method for monotone variational inequalities in Hilbert spaces. Numerical Algorithms. Springer Nature. https://doi.org/10.1007/s11075-019-00758-y chicago: Shehu, Yekini, Xiao-Huan Li, and Qiao-Li Dong. “An Efficient Projection-Type Method for Monotone Variational Inequalities in Hilbert Spaces.” Numerical Algorithms. Springer Nature, 2020. https://doi.org/10.1007/s11075-019-00758-y. ieee: Y. Shehu, X.-H. Li, and Q.-L. Dong, “An efficient projection-type method for monotone variational inequalities in Hilbert spaces,” Numerical Algorithms, vol. 84. Springer Nature, pp. 365–388, 2020. ista: Shehu Y, Li X-H, Dong Q-L. 2020. An efficient projection-type method for monotone variational inequalities in Hilbert spaces. Numerical Algorithms. 84, 365–388. mla: Shehu, Yekini, et al. “An Efficient Projection-Type Method for Monotone Variational Inequalities in Hilbert Spaces.” Numerical Algorithms, vol. 84, Springer Nature, 2020, pp. 365–88, doi:10.1007/s11075-019-00758-y. short: Y. Shehu, X.-H. Li, Q.-L. Dong, Numerical Algorithms 84 (2020) 365–388. date_created: 2019-06-27T20:09:33Z date_published: 2020-05-01T00:00:00Z date_updated: 2023-08-17T13:51:18Z day: '01' ddc: - '000' department: - _id: VlKo doi: 10.1007/s11075-019-00758-y ec_funded: 1 external_id: isi: - '000528979000015' file: - access_level: open_access checksum: bb1a1eb3ebb2df380863d0db594673ba content_type: application/pdf creator: kschuh date_created: 2019-10-01T13:14:10Z date_updated: 2020-07-14T12:47:34Z file_id: '6927' file_name: ExtragradientMethodPaper.pdf file_size: 359654 relation: main_file file_date_updated: 2020-07-14T12:47:34Z has_accepted_license: '1' intvolume: ' 84' isi: 1 language: - iso: eng month: '05' oa: 1 oa_version: Submitted Version page: 365-388 project: - _id: 25FBA906-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '616160' name: 'Discrete Optimization in Computer Vision: Theory and Practice' publication: Numerical Algorithms publication_identifier: eissn: - 1572-9265 issn: - 1017-1398 publication_status: published publisher: Springer Nature quality_controlled: '1' scopus_import: '1' status: public title: An efficient projection-type method for monotone variational inequalities in Hilbert spaces type: journal_article user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8 volume: 84 year: '2020' ... --- _id: '8077' abstract: - lang: eng text: The projection methods with vanilla inertial extrapolation step for variational inequalities have been of interest to many authors recently due to the improved convergence speed contributed by the presence of inertial extrapolation step. However, it is discovered that these projection methods with inertial steps lose the Fejér monotonicity of the iterates with respect to the solution, which is being enjoyed by their corresponding non-inertial projection methods for variational inequalities. This lack of Fejér monotonicity makes projection methods with vanilla inertial extrapolation step for variational inequalities not to converge faster than their corresponding non-inertial projection methods at times. Also, it has recently been proved that the projection methods with vanilla inertial extrapolation step may provide convergence rates that are worse than the classical projected gradient methods for strongly convex functions. In this paper, we introduce projection methods with alternated inertial extrapolation step for solving variational inequalities. We show that the sequence of iterates generated by our methods converges weakly to a solution of the variational inequality under some appropriate conditions. The Fejér monotonicity of even subsequence is recovered in these methods and linear rate of convergence is obtained. The numerical implementations of our methods compared with some other inertial projection methods show that our method is more efficient and outperforms some of these inertial projection methods. acknowledgement: The authors are grateful to the two anonymous referees for their insightful comments and suggestions which have improved the earlier version of the manuscript greatly. The first author has received funding from the European Research Council (ERC) under the European Union Seventh Framework Programme (FP7 - 2007-2013) (Grant agreement No. 616160). article_processing_charge: No article_type: original author: - first_name: Yekini full_name: Shehu, Yekini id: 3FC7CB58-F248-11E8-B48F-1D18A9856A87 last_name: Shehu orcid: 0000-0001-9224-7139 - first_name: Olaniyi S. full_name: Iyiola, Olaniyi S. last_name: Iyiola citation: ama: 'Shehu Y, Iyiola OS. Projection methods with alternating inertial steps for variational inequalities: Weak and linear convergence. Applied Numerical Mathematics. 2020;157:315-337. doi:10.1016/j.apnum.2020.06.009' apa: 'Shehu, Y., & Iyiola, O. S. (2020). Projection methods with alternating inertial steps for variational inequalities: Weak and linear convergence. Applied Numerical Mathematics. Elsevier. https://doi.org/10.1016/j.apnum.2020.06.009' chicago: 'Shehu, Yekini, and Olaniyi S. Iyiola. “Projection Methods with Alternating Inertial Steps for Variational Inequalities: Weak and Linear Convergence.” Applied Numerical Mathematics. Elsevier, 2020. https://doi.org/10.1016/j.apnum.2020.06.009.' ieee: 'Y. Shehu and O. S. Iyiola, “Projection methods with alternating inertial steps for variational inequalities: Weak and linear convergence,” Applied Numerical Mathematics, vol. 157. Elsevier, pp. 315–337, 2020.' ista: 'Shehu Y, Iyiola OS. 2020. Projection methods with alternating inertial steps for variational inequalities: Weak and linear convergence. Applied Numerical Mathematics. 157, 315–337.' mla: 'Shehu, Yekini, and Olaniyi S. Iyiola. “Projection Methods with Alternating Inertial Steps for Variational Inequalities: Weak and Linear Convergence.” Applied Numerical Mathematics, vol. 157, Elsevier, 2020, pp. 315–37, doi:10.1016/j.apnum.2020.06.009.' short: Y. Shehu, O.S. Iyiola, Applied Numerical Mathematics 157 (2020) 315–337. date_created: 2020-07-02T09:02:33Z date_published: 2020-11-01T00:00:00Z date_updated: 2023-08-22T07:50:43Z day: '01' ddc: - '510' department: - _id: VlKo doi: 10.1016/j.apnum.2020.06.009 ec_funded: 1 external_id: isi: - '000564648400018' file: - access_level: open_access checksum: 87d81324a62c82baa925c009dfcb0200 content_type: application/pdf creator: dernst date_created: 2020-07-02T09:08:59Z date_updated: 2020-07-14T12:48:09Z file_id: '8078' file_name: 2020_AppliedNumericalMath_Shehu.pdf file_size: 2874203 relation: main_file file_date_updated: 2020-07-14T12:48:09Z has_accepted_license: '1' intvolume: ' 157' isi: 1 language: - iso: eng month: '11' oa: 1 oa_version: Submitted Version page: 315-337 project: - _id: 25FBA906-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '616160' name: 'Discrete Optimization in Computer Vision: Theory and Practice' publication: Applied Numerical Mathematics publication_identifier: issn: - 0168-9274 publication_status: published publisher: Elsevier quality_controlled: '1' scopus_import: '1' status: public title: 'Projection methods with alternating inertial steps for variational inequalities: Weak and linear convergence' type: journal_article user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8 volume: 157 year: '2020' ... --- _id: '7161' abstract: - lang: eng text: In this paper, we introduce an inertial projection-type method with different updating strategies for solving quasi-variational inequalities with strongly monotone and Lipschitz continuous operators in real Hilbert spaces. Under standard assumptions, we establish different strong convergence results for the proposed algorithm. Primary numerical experiments demonstrate the potential applicability of our scheme compared with some related methods in the literature. acknowledgement: We are grateful to the anonymous referees and editor whose insightful comments helped to considerably improve an earlier version of this paper. The research of the first author is supported by an ERC Grant from the Institute of Science and Technology (IST). article_processing_charge: No article_type: original author: - first_name: Yekini full_name: Shehu, Yekini id: 3FC7CB58-F248-11E8-B48F-1D18A9856A87 last_name: Shehu orcid: 0000-0001-9224-7139 - first_name: Aviv full_name: Gibali, Aviv last_name: Gibali - first_name: Simone full_name: Sagratella, Simone last_name: Sagratella citation: ama: Shehu Y, Gibali A, Sagratella S. Inertial projection-type methods for solving quasi-variational inequalities in real Hilbert spaces. Journal of Optimization Theory and Applications. 2020;184:877–894. doi:10.1007/s10957-019-01616-6 apa: Shehu, Y., Gibali, A., & Sagratella, S. (2020). Inertial projection-type methods for solving quasi-variational inequalities in real Hilbert spaces. Journal of Optimization Theory and Applications. Springer Nature. https://doi.org/10.1007/s10957-019-01616-6 chicago: Shehu, Yekini, Aviv Gibali, and Simone Sagratella. “Inertial Projection-Type Methods for Solving Quasi-Variational Inequalities in Real Hilbert Spaces.” Journal of Optimization Theory and Applications. Springer Nature, 2020. https://doi.org/10.1007/s10957-019-01616-6. ieee: Y. Shehu, A. Gibali, and S. Sagratella, “Inertial projection-type methods for solving quasi-variational inequalities in real Hilbert spaces,” Journal of Optimization Theory and Applications, vol. 184. Springer Nature, pp. 877–894, 2020. ista: Shehu Y, Gibali A, Sagratella S. 2020. Inertial projection-type methods for solving quasi-variational inequalities in real Hilbert spaces. Journal of Optimization Theory and Applications. 184, 877–894. mla: Shehu, Yekini, et al. “Inertial Projection-Type Methods for Solving Quasi-Variational Inequalities in Real Hilbert Spaces.” Journal of Optimization Theory and Applications, vol. 184, Springer Nature, 2020, pp. 877–894, doi:10.1007/s10957-019-01616-6. short: Y. Shehu, A. Gibali, S. Sagratella, Journal of Optimization Theory and Applications 184 (2020) 877–894. date_created: 2019-12-09T21:33:44Z date_published: 2020-03-01T00:00:00Z date_updated: 2023-09-06T11:27:15Z day: '01' ddc: - '518' - '510' - '515' department: - _id: VlKo doi: 10.1007/s10957-019-01616-6 ec_funded: 1 external_id: isi: - '000511805200009' file: - access_level: open_access checksum: 9f6dc6c6bf2b48cb3a2091a9ed5feaf2 content_type: application/pdf creator: dernst date_created: 2020-10-12T10:40:27Z date_updated: 2021-03-16T23:30:04Z embargo: 2021-03-15 file_id: '8647' file_name: 2020_JourOptimizationTheoryApplic_Shehu.pdf file_size: 332641 relation: main_file file_date_updated: 2021-03-16T23:30:04Z has_accepted_license: '1' intvolume: ' 184' isi: 1 language: - iso: eng month: '03' oa: 1 oa_version: Submitted Version page: 877–894 project: - _id: 25FBA906-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '616160' name: 'Discrete Optimization in Computer Vision: Theory and Practice' publication: Journal of Optimization Theory and Applications publication_identifier: eissn: - 1573-2878 issn: - 0022-3239 publication_status: published publisher: Springer Nature quality_controlled: '1' scopus_import: '1' status: public title: Inertial projection-type methods for solving quasi-variational inequalities in real Hilbert spaces type: journal_article user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1 volume: 184 year: '2020' ... --- _id: '6725' abstract: - lang: eng text: "A Valued Constraint Satisfaction Problem (VCSP) provides a common framework that can express a wide range of discrete optimization problems. A VCSP instance is given by a finite set of variables, a finite domain of labels, and an objective function to be minimized. This function is represented as a sum of terms where each term depends on a subset of the variables. To obtain different classes of optimization problems, one can restrict all terms to come from a fixed set Γ of cost functions, called a language. \r\nRecent breakthrough results have established a complete complexity classification of such classes with respect to language Γ: if all cost functions in Γ satisfy a certain algebraic condition then all Γ-instances can be solved in polynomial time, otherwise the problem is NP-hard. Unfortunately, testing this condition for a given language Γ is known to be NP-hard. We thus study exponential algorithms for this meta-problem. We show that the tractability condition of a finite-valued language Γ can be tested in O(3‾√3|D|⋅poly(size(Γ))) time, where D is the domain of Γ and poly(⋅) is some fixed polynomial. We also obtain a matching lower bound under the Strong Exponential Time Hypothesis (SETH). More precisely, we prove that for any constant δ<1 there is no O(3‾√3δ|D|) algorithm, assuming that SETH holds." alternative_title: - LIPIcs author: - first_name: Vladimir full_name: Kolmogorov, Vladimir id: 3D50B0BA-F248-11E8-B48F-1D18A9856A87 last_name: Kolmogorov citation: ama: 'Kolmogorov V. Testing the complexity of a valued CSP language. In: 46th International Colloquium on Automata, Languages and Programming. Vol 132. Schloss Dagstuhl - Leibniz-Zentrum für Informatik; 2019:77:1-77:12. doi:10.4230/LIPICS.ICALP.2019.77' apa: 'Kolmogorov, V. (2019). Testing the complexity of a valued CSP language. In 46th International Colloquium on Automata, Languages and Programming (Vol. 132, p. 77:1-77:12). Patras, Greece: Schloss Dagstuhl - Leibniz-Zentrum für Informatik. https://doi.org/10.4230/LIPICS.ICALP.2019.77' chicago: Kolmogorov, Vladimir. “Testing the Complexity of a Valued CSP Language.” In 46th International Colloquium on Automata, Languages and Programming, 132:77:1-77:12. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2019. https://doi.org/10.4230/LIPICS.ICALP.2019.77. ieee: V. Kolmogorov, “Testing the complexity of a valued CSP language,” in 46th International Colloquium on Automata, Languages and Programming, Patras, Greece, 2019, vol. 132, p. 77:1-77:12. ista: 'Kolmogorov V. 2019. Testing the complexity of a valued CSP language. 46th International Colloquium on Automata, Languages and Programming. ICALP 2019: International Colloquim on Automata, Languages and Programming, LIPIcs, vol. 132, 77:1-77:12.' mla: Kolmogorov, Vladimir. “Testing the Complexity of a Valued CSP Language.” 