---
_id: '7577'
abstract:
- lang: eng
text: Weak convergence of inertial iterative method for solving variational inequalities
is the focus of this paper. The cost function is assumed to be non-Lipschitz and
monotone. We propose a projection-type method with inertial terms and give weak
convergence analysis under appropriate conditions. Some test results are performed
and compared with relevant methods in the literature to show the efficiency and
advantages given by our proposed methods.
acknowledgement: The project of the first author has received funding from the European
Research Council (ERC) under the European Union's Seventh Framework Program (FP7
- 2007-2013) (Grant agreement No. 616160).
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Yekini
full_name: Shehu, Yekini
id: 3FC7CB58-F248-11E8-B48F-1D18A9856A87
last_name: Shehu
orcid: 0000-0001-9224-7139
- first_name: Olaniyi S.
full_name: Iyiola, Olaniyi S.
last_name: Iyiola
citation:
ama: Shehu Y, Iyiola OS. Weak convergence for variational inequalities with inertial-type
method. Applicable Analysis. 2022;101(1):192-216. doi:10.1080/00036811.2020.1736287
apa: Shehu, Y., & Iyiola, O. S. (2022). Weak convergence for variational inequalities
with inertial-type method. Applicable Analysis. Taylor & Francis. https://doi.org/10.1080/00036811.2020.1736287
chicago: Shehu, Yekini, and Olaniyi S. Iyiola. “Weak Convergence for Variational
Inequalities with Inertial-Type Method.” Applicable Analysis. Taylor &
Francis, 2022. https://doi.org/10.1080/00036811.2020.1736287.
ieee: Y. Shehu and O. S. Iyiola, “Weak convergence for variational inequalities
with inertial-type method,” Applicable Analysis, vol. 101, no. 1. Taylor
& Francis, pp. 192–216, 2022.
ista: Shehu Y, Iyiola OS. 2022. Weak convergence for variational inequalities with
inertial-type method. Applicable Analysis. 101(1), 192–216.
mla: Shehu, Yekini, and Olaniyi S. Iyiola. “Weak Convergence for Variational Inequalities
with Inertial-Type Method.” Applicable Analysis, vol. 101, no. 1, Taylor
& Francis, 2022, pp. 192–216, doi:10.1080/00036811.2020.1736287.
short: Y. Shehu, O.S. Iyiola, Applicable Analysis 101 (2022) 192–216.
date_created: 2020-03-09T07:06:52Z
date_published: 2022-01-01T00:00:00Z
date_updated: 2024-03-05T14:01:52Z
day: '01'
ddc:
- '510'
- '515'
- '518'
department:
- _id: VlKo
doi: 10.1080/00036811.2020.1736287
ec_funded: 1
external_id:
arxiv:
- '2101.08057'
isi:
- '000518364100001'
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oa_version: Submitted Version
page: 192-216
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call_identifier: FP7
grant_number: '616160'
name: 'Discrete Optimization in Computer Vision: Theory and Practice'
publication: Applicable Analysis
publication_identifier:
eissn:
- 1563-504X
issn:
- 0003-6811
publication_status: published
publisher: Taylor & Francis
quality_controlled: '1'
scopus_import: '1'
status: public
title: Weak convergence for variational inequalities with inertial-type method
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 101
year: '2022'
...
---
_id: '7925'
abstract:
- lang: eng
text: In this paper, we introduce a relaxed CQ method with alternated inertial step
for solving split feasibility problems. We give convergence of the sequence generated
by our method under some suitable assumptions. Some numerical implementations
from sparse signal and image deblurring are reported to show the efficiency of
our method.
acknowledgement: Open access funding provided by Institute of Science and Technology
(IST Austria). The authors are grateful to the referees for their insightful comments
which have improved the earlier version of the manuscript greatly. The first author
has received funding from the European Research Council (ERC) under the European
Union’s Seventh Framework Program (FP7-2007-2013) (Grant agreement No. 616160).
article_processing_charge: Yes (via OA deal)
article_type: original
author:
- first_name: Yekini
full_name: Shehu, Yekini
id: 3FC7CB58-F248-11E8-B48F-1D18A9856A87
last_name: Shehu
orcid: 0000-0001-9224-7139
- first_name: Aviv
full_name: Gibali, Aviv
last_name: Gibali
citation:
ama: Shehu Y, Gibali A. New inertial relaxed method for solving split feasibilities.
