---
_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
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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:
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department:
- _id: VlKo
doi: 10.1109/CVPR.2017.526
ec_funded: 1
external_id:
isi:
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page: 4950-4960
project:
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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'
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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:
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checksum: 89db06a0e8083524449cb59b56bf4e5b
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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
license: https://creativecommons.org/licenses/by/4.0/
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:
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legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
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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:
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legal_code_url: https://creativecommons.org/publicdomain/zero/1.0/legalcode
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short: CC0 (1.0)
type: research_data
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...