46th International Colloquium on Automata, Languages and Programming, vol. 132, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2019, p. 77:1-77:12, doi:10.4230/LIPICS.ICALP.2019.77. short: V. Kolmogorov, in:, 46th International Colloquium on Automata, Languages and Programming, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2019, p. 77:1-77:12. conference: end_date: 2019-07-12 location: Patras, Greece name: 'ICALP 2019: International Colloquim on Automata, Languages and Programming' start_date: 2019-07-08 date_created: 2019-07-29T12:23:29Z date_published: 2019-07-01T00:00:00Z date_updated: 2021-01-12T08:08:40Z day: '01' ddc: - '000' department: - _id: VlKo doi: 10.4230/LIPICS.ICALP.2019.77 ec_funded: 1 external_id: arxiv: - '1803.02289' file: - access_level: open_access checksum: f5ebee8eec6ae09e30365578ee63a492 content_type: application/pdf creator: dernst date_created: 2019-07-31T07:01:45Z date_updated: 2020-07-14T12:47:38Z file_id: '6738' file_name: 2019_LIPICS_Kolmogorov.pdf file_size: 575475 relation: main_file file_date_updated: 2020-07-14T12:47:38Z has_accepted_license: '1' intvolume: ' 132' language: - iso: eng month: '07' oa: 1 oa_version: Published Version page: 77:1-77:12 project: - _id: 25FBA906-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '616160' name: 'Discrete Optimization in Computer Vision: Theory and Practice' publication: 46th International Colloquium on Automata, Languages and Programming publication_identifier: isbn: - 978-3-95977-109-2 issn: - 1868-8969 publication_status: published publisher: Schloss Dagstuhl - Leibniz-Zentrum für Informatik quality_controlled: '1' scopus_import: 1 status: public title: Testing the complexity of a valued CSP language tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 132 year: '2019' ... --- _id: '6596' abstract: - lang: eng text: It is well known that many problems in image recovery, signal processing, and machine learning can be modeled as finding zeros of the sum of maximal monotone and Lipschitz continuous monotone operators. Many papers have studied forward-backward splitting methods for finding zeros of the sum of two monotone operators in Hilbert spaces. Most of the proposed splitting methods in the literature have been proposed for the sum of maximal monotone and inverse-strongly monotone operators in Hilbert spaces. In this paper, we consider splitting methods for finding zeros of the sum of maximal monotone operators and Lipschitz continuous monotone operators in Banach spaces. We obtain weak and strong convergence results for the zeros of the sum of maximal monotone and Lipschitz continuous monotone operators in Banach spaces. Many already studied problems in the literature can be considered as special cases of this paper. article_number: '138' article_processing_charge: Yes (via OA deal) article_type: original author: - first_name: Yekini full_name: Shehu, Yekini id: 3FC7CB58-F248-11E8-B48F-1D18A9856A87 last_name: Shehu orcid: 0000-0001-9224-7139 citation: ama: Shehu Y. Convergence results of forward-backward algorithms for sum of monotone operators in Banach spaces. Results in Mathematics. 2019;74(4). doi:10.1007/s00025-019-1061-4 apa: Shehu, Y. (2019). Convergence results of forward-backward algorithms for sum of monotone operators in Banach spaces. Results in Mathematics. Springer. https://doi.org/10.1007/s00025-019-1061-4 chicago: Shehu, Yekini. “Convergence Results of Forward-Backward Algorithms for Sum of Monotone Operators in Banach Spaces.” Results in Mathematics. Springer, 2019. https://doi.org/10.1007/s00025-019-1061-4. ieee: Y. Shehu, “Convergence results of forward-backward algorithms for sum of monotone operators in Banach spaces,” Results in Mathematics, vol. 74, no. 4. Springer, 2019. ista: Shehu Y. 2019. Convergence results of forward-backward algorithms for sum of monotone operators in Banach spaces. Results in Mathematics. 74(4), 138. mla: Shehu, Yekini. “Convergence Results of Forward-Backward Algorithms for Sum of Monotone Operators in Banach Spaces.” Results in Mathematics, vol. 74, no. 4, 138, Springer, 2019, doi:10.1007/s00025-019-1061-4. short: Y. Shehu, Results in Mathematics 74 (2019). date_created: 2019-06-29T10:11:30Z date_published: 2019-12-01T00:00:00Z date_updated: 2023-08-28T12:26:22Z day: '01' ddc: - '000' department: - _id: VlKo doi: 10.1007/s00025-019-1061-4 ec_funded: 1 external_id: arxiv: - '2101.09068' isi: - '000473237500002' file: - access_level: open_access checksum: c6d18cb1e16fc0c36a0e0f30b4ebbc2d content_type: application/pdf creator: kschuh date_created: 2019-07-03T15:20:40Z date_updated: 2020-07-14T12:47:34Z file_id: '6605' file_name: Springer_2019_Shehu.pdf file_size: 466942 relation: main_file file_date_updated: 2020-07-14T12:47:34Z has_accepted_license: '1' intvolume: ' 74' isi: 1 issue: '4' language: - iso: eng month: '12' oa: 1 oa_version: Published Version project: - _id: 25FBA906-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '616160' name: 'Discrete Optimization in Computer Vision: Theory and Practice' - _id: B67AFEDC-15C9-11EA-A837-991A96BB2854 name: IST Austria Open Access Fund publication: Results in Mathematics publication_identifier: eissn: - 1420-9012 issn: - 1422-6383 publication_status: published publisher: Springer quality_controlled: '1' scopus_import: '1' status: public title: Convergence results of forward-backward algorithms for sum of monotone operators in Banach spaces tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8 volume: 74 year: '2019' ... --- _id: '7000' abstract: - lang: eng text: The main contributions of this paper are the proposition and the convergence analysis of a class of inertial projection-type algorithm for solving variational inequality problems in real Hilbert spaces where the underline operator is monotone and uniformly continuous. We carry out a unified analysis of the proposed method under very mild assumptions. In particular, weak convergence of the generated sequence is established and nonasymptotic O(1 / n) rate of convergence is established, where n denotes the iteration counter. We also present some experimental results to illustrate the profits gained by introducing the inertial extrapolation steps. article_number: '161' article_processing_charge: No article_type: original author: - first_name: Yekini full_name: Shehu, Yekini id: 3FC7CB58-F248-11E8-B48F-1D18A9856A87 last_name: Shehu orcid: 0000-0001-9224-7139 - first_name: Olaniyi S. full_name: Iyiola, Olaniyi S. last_name: Iyiola - first_name: Xiao-Huan full_name: Li, Xiao-Huan last_name: Li - first_name: Qiao-Li full_name: Dong, Qiao-Li last_name: Dong citation: ama: Shehu Y, Iyiola OS, Li X-H, Dong Q-L. Convergence analysis of projection method for variational inequalities. Computational and Applied Mathematics. 2019;38(4). doi:10.1007/s40314-019-0955-9 apa: Shehu, Y., Iyiola, O. S., Li, X.-H., & Dong, Q.-L. (2019). Convergence analysis of projection method for variational inequalities. Computational and Applied Mathematics. Springer Nature. https://doi.org/10.1007/s40314-019-0955-9 chicago: Shehu, Yekini, Olaniyi S. Iyiola, Xiao-Huan Li, and Qiao-Li Dong. “Convergence Analysis of Projection Method for Variational Inequalities.” Computational and Applied Mathematics. Springer Nature, 2019. https://doi.org/10.1007/s40314-019-0955-9. ieee: Y. Shehu, O. S. Iyiola, X.-H. Li, and Q.-L. Dong, “Convergence analysis of projection method for variational inequalities,” Computational and Applied Mathematics, vol. 38, no. 4. Springer Nature, 2019. ista: Shehu Y, Iyiola OS, Li X-H, Dong Q-L. 2019. Convergence analysis of projection method for variational inequalities. Computational and Applied Mathematics. 38(4), 161. mla: Shehu, Yekini, et al. “Convergence Analysis of Projection Method for Variational Inequalities.” Computational and Applied Mathematics, vol. 38, no. 4, 161, Springer Nature, 2019, doi:10.1007/s40314-019-0955-9. short: Y. Shehu, O.S. Iyiola, X.-H. Li, Q.-L. Dong, Computational and Applied Mathematics 38 (2019). date_created: 2019-11-12T12:41:44Z date_published: 2019-12-01T00:00:00Z date_updated: 2023-08-30T07:20:32Z day: '01' ddc: - '510' - '515' - '518' department: - _id: VlKo doi: 10.1007/s40314-019-0955-9 ec_funded: 1 external_id: arxiv: - '2101.09081' isi: - '000488973100005' has_accepted_license: '1' intvolume: ' 38' isi: 1 issue: '4' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.1007/s40314-019-0955-9 month: '12' oa: 1 oa_version: Published Version project: - _id: 25FBA906-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '616160' name: 'Discrete Optimization in Computer Vision: Theory and Practice' publication: Computational and Applied Mathematics publication_identifier: eissn: - 1807-0302 issn: - 2238-3603 publication_status: published publisher: Springer Nature quality_controlled: '1' scopus_import: '1' status: public title: Convergence analysis of projection method for variational inequalities type: journal_article user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8 volume: 38 year: '2019' ... --- _id: '7412' abstract: - lang: eng text: We develop a framework for the rigorous analysis of focused stochastic local search algorithms. These algorithms search a state space by repeatedly selecting some constraint that is violated in the current state and moving to a random nearby state that addresses the violation, while (we hope) not introducing many new violations. An important class of focused local search algorithms with provable performance guarantees has recently arisen from algorithmizations of the Lovász local lemma (LLL), a nonconstructive tool for proving the existence of satisfying states by introducing a background measure on the state space. While powerful, the state transitions of algorithms in this class must be, in a precise sense, perfectly compatible with the background measure. In many applications this is a very restrictive requirement, and one needs to step outside the class. Here we introduce the notion of measure distortion and develop a framework for analyzing arbitrary focused stochastic local search algorithms, recovering LLL algorithmizations as the special case of no distortion. Our framework takes as input an arbitrary algorithm of such type and an arbitrary probability measure and shows how to use the measure as a yardstick of algorithmic progress, even for algorithms designed independently of the measure. article_processing_charge: No article_type: original author: - first_name: Dimitris full_name: Achlioptas, Dimitris last_name: Achlioptas - first_name: Fotis full_name: Iliopoulos, Fotis last_name: Iliopoulos - first_name: Vladimir full_name: Kolmogorov, Vladimir id: 3D50B0BA-F248-11E8-B48F-1D18A9856A87 last_name: Kolmogorov citation: ama: Achlioptas D, Iliopoulos F, Kolmogorov V. A local lemma for focused stochastical algorithms. SIAM Journal on Computing. 2019;48(5):1583-1602. doi:10.1137/16m109332x apa: Achlioptas, D., Iliopoulos, F., & Kolmogorov, V. (2019). A local lemma for focused stochastical algorithms. SIAM Journal on Computing. SIAM. https://doi.org/10.1137/16m109332x chicago: Achlioptas, Dimitris, Fotis Iliopoulos, and Vladimir Kolmogorov. “A Local Lemma for Focused Stochastical Algorithms.” SIAM Journal on Computing. SIAM, 2019. https://doi.org/10.1137/16m109332x. ieee: D. Achlioptas, F. Iliopoulos, and V. Kolmogorov, “A local lemma for focused stochastical algorithms,” SIAM Journal on Computing, vol. 48, no. 5. SIAM, pp. 1583–1602, 2019. ista: Achlioptas D, Iliopoulos F, Kolmogorov V. 2019. A local lemma for focused stochastical algorithms. SIAM Journal on Computing. 48(5), 1583–1602. mla: Achlioptas, Dimitris, et al. “A Local Lemma for Focused Stochastical Algorithms.” SIAM Journal on Computing, vol. 48, no. 5, SIAM, 2019, pp. 1583–602, doi:10.1137/16m109332x. short: D. Achlioptas, F. Iliopoulos, V. Kolmogorov, SIAM Journal on Computing 48 (2019) 1583–1602. date_created: 2020-01-30T09:27:32Z date_published: 2019-10-31T00:00:00Z date_updated: 2023-09-06T15:25:29Z day: '31' department: - _id: VlKo doi: 10.1137/16m109332x ec_funded: 1 external_id: arxiv: - '1809.01537' isi: - '000493900200005' intvolume: ' 48' isi: 1 issue: '5' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/1809.01537 month: '10' oa: 1 oa_version: Preprint page: 1583-1602 project: - _id: 25FBA906-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '616160' name: 'Discrete Optimization in Computer Vision: Theory and Practice' publication: SIAM Journal on Computing publication_identifier: eissn: - 1095-7111 issn: - 0097-5397 publication_status: published publisher: SIAM quality_controlled: '1' scopus_import: '1' status: public title: A local lemma for focused stochastical algorithms type: journal_article user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1 volume: 48 year: '2019' ... --- _id: '7468' abstract: - lang: eng text: We present a new proximal bundle method for Maximum-A-Posteriori (MAP) inference in structured energy minimization problems. The method optimizes a Lagrangean relaxation of the original energy minimization problem using a multi plane block-coordinate Frank-Wolfe method that takes advantage of the specific structure of the Lagrangean decomposition. We show empirically that our method outperforms state-of-the-art Lagrangean decomposition based algorithms on some challenging Markov Random Field, multi-label discrete tomography and graph matching problems. article_number: 11138-11147 article_processing_charge: No author: - first_name: Paul full_name: Swoboda, Paul id: 446560C6-F248-11E8-B48F-1D18A9856A87 last_name: Swoboda - first_name: Vladimir full_name: Kolmogorov, Vladimir id: 3D50B0BA-F248-11E8-B48F-1D18A9856A87 last_name: Kolmogorov citation: ama: 'Swoboda P, Kolmogorov V. Map inference via block-coordinate Frank-Wolfe algorithm. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Vol 2019-June. IEEE; 2019. doi:10.1109/CVPR.2019.01140' apa: 'Swoboda, P., & Kolmogorov, V. (2019). Map inference via block-coordinate Frank-Wolfe algorithm. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Vol. 2019–June). Long Beach, CA, United States: IEEE. https://doi.org/10.1109/CVPR.2019.01140' chicago: Swoboda, Paul, and Vladimir Kolmogorov. “Map Inference via Block-Coordinate Frank-Wolfe Algorithm.” In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 2019–June. IEEE, 2019. https://doi.org/10.1109/CVPR.2019.01140. ieee: P. Swoboda and V. Kolmogorov, “Map inference via block-coordinate Frank-Wolfe algorithm,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Long Beach, CA, United States, 2019, vol. 2019–June. ista: 'Swoboda P, Kolmogorov V. 2019. Map inference via block-coordinate Frank-Wolfe algorithm. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR: Conference on Computer Vision and Pattern Recognition vol. 2019–June, 11138–11147.' mla: Swoboda, Paul, and Vladimir Kolmogorov. “Map Inference via Block-Coordinate Frank-Wolfe Algorithm.” Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019–June, 11138–11147, IEEE, 2019, doi:10.1109/CVPR.2019.01140. short: P. Swoboda, V. Kolmogorov, in:, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE, 2019. conference: end_date: 2019-06-20 location: Long Beach, CA, United States name: 'CVPR: Conference on Computer Vision and Pattern Recognition' start_date: 2019-06-15 date_created: 2020-02-09T23:00:52Z date_published: 2019-06-01T00:00:00Z date_updated: 2023-09-07T14:54:24Z day: '01' department: - _id: VlKo doi: 10.1109/CVPR.2019.01140 ec_funded: 1 external_id: arxiv: - '1806.05049' isi: - '000542649304076' isi: 1 language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/1806.05049 month: '06' oa: 1 oa_version: Preprint project: - _id: 25FBA906-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '616160' name: 'Discrete Optimization in Computer Vision: Theory and Practice' publication: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition publication_identifier: isbn: - '9781728132938' issn: - '10636919' publication_status: published publisher: IEEE quality_controlled: '1' scopus_import: '1' status: public title: Map inference via block-coordinate Frank-Wolfe algorithm type: conference user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1 volume: 2019-June year: '2019' ... --- _id: '7639' abstract: - lang: eng text: Deep neural networks (DNNs) have become increasingly important due to their excellent empirical performance on a wide range of problems. However, regularization is generally achieved by indirect means, largely due to the complex set of functions defined by a network and the difficulty in measuring function complexity. There exists no method in the literature for additive regularization based on a norm of the function, as is classically considered in statistical learning theory. In this work, we study the tractability of function norms for deep neural networks with ReLU activations. We provide, to the best of our knowledge, the first proof in the literature of the NP-hardness of computing function norms of DNNs of 3 or more layers. We also highlight a fundamental difference between shallow and deep networks. In the light on these results, we propose a new regularization strategy based on approximate function norms, and show its efficiency on a segmentation task with a DNN. article_number: 748-752 article_processing_charge: No author: - first_name: Amal full_name: Rannen-Triki, Amal last_name: Rannen-Triki - first_name: Maxim full_name: Berman, Maxim last_name: Berman - first_name: Vladimir full_name: Kolmogorov, Vladimir id: 3D50B0BA-F248-11E8-B48F-1D18A9856A87 last_name: Kolmogorov - first_name: Matthew B. full_name: Blaschko, Matthew B. last_name: Blaschko citation: ama: 'Rannen-Triki A, Berman M, Kolmogorov V, Blaschko MB. Function norms for neural networks. In: Proceedings of the 2019 International Conference on Computer Vision Workshop. IEEE; 2019. doi:10.1109/ICCVW.2019.00097' apa: 'Rannen-Triki, A., Berman, M., Kolmogorov, V., & Blaschko, M. B. (2019). Function norms for neural networks. In Proceedings of the 2019 International Conference on Computer Vision Workshop. Seoul, South Korea: IEEE. https://doi.org/10.1109/ICCVW.2019.00097' chicago: Rannen-Triki, Amal, Maxim Berman, Vladimir Kolmogorov, and Matthew B. Blaschko. “Function Norms for Neural Networks.” In Proceedings of the 2019 International Conference on Computer Vision Workshop. IEEE, 2019. https://doi.org/10.1109/ICCVW.2019.00097. ieee: A. Rannen-Triki, M. Berman, V. Kolmogorov, and M. B. Blaschko, “Function norms for neural networks,” in Proceedings of the 2019 International Conference on Computer Vision Workshop, Seoul, South Korea, 2019. ista: 'Rannen-Triki A, Berman M, Kolmogorov V, Blaschko MB. 2019. Function norms for neural networks. Proceedings of the 2019 International Conference on Computer Vision Workshop. ICCVW: International Conference on Computer Vision Workshop, 748–752.' mla: Rannen-Triki, Amal, et al. “Function Norms for Neural Networks.” Proceedings of the 2019 International Conference on Computer Vision Workshop, 748–752, IEEE, 2019, doi:10.1109/ICCVW.2019.00097. short: A. Rannen-Triki, M. Berman, V. Kolmogorov, M.B. Blaschko, in:, Proceedings of the 2019 International Conference on Computer Vision Workshop, IEEE, 2019. conference: end_date: 2019-10-28 location: Seoul, South Korea name: 'ICCVW: International Conference on Computer Vision Workshop' start_date: 2019-10-27 date_created: 2020-04-05T22:00:50Z date_published: 2019-10-01T00:00:00Z date_updated: 2023-09-08T11:19:12Z day: '01' department: - _id: VlKo doi: 10.1109/ICCVW.2019.00097 external_id: isi: - '000554591600090' isi: 1 language: - iso: eng month: '10' oa_version: None publication: Proceedings of the 2019 International Conference on Computer Vision Workshop publication_identifier: isbn: - '9781728150239' publication_status: published publisher: IEEE quality_controlled: '1' scopus_import: '1' status: public title: Function norms for neural networks type: conference user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1 year: '2019' ... --- _id: '703' abstract: - lang: eng text: We consider the NP-hard problem of MAP-inference for undirected discrete graphical models. We propose a polynomial time and practically efficient algorithm for finding a part of its optimal solution. Specifically, our algorithm marks some labels of the considered graphical model either as (i) optimal, meaning that they belong to all optimal solutions of the inference problem; (ii) non-optimal if they provably do not belong to any solution. With access to an exact solver of a linear programming relaxation to the MAP-inference problem, our algorithm marks the maximal possible (in a specified sense) number of labels. We also present a version of the algorithm, which has access to a suboptimal dual solver only and still can ensure the (non-)optimality for the marked labels, although the overall number of the marked labels may decrease. We propose an efficient implementation, which runs in time comparable to a single run of a suboptimal dual solver. Our method is well-scalable and shows state-of-the-art results on computational benchmarks from machine learning and computer vision. author: - first_name: Alexander full_name: Shekhovtsov, Alexander last_name: Shekhovtsov - first_name: Paul full_name: Swoboda, Paul id: 446560C6-F248-11E8-B48F-1D18A9856A87 last_name: Swoboda - first_name: Bogdan full_name: Savchynskyy, Bogdan last_name: Savchynskyy citation: ama: Shekhovtsov A, Swoboda P, Savchynskyy B. Maximum persistency via iterative relaxed inference with graphical models. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2018;40(7):1668-1682. doi:10.1109/TPAMI.2017.2730884 apa: Shekhovtsov, A., Swoboda, P., & Savchynskyy, B. (2018). Maximum persistency via iterative relaxed inference with graphical models. IEEE Transactions on Pattern Analysis and Machine Intelligence. IEEE. https://doi.org/10.1109/TPAMI.2017.2730884 chicago: Shekhovtsov, Alexander, Paul Swoboda, and Bogdan Savchynskyy. “Maximum Persistency via Iterative Relaxed Inference with Graphical Models.” IEEE Transactions on Pattern Analysis and Machine Intelligence. IEEE, 2018. https://doi.org/10.1109/TPAMI.2017.2730884. ieee: A. Shekhovtsov, P. Swoboda, and B. Savchynskyy, “Maximum persistency via iterative relaxed inference with graphical models,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 40, no. 7. IEEE, pp. 1668–1682, 2018. ista: Shekhovtsov A, Swoboda P, Savchynskyy B. 2018. Maximum persistency via iterative relaxed inference with graphical models. IEEE Transactions on Pattern Analysis and Machine Intelligence. 40(7), 1668–1682. mla: Shekhovtsov, Alexander, et al. “Maximum Persistency via Iterative Relaxed Inference with Graphical Models.” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 40, no. 7, IEEE, 2018, pp. 1668–82, doi:10.1109/TPAMI.2017.2730884. short: A. Shekhovtsov, P. Swoboda, B. Savchynskyy, IEEE Transactions on Pattern Analysis and Machine Intelligence 40 (2018) 1668–1682. date_created: 2018-12-11T11:48:01Z date_published: 2018-07-01T00:00:00Z date_updated: 2021-01-12T08:11:32Z day: '01' department: - _id: VlKo doi: 10.1109/TPAMI.2017.2730884 external_id: arxiv: - '1508.07902' intvolume: ' 40' issue: '7' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/1508.07902 month: '07' oa: 1 oa_version: Preprint page: 1668-1682 publication: IEEE Transactions on Pattern Analysis and Machine Intelligence publication_identifier: issn: - '01628828' publication_status: published publisher: IEEE publist_id: '6992' quality_controlled: '1' scopus_import: 1 status: public title: Maximum persistency via iterative relaxed inference with graphical models type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 40 year: '2018' ... --- _id: '10864' abstract: - lang: eng text: We prove that every congruence distributive variety has directed Jónsson terms, and every congruence modular variety has directed Gumm terms. The directed terms we construct witness every case of absorption witnessed by the original Jónsson or Gumm terms. This result is equivalent to a pair of claims about absorption for admissible preorders in congruence distributive and congruence modular varieties, respectively. For finite algebras, these absorption theorems have already seen significant applications, but until now, it was not clear if the theorems hold for general algebras as well. Our method also yields a novel proof of a result by P. Lipparini about the existence of a chain of terms (which we call Pixley terms) in varieties that are at the same time congruence distributive and k-permutable for some k. acknowledgement: The second author was supported by National Science Center grant DEC-2011-/01/B/ST6/01006. article_processing_charge: No author: - first_name: Alexandr full_name: Kazda, Alexandr id: 3B32BAA8-F248-11E8-B48F-1D18A9856A87 last_name: Kazda - first_name: Marcin full_name: Kozik, Marcin last_name: Kozik - first_name: Ralph full_name: McKenzie, Ralph last_name: McKenzie - first_name: Matthew full_name: Moore, Matthew last_name: Moore citation: ama: 'Kazda A, Kozik M, McKenzie R, Moore M. Absorption and directed Jónsson terms. In: Czelakowski J, ed. Don Pigozzi on Abstract Algebraic Logic, Universal Algebra, and Computer Science. Vol 16. OCTR. Cham: Springer Nature; 2018:203-220. doi:10.1007/978-3-319-74772-9_7' apa: 'Kazda, A., Kozik, M., McKenzie, R., & Moore, M. (2018). Absorption and directed Jónsson terms. In J. Czelakowski (Ed.), Don Pigozzi on Abstract Algebraic Logic, Universal Algebra, and Computer Science (Vol. 16, pp. 203–220). Cham: Springer Nature. https://doi.org/10.1007/978-3-319-74772-9_7' chicago: 'Kazda, Alexandr, Marcin Kozik, Ralph McKenzie, and Matthew Moore. “Absorption and Directed Jónsson Terms.” In Don Pigozzi on Abstract Algebraic Logic, Universal Algebra, and Computer Science, edited by J Czelakowski, 16:203–20. OCTR. Cham: Springer Nature, 2018. https://doi.org/10.1007/978-3-319-74772-9_7.' ieee: 'A. Kazda, M. Kozik, R. McKenzie, and M. Moore, “Absorption and directed Jónsson terms,” in Don Pigozzi on Abstract Algebraic Logic, Universal Algebra, and Computer Science, vol. 16, J. Czelakowski, Ed. Cham: Springer Nature, 2018, pp. 203–220.' ista: 'Kazda A, Kozik M, McKenzie R, Moore M. 2018.Absorption and directed Jónsson terms. In: Don Pigozzi on Abstract Algebraic Logic, Universal Algebra, and Computer Science. vol. 16, 203–220.' mla: Kazda, Alexandr, et al. “Absorption and Directed Jónsson Terms.” Don Pigozzi on Abstract Algebraic Logic, Universal Algebra, and Computer Science, edited by J Czelakowski, vol. 16, Springer Nature, 2018, pp. 203–20, doi:10.1007/978-3-319-74772-9_7. short: A. Kazda, M. Kozik, R. McKenzie, M. Moore, in:, J. Czelakowski (Ed.), Don Pigozzi on Abstract Algebraic Logic, Universal Algebra, and Computer Science, Springer Nature, Cham, 2018, pp. 203–220. date_created: 2022-03-18T10:30:32Z date_published: 2018-03-21T00:00:00Z date_updated: 2023-09-05T15:37:18Z day: '21' department: - _id: VlKo doi: 10.1007/978-3-319-74772-9_7 editor: - first_name: J full_name: Czelakowski, J last_name: Czelakowski external_id: arxiv: - '1502.01072' intvolume: ' 16' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/1502.01072 month: '03' oa: 1 oa_version: Preprint page: 203-220 place: Cham publication: Don Pigozzi on Abstract Algebraic Logic, Universal Algebra, and Computer Science publication_identifier: eisbn: - '9783319747729' eissn: - 2211-2766 isbn: - '9783319747712' issn: - 2211-2758 publication_status: published publisher: Springer Nature quality_controlled: '1' scopus_import: '1' series_title: OCTR status: public title: Absorption and directed Jónsson terms type: book_chapter user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1 volume: 16 year: '2018' ... --- _id: '273' abstract: - lang: eng text: The accuracy of information retrieval systems is often measured using complex loss functions such as the average precision (AP) or the normalized discounted cumulative gain (NDCG). Given a set of positive and negative samples, the parameters of a retrieval system can be estimated by minimizing these loss functions. However, the non-differentiability and non-decomposability of these loss functions does not allow for simple gradient based optimization algorithms. This issue is generally circumvented by either optimizing a structured hinge-loss upper bound to the loss function or by using asymptotic methods like the direct-loss minimization framework. Yet, the high computational complexity of loss-augmented inference, which is necessary for both the frameworks, prohibits its use in large training data sets. To alleviate this deficiency, we present a novel quicksort flavored algorithm for a large class of non-decomposable loss functions. We provide a complete characterization of the loss functions that are amenable to our algorithm, and show that it includes both AP and NDCG based loss functions. Furthermore, we prove that no comparison based algorithm can improve upon the computational complexity of our approach asymptotically. We demonstrate the effectiveness of our approach in the context of optimizing the structured hinge loss upper bound of AP and NDCG loss for learning models for a variety of vision tasks. We show that our approach provides significantly better results than simpler decomposable loss functions, while requiring a comparable training time. article_processing_charge: No author: - first_name: Pritish full_name: Mohapatra, Pritish last_name: Mohapatra - first_name: Michal full_name: Rolinek, Michal id: 3CB3BC06-F248-11E8-B48F-1D18A9856A87 last_name: Rolinek - first_name: C V full_name: Jawahar, C V last_name: Jawahar - first_name: Vladimir full_name: Kolmogorov, Vladimir id: 3D50B0BA-F248-11E8-B48F-1D18A9856A87 last_name: Kolmogorov - first_name: M Pawan full_name: Kumar, M Pawan last_name: Kumar citation: ama: 'Mohapatra P, Rolinek M, Jawahar CV, Kolmogorov V, Kumar MP. Efficient optimization for rank-based loss functions. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE; 2018:3693-3701. doi:10.1109/cvpr.2018.00389' apa: 'Mohapatra, P., Rolinek, M., Jawahar, C. V., Kolmogorov, V., & Kumar, M. P. (2018). Efficient optimization for rank-based loss functions. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 3693–3701). Salt Lake City, UT, USA: IEEE. https://doi.org/10.1109/cvpr.2018.00389' chicago: Mohapatra, Pritish, Michal Rolinek, C V Jawahar, Vladimir Kolmogorov, and M Pawan Kumar. “Efficient Optimization for Rank-Based Loss Functions.” In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 3693–3701. IEEE, 2018. https://doi.org/10.1109/cvpr.2018.00389. ieee: P. Mohapatra, M. Rolinek, C. V. Jawahar, V. Kolmogorov, and M. P. Kumar, “Efficient optimization for rank-based loss functions,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 2018, pp. 3693–3701. ista: 'Mohapatra P, Rolinek M, Jawahar CV, Kolmogorov V, Kumar MP. 2018. Efficient optimization for rank-based loss functions. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. CVPR: Conference on Computer Vision and Pattern Recognition, 3693–3701.' mla: Mohapatra, Pritish, et al. “Efficient Optimization for Rank-Based Loss Functions.” 