Optimization Letters. 2021;15:2109-2126. doi:10.1007/s11590-020-01603-1
apa: Shehu, Y., & Gibali, A. (2021). New inertial relaxed method for solving
split feasibilities. Optimization Letters. Springer Nature. https://doi.org/10.1007/s11590-020-01603-1
chicago: Shehu, Yekini, and Aviv Gibali. “New Inertial Relaxed Method for Solving
Split Feasibilities.” Optimization Letters. Springer Nature, 2021. https://doi.org/10.1007/s11590-020-01603-1.
ieee: Y. Shehu and A. Gibali, “New inertial relaxed method for solving split feasibilities,”
Optimization Letters, vol. 15. Springer Nature, pp. 2109–2126, 2021.
ista: Shehu Y, Gibali A. 2021. New inertial relaxed method for solving split feasibilities.
Optimization Letters. 15, 2109–2126.
mla: Shehu, Yekini, and Aviv Gibali. “New Inertial Relaxed Method for Solving Split
Feasibilities.” Optimization Letters, vol. 15, Springer Nature, 2021, pp.
2109–26, doi:10.1007/s11590-020-01603-1.
short: Y. Shehu, A. Gibali, Optimization Letters 15 (2021) 2109–2126.
date_created: 2020-06-04T11:28:33Z
date_published: 2021-09-01T00:00:00Z
date_updated: 2024-03-07T15:00:43Z
day: '01'
ddc:
- '510'
department:
- _id: VlKo
doi: 10.1007/s11590-020-01603-1
ec_funded: 1
external_id:
isi:
- '000537342300001'
file:
- access_level: open_access
checksum: 63c5f31cd04626152a19f97a2476281b
content_type: application/pdf
creator: kschuh
date_created: 2024-03-07T14:58:51Z
date_updated: 2024-03-07T14:58:51Z
file_id: '15089'
file_name: 2021_OptimizationLetters_Shehu.pdf
file_size: 2148882
relation: main_file
success: 1
file_date_updated: 2024-03-07T14:58:51Z
has_accepted_license: '1'
intvolume: ' 15'
isi: 1
language:
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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
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user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 15
year: '2021'
...
---
_id: '8196'
abstract:
- lang: eng
text: This paper aims to obtain a strong convergence result for a Douglas–Rachford
splitting method with inertial extrapolation step for finding a zero of the sum
of two set-valued maximal monotone operators without any further assumption of
uniform monotonicity on any of the involved maximal monotone operators. Furthermore,
our proposed method is easy to implement and the inertial factor in our proposed
method is a natural choice. Our method of proof is of independent interest. Finally,
some numerical implementations are given to confirm the theoretical analysis.
acknowledgement: Open access funding provided by Institute of Science and Technology
(IST Austria). The project of Yekini Shehu has received funding from the European
Research Council (ERC) under the European Union’s Seventh Framework Program (FP7—2007–2013)
(Grant Agreement No. 616160). The authors are grateful to the anonymous referees
and the handling Editor for their comments and suggestions which have improved the
earlier version of the manuscript greatly.
article_processing_charge: Yes (via OA deal)
article_type: original
author:
- first_name: Yekini
full_name: Shehu, Yekini
id: 3FC7CB58-F248-11E8-B48F-1D18A9856A87
last_name: Shehu
orcid: 0000-0001-9224-7139
- first_name: Qiao-Li
full_name: Dong, Qiao-Li
last_name: Dong
- first_name: Lu-Lu
full_name: Liu, Lu-Lu
last_name: Liu
- first_name: Jen-Chih
full_name: Yao, Jen-Chih
last_name: Yao
citation:
ama: Shehu Y, Dong Q-L, Liu L-L, Yao J-C. New strong convergence method for the
sum of two maximal monotone operators. Optimization and Engineering. 2021;22:2627-2653.
doi:10.1007/s11081-020-09544-5
apa: Shehu, Y., Dong, Q.-L., Liu, L.-L., & Yao, J.-C. (2021). New strong convergence
method for the sum of two maximal monotone operators. Optimization and Engineering.
