---
_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
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isi: 1
language:
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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
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file_size: 2148882
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success: 1
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intvolume: ' 15'
isi: 1
language:
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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
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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
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