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, 2018, pp. 3693–701, doi:10.1109/cvpr.2018.00389. short: P. Mohapatra, M. Rolinek, C.V. Jawahar, V. Kolmogorov, M.P. Kumar, in:, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, 2018, pp. 3693–3701. conference: end_date: 2018-06-22 location: Salt Lake City, UT, USA name: 'CVPR: Conference on Computer Vision and Pattern Recognition' start_date: 2018-06-18 date_created: 2018-12-11T11:45:33Z date_published: 2018-06-28T00:00:00Z date_updated: 2023-09-11T13:24:43Z day: '28' department: - _id: VlKo doi: 10.1109/cvpr.2018.00389 ec_funded: 1 external_id: arxiv: - '1604.08269' isi: - '000457843603087' isi: 1 language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/1604.08269 month: '06' oa: 1 oa_version: Preprint page: 3693-3701 project: - _id: 25FBA906-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '616160' name: 'Discrete Optimization in Computer Vision: Theory and Practice' publication: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition publication_identifier: isbn: - '9781538664209' publication_status: published publisher: IEEE quality_controlled: '1' scopus_import: '1' status: public title: Efficient optimization for rank-based loss functions type: conference user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1 year: '2018' ... --- _id: '193' abstract: - lang: eng text: 'We show attacks on five data-independent memory-hard functions (iMHF) that were submitted to the password hashing competition (PHC). Informally, an MHF is a function which cannot be evaluated on dedicated hardware, like ASICs, at significantly lower hardware and/or energy cost than evaluating a single instance on a standard single-core architecture. Data-independent means the memory access pattern of the function is independent of the input; this makes iMHFs harder to construct than data-dependent ones, but the latter can be attacked by various side-channel attacks. Following [Alwen-Blocki''16], we capture the evaluation of an iMHF as a directed acyclic graph (DAG). The cumulative parallel pebbling complexity of this DAG is a measure for the hardware cost of evaluating the iMHF on an ASIC. Ideally, one would like the complexity of a DAG underlying an iMHF to be as close to quadratic in the number of nodes of the graph as possible. Instead, we show that (the DAGs underlying) the following iMHFs are far from this bound: Rig.v2, TwoCats and Gambit each having an exponent no more than 1.75. Moreover, we show that the complexity of the iMHF modes of the PHC finalists Pomelo and Lyra2 have exponents at most 1.83 and 1.67 respectively. To show this we investigate a combinatorial property of each underlying DAG (called its depth-robustness. By establishing upper bounds on this property we are then able to apply the general technique of [Alwen-Block''16] for analyzing the hardware costs of an iMHF.' acknowledgement: Leonid Reyzin was supported in part by IST Austria and by US NSF grants 1012910, 1012798, and 1422965; this research was performed while he was visiting IST Austria. article_processing_charge: No author: - first_name: Joel F full_name: Alwen, Joel F id: 2A8DFA8C-F248-11E8-B48F-1D18A9856A87 last_name: Alwen - first_name: Peter full_name: Gazi, Peter last_name: Gazi - first_name: Chethan full_name: Kamath Hosdurg, Chethan id: 4BD3F30E-F248-11E8-B48F-1D18A9856A87 last_name: Kamath Hosdurg - first_name: Karen full_name: Klein, Karen id: 3E83A2F8-F248-11E8-B48F-1D18A9856A87 last_name: Klein - first_name: Georg F full_name: Osang, Georg F id: 464B40D6-F248-11E8-B48F-1D18A9856A87 last_name: Osang orcid: 0000-0002-8882-5116 - first_name: Krzysztof Z full_name: Pietrzak, Krzysztof Z id: 3E04A7AA-F248-11E8-B48F-1D18A9856A87 last_name: Pietrzak orcid: 0000-0002-9139-1654 - first_name: Lenoid full_name: Reyzin, Lenoid last_name: Reyzin - first_name: Michal full_name: Rolinek, Michal id: 3CB3BC06-F248-11E8-B48F-1D18A9856A87 last_name: Rolinek - first_name: Michal full_name: Rybar, Michal id: 2B3E3DE8-F248-11E8-B48F-1D18A9856A87 last_name: Rybar citation: ama: 'Alwen JF, Gazi P, Kamath Hosdurg C, et al. On the memory hardness of data independent password hashing functions. In: Proceedings of the 2018 on Asia Conference on Computer and Communication Security. ACM; 2018:51-65. doi:10.1145/3196494.3196534' apa: 'Alwen, J. F., Gazi, P., Kamath Hosdurg, C., Klein, K., Osang, G. F., Pietrzak, K. Z., … Rybar, M. (2018). On the memory hardness of data independent password hashing functions. In Proceedings of the 2018 on Asia Conference on Computer and Communication Security (pp. 51–65). Incheon, Republic of Korea: ACM. https://doi.org/10.1145/3196494.3196534' chicago: Alwen, Joel F, Peter Gazi, Chethan Kamath Hosdurg, Karen Klein, Georg F Osang, Krzysztof Z Pietrzak, Lenoid Reyzin, Michal Rolinek, and Michal Rybar. “On the Memory Hardness of Data Independent Password Hashing Functions.” In Proceedings of the 2018 on Asia Conference on Computer and Communication Security, 51–65. ACM, 2018. https://doi.org/10.1145/3196494.3196534. ieee: J. F. Alwen et al., “On the memory hardness of data independent password hashing functions,” in Proceedings of the 2018 on Asia Conference on Computer and Communication Security, Incheon, Republic of Korea, 2018, pp. 51–65. ista: 'Alwen JF, Gazi P, Kamath Hosdurg C, Klein K, Osang GF, Pietrzak KZ, Reyzin L, Rolinek M, Rybar M. 2018. On the memory hardness of data independent password hashing functions. Proceedings of the 2018 on Asia Conference on Computer and Communication Security. ASIACCS: Asia Conference on Computer and Communications Security , 51–65.' mla: Alwen, Joel F., et al. “On the Memory Hardness of Data Independent Password Hashing Functions.” Proceedings of the 2018 on Asia Conference on Computer and Communication Security, ACM, 2018, pp. 51–65, doi:10.1145/3196494.3196534. short: J.F. Alwen, P. Gazi, C. Kamath Hosdurg, K. Klein, G.F. Osang, K.Z. Pietrzak, L. Reyzin, M. Rolinek, M. Rybar, in:, Proceedings of the 2018 on Asia Conference on Computer and Communication Security, ACM, 2018, pp. 51–65. conference: end_date: 2018-06-08 location: Incheon, Republic of Korea name: 'ASIACCS: Asia Conference on Computer and Communications Security ' start_date: 2018-06-04 date_created: 2018-12-11T11:45:07Z date_published: 2018-06-01T00:00:00Z date_updated: 2023-09-13T09:13:12Z day: '01' department: - _id: KrPi - _id: HeEd - _id: VlKo doi: 10.1145/3196494.3196534 ec_funded: 1 external_id: isi: - '000516620100005' isi: 1 language: - iso: eng main_file_link: - open_access: '1' url: https://eprint.iacr.org/2016/783 month: '06' oa: 1 oa_version: Submitted Version page: 51 - 65 project: - _id: 25FBA906-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '616160' name: 'Discrete Optimization in Computer Vision: Theory and Practice' - _id: 258AA5B2-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '682815' name: Teaching Old Crypto New Tricks publication: Proceedings of the 2018 on Asia Conference on Computer and Communication Security publication_status: published publisher: ACM publist_id: '7723' quality_controlled: '1' scopus_import: '1' status: public title: On the memory hardness of data independent password hashing functions type: conference user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1 year: '2018' ... --- _id: '5975' abstract: - lang: eng text: We consider the recent formulation of the algorithmic Lov ́asz Local Lemma [N. Har-vey and J. Vondr ́ak, inProceedings of FOCS, 2015, pp. 1327–1345; D. Achlioptas and F. Iliopoulos,inProceedings of SODA, 2016, pp. 2024–2038; D. Achlioptas, F. Iliopoulos, and V. Kolmogorov,ALocal Lemma for Focused Stochastic Algorithms, arXiv preprint, 2018] for finding objects that avoid“bad features,” or “flaws.” It extends the Moser–Tardos resampling algorithm [R. A. Moser andG. Tardos,J. ACM, 57 (2010), 11] to more general discrete spaces. At each step the method picks aflaw present in the current state and goes to a new state according to some prespecified probabilitydistribution (which depends on the current state and the selected flaw). However, the recent formu-lation is less flexible than the Moser–Tardos method since it requires a specific flaw selection rule,whereas the algorithm of Moser and Tardos allows an arbitrary rule (and thus can potentially beimplemented more efficiently). We formulate a new “commutativity” condition and prove that it issufficient for an arbitrary rule to work. It also enables an efficient parallelization under an additionalassumption. We then show that existing resampling oracles for perfect matchings and permutationsdo satisfy this condition. article_processing_charge: No author: - first_name: Vladimir full_name: Kolmogorov, Vladimir id: 3D50B0BA-F248-11E8-B48F-1D18A9856A87 last_name: Kolmogorov citation: ama: Kolmogorov V. Commutativity in the algorithmic Lovász local lemma. SIAM Journal on Computing. 2018;47(6):2029-2056. doi:10.1137/16m1093306 apa: Kolmogorov, V. (2018). Commutativity in the algorithmic Lovász local lemma. SIAM Journal on Computing. Society for Industrial & Applied Mathematics (SIAM). https://doi.org/10.1137/16m1093306 chicago: Kolmogorov, Vladimir. “Commutativity in the Algorithmic Lovász Local Lemma.” SIAM Journal on Computing. Society for Industrial & Applied Mathematics (SIAM), 2018. https://doi.org/10.1137/16m1093306. ieee: V. Kolmogorov, “Commutativity in the algorithmic Lovász local lemma,” SIAM Journal on Computing, vol. 47, no. 6. Society for Industrial & Applied Mathematics (SIAM), pp. 2029–2056, 2018. ista: Kolmogorov V. 2018. Commutativity in the algorithmic Lovász local lemma. SIAM Journal on Computing. 47(6), 2029–2056. mla: Kolmogorov, Vladimir. “Commutativity in the Algorithmic Lovász Local Lemma.” SIAM Journal on Computing, vol. 47, no. 6, Society for Industrial & Applied Mathematics (SIAM), 2018, pp. 2029–56, doi:10.1137/16m1093306. short: V. Kolmogorov, SIAM Journal on Computing 47 (2018) 2029–2056. date_created: 2019-02-13T12:59:33Z date_published: 2018-11-08T00:00:00Z date_updated: 2023-09-19T14:24:58Z day: '08' department: - _id: VlKo doi: 10.1137/16m1093306 ec_funded: 1 external_id: arxiv: - '1506.08547' isi: - '000453785100001' intvolume: ' 47' isi: 1 issue: '6' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/1506.08547 month: '11' oa: 1 oa_version: Preprint page: 2029-2056 project: - _id: 25FBA906-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '616160' name: 'Discrete Optimization in Computer Vision: Theory and Practice' publication: SIAM Journal on Computing publication_identifier: eissn: - 1095-7111 issn: - 0097-5397 publication_status: published publisher: Society for Industrial & Applied Mathematics (SIAM) quality_controlled: '1' related_material: record: - id: '1193' relation: earlier_version status: public scopus_import: '1' status: public title: Commutativity in the algorithmic Lovász local lemma type: journal_article user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1 volume: 47 year: '2018' ... --- _id: '5978' abstract: - lang: eng text: 'We consider the MAP-inference problem for graphical models,which is a valued constraint satisfaction problem defined onreal numbers with a natural summation operation. We proposea family of relaxations (different from the famous Sherali-Adams hierarchy), which naturally define lower bounds for itsoptimum. This family always contains a tight relaxation andwe give an algorithm able to find it and therefore, solve theinitial non-relaxed NP-hard problem.The relaxations we consider decompose the original probleminto two non-overlapping parts: an easy LP-tight part and adifficult one. For the latter part a combinatorial solver must beused. As we show in our experiments, in a number of applica-tions the second, difficult part constitutes only a small fractionof the whole problem. This property allows to significantlyreduce the computational time of the combinatorial solver andtherefore solve problems which were out of reach before.' article_processing_charge: No author: - first_name: Stefan full_name: Haller, Stefan last_name: Haller - first_name: Paul full_name: Swoboda, Paul id: 446560C6-F248-11E8-B48F-1D18A9856A87 last_name: Swoboda - first_name: Bogdan full_name: Savchynskyy, Bogdan last_name: Savchynskyy citation: ama: 'Haller S, Swoboda P, Savchynskyy B. Exact MAP-inference by confining combinatorial search with LP relaxation. In: Proceedings of the 32st AAAI Conference on Artificial Intelligence. AAAI Press; 2018:6581-6588.' apa: 'Haller, S., Swoboda, P., & Savchynskyy, B. (2018). Exact MAP-inference by confining combinatorial search with LP relaxation. In Proceedings of the 32st AAAI Conference on Artificial Intelligence (pp. 6581–6588). New Orleans, LU, United States: AAAI Press.' chicago: Haller, Stefan, Paul Swoboda, and Bogdan Savchynskyy. “Exact MAP-Inference by Confining Combinatorial Search with LP Relaxation.” In Proceedings of the 32st AAAI Conference on Artificial Intelligence, 6581–88. AAAI Press, 2018. ieee: S. Haller, P. Swoboda, and B. Savchynskyy, “Exact MAP-inference by confining combinatorial search with LP relaxation,” in Proceedings of the 32st AAAI Conference on Artificial Intelligence, New Orleans, LU, United States, 2018, pp. 6581–6588. ista: 'Haller S, Swoboda P, Savchynskyy B. 2018. Exact MAP-inference by confining combinatorial search with LP relaxation. Proceedings of the 32st AAAI Conference on Artificial Intelligence. AAAI: Conference on Artificial Intelligence, 6581–6588.' mla: Haller, Stefan, et al. “Exact MAP-Inference by Confining Combinatorial Search with LP Relaxation.” Proceedings of the 32st AAAI Conference on Artificial Intelligence, AAAI Press, 2018, pp. 6581–88. short: S. Haller, P. Swoboda, B. Savchynskyy, in:, Proceedings of the 32st AAAI Conference on Artificial Intelligence, AAAI Press, 2018, pp. 6581–6588. conference: end_date: 2018-02-07 location: New Orleans, LU, United States name: 'AAAI: Conference on Artificial Intelligence' start_date: 2018-02-02 date_created: 2019-02-13T13:32:48Z date_published: 2018-02-01T00:00:00Z date_updated: 2023-09-19T14:26:52Z day: '01' department: - _id: VlKo external_id: arxiv: - '2004.06370' isi: - '000485488906082' isi: 1 language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/2004.06370 month: '02' oa: 1 oa_version: Preprint page: 6581-6588 publication: Proceedings of the 32st AAAI Conference on Artificial Intelligence publication_status: published publisher: AAAI Press quality_controlled: '1' scopus_import: '1' status: public title: Exact MAP-inference by confining combinatorial search with LP relaxation type: conference user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1 year: '2018' ... --- _id: '18' abstract: - lang: eng text: An N-superconcentrator is a directed, acyclic graph with N input nodes and N output nodes such that every subset of the inputs and every subset of the outputs of same cardinality can be connected by node-disjoint paths. It is known that linear-size and bounded-degree superconcentrators exist. We prove the existence of such superconcentrators with asymptotic density 25.3 (where the density is the number of edges divided by N). The previously best known densities were 28 [12] and 27.4136 [17]. article_processing_charge: No author: - first_name: Vladimir full_name: Kolmogorov, Vladimir id: 3D50B0BA-F248-11E8-B48F-1D18A9856A87 last_name: Kolmogorov - first_name: Michal full_name: Rolinek, Michal id: 3CB3BC06-F248-11E8-B48F-1D18A9856A87 last_name: Rolinek citation: ama: Kolmogorov V, Rolinek M. Superconcentrators of density 25.3. Ars Combinatoria. 2018;141(10):269-304. apa: Kolmogorov, V., & Rolinek, M. (2018). Superconcentrators of density 25.3. Ars Combinatoria. Charles Babbage Research Centre. chicago: Kolmogorov, Vladimir, and Michal Rolinek. “Superconcentrators of Density 25.3.” Ars Combinatoria. Charles Babbage Research Centre, 2018. ieee: V. Kolmogorov and M. Rolinek, “Superconcentrators of density 25.3,” Ars Combinatoria, vol. 141, no. 10. Charles Babbage Research Centre, pp. 269–304, 2018. ista: Kolmogorov V, Rolinek M. 2018. Superconcentrators of density 25.3. Ars Combinatoria. 