Springer Nature. https://doi.org/10.1007/s11081-020-09544-5
chicago: Shehu, Yekini, Qiao-Li Dong, Lu-Lu Liu, and Jen-Chih Yao. “New Strong Convergence
Method for the Sum of Two Maximal Monotone Operators.” Optimization and Engineering.
Springer Nature, 2021. https://doi.org/10.1007/s11081-020-09544-5.
ieee: Y. Shehu, Q.-L. Dong, L.-L. Liu, and J.-C. Yao, “New strong convergence method
for the sum of two maximal monotone operators,” Optimization and Engineering,
vol. 22. Springer Nature, pp. 2627–2653, 2021.
ista: Shehu Y, Dong Q-L, Liu L-L, Yao J-C. 2021. New strong convergence method for
the sum of two maximal monotone operators. Optimization and Engineering. 22, 2627–2653.
mla: Shehu, Yekini, et al. “New Strong Convergence Method for the Sum of Two Maximal
Monotone Operators.” Optimization and Engineering, vol. 22, Springer Nature,
2021, pp. 2627–53, doi:10.1007/s11081-020-09544-5.
short: Y. Shehu, Q.-L. Dong, L.-L. Liu, J.-C. Yao, Optimization and Engineering
22 (2021) 2627–2653.
date_created: 2020-08-03T14:29:57Z
date_published: 2021-02-25T00:00:00Z
date_updated: 2024-03-07T14:39:29Z
day: '25'
ddc:
- '510'
department:
- _id: VlKo
doi: 10.1007/s11081-020-09544-5
ec_funded: 1
external_id:
isi:
- '000559345400001'
file:
- access_level: open_access
content_type: application/pdf
creator: dernst
date_created: 2020-08-03T15:24:39Z
date_updated: 2020-08-03T15:24:39Z
file_id: '8197'
file_name: 2020_OptimizationEngineering_Shehu.pdf
file_size: 2137860
relation: main_file
success: 1
file_date_updated: 2020-08-03T15:24:39Z
has_accepted_license: '1'
intvolume: ' 22'
isi: 1
language:
- iso: eng
month: '02'
oa: 1
oa_version: Published Version
page: 2627-2653
project:
- _id: B67AFEDC-15C9-11EA-A837-991A96BB2854
name: IST Austria Open Access Fund
- _id: 25FBA906-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '616160'
name: 'Discrete Optimization in Computer Vision: Theory and Practice'
publication: Optimization and Engineering
publication_identifier:
eissn:
- 1573-2924
issn:
- 1389-4420
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: New strong convergence method for the sum of two maximal monotone operators
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 22
year: '2021'
...
---
_id: '8817'
abstract:
- lang: eng
text: The paper introduces an inertial extragradient subgradient method with self-adaptive
step sizes for solving equilibrium problems in real Hilbert spaces. Weak convergence
of the proposed method is obtained under the condition that the bifunction is
pseudomonotone and Lipchitz continuous. Linear convergence is also given when
the bifunction is strongly pseudomonotone and Lipchitz continuous. Numerical implementations
and comparisons with other related inertial methods are given using test problems
including a real-world application to Nash–Cournot oligopolistic electricity market
equilibrium model.
acknowledgement: The authors are grateful to the two referees and the Associate Editor
for their comments and suggestions which have improved the earlier version of the
paper greatly. The project of Yekini Shehu has received funding from the European
Research Council (ERC) under the European Union’s Seventh Framework Program (FP7
- 2007-2013) (Grant agreement No. 616160).
article_processing_charge: No
article_type: original
author:
- first_name: Yekini
full_name: Shehu, Yekini
id: 3FC7CB58-F248-11E8-B48F-1D18A9856A87
last_name: Shehu
orcid: 0000-0001-9224-7139
- first_name: Olaniyi S.
full_name: Iyiola, Olaniyi S.
last_name: Iyiola
- first_name: Duong Viet
full_name: Thong, Duong Viet
last_name: Thong
- first_name: Nguyen Thi Cam
full_name: Van, Nguyen Thi Cam
last_name: Van
citation:
ama: Shehu Y, Iyiola OS, Thong DV, Van NTC. An inertial subgradient extragradient
algorithm extended to pseudomonotone equilibrium problems. Mathematical Methods
of Operations Research. 2021;93(2):213-242. doi:10.1007/s00186-020-00730-w
apa: Shehu, Y., Iyiola, O. S., Thong, D. V., & Van, N. T. C. (2021). An inertial
subgradient extragradient algorithm extended to pseudomonotone equilibrium problems.