141(10), 269–304. mla: Kolmogorov, Vladimir, and Michal Rolinek. “Superconcentrators of Density 25.3.” Ars Combinatoria, vol. 141, no. 10, Charles Babbage Research Centre, 2018, pp. 269–304. short: V. Kolmogorov, M. Rolinek, Ars Combinatoria 141 (2018) 269–304. date_created: 2018-12-11T11:44:11Z date_published: 2018-10-01T00:00:00Z date_updated: 2023-09-19T14:46:18Z day: '01' department: - _id: VlKo external_id: arxiv: - '1405.7828' isi: - '000446809500022' intvolume: ' 141' isi: 1 issue: '10' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/1405.7828 month: '10' oa: 1 oa_version: Preprint page: 269 - 304 publication: Ars Combinatoria publication_identifier: issn: - 0381-7032 publication_status: published publisher: Charles Babbage Research Centre publist_id: '8037' quality_controlled: '1' scopus_import: '1' status: public title: Superconcentrators of density 25.3 type: journal_article user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1 volume: 141 year: '2018' ... --- _id: '6032' abstract: - lang: eng text: The main result of this article is a generalization of the classical blossom algorithm for finding perfect matchings. Our algorithm can efficiently solve Boolean CSPs where each variable appears in exactly two constraints (we call it edge CSP) and all constraints are even Δ-matroid relations (represented by lists of tuples). As a consequence of this, we settle the complexity classification of planar Boolean CSPs started by Dvorak and Kupec. Using a reduction to even Δ-matroids, we then extend the tractability result to larger classes of Δ-matroids that we call efficiently coverable. It properly includes classes that were known to be tractable before, namely, co-independent, compact, local, linear, and binary, with the following caveat:We represent Δ-matroids by lists of tuples, while the last two use a representation by matrices. Since an n ×n matrix can represent exponentially many tuples, our tractability result is not strictly stronger than the known algorithm for linear and binary Δ-matroids. article_number: '22' article_processing_charge: No article_type: original author: - first_name: Alexandr full_name: Kazda, Alexandr id: 3B32BAA8-F248-11E8-B48F-1D18A9856A87 last_name: Kazda - first_name: Vladimir full_name: Kolmogorov, Vladimir id: 3D50B0BA-F248-11E8-B48F-1D18A9856A87 last_name: Kolmogorov - first_name: Michal full_name: Rolinek, Michal id: 3CB3BC06-F248-11E8-B48F-1D18A9856A87 last_name: Rolinek citation: ama: Kazda A, Kolmogorov V, Rolinek M. Even delta-matroids and the complexity of planar boolean CSPs. ACM Transactions on Algorithms. 2018;15(2). doi:10.1145/3230649 apa: Kazda, A., Kolmogorov, V., & Rolinek, M. (2018). Even delta-matroids and the complexity of planar boolean CSPs. ACM Transactions on Algorithms. ACM. https://doi.org/10.1145/3230649 chicago: Kazda, Alexandr, Vladimir Kolmogorov, and Michal Rolinek. “Even Delta-Matroids and the Complexity of Planar Boolean CSPs.” ACM Transactions on Algorithms. ACM, 2018. https://doi.org/10.1145/3230649. ieee: A. Kazda, V. Kolmogorov, and M. Rolinek, “Even delta-matroids and the complexity of planar boolean CSPs,” ACM Transactions on Algorithms, vol. 15, no. 2. ACM, 2018. ista: Kazda A, Kolmogorov V, Rolinek M. 2018. Even delta-matroids and the complexity of planar boolean CSPs. ACM Transactions on Algorithms. 15(2), 22. mla: Kazda, Alexandr, et al. “Even Delta-Matroids and the Complexity of Planar Boolean CSPs.” ACM Transactions on Algorithms, vol. 15, no. 2, 22, ACM, 2018, doi:10.1145/3230649. short: A. Kazda, V. Kolmogorov, M. Rolinek, ACM Transactions on Algorithms 15 (2018). date_created: 2019-02-17T22:59:25Z date_published: 2018-12-01T00:00:00Z date_updated: 2023-09-20T11:20:26Z day: '01' department: - _id: VlKo doi: 10.1145/3230649 ec_funded: 1 external_id: arxiv: - '1602.03124' isi: - '000468036500007' intvolume: ' 15' isi: 1 issue: '2' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/1602.03124 month: '12' oa: 1 oa_version: Preprint project: - _id: 25FBA906-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '616160' name: 'Discrete Optimization in Computer Vision: Theory and Practice' publication: ACM Transactions on Algorithms publication_status: published publisher: ACM quality_controlled: '1' related_material: record: - id: '1192' relation: earlier_version status: public scopus_import: '1' status: public title: Even delta-matroids and the complexity of planar boolean CSPs type: journal_article user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1 volume: 15 year: '2018' ... --- _id: '5573' abstract: - lang: eng text: Graph matching problems for large displacement optical flow of RGB-D images. article_processing_charge: No author: - first_name: Hassan full_name: Alhaija, Hassan last_name: Alhaija - first_name: Anita full_name: Sellent, Anita last_name: Sellent - first_name: Daniel full_name: Kondermann, Daniel last_name: Kondermann - first_name: Carsten full_name: Rother, Carsten last_name: Rother citation: ama: Alhaija H, Sellent A, Kondermann D, Rother C. Graph matching problems for GraphFlow – 6D Large Displacement Scene Flow. 2018. doi:10.15479/AT:ISTA:82 apa: Alhaija, H., Sellent, A., Kondermann, D., & Rother, C. (2018). Graph matching problems for GraphFlow – 6D Large Displacement Scene Flow. Institute of Science and Technology Austria. https://doi.org/10.15479/AT:ISTA:82 chicago: Alhaija, Hassan, Anita Sellent, Daniel Kondermann, and Carsten Rother. “Graph Matching Problems for GraphFlow – 6D Large Displacement Scene Flow.” Institute of Science and Technology Austria, 2018. https://doi.org/10.15479/AT:ISTA:82. ieee: H. Alhaija, A. Sellent, D. Kondermann, and C. Rother, “Graph matching problems for GraphFlow – 6D Large Displacement Scene Flow.” Institute of Science and Technology Austria, 2018. ista: Alhaija H, Sellent A, Kondermann D, Rother C. 2018. Graph matching problems for GraphFlow – 6D Large Displacement Scene Flow, Institute of Science and Technology Austria, 10.15479/AT:ISTA:82. mla: Alhaija, Hassan, et al. Graph Matching Problems for GraphFlow – 6D Large Displacement Scene Flow. Institute of Science and Technology Austria, 2018, doi:10.15479/AT:ISTA:82. short: H. Alhaija, A. Sellent, D. Kondermann, C. Rother, (2018). contributor: - contributor_type: researcher first_name: Paul id: 446560C6-F248-11E8-B48F-1D18A9856A87 last_name: Swoboda datarep_id: '82' date_created: 2018-12-12T12:31:36Z date_published: 2018-01-04T00:00:00Z date_updated: 2024-02-21T13:41:17Z day: '04' ddc: - '001' department: - _id: VlKo doi: 10.15479/AT:ISTA:82 file: - access_level: open_access checksum: 53c17082848e12f3c2e1b4185b578208 content_type: application/zip creator: system date_created: 2018-12-12T13:02:34Z date_updated: 2020-07-14T12:47:05Z file_id: '5600' file_name: IST-2018-82-v1+1_GraphFlowMatchingProblems.zip file_size: 1737958 relation: main_file file_date_updated: 2020-07-14T12:47:05Z has_accepted_license: '1' keyword: - graph matching - quadratic assignment problem< license: https://creativecommons.org/publicdomain/zero/1.0/ month: '01' oa: 1 oa_version: Published Version publisher: Institute of Science and Technology Austria related_material: link: - relation: research_paper url: https://doi.org/10.1007/978-3-319-24947-6_23 status: public title: Graph matching problems for GraphFlow – 6D Large Displacement Scene Flow tmp: image: /images/cc_0.png legal_code_url: https://creativecommons.org/publicdomain/zero/1.0/legalcode name: Creative Commons Public Domain Dedication (CC0 1.0) short: CC0 (1.0) type: research_data user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2018' ... --- _id: '641' abstract: - lang: eng text: 'We introduce two novel methods for learning parameters of graphical models for image labelling. The following two tasks underline both methods: (i) perturb model parameters based on given features and ground truth labelings, so as to exactly reproduce these labelings as optima of the local polytope relaxation of the labelling problem; (ii) train a predictor for the perturbed model parameters so that improved model parameters can be applied to the labelling of novel data. Our first method implements task (i) by inverse linear programming and task (ii) using a regressor e.g. a Gaussian process. Our second approach simultaneously solves tasks (i) and (ii) in a joint manner, while being restricted to linearly parameterised predictors. Experiments demonstrate the merits of both approaches.' alternative_title: - LNCS author: - first_name: Vera full_name: Trajkovska, Vera last_name: Trajkovska - first_name: Paul full_name: Swoboda, Paul id: 446560C6-F248-11E8-B48F-1D18A9856A87 last_name: Swoboda - first_name: Freddie full_name: Åström, Freddie last_name: Åström - first_name: Stefanie full_name: Petra, Stefanie last_name: Petra citation: ama: 'Trajkovska V, Swoboda P, Åström F, Petra S. Graphical model parameter learning by inverse linear programming. In: Lauze F, Dong Y, Bjorholm Dahl A, eds. Vol 10302. Springer; 2017:323-334. doi:10.1007/978-3-319-58771-4_26' apa: 'Trajkovska, V., Swoboda, P., Åström, F., & Petra, S. (2017). Graphical model parameter learning by inverse linear programming. In F. Lauze, Y. Dong, & A. Bjorholm Dahl (Eds.) (Vol. 10302, pp. 323–334). Presented at the SSVM: Scale Space and Variational Methods in Computer Vision, Kolding, Denmark: Springer. https://doi.org/10.1007/978-3-319-58771-4_26' chicago: Trajkovska, Vera, Paul Swoboda, Freddie Åström, and Stefanie Petra. “Graphical Model Parameter Learning by Inverse Linear Programming.” edited by François Lauze, Yiqiu Dong, and Anders Bjorholm Dahl, 10302:323–34. Springer, 2017. https://doi.org/10.1007/978-3-319-58771-4_26. ieee: 'V. Trajkovska, P. Swoboda, F. Åström, and S. Petra, “Graphical model parameter learning by inverse linear programming,” presented at the SSVM: Scale Space and Variational Methods in Computer Vision, Kolding, Denmark, 2017, vol. 10302, pp. 323–334.' ista: 'Trajkovska V, Swoboda P, Åström F, Petra S. 2017. Graphical model parameter learning by inverse linear programming. SSVM: Scale Space and Variational Methods in Computer Vision, LNCS, vol. 10302, 323–334.' mla: Trajkovska, Vera, et al. Graphical Model Parameter Learning by Inverse Linear Programming. Edited by François Lauze et al., vol. 10302, Springer, 2017, pp. 323–34, doi:10.1007/978-3-319-58771-4_26. short: V. Trajkovska, P. Swoboda, F. Åström, S. Petra, in:, F. Lauze, Y. Dong, A. Bjorholm Dahl (Eds.), Springer, 2017, pp. 323–334. conference: end_date: 2017-06-08 location: Kolding, Denmark name: 'SSVM: Scale Space and Variational Methods in Computer Vision' start_date: 2017-06-04 date_created: 2018-12-11T11:47:39Z date_published: 2017-01-01T00:00:00Z date_updated: 2021-01-12T08:07:23Z day: '01' department: - _id: VlKo doi: 10.1007/978-3-319-58771-4_26 editor: - first_name: François full_name: Lauze, François last_name: Lauze - first_name: Yiqiu full_name: Dong, Yiqiu last_name: Dong - first_name: Anders full_name: Bjorholm Dahl, Anders last_name: Bjorholm Dahl intvolume: ' 10302' language: - iso: eng month: '01' oa_version: None page: 323 - 334 publication_identifier: isbn: - 978-331958770-7 publication_status: published publisher: Springer publist_id: '7147' quality_controlled: '1' scopus_import: 1 status: public title: Graphical model parameter learning by inverse linear programming type: conference user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87 volume: 10302 year: '2017' ... --- _id: '644' abstract: - lang: eng text: An instance of the valued constraint satisfaction problem (VCSP) is given by a finite set of variables, a finite domain of labels, and a sum of functions, each function depending on a subset of the variables. Each function can take finite values specifying costs of assignments of labels to its variables or the infinite value, which indicates an infeasible assignment. The goal is to find an assignment of labels to the variables that minimizes the sum. We study, assuming that P 6= NP, how the complexity of this very general problem depends on the set of functions allowed in the instances, the so-called constraint language. The case when all allowed functions take values in f0;1g corresponds to ordinary CSPs, where one deals only with the feasibility issue, and there is no optimization. This case is the subject of the algebraic CSP dichotomy conjecture predicting for which constraint languages CSPs are tractable (i.e., solvable in polynomial time) and for which they are NP-hard. The case when all allowed functions take only finite values corresponds to a finitevalued CSP, where the feasibility aspect is trivial and one deals only with the optimization issue. The complexity of finite-valued CSPs was fully classified by Thapper and Živný. An algebraic necessary condition for tractability of a general-valued CSP with a fixed constraint language was recently given by Kozik and Ochremiak. As our main result, we prove that if a constraint language satisfies this algebraic necessary condition, and the feasibility CSP (i.e., the problem of deciding whether a given instance has a feasible solution) corresponding to the VCSP with this language is tractable, then the VCSP is tractable. The algorithm is a simple combination of the assumed algorithm for the feasibility CSP and the standard LP relaxation. As a corollary, we obtain that a dichotomy for ordinary CSPs would imply a dichotomy for general-valued CSPs. author: - first_name: Vladimir full_name: Kolmogorov, Vladimir id: 3D50B0BA-F248-11E8-B48F-1D18A9856A87 last_name: Kolmogorov - first_name: Andrei full_name: Krokhin, Andrei last_name: Krokhin - first_name: Michal full_name: Rolinek, Michal id: 3CB3BC06-F248-11E8-B48F-1D18A9856A87 last_name: Rolinek citation: ama: Kolmogorov V, Krokhin A, Rolinek M. The complexity of general-valued CSPs. SIAM Journal on Computing. 2017;46(3):1087-1110. doi:10.1137/16M1091836 apa: Kolmogorov, V., Krokhin, A., & Rolinek, M. (2017). The complexity of general-valued CSPs. SIAM Journal on Computing. SIAM. https://doi.org/10.1137/16M1091836 chicago: Kolmogorov, Vladimir, Andrei Krokhin, and Michal Rolinek. “The Complexity of General-Valued CSPs.” SIAM Journal on Computing. SIAM, 2017. https://doi.org/10.1137/16M1091836. ieee: V. Kolmogorov, A. Krokhin, and M. Rolinek, “The complexity of general-valued CSPs,” SIAM Journal on Computing, vol. 46, no. 3. SIAM, pp. 1087–1110, 2017. ista: Kolmogorov V, Krokhin A, Rolinek M. 2017. The complexity of general-valued CSPs. SIAM Journal on Computing. 46(3), 1087–1110. mla: Kolmogorov, Vladimir, et al. “The Complexity of General-Valued CSPs.” SIAM Journal on Computing, vol. 46, no. 3, SIAM, 2017, pp. 1087–110, doi:10.1137/16M1091836. short: V. Kolmogorov, A. Krokhin, M. Rolinek, SIAM Journal on Computing 46 (2017) 1087–1110. date_created: 2018-12-11T11:47:40Z date_published: 2017-06-29T00:00:00Z date_updated: 2023-02-23T10:07:49Z day: '29' department: - _id: VlKo doi: 10.1137/16M1091836 ec_funded: 1 intvolume: ' 46' issue: '3' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/1502.07327 month: '06' oa: 1 oa_version: Preprint page: 1087 - 1110 project: - _id: 25FBA906-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '616160' name: 'Discrete Optimization in Computer Vision: Theory and Practice' publication: SIAM Journal on Computing publication_status: published publisher: SIAM publist_id: '7138' quality_controlled: '1' related_material: record: - id: '1637' relation: other status: public scopus_import: 1 status: public title: The complexity of general-valued CSPs type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 46 year: '2017' ... --- _id: '646' abstract: - lang: eng text: We present a novel convex relaxation and a corresponding inference algorithm for the non-binary discrete tomography problem, that is, reconstructing discrete-valued images from few linear measurements. In contrast to state of the art approaches that split the problem into a continuous reconstruction problem for the linear measurement constraints and a discrete labeling problem to enforce discrete-valued reconstructions, we propose a joint formulation that addresses both problems simultaneously, resulting in a tighter convex relaxation. For this purpose a constrained graphical model is set up and evaluated using a novel relaxation optimized by dual decomposition. We evaluate our approach experimentally and show superior solutions both mathematically (tighter relaxation) and experimentally in comparison to previously proposed relaxations. alternative_title: - LNCS author: - first_name: Jan full_name: Kuske, Jan last_name: Kuske - first_name: Paul full_name: Swoboda, Paul id: 446560C6-F248-11E8-B48F-1D18A9856A87 last_name: Swoboda - first_name: Stefanie full_name: Petra, Stefanie last_name: Petra citation: ama: 'Kuske J, Swoboda P, Petra S. A novel convex relaxation for non binary discrete tomography. In: Lauze F, Dong Y, Bjorholm Dahl A, eds. Vol 10302. Springer; 2017:235-246. doi:10.1007/978-3-319-58771-4_19' apa: 'Kuske, J., Swoboda, P., & Petra, S. (2017). A novel convex relaxation for non binary discrete tomography. In F. Lauze, Y. Dong, & A. Bjorholm Dahl (Eds.) (Vol. 10302, pp. 235–246). Presented at the SSVM: Scale Space and Variational Methods in Computer Vision, Kolding, Denmark: Springer. https://doi.org/10.1007/978-3-319-58771-4_19' chicago: Kuske, Jan, Paul Swoboda, and Stefanie Petra. “A Novel Convex Relaxation for Non Binary Discrete Tomography.” edited by François Lauze, Yiqiu Dong, and Anders Bjorholm Dahl, 10302:235–46. Springer, 2017. https://doi.org/10.1007/978-3-319-58771-4_19. ieee: 'J. Kuske, P. Swoboda, and S. Petra, “A novel convex relaxation for non binary discrete tomography,” presented at the SSVM: Scale Space and Variational Methods in Computer Vision, Kolding, Denmark, 2017, vol. 10302, pp. 235–246.' ista: 'Kuske J, Swoboda P, Petra S. 2017. A novel convex relaxation for non binary discrete tomography. SSVM: Scale Space and Variational Methods in Computer Vision, LNCS, vol. 10302, 235–246.' mla: Kuske, Jan, et al. A Novel Convex Relaxation for Non Binary Discrete Tomography. Edited by François Lauze et al., vol. 10302, Springer, 2017, pp. 235–46, doi:10.1007/978-3-319-58771-4_19. short: J. Kuske, P. Swoboda, S. Petra, in:, F. Lauze, Y. Dong, A. Bjorholm Dahl (Eds.), Springer, 2017, pp. 235–246. conference: end_date: 2017-06-08 location: Kolding, Denmark name: 'SSVM: Scale Space and Variational Methods in Computer Vision' start_date: 2017-06-04 date_created: 2018-12-11T11:47:41Z date_published: 2017-06-01T00:00:00Z date_updated: 2021-01-12T08:07:34Z day: '01' department: - _id: VlKo doi: 10.1007/978-3-319-58771-4_19 ec_funded: 1 editor: - first_name: François full_name: Lauze, François last_name: Lauze - first_name: Yiqiu full_name: Dong, Yiqiu last_name: Dong - first_name: Anders full_name: Bjorholm Dahl, Anders last_name: Bjorholm Dahl intvolume: ' 10302' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/1703.03769 month: '06' oa: 1 oa_version: Submitted Version page: 235 - 246 project: - _id: 25FBA906-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '616160' name: 'Discrete Optimization in Computer Vision: Theory and Practice' publication_identifier: isbn: - 978-331958770-7 publication_status: published publisher: Springer publist_id: '7132' quality_controlled: '1' scopus_import: 1 status: public title: A novel convex relaxation for non binary discrete tomography type: conference user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87 volume: 10302 year: '2017' ... --- _id: '992' abstract: - lang: eng text: "An instance of the Constraint Satisfaction Problem (CSP) is given by a finite set of\r\nvariables, a finite domain of labels, and a set of constraints, each constraint acting on\r\na subset of the variables. The goal is to find an assignment of labels to its variables\r\nthat satisfies all constraints (or decide whether one exists). If we allow more general\r\n“soft” constraints, which come with (possibly infinite) costs of particular assignments,\r\nwe obtain instances from a richer class called Valued Constraint Satisfaction Problem\r\n(VCSP). There the goal is to find an assignment with minimum total cost.\r\nIn this thesis, we focus (assuming that P\r\n6\r\n=\r\nNP) on classifying computational com-\r\nplexity of CSPs and VCSPs under certain restricting conditions. Two results are the core\r\ncontent of the work. In one of them, we consider VCSPs parametrized by a constraint\r\nlanguage, that is the set of “soft” constraints allowed to form the instances, and finish\r\nthe complexity classification modulo (missing pieces of) complexity classification for\r\nanalogously parametrized CSP. The other result is a generalization of Edmonds’ perfect\r\nmatching algorithm. This generalization contributes to complexity classfications in two\r\nways. First, it gives a new (largest known) polynomial-time solvable class of Boolean\r\nCSPs in which every variable may appear in at most two constraints and second, it\r\nsettles full classification of Boolean CSPs with planar drawing (again parametrized by a\r\nconstraint language)." acknowledgement: FP7/2007-2013/ERC grant agreement no 616160 alternative_title: - ISTA Thesis article_processing_charge: No author: - first_name: Michal full_name: Rolinek, Michal id: 3CB3BC06-F248-11E8-B48F-1D18A9856A87 last_name: Rolinek citation: ama: Rolinek M. Complexity of constraint satisfaction. 2017. doi:10.15479/AT:ISTA:th_815 apa: Rolinek, M. (2017). Complexity of constraint satisfaction. Institute of Science and Technology Austria. https://doi.org/10.15479/AT:ISTA:th_815 chicago: Rolinek, Michal. “Complexity of Constraint Satisfaction.” Institute of Science and Technology Austria, 2017. https://doi.org/10.15479/AT:ISTA:th_815. ieee: M. Rolinek, “Complexity of constraint satisfaction,” Institute of Science and Technology Austria, 2017. ista: Rolinek M. 2017. Complexity of constraint satisfaction. Institute of Science and Technology Austria. mla: Rolinek, Michal. Complexity of Constraint Satisfaction. Institute of Science and Technology Austria, 2017, doi:10.15479/AT:ISTA:th_815. short: M. Rolinek, Complexity of Constraint Satisfaction, Institute of Science and Technology Austria, 2017. date_created: 2018-12-11T11:49:35Z date_published: 2017-05-01T00:00:00Z date_updated: 2023-09-07T12:05:41Z day: '01' ddc: - '004' degree_awarded: PhD department: - _id: VlKo doi: 10.15479/AT:ISTA:th_815 ec_funded: 1 file: - access_level: open_access checksum: 81761fb939acb7585c36629f765b4373 content_type: application/pdf creator: system date_created: 2018-12-12T10:07:55Z date_updated: 2020-07-14T12:48:18Z file_id: '4654' file_name: IST-2017-815-v1+3_final_blank_signature_maybe_pdfa.pdf file_size: 786145 relation: main_file - access_level: closed checksum: 2b2d7e1d6c1c79a9795a7aa0f860baf3 content_type: application/zip creator: dernst date_created: 2019-04-05T08:43:24Z date_updated: 2020-07-14T12:48:18Z file_id: '6208' file_name: 2017_Thesis_Rolinek_source.zip file_size: 5936337 relation: source_file file_date_updated: 2020-07-14T12:48:18Z has_accepted_license: '1' language: - iso: eng month: '05' oa: 1 oa_version: Published Version page: '97' project: - _id: 25FBA906-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '616160' name: 'Discrete Optimization in Computer Vision: Theory and Practice' publication_identifier: issn: - 2663-337X publication_status: published publisher: Institute of Science and Technology Austria publist_id: '6407' pubrep_id: '815' status: public supervisor: - first_name: Vladimir full_name: Kolmogorov, Vladimir id: 3D50B0BA-F248-11E8-B48F-1D18A9856A87 last_name: Kolmogorov title: Complexity of constraint satisfaction type: dissertation user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1 year: '2017' ... --- _id: '1192' abstract: - lang: eng text: The main result of this paper is a generalization of the classical blossom algorithm for finding perfect matchings. Our algorithm can efficiently solve Boolean CSPs where each variable appears in exactly two constraints (we call it edge CSP) and all constraints are even Δ-matroid relations (represented by lists of tuples). As a consequence of this, we settle the complexity classification of planar Boolean CSPs started by Dvorak and Kupec. Knowing that edge CSP is tractable for even Δ-matroid constraints allows us to extend the tractability result to a larger class of Δ-matroids that includes many classes that were known to be tractable before, namely co-independent, compact, local and binary. article_processing_charge: No author: - first_name: Alexandr full_name: Kazda, Alexandr id: 3B32BAA8-F248-11E8-B48F-1D18A9856A87 last_name: Kazda - first_name: Vladimir full_name: Kolmogorov, Vladimir id: 3D50B0BA-F248-11E8-B48F-1D18A9856A87 last_name: Kolmogorov - first_name: Michal full_name: Rolinek, Michal id: 3CB3BC06-F248-11E8-B48F-1D18A9856A87 last_name: Rolinek citation: ama: 'Kazda A, Kolmogorov V, Rolinek M. Even delta-matroids and the complexity of planar Boolean CSPs. In: SIAM; 2017:307-326. doi:10.1137/1.9781611974782.20' apa: 'Kazda, A., Kolmogorov, V., & Rolinek, M. (2017). Even delta-matroids and the complexity of planar Boolean CSPs (pp. 307–326). Presented at the SODA: Symposium on Discrete Algorithms, Barcelona, Spain: SIAM. https://doi.org/10.1137/1.9781611974782.20' chicago: Kazda, Alexandr, Vladimir Kolmogorov, and Michal Rolinek. “Even Delta-Matroids and the Complexity of Planar Boolean CSPs,” 307–26. SIAM, 2017. https://doi.org/10.1137/1.9781611974782.20. ieee: 'A. Kazda, V. Kolmogorov, and M. Rolinek, “Even delta-matroids and the complexity of planar Boolean CSPs,” presented at the SODA: Symposium on Discrete Algorithms, Barcelona, Spain, 2017, pp. 307–326.' ista: 'Kazda A, Kolmogorov V, Rolinek M. 2017. Even delta-matroids and the complexity of planar Boolean CSPs. SODA: Symposium on Discrete Algorithms, 307–326.' mla: Kazda, Alexandr, et al. Even Delta-Matroids and the Complexity of Planar Boolean CSPs. SIAM, 2017, pp. 307–26, doi:10.1137/1.9781611974782.20. short: A. Kazda, V. Kolmogorov, M. Rolinek, in:, SIAM, 2017, pp. 307–326. conference: end_date: 2017-01019 location: Barcelona, Spain name: 'SODA: Symposium on Discrete Algorithms' start_date: 2017-01-16 date_created: 2018-12-11T11:50:38Z date_published: 2017-01-01T00:00:00Z date_updated: 2023-09-20T11:20:26Z day: '01' department: - _id: VlKo doi: 10.1137/1.9781611974782.20 ec_funded: 1 external_id: isi: - '000426965800020' isi: 1 language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/1602.03124 month: '01' oa: 1 oa_version: Submitted Version page: 307 - 326 project: - _id: 25FBA906-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '616160' name: 'Discrete Optimization in Computer Vision: Theory and Practice' publication_identifier: isbn: - 978-161197478-2 publication_status: published publisher: SIAM publist_id: '6159' quality_controlled: '1' related_material: record: - id: '6032' relation: later_version status: public status: public title: Even delta-matroids and the complexity of planar Boolean CSPs type: conference user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1 year: '2017' ... --- _id: '916' abstract: - lang: eng text: We study the quadratic assignment problem, in computer vision also known as graph matching. Two leading solvers for this problem optimize the Lagrange decomposition duals with sub-gradient and dual ascent (also known as message passing) updates. We explore this direction further and propose several additional Lagrangean relaxations of the graph matching problem along with corresponding algorithms, which are all based on a common dual ascent framework. Our extensive empirical evaluation gives several theoretical insights and suggests a new state-of-the-art anytime solver for the considered problem. Our improvement over state-of-the-art is particularly visible on a new dataset with large-scale sparse problem instances containing more than 500 graph nodes each. article_processing_charge: No author: - first_name: Paul full_name: Swoboda, Paul id: 446560C6-F248-11E8-B48F-1D18A9856A87 last_name: Swoboda - first_name: Carsten full_name: Rother, Carsten last_name: Rother - first_name: Carsten full_name: Abu Alhaija, Carsten last_name: Abu Alhaija - first_name: Dagmar full_name: Kainmueller, Dagmar last_name: Kainmueller - first_name: Bogdan full_name: Savchynskyy, Bogdan last_name: Savchynskyy citation: ama: 'Swoboda P, Rother C, Abu Alhaija C, Kainmueller D, Savchynskyy B. A study of lagrangean decompositions and dual ascent solvers for graph matching. In: Vol 2017. IEEE; 2017:7062-7071. doi:10.1109/CVPR.2017.747' apa: 'Swoboda, P., Rother, C., Abu Alhaija, C., Kainmueller, D., & Savchynskyy, B. (2017). A study of lagrangean decompositions and dual ascent solvers for graph matching (Vol. 2017, pp. 7062–7071). Presented at the CVPR: Computer Vision and Pattern Recognition, Honolulu, HA, United States: IEEE. https://doi.org/10.1109/CVPR.2017.747' chicago: Swoboda, Paul, Carsten Rother, Carsten Abu Alhaija, Dagmar Kainmueller, and Bogdan Savchynskyy. “A Study of Lagrangean Decompositions and Dual Ascent Solvers for Graph Matching,” 2017:7062–71. IEEE, 2017. https://doi.org/10.1109/CVPR.2017.747. ieee: 'P. Swoboda, C. Rother, C. Abu Alhaija, D. Kainmueller, and B. Savchynskyy, “A study of lagrangean decompositions and dual ascent solvers for graph matching,” presented at the CVPR: Computer Vision and Pattern Recognition, Honolulu, HA, United States, 2017, vol. 2017, pp. 7062–7071.' ista: 'Swoboda P, Rother C, Abu Alhaija C, Kainmueller D, Savchynskyy B. 2017. A study of lagrangean decompositions and dual ascent solvers for graph matching. CVPR: Computer Vision and Pattern Recognition vol. 2017, 7062–7071.' mla: Swoboda, Paul, et al. A Study of Lagrangean Decompositions and Dual Ascent Solvers for Graph Matching. Vol. 2017, IEEE, 2017, pp. 7062–71, doi:10.1109/CVPR.2017.747. short: P. Swoboda, C. Rother, C. Abu Alhaija, D. Kainmueller, B. Savchynskyy, in:, IEEE, 2017, pp. 7062–7071. conference: end_date: 2017-07-26 location: Honolulu, HA, United States name: 'CVPR: Computer Vision and Pattern Recognition' start_date: 2017-07-21 date_created: 2018-12-11T11:49:11Z date_published: 2017-01-01T00:00:00Z date_updated: 2023-09-26T15:41:40Z day: '01' ddc: - '000' department: - _id: VlKo doi: 10.1109/CVPR.2017.747 ec_funded: 1 external_id: isi: - '000418371407018' file: - access_level: open_access checksum: e38a2740daad1ea178465843b5072906 content_type: application/pdf creator: dernst date_created: 2019-01-18T12:49:38Z date_updated: 2020-07-14T12:48:15Z file_id: '5848' file_name: 2017_CVPR_Swoboda2.pdf file_size: 944332 relation: main_file file_date_updated: 2020-07-14T12:48:15Z has_accepted_license: '1' intvolume: ' 2017' isi: 1 language: - iso: eng month: '01' oa: 1 oa_version: Submitted Version page: 7062-7071 project: - _id: 25FBA906-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '616160' name: 'Discrete Optimization in Computer Vision: Theory and Practice' publication_identifier: isbn: - 978-153860457-1 publication_status: published publisher: IEEE publist_id: '6525' quality_controlled: '1' scopus_import: '1' status: public title: A study of lagrangean decompositions and dual ascent solvers for graph matching type: conference user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1 volume: 2017 year: '2017' ... --- _id: '915' abstract: - lang: eng text: We propose a dual decomposition and linear program relaxation of the NP-hard minimum cost multicut problem. Unlike other polyhedral relaxations of the multicut polytope, it is amenable to efficient optimization by message passing. Like other polyhedral relaxations, it can be tightened efficiently by cutting planes. We define an algorithm that alternates between message passing and