Mathematical Methods of Operations Research. Springer Nature. https://doi.org/10.1007/s00186-020-00730-w
chicago: Shehu, Yekini, Olaniyi S. Iyiola, Duong Viet Thong, and Nguyen Thi Cam
Van. “An Inertial Subgradient Extragradient Algorithm Extended to Pseudomonotone
Equilibrium Problems.” Mathematical Methods of Operations Research. Springer
Nature, 2021. https://doi.org/10.1007/s00186-020-00730-w.
ieee: Y. Shehu, O. S. Iyiola, D. V. Thong, and N. T. C. Van, “An inertial subgradient
extragradient algorithm extended to pseudomonotone equilibrium problems,” Mathematical
Methods of Operations Research, vol. 93, no. 2. Springer Nature, pp. 213–242,
2021.
ista: Shehu Y, Iyiola OS, Thong DV, Van NTC. 2021. An inertial subgradient extragradient
algorithm extended to pseudomonotone equilibrium problems. Mathematical Methods
of Operations Research. 93(2), 213–242.
mla: Shehu, Yekini, et al. “An Inertial Subgradient Extragradient Algorithm Extended
to Pseudomonotone Equilibrium Problems.” Mathematical Methods of Operations
Research, vol. 93, no. 2, Springer Nature, 2021, pp. 213–42, doi:10.1007/s00186-020-00730-w.
short: Y. Shehu, O.S. Iyiola, D.V. Thong, N.T.C. Van, Mathematical Methods of Operations
Research 93 (2021) 213–242.
date_created: 2020-11-29T23:01:18Z
date_published: 2021-04-01T00:00:00Z
date_updated: 2023-10-10T09:30:23Z
day: '01'
department:
- _id: VlKo
doi: 10.1007/s00186-020-00730-w
ec_funded: 1
external_id:
isi:
- '000590497300001'
intvolume: ' 93'
isi: 1
issue: '2'
language:
- iso: eng
month: '04'
oa_version: None
page: 213-242
project:
- _id: 25FBA906-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '616160'
name: 'Discrete Optimization in Computer Vision: Theory and Practice'
publication: Mathematical Methods of Operations Research
publication_identifier:
eissn:
- 1432-5217
issn:
- 1432-2994
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: An inertial subgradient extragradient algorithm extended to pseudomonotone
equilibrium problems
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 93
year: '2021'
...
---
_id: '9234'
abstract:
- lang: eng
text: In this paper, we present two new inertial projection-type methods for solving
multivalued variational inequality problems in finite-dimensional spaces. We establish
the convergence of the sequence generated by these methods when the multivalued
mapping associated with the problem is only required to be locally bounded without
any monotonicity assumption. Furthermore, the inertial techniques that we employ
in this paper are quite different from the ones used in most papers. Moreover,
based on the weaker assumptions on the inertial factor in our methods, we derive
several special cases of our methods. Finally, we present some experimental results
to illustrate the profits that we gain by introducing the inertial extrapolation
steps.
acknowledgement: 'The authors sincerely thank the Editor-in-Chief and anonymous referees
for their careful reading, constructive comments and fruitful suggestions that help
improve the manuscript. The research of the first author is supported by the National
Research Foundation (NRF) South Africa (S& F-DSI/NRF Free Standing Postdoctoral
Fellowship; Grant Number: 120784). The first author also acknowledges the financial
support from DSI/NRF, South Africa Center of Excellence in Mathematical and Statistical
Sciences (CoE-MaSS) Postdoctoral Fellowship. The second author has received funding
from the European Research Council (ERC) under the European Union’s Seventh Framework
Program (FP7 - 2007-2013) (Grant agreement No. 616160). Open Access funding provided
by Institute of Science and Technology (IST Austria).'
article_processing_charge: Yes (via OA deal)
article_type: original
author:
- first_name: Chinedu
full_name: Izuchukwu, Chinedu
last_name: Izuchukwu
- first_name: Yekini
full_name: Shehu, Yekini
id: 3FC7CB58-F248-11E8-B48F-1D18A9856A87
last_name: Shehu
orcid: 0000-0001-9224-7139
citation:
ama: Izuchukwu C, Shehu Y. New inertial projection methods for solving multivalued
variational inequality problems beyond monotonicity. Networks and Spatial Economics.
2021;21(2):291-323. doi:10.1007/s11067-021-09517-w
apa: Izuchukwu, C., & Shehu, Y. (2021). New inertial projection methods for
solving multivalued variational inequality problems beyond monotonicity. Networks
and Spatial Economics. Springer Nature. https://doi.org/10.1007/s11067-021-09517-w
chicago: Izuchukwu, Chinedu, and Yekini Shehu. “New Inertial Projection Methods
for Solving Multivalued Variational Inequality Problems beyond Monotonicity.”
Networks and Spatial Economics. Springer Nature, 2021. https://doi.org/10.1007/s11067-021-09517-w.
ieee: C. Izuchukwu and Y. Shehu, “New inertial projection methods for solving multivalued
variational inequality problems beyond monotonicity,” Networks and Spatial
Economics, vol. 21, no. 2. Springer Nature, pp. 291–323, 2021.
ista: Izuchukwu C, Shehu Y. 2021. New inertial projection methods for solving multivalued
variational inequality problems beyond monotonicity. Networks and Spatial Economics.
21(2), 291–323.
mla: Izuchukwu, Chinedu, and Yekini Shehu. “New Inertial Projection Methods for
Solving Multivalued Variational Inequality Problems beyond Monotonicity.” Networks
and Spatial Economics, vol. 21, no. 2, Springer Nature, 2021, pp. 291–323,
doi:10.1007/s11067-021-09517-w.
short: C. Izuchukwu, Y. Shehu, Networks and Spatial Economics 21 (2021) 291–323.
date_created: 2021-03-10T12:18:47Z
date_published: 2021-06-01T00:00:00Z
date_updated: 2023-09-05T15:32:32Z
day: '01'
ddc:
- '510'
department:
- _id: VlKo
doi: 10.1007/s11067-021-09517-w
ec_funded: 1
external_id:
isi:
- '000625002100001'
file:
- access_level: open_access
checksum: 22b4253a2e5da843622a2df713784b4c
content_type: application/pdf
creator: kschuh
date_created: 2021-08-11T12:44:16Z
date_updated: 2021-08-11T12:44:16Z
file_id: '9884'
file_name: 2021_NetworksSpatialEconomics_Shehu.pdf
file_size: 834964
relation: main_file
success: 1
file_date_updated: 2021-08-11T12:44:16Z
has_accepted_license: '1'
intvolume: ' 21'
isi: 1
issue: '2'
keyword:
- Computer Networks and Communications
- Software
- Artificial Intelligence
language:
- iso: eng
month: '06'
oa: 1
oa_version: Published Version
page: 291-323
project:
- _id: 25FBA906-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '616160'
name: 'Discrete Optimization in Computer Vision: Theory and Practice'
- _id: B67AFEDC-15C9-11EA-A837-991A96BB2854
name: IST Austria Open Access Fund
publication: Networks and Spatial Economics
publication_identifier:
eissn:
- 1572-9427
issn:
- 1566-113X
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: New inertial projection methods for solving multivalued variational inequality
problems beyond monotonicity
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
volume: 21
year: '2021'
...