efficient separation of cycle- and odd-wheel inequalities. This algorithm is more efficient than state-of-the-art algorithms based on linear programming, including algorithms written in the framework of leading commercial software, as we show in experiments with large instances of the problem from applications in computer vision, biomedical image analysis and data mining. article_processing_charge: No author: - first_name: Paul full_name: Swoboda, Paul id: 446560C6-F248-11E8-B48F-1D18A9856A87 last_name: Swoboda - first_name: Bjoern full_name: Andres, Bjoern last_name: Andres citation: ama: 'Swoboda P, Andres B. A message passing algorithm for the minimum cost multicut problem. In: Vol 2017. IEEE; 2017:4990-4999. doi:10.1109/CVPR.2017.530' apa: 'Swoboda, P., & Andres, B. (2017). A message passing algorithm for the minimum cost multicut problem (Vol. 2017, pp. 4990–4999). Presented at the CVPR: Computer Vision and Pattern Recognition, Honolulu, HA, United States: IEEE. https://doi.org/10.1109/CVPR.2017.530' chicago: Swoboda, Paul, and Bjoern Andres. “A Message Passing Algorithm for the Minimum Cost Multicut Problem,” 2017:4990–99. IEEE, 2017. https://doi.org/10.1109/CVPR.2017.530. ieee: 'P. Swoboda and B. Andres, “A message passing algorithm for the minimum cost multicut problem,” presented at the CVPR: Computer Vision and Pattern Recognition, Honolulu, HA, United States, 2017, vol. 2017, pp. 4990–4999.' ista: 'Swoboda P, Andres B. 2017. A message passing algorithm for the minimum cost multicut problem. CVPR: Computer Vision and Pattern Recognition vol. 2017, 4990–4999.' mla: Swoboda, Paul, and Bjoern Andres. A Message Passing Algorithm for the Minimum Cost Multicut Problem. Vol. 2017, IEEE, 2017, pp. 4990–99, doi:10.1109/CVPR.2017.530. short: P. Swoboda, B. Andres, in:, IEEE, 2017, pp. 4990–4999. conference: end_date: 2017-07-26 location: Honolulu, HA, United States name: 'CVPR: Computer Vision and Pattern Recognition' start_date: 2017-07-21 date_created: 2018-12-11T11:49:11Z date_published: 2017-07-01T00:00:00Z date_updated: 2023-09-26T15:43:27Z day: '01' ddc: - '000' department: - _id: VlKo doi: 10.1109/CVPR.2017.530 ec_funded: 1 external_id: isi: - '000418371405009' file: - access_level: open_access checksum: 7e51dacefa693574581a32da3eff63dc content_type: application/pdf creator: dernst date_created: 2019-01-18T12:52:46Z date_updated: 2020-07-14T12:48:15Z file_id: '5849' file_name: Swoboda_A_Message_Passing_CVPR_2017_paper.pdf file_size: 883264 relation: main_file file_date_updated: 2020-07-14T12:48:15Z has_accepted_license: '1' intvolume: ' 2017' isi: 1 language: - iso: eng month: '07' oa: 1 oa_version: Submitted Version page: 4990-4999 project: - _id: 25FBA906-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '616160' name: 'Discrete Optimization in Computer Vision: Theory and Practice' publication_identifier: isbn: - 978-153860457-1 publication_status: published publisher: IEEE publist_id: '6526' quality_controlled: '1' scopus_import: '1' status: public title: A message passing algorithm for the minimum cost multicut problem type: conference user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1 volume: 2017 year: '2017' ... --- _id: '917' abstract: - lang: eng text: We propose a general dual ascent framework for Lagrangean decomposition of combinatorial problems. Although methods of this type have shown their efficiency for a number of problems, so far there was no general algorithm applicable to multiple problem types. In this work, we propose such a general algorithm. It depends on several parameters, which can be used to optimize its performance in each particular setting. We demonstrate efficacy of our method on graph matching and multicut problems, where it outperforms state-of-the-art solvers including those based on subgradient optimization and off-the-shelf linear programming solvers. article_processing_charge: No author: - first_name: Paul full_name: Swoboda, Paul id: 446560C6-F248-11E8-B48F-1D18A9856A87 last_name: Swoboda - first_name: Jan full_name: Kuske, Jan last_name: Kuske - first_name: Bogdan full_name: Savchynskyy, Bogdan last_name: Savchynskyy citation: ama: 'Swoboda P, Kuske J, Savchynskyy B. A dual ascent framework for Lagrangean decomposition of combinatorial problems. In: Vol 2017. IEEE; 2017:4950-4960. doi:10.1109/CVPR.2017.526' apa: 'Swoboda, P., Kuske, J., & Savchynskyy, B. (2017). A dual ascent framework for Lagrangean decomposition of combinatorial problems (Vol. 2017, pp. 4950–4960). Presented at the CVPR: Computer Vision and Pattern Recognition, Honolulu, HA, United States: IEEE. https://doi.org/10.1109/CVPR.2017.526' chicago: Swoboda, Paul, Jan Kuske, and Bogdan Savchynskyy. “A Dual Ascent Framework for Lagrangean Decomposition of Combinatorial Problems,” 2017:4950–60. IEEE, 2017. https://doi.org/10.1109/CVPR.2017.526. ieee: 'P. Swoboda, J. Kuske, and B. Savchynskyy, “A dual ascent framework for Lagrangean decomposition of combinatorial problems,” presented at the CVPR: Computer Vision and Pattern Recognition, Honolulu, HA, United States, 2017, vol. 2017, pp. 4950–4960.' ista: 'Swoboda P, Kuske J, Savchynskyy B. 2017. A dual ascent framework for Lagrangean decomposition of combinatorial problems. CVPR: Computer Vision and Pattern Recognition vol. 2017, 4950–4960.' mla: Swoboda, Paul, et al. A Dual Ascent Framework for Lagrangean Decomposition of Combinatorial Problems. Vol. 2017, IEEE, 2017, pp. 4950–60, doi:10.1109/CVPR.2017.526. short: P. Swoboda, J. Kuske, B. Savchynskyy, in:, IEEE, 2017, pp. 4950–4960. conference: end_date: 2017-07-26 location: Honolulu, HA, United States name: 'CVPR: Computer Vision and Pattern Recognition' start_date: 2017-07-21 date_created: 2018-12-11T11:49:11Z date_published: 2017-07-01T00:00:00Z date_updated: 2023-09-26T15:41:11Z day: '01' ddc: - '000' department: - _id: VlKo doi: 10.1109/CVPR.2017.526 ec_funded: 1 external_id: isi: - '000418371405005' file: - access_level: open_access checksum: 72fd291046bd8e5717961bd68f6b6f03 content_type: application/pdf creator: dernst date_created: 2019-01-18T12:45:55Z date_updated: 2020-07-14T12:48:15Z file_id: '5847' file_name: 2017_CVPR_Swoboda.pdf file_size: 898652 relation: main_file file_date_updated: 2020-07-14T12:48:15Z has_accepted_license: '1' intvolume: ' 2017' isi: 1 language: - iso: eng month: '07' oa: 1 oa_version: Submitted Version page: 4950-4960 project: - _id: 25FBA906-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '616160' name: 'Discrete Optimization in Computer Vision: Theory and Practice' publication_identifier: isbn: - 978-153860457-1 publication_status: published publisher: IEEE publist_id: '6524' quality_controlled: '1' scopus_import: '1' status: public title: A dual ascent framework for Lagrangean decomposition of combinatorial problems type: conference user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1 volume: 2017 year: '2017' ... --- _id: '274' abstract: - lang: eng text: We consider the problem of estimating the partition function Z(β)=∑xexp(−β(H(x)) of a Gibbs distribution with a Hamilton H(⋅), or more precisely the logarithm of the ratio q=lnZ(0)/Z(β). It has been recently shown how to approximate q with high probability assuming the existence of an oracle that produces samples from the Gibbs distribution for a given parameter value in [0,β]. The current best known approach due to Huber [9] uses O(qlnn⋅[lnq+lnlnn+ε−2]) oracle calls on average where ε is the desired accuracy of approximation and H(⋅) is assumed to lie in {0}∪[1,n]. We improve the complexity to O(qlnn⋅ε−2) oracle calls. We also show that the same complexity can be achieved if exact oracles are replaced with approximate sampling oracles that are within O(ε2qlnn) variation distance from exact oracles. Finally, we prove a lower bound of Ω(q⋅ε−2) oracle calls under a natural model of computation. article_processing_charge: No author: - first_name: Vladimir full_name: Kolmogorov, Vladimir id: 3D50B0BA-F248-11E8-B48F-1D18A9856A87 last_name: Kolmogorov citation: ama: 'Kolmogorov V. A faster approximation algorithm for the Gibbs partition function. In: Proceedings of the 31st Conference On Learning Theory. Vol 75. ML Research Press; 2017:228-249.' apa: Kolmogorov, V. (2017). A faster approximation algorithm for the Gibbs partition function. In Proceedings of the 31st Conference On Learning Theory (Vol. 75, pp. 228–249). ML Research Press. chicago: Kolmogorov, Vladimir. “A Faster Approximation Algorithm for the Gibbs Partition Function.” In Proceedings of the 31st Conference On Learning Theory, 75:228–49. ML Research Press, 2017. ieee: V. Kolmogorov, “A faster approximation algorithm for the Gibbs partition function,” in Proceedings of the 31st Conference On Learning Theory, 2017, vol. 75, pp. 228–249. ista: 'Kolmogorov V. 2017. A faster approximation algorithm for the Gibbs partition function. Proceedings of the 31st Conference On Learning Theory. COLT: Annual Conference on Learning Theory vol. 75, 228–249.' mla: Kolmogorov, Vladimir. “A Faster Approximation Algorithm for the Gibbs Partition Function.” Proceedings of the 31st Conference On Learning Theory, vol. 75, ML Research Press, 2017, pp. 228–49. short: V. Kolmogorov, in:, Proceedings of the 31st Conference On Learning Theory, ML Research Press, 2017, pp. 228–249. conference: end_date: 2018-07-09 name: 'COLT: Annual Conference on Learning Theory ' start_date: 2018-07-06 date_created: 2018-12-11T11:45:33Z date_published: 2017-12-27T00:00:00Z date_updated: 2023-10-17T12:32:13Z day: '27' ddc: - '510' department: - _id: VlKo ec_funded: 1 external_id: arxiv: - '1608.04223' file: - access_level: open_access checksum: 89db06a0e8083524449cb59b56bf4e5b content_type: application/pdf creator: dernst date_created: 2020-05-12T09:23:27Z date_updated: 2020-07-14T12:45:45Z file_id: '7820' file_name: 2018_PMLR_Kolmogorov.pdf file_size: 408974 relation: main_file file_date_updated: 2020-07-14T12:45:45Z has_accepted_license: '1' intvolume: ' 75' language: - iso: eng month: '12' oa: 1 oa_version: Published Version page: 228-249 project: - _id: 25FBA906-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '616160' name: 'Discrete Optimization in Computer Vision: Theory and Practice' publication: Proceedings of the 31st Conference On Learning Theory publication_status: published publisher: ML Research Press publist_id: '7628' quality_controlled: '1' status: public title: A faster approximation algorithm for the Gibbs partition function tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 75 year: '2017' ... --- _id: '5561' abstract: - lang: eng text: 'Graph matching problems as described in "Active Graph Matching for Automatic Joint Segmentation and Annotation of C. Elegans." by Kainmueller, Dagmar and Jug, Florian and Rother, Carsten and Myers, Gene, MICCAI 2014. Problems are in OpenGM2 hdf5 format (see http://hciweb2.iwr.uni-heidelberg.de/opengm/) and a custom text format used by the feature matching solver described in "Feature Correspondence via Graph Matching: Models and Global Optimization." by Lorenzo Torresani, Vladimir Kolmogorov and Carsten Rother, ECCV 2008, code at http://pub.ist.ac.at/~vnk/software/GraphMatching-v1.02.src.zip. ' acknowledgement: We thank Vladimir Kolmogorov and Stephan Saalfeld forinspiring discussions. article_processing_charge: No author: - first_name: Dagmar full_name: Kainmueller, Dagmar last_name: Kainmueller - first_name: Florian full_name: Jug, Florian last_name: Jug - first_name: Carsten full_name: Rother, Carsten last_name: Rother - first_name: Gene full_name: Meyers, Gene last_name: Meyers citation: ama: Kainmueller D, Jug F, Rother C, Meyers G. Graph matching problems for annotating C. Elegans. 2017. doi:10.15479/AT:ISTA:57 apa: Kainmueller, D., Jug, F., Rother, C., & Meyers, G. (2017). Graph matching problems for annotating C. Elegans. Institute of Science and Technology Austria. https://doi.org/10.15479/AT:ISTA:57 chicago: Kainmueller, Dagmar, Florian Jug, Carsten Rother, and Gene Meyers. “Graph Matching Problems for Annotating C. Elegans.” Institute of Science and Technology Austria, 2017. https://doi.org/10.15479/AT:ISTA:57. ieee: D. Kainmueller, F. Jug, C. Rother, and G. Meyers, “Graph matching problems for annotating C. Elegans.” Institute of Science and Technology Austria, 2017. ista: Kainmueller D, Jug F, Rother C, Meyers G. 2017. Graph matching problems for annotating C. Elegans, Institute of Science and Technology Austria, 10.15479/AT:ISTA:57. mla: Kainmueller, Dagmar, et al. Graph Matching Problems for Annotating C. Elegans. Institute of Science and Technology Austria, 2017, doi:10.15479/AT:ISTA:57. short: D. Kainmueller, F. Jug, C. Rother, G. Meyers, (2017). datarep_id: '57' date_created: 2018-12-12T12:31:32Z date_published: 2017-02-13T00:00:00Z date_updated: 2024-02-21T13:46:31Z day: '13' ddc: - '000' department: - _id: VlKo doi: 10.15479/AT:ISTA:57 file: - access_level: open_access checksum: 3dc3e1306a66028a34181ebef2923139 content_type: application/zip creator: system date_created: 2018-12-12T13:02:54Z date_updated: 2020-07-14T12:47:03Z file_id: '5614' file_name: IST-2017-57-v1+1_wormMatchingProblems.zip file_size: 327042819 relation: main_file file_date_updated: 2020-07-14T12:47:03Z has_accepted_license: '1' keyword: - graph matching - feature matching - QAP - MAP-inference month: '02' oa: 1 oa_version: Published Version publisher: Institute of Science and Technology Austria status: public title: Graph matching problems for annotating C. Elegans tmp: image: /images/cc_0.png legal_code_url: https://creativecommons.org/publicdomain/zero/1.0/legalcode name: Creative Commons Public Domain Dedication (CC0 1.0) short: CC0 (1.0) type: research_data user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2017' ... --- _id: '1231' abstract: - lang: eng text: 'We study the time-and memory-complexities of the problem of computing labels of (multiple) randomly selected challenge-nodes in a directed acyclic graph. The w-bit label of a node is the hash of the labels of its parents, and the hash function is modeled as a random oracle. Specific instances of this problem underlie both proofs of space [Dziembowski et al. CRYPTO’15] as well as popular memory-hard functions like scrypt. As our main tool, we introduce the new notion of a probabilistic parallel entangled pebbling game, a new type of combinatorial pebbling game on a graph, which is closely related to the labeling game on the same graph. As a first application of our framework, we prove that for scrypt, when the underlying hash function is invoked n times, the cumulative memory complexity (CMC) (a notion recently introduced by Alwen and Serbinenko (STOC’15) to capture amortized memory-hardness for parallel adversaries) is at least Ω(w · (n/ log(n))2). This bound holds for adversaries that can store many natural functions of the labels (e.g., linear combinations), but still not arbitrary functions thereof. We then introduce and study a combinatorial quantity, and show how a sufficiently small upper bound on it (which we conjecture) extends our CMC bound for scrypt to hold against arbitrary adversaries. We also show that such an upper bound solves the main open problem for proofs-of-space protocols: namely, establishing that the time complexity of computing the label of a random node in a graph on n nodes (given an initial kw-bit state) reduces tightly to the time complexity for black pebbling on the same graph (given an initial k-node pebbling).' acknowledgement: "Joël Alwen, Chethan Kamath, and Krzysztof Pietrzak’s research is partially supported by an ERC starting grant (259668-PSPC). Vladimir Kolmogorov is partially supported by an ERC consolidator grant (616160-DOICV). Binyi Chen was partially supported by NSF grants CNS-1423566 and CNS-1514526, and a gift from the Gareatis Foundation. Stefano Tessaro was partially supported by NSF grants CNS-1423566, CNS-1528178, a Hellman Fellowship, and the Glen and Susanne Culler Chair.