---
_id: '9365'
abstract:
- lang: eng
text: In this paper, we propose a new iterative method with alternated inertial
step for solving split common null point problem in real Hilbert spaces. We obtain
weak convergence of the proposed iterative algorithm. Furthermore, we introduce
the notion of bounded linear regularity property for the split common null point
problem and obtain the linear convergence property for the new algorithm under
some mild assumptions. Finally, we provide some numerical examples to demonstrate
the performance and efficiency of the proposed method.
acknowledgement: The second author has received funding from the European Research
Council (ERC) under the European Union's Seventh Framework Program (FP7-2007-2013)
(Grant agreement No. 616160).
article_processing_charge: No
article_type: original
author:
- first_name: Ferdinard U.
full_name: Ogbuisi, Ferdinard U.
last_name: Ogbuisi
- first_name: Yekini
full_name: Shehu, Yekini
id: 3FC7CB58-F248-11E8-B48F-1D18A9856A87
last_name: Shehu
orcid: 0000-0001-9224-7139
- first_name: Jen Chih
full_name: Yao, Jen Chih
last_name: Yao
citation:
ama: Ogbuisi FU, Shehu Y, Yao JC. Convergence analysis of new inertial method for
the split common null point problem. Optimization. 2021. doi:10.1080/02331934.2021.1914035
apa: Ogbuisi, F. U., Shehu, Y., & Yao, J. C. (2021). Convergence analysis of
new inertial method for the split common null point problem. Optimization.
Taylor and Francis. https://doi.org/10.1080/02331934.2021.1914035
chicago: Ogbuisi, Ferdinard U., Yekini Shehu, and Jen Chih Yao. “Convergence Analysis
of New Inertial Method for the Split Common Null Point Problem.” Optimization.
Taylor and Francis, 2021. https://doi.org/10.1080/02331934.2021.1914035.
ieee: F. U. Ogbuisi, Y. Shehu, and J. C. Yao, “Convergence analysis of new inertial
method for the split common null point problem,” Optimization. Taylor and
Francis, 2021.
ista: Ogbuisi FU, Shehu Y, Yao JC. 2021. Convergence analysis of new inertial method
for the split common null point problem. Optimization.
mla: Ogbuisi, Ferdinard U., et al. “Convergence Analysis of New Inertial Method
for the Split Common Null Point Problem.” Optimization, Taylor and Francis,
2021, doi:10.1080/02331934.2021.1914035.
short: F.U. Ogbuisi, Y. Shehu, J.C. Yao, Optimization (2021).
date_created: 2021-05-02T22:01:29Z
date_published: 2021-04-14T00:00:00Z
date_updated: 2023-10-10T09:48:41Z
day: '14'
department:
- _id: VlKo
doi: 10.1080/02331934.2021.1914035
ec_funded: 1
external_id:
isi:
- '000640109300001'
isi: 1
language:
- iso: eng
month: '04'
oa_version: None
project:
- _id: 25FBA906-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '616160'
name: 'Discrete Optimization in Computer Vision: Theory and Practice'
publication: Optimization
publication_identifier:
eissn:
- 1029-4945
issn:
- 0233-1934
publication_status: published
publisher: Taylor and Francis
quality_controlled: '1'
scopus_import: '1'
status: public
title: Convergence analysis of new inertial method for the split common null point
problem
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2021'
...
---
_id: '9469'
abstract:
- lang: eng
text: In this paper, we consider reflected three-operator splitting methods for
monotone inclusion problems in real Hilbert spaces. To do this, we first obtain
weak convergence analysis and nonasymptotic O(1/n) convergence rate of the reflected
Krasnosel'skiĭ-Mann iteration for finding a fixed point of nonexpansive mapping
in real Hilbert spaces under some seemingly easy to implement conditions on the
iterative parameters. We then apply our results to three-operator splitting for
the monotone inclusion problem and consequently obtain the corresponding convergence
analysis. Furthermore, we derive reflected primal-dual algorithms for highly structured
monotone inclusion problems. Some numerical implementations are drawn from splitting
methods to support the theoretical analysis.
acknowledgement: The authors are grateful to the anonymous referees and the handling
Editor for their insightful comments which have improved the earlier version of
the manuscript greatly. The second author is grateful to the University of Hafr
Al Batin. The last author has received funding from the European Research Council
(ERC) under the European Union's Seventh Framework Program (FP7-2007-2013) (Grant
agreement No. 616160).
article_processing_charge: No
article_type: original
author:
- first_name: Olaniyi S.
full_name: Iyiola, Olaniyi S.
last_name: Iyiola
- first_name: Cyril D.
full_name: Enyi, Cyril D.
last_name: Enyi
- first_name: Yekini
full_name: Shehu, Yekini
id: 3FC7CB58-F248-11E8-B48F-1D18A9856A87
last_name: Shehu
orcid: 0000-0001-9224-7139
citation:
ama: Iyiola OS, Enyi CD, Shehu Y. Reflected three-operator splitting method for
monotone inclusion problem. Optimization Methods and Software. 2021. doi:10.1080/10556788.2021.1924715
apa: Iyiola, O. S., Enyi, C. D., & Shehu, Y. (2021). Reflected three-operator
splitting method for monotone inclusion problem. Optimization Methods and Software.
Taylor and Francis. https://doi.org/10.1080/10556788.2021.1924715
chicago: Iyiola, Olaniyi S., Cyril D. Enyi, and Yekini Shehu. “Reflected Three-Operator
Splitting Method for Monotone Inclusion Problem.” Optimization Methods and
Software. Taylor and Francis, 2021. https://doi.org/10.1080/10556788.2021.1924715.
ieee: O. S. Iyiola, C. D. Enyi, and Y. Shehu, “Reflected three-operator splitting
method for monotone inclusion problem,” Optimization Methods and Software.
Taylor and Francis, 2021.
ista: Iyiola OS, Enyi CD, Shehu Y. 2021. Reflected three-operator splitting method
for monotone inclusion problem. Optimization Methods and Software.
mla: Iyiola, Olaniyi S., et al. “Reflected Three-Operator Splitting Method for Monotone
Inclusion Problem.” Optimization Methods and Software, Taylor and Francis,
2021, doi:10.1080/10556788.2021.1924715.
short: O.S. Iyiola, C.D. Enyi, Y. Shehu, Optimization Methods and Software (2021).
date_created: 2021-06-06T22:01:30Z
date_published: 2021-05-12T00:00:00Z
date_updated: 2023-08-08T13:57:43Z
day: '12'
department:
- _id: VlKo
doi: 10.1080/10556788.2021.1924715
ec_funded: 1
external_id:
isi:
- '000650507600001'
isi: 1
language:
- iso: eng
month: '05'
oa_version: None
project:
- _id: 25FBA906-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '616160'
name: 'Discrete Optimization in Computer Vision: Theory and Practice'
publication: Optimization Methods and Software
publication_identifier:
eissn:
- 1029-4937
issn:
- 1055-6788
publication_status: published
publisher: Taylor and Francis
quality_controlled: '1'
scopus_import: '1'
status: public
title: Reflected three-operator splitting method for monotone inclusion problem
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
year: '2021'
...
---
_id: '10072'
abstract:
- lang: eng
text: The Lovász Local Lemma (LLL) is a powerful tool in probabilistic combinatorics
which can be used to establish the existence of objects that satisfy certain properties.
The breakthrough paper of Moser and Tardos and follow-up works revealed that the
LLL has intimate connections with a class of stochastic local search algorithms
for finding such desirable objects. In particular, it can be seen as a sufficient
condition for this type of algorithms to converge fast. Besides conditions for
existence of and fast convergence to desirable objects, one may naturally ask
further questions regarding properties of these algorithms. For instance, "are
they parallelizable?", "how many solutions can they output?", "what is the expected
"weight" of a solution?", etc. These questions and more have been answered for
a class of LLL-inspired algorithms called commutative. In this paper we introduce
a new, very natural and more general notion of commutativity (essentially matrix
commutativity) which allows us to show a number of new refined properties of LLL-inspired
local search algorithms with significantly simpler proofs.
acknowledgement: "Fotis Iliopoulos: This material is based upon work directly supported
by the IAS Fund for Math and indirectly supported by the National Science Foundation
Grant No. CCF-1900460. Any opinions, findings and conclusions or recommendations
expressed in this material are those of the author(s) and do not necessarily reflect
the views of the National Science Foundation. This work is also supported by the
National Science Foundation Grant No. CCF-1815328.\r\nVladimir Kolmogorov: Supported
by the European Research Council under the European Unions Seventh Framework Programme
(FP7/2007-2013)/ERC grant agreement no 616160."