\r\n\r\nThis work was done in part while the authors were visiting the Simons Institute for the Theory of Computing, supported by the Simons Foundation and by the DIMACS/Simons Collaboration in Cryptography through NSF grant CNS-1523467." alternative_title: - LNCS author: - first_name: Joel F full_name: Alwen, Joel F id: 2A8DFA8C-F248-11E8-B48F-1D18A9856A87 last_name: Alwen - first_name: Binyi full_name: Chen, Binyi last_name: Chen - first_name: Chethan full_name: Kamath Hosdurg, Chethan id: 4BD3F30E-F248-11E8-B48F-1D18A9856A87 last_name: Kamath Hosdurg - first_name: Vladimir full_name: Kolmogorov, Vladimir id: 3D50B0BA-F248-11E8-B48F-1D18A9856A87 last_name: Kolmogorov - first_name: Krzysztof Z full_name: Pietrzak, Krzysztof Z id: 3E04A7AA-F248-11E8-B48F-1D18A9856A87 last_name: Pietrzak orcid: 0000-0002-9139-1654 - first_name: Stefano full_name: Tessaro, Stefano last_name: Tessaro citation: ama: 'Alwen JF, Chen B, Kamath Hosdurg C, Kolmogorov V, Pietrzak KZ, Tessaro S. On the complexity of scrypt and proofs of space in the parallel random oracle model. In: Vol 9666. Springer; 2016:358-387. doi:10.1007/978-3-662-49896-5_13' apa: 'Alwen, J. F., Chen, B., Kamath Hosdurg, C., Kolmogorov, V., Pietrzak, K. Z., & Tessaro, S. (2016). On the complexity of scrypt and proofs of space in the parallel random oracle model (Vol. 9666, pp. 358–387). Presented at the EUROCRYPT: Theory and Applications of Cryptographic Techniques, Vienna, Austria: Springer. https://doi.org/10.1007/978-3-662-49896-5_13' chicago: Alwen, Joel F, Binyi Chen, Chethan Kamath Hosdurg, Vladimir Kolmogorov, Krzysztof Z Pietrzak, and Stefano Tessaro. “On the Complexity of Scrypt and Proofs of Space in the Parallel Random Oracle Model,” 9666:358–87. Springer, 2016. https://doi.org/10.1007/978-3-662-49896-5_13. ieee: 'J. F. Alwen, B. Chen, C. Kamath Hosdurg, V. Kolmogorov, K. Z. Pietrzak, and S. Tessaro, “On the complexity of scrypt and proofs of space in the parallel random oracle model,” presented at the EUROCRYPT: Theory and Applications of Cryptographic Techniques, Vienna, Austria, 2016, vol. 9666, pp. 358–387.' ista: 'Alwen JF, Chen B, Kamath Hosdurg C, Kolmogorov V, Pietrzak KZ, Tessaro S. 2016. On the complexity of scrypt and proofs of space in the parallel random oracle model. EUROCRYPT: Theory and Applications of Cryptographic Techniques, LNCS, vol. 9666, 358–387.' mla: Alwen, Joel F., et al. On the Complexity of Scrypt and Proofs of Space in the Parallel Random Oracle Model. Vol. 9666, Springer, 2016, pp. 358–87, doi:10.1007/978-3-662-49896-5_13. short: J.F. Alwen, B. Chen, C. Kamath Hosdurg, V. Kolmogorov, K.Z. Pietrzak, S. Tessaro, in:, Springer, 2016, pp. 358–387. conference: end_date: 2016-05-12 location: Vienna, Austria name: 'EUROCRYPT: Theory and Applications of Cryptographic Techniques' start_date: 2016-05-08 date_created: 2018-12-11T11:50:51Z date_published: 2016-04-28T00:00:00Z date_updated: 2021-01-12T06:49:15Z day: '28' department: - _id: KrPi - _id: VlKo doi: 10.1007/978-3-662-49896-5_13 ec_funded: 1 intvolume: ' 9666' language: - iso: eng main_file_link: - open_access: '1' url: https://eprint.iacr.org/2016/100 month: '04' oa: 1 oa_version: Submitted Version page: 358 - 387 project: - _id: 258C570E-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '259668' name: Provable Security for Physical Cryptography - _id: 25FBA906-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '616160' name: 'Discrete Optimization in Computer Vision: Theory and Practice' publication_status: published publisher: Springer publist_id: '6103' quality_controlled: '1' scopus_import: 1 status: public title: On the complexity of scrypt and proofs of space in the parallel random oracle model type: conference user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87 volume: 9666 year: '2016' ... --- _id: '1353' abstract: - lang: eng text: We characterize absorption in finite idempotent algebras by means of Jónsson absorption and cube term blockers. As an application we show that it is decidable whether a given subset is an absorbing subuniverse of an algebra given by the tables of its basic operations. acknowledgement: 'Libor Barto and Alexandr Kazda were supported by the the Grant Agency of the Czech Republic, grant GACR 13-01832S. ' author: - first_name: Libor full_name: Barto, Libor last_name: Barto - first_name: Alexandr full_name: Kazda, Alexandr id: 3B32BAA8-F248-11E8-B48F-1D18A9856A87 last_name: Kazda citation: ama: Barto L, Kazda A. Deciding absorption. International Journal of Algebra and Computation. 2016;26(5):1033-1060. doi:10.1142/S0218196716500430 apa: Barto, L., & Kazda, A. (2016). Deciding absorption. International Journal of Algebra and Computation. World Scientific Publishing. https://doi.org/10.1142/S0218196716500430 chicago: Barto, Libor, and Alexandr Kazda. “Deciding Absorption.” International Journal of Algebra and Computation. World Scientific Publishing, 2016. https://doi.org/10.1142/S0218196716500430. ieee: L. Barto and A. Kazda, “Deciding absorption,” International Journal of Algebra and Computation, vol. 26, no. 5. World Scientific Publishing, pp. 1033–1060, 2016. ista: Barto L, Kazda A. 2016. Deciding absorption. International Journal of Algebra and Computation. 26(5), 1033–1060. mla: Barto, Libor, and Alexandr Kazda. “Deciding Absorption.” International Journal of Algebra and Computation, vol. 26, no. 5, World Scientific Publishing, 2016, pp. 1033–60, doi:10.1142/S0218196716500430. short: L. Barto, A. Kazda, International Journal of Algebra and Computation 26 (2016) 1033–1060. date_created: 2018-12-11T11:51:32Z date_published: 2016-07-20T00:00:00Z date_updated: 2021-01-12T06:50:06Z day: '20' department: - _id: VlKo doi: 10.1142/S0218196716500430 intvolume: ' 26' issue: '5' language: - iso: eng main_file_link: - open_access: '1' url: http://arxiv.org/abs/1512.07009 month: '07' oa: 1 oa_version: Preprint page: 1033 - 1060 publication: International Journal of Algebra and Computation publication_status: published publisher: World Scientific Publishing publist_id: '5893' quality_controlled: '1' scopus_import: 1 status: public title: Deciding absorption type: journal_article user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87 volume: 26 year: '2016' ... --- _id: '1377' abstract: - lang: eng text: We consider the problem of minimizing the continuous valued total variation subject to different unary terms on trees and propose fast direct algorithms based on dynamic programming to solve these problems. We treat both the convex and the nonconvex case and derive worst-case complexities that are equal to or better than existing methods. We show applications to total variation based two dimensional image processing and computer vision problems based on a Lagrangian decomposition approach. The resulting algorithms are very effcient, offer a high degree of parallelism, and come along with memory requirements which are only in the order of the number of image pixels. author: - first_name: Vladimir full_name: Kolmogorov, Vladimir id: 3D50B0BA-F248-11E8-B48F-1D18A9856A87 last_name: Kolmogorov - first_name: Thomas full_name: Pock, Thomas last_name: Pock - first_name: Michal full_name: Rolinek, Michal id: 3CB3BC06-F248-11E8-B48F-1D18A9856A87 last_name: Rolinek citation: ama: Kolmogorov V, Pock T, Rolinek M. Total variation on a tree. SIAM Journal on Imaging Sciences. 2016;9(2):605-636. doi:10.1137/15M1010257 apa: Kolmogorov, V., Pock, T., & Rolinek, M. (2016). Total variation on a tree. SIAM Journal on Imaging Sciences. Society for Industrial and Applied Mathematics . https://doi.org/10.1137/15M1010257 chicago: Kolmogorov, Vladimir, Thomas Pock, and Michal Rolinek. “Total Variation on a Tree.” SIAM Journal on Imaging Sciences. Society for Industrial and Applied Mathematics , 2016. https://doi.org/10.1137/15M1010257. ieee: V. Kolmogorov, T. Pock, and M. Rolinek, “Total variation on a tree,” SIAM Journal on Imaging Sciences, vol. 9, no. 2. Society for Industrial and Applied Mathematics , pp. 605–636, 2016. ista: Kolmogorov V, Pock T, Rolinek M. 2016. Total variation on a tree. SIAM Journal on Imaging Sciences. 9(2), 605–636. mla: Kolmogorov, Vladimir, et al. “Total Variation on a Tree.” SIAM Journal on Imaging Sciences, vol. 9, no. 2, Society for Industrial and Applied Mathematics , 2016, pp. 605–36, doi:10.1137/15M1010257. short: V. Kolmogorov, T. Pock, M. Rolinek, SIAM Journal on Imaging Sciences 9 (2016) 605–636. date_created: 2018-12-11T11:51:40Z date_published: 2016-05-03T00:00:00Z date_updated: 2021-01-12T06:50:15Z day: '03' department: - _id: VlKo doi: 10.1137/15M1010257 ec_funded: 1 intvolume: ' 9' issue: '2' language: - iso: eng main_file_link: - open_access: '1' url: http://arxiv.org/abs/1502.07770 month: '05' oa: 1 oa_version: Preprint page: 605 - 636 project: - _id: 25FBA906-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '616160' name: 'Discrete Optimization in Computer Vision: Theory and Practice' publication: SIAM Journal on Imaging Sciences publication_status: published publisher: 'Society for Industrial and Applied Mathematics ' publist_id: '5834' quality_controlled: '1' scopus_import: 1 status: public title: Total variation on a tree type: journal_article user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87 volume: 9 year: '2016' ... --- _id: '1612' abstract: - lang: eng text: We prove that whenever A is a 3-conservative relational structure with only binary and unary relations,then the algebra of polymorphisms of A either has no Taylor operation (i.e.,CSP(A)is NP-complete),or it generates an SD(∧) variety (i.e.,CSP(A)has bounded width). author: - first_name: Alexandr full_name: Kazda, Alexandr id: 3B32BAA8-F248-11E8-B48F-1D18A9856A87 last_name: Kazda citation: ama: Kazda A. CSP for binary conservative relational structures. Algebra Universalis. 2016;75(1):75-84. doi:10.1007/s00012-015-0358-8 apa: Kazda, A. (2016). CSP for binary conservative relational structures. Algebra Universalis. Springer. https://doi.org/10.1007/s00012-015-0358-8 chicago: Kazda, Alexandr. “CSP for Binary Conservative Relational Structures.” Algebra Universalis. Springer, 2016. https://doi.org/10.1007/s00012-015-0358-8. ieee: A. Kazda, “CSP for binary conservative relational structures,” Algebra Universalis, vol. 75, no. 1. Springer, pp. 75–84, 2016. ista: Kazda A. 2016. CSP for binary conservative relational structures. Algebra Universalis. 75(1), 75–84. mla: Kazda, Alexandr. “CSP for Binary Conservative Relational Structures.” Algebra Universalis, vol. 75, no. 1, Springer, 2016, pp. 75–84, doi:10.1007/s00012-015-0358-8. short: A. Kazda, Algebra Universalis 75 (2016) 75–84. date_created: 2018-12-11T11:53:01Z date_published: 2016-02-01T00:00:00Z date_updated: 2021-01-12T06:52:00Z day: '01' department: - _id: VlKo doi: 10.1007/s00012-015-0358-8 intvolume: ' 75' issue: '1' language: - iso: eng main_file_link: - open_access: '1' url: http://arxiv.org/abs/1112.1099 month: '02' oa: 1 oa_version: Preprint page: 75 - 84 publication: Algebra Universalis publication_status: published publisher: Springer publist_id: '5554' quality_controlled: '1' scopus_import: 1 status: public title: CSP for binary conservative relational structures type: journal_article user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87 volume: 75 year: '2016' ... --- _id: '1193' abstract: - lang: eng text: We consider the recent formulation of the Algorithmic Lovász Local Lemma [1], [2] for finding objects that avoid "bad features", or "flaws". It extends the Moser-Tardos resampling algorithm [3] to more general discrete spaces. At each step the method picks a flaw present in the current state and "resamples" it using a "resampling oracle" provided by the user. However, it is less flexible than the Moser-Tardos method since [1], [2] require a specific flaw selection rule, whereas [3] allows an arbitrary rule (and thus can potentially be implemented more efficiently). We formulate a new "commutativity" condition, and prove that it is sufficient for an arbitrary rule to work. It also enables an efficient parallelization under an additional assumption. We then show that existing resampling oracles for perfect matchings and permutations do satisfy this condition. Finally, we generalize the precondition in [2] (in the case of symmetric potential causality graphs). This unifies special cases that previously were treated separately. acknowledgement: European Unions Seventh Framework Programme (FP7/2007-2013)/ERC grant agreement no 616160 article_number: '7782993' article_processing_charge: No author: - first_name: Vladimir full_name: Kolmogorov, Vladimir id: 3D50B0BA-F248-11E8-B48F-1D18A9856A87 last_name: Kolmogorov citation: ama: 'Kolmogorov V. Commutativity in the algorithmic Lovasz local lemma. In: Proceedings - Annual IEEE Symposium on Foundations of Computer Science. Vol 2016-December. IEEE; 2016. doi:10.1109/FOCS.2016.88' apa: 'Kolmogorov, V. (2016). Commutativity in the algorithmic Lovasz local lemma. In Proceedings - Annual IEEE Symposium on Foundations of Computer Science (Vol. 2016–December). New Brunswick, NJ, USA : IEEE. https://doi.org/10.1109/FOCS.2016.88' chicago: Kolmogorov, Vladimir. “Commutativity in the Algorithmic Lovasz Local Lemma.” In Proceedings - Annual IEEE Symposium on Foundations of Computer Science, Vol. 2016–December. IEEE, 2016. https://doi.org/10.1109/FOCS.2016.88. ieee: V. Kolmogorov, “Commutativity in the algorithmic Lovasz local lemma,” in Proceedings - Annual IEEE Symposium on Foundations of Computer Science, New Brunswick, NJ, USA , 2016, vol. 2016–December. ista: 'Kolmogorov V. 2016. Commutativity in the algorithmic Lovasz local lemma. Proceedings - Annual IEEE Symposium on Foundations of Computer Science. FOCS: Foundations of Computer Science vol. 2016–December, 7782993.' mla: Kolmogorov, Vladimir. “Commutativity in the Algorithmic Lovasz Local Lemma.” Proceedings - Annual IEEE Symposium on Foundations of Computer Science, vol. 2016–December, 7782993, IEEE, 2016, doi:10.1109/FOCS.2016.88. short: V. Kolmogorov, in:, Proceedings - Annual IEEE Symposium on Foundations of Computer Science, IEEE, 2016. conference: end_date: 2016-09-11 location: 'New Brunswick, NJ, USA ' name: 'FOCS: Foundations of Computer Science' start_date: 2016-09-09 date_created: 2018-12-11T11:50:38Z date_published: 2016-12-15T00:00:00Z date_updated: 2023-09-19T14:24:57Z day: '15' department: - _id: VlKo doi: 10.1109/FOCS.2016.88 ec_funded: 1 external_id: arxiv: - '1506.08547' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/1506.08547v7 month: '12' oa: 1 oa_version: Preprint project: - _id: 25FBA906-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '616160' name: 'Discrete Optimization in Computer Vision: Theory and Practice' publication: Proceedings - Annual IEEE Symposium on Foundations of Computer Science publication_status: published publisher: IEEE publist_id: '6158' quality_controlled: '1' related_material: record: - id: '5975' relation: later_version status: public scopus_import: 1 status: public title: Commutativity in the algorithmic Lovasz local lemma type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 2016-December year: '2016' ...