alternative_title:
- LIPIcs
article_number: '31'
article_processing_charge: Yes
arxiv: 1
author:
- first_name: David G.
full_name: Harris, David G.
last_name: Harris
- first_name: Fotis
full_name: Iliopoulos, Fotis
last_name: Iliopoulos
- first_name: Vladimir
full_name: Kolmogorov, Vladimir
id: 3D50B0BA-F248-11E8-B48F-1D18A9856A87
last_name: Kolmogorov
citation:
ama: 'Harris DG, Iliopoulos F, Kolmogorov V. A new notion of commutativity for the
algorithmic Lovász Local Lemma. In: Approximation, Randomization, and Combinatorial
Optimization. Algorithms and Techniques. Vol 207. Schloss Dagstuhl - Leibniz
Zentrum für Informatik; 2021. doi:10.4230/LIPIcs.APPROX/RANDOM.2021.31'
apa: 'Harris, D. G., Iliopoulos, F., & Kolmogorov, V. (2021). A new notion of
commutativity for the algorithmic Lovász Local Lemma. In Approximation, Randomization,
and Combinatorial Optimization. Algorithms and Techniques (Vol. 207). Virtual:
Schloss Dagstuhl - Leibniz Zentrum für Informatik. https://doi.org/10.4230/LIPIcs.APPROX/RANDOM.2021.31'
chicago: Harris, David G., Fotis Iliopoulos, and Vladimir Kolmogorov. “A New Notion
of Commutativity for the Algorithmic Lovász Local Lemma.” In Approximation,
Randomization, and Combinatorial Optimization. Algorithms and Techniques,
Vol. 207. Schloss Dagstuhl - Leibniz Zentrum für Informatik, 2021. https://doi.org/10.4230/LIPIcs.APPROX/RANDOM.2021.31.
ieee: D. G. Harris, F. Iliopoulos, and V. Kolmogorov, “A new notion of commutativity
for the algorithmic Lovász Local Lemma,” in Approximation, Randomization, and
Combinatorial Optimization. Algorithms and Techniques, Virtual, 2021, vol.
207.
ista: 'Harris DG, Iliopoulos F, Kolmogorov V. 2021. A new notion of commutativity
for the algorithmic Lovász Local Lemma. Approximation, Randomization, and Combinatorial
Optimization. Algorithms and Techniques. APPROX/RANDOM: Approximation Algorithms
for Combinatorial Optimization Problems/ Randomization and Computation, LIPIcs,
vol. 207, 31.'
mla: Harris, David G., et al. “A New Notion of Commutativity for the Algorithmic
Lovász Local Lemma.” Approximation, Randomization, and Combinatorial Optimization.
Algorithms and Techniques, vol. 207, 31, Schloss Dagstuhl - Leibniz Zentrum
für Informatik, 2021, doi:10.4230/LIPIcs.APPROX/RANDOM.2021.31.
short: D.G. Harris, F. Iliopoulos, V. Kolmogorov, in:, Approximation, Randomization,
and Combinatorial Optimization. Algorithms and Techniques, Schloss Dagstuhl -
Leibniz Zentrum für Informatik, 2021.
conference:
end_date: 2021-08-18
location: Virtual
name: 'APPROX/RANDOM: Approximation Algorithms for Combinatorial Optimization Problems/
Randomization and Computation'
start_date: 2021-08-16
date_created: 2021-10-03T22:01:22Z
date_published: 2021-09-15T00:00:00Z
date_updated: 2022-03-18T10:08:25Z
day: '15'
ddc:
- '000'
department:
- _id: VlKo
doi: 10.4230/LIPIcs.APPROX/RANDOM.2021.31
ec_funded: 1
external_id:
arxiv:
- '2008.05569'
file:
- access_level: open_access
checksum: 9d2544d53aa5b01565c6891d97a4d765
content_type: application/pdf
creator: cchlebak
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date_updated: 2021-10-06T13:51:54Z
file_id: '10098'
file_name: 2021_LIPIcs_Harris.pdf
file_size: 804472
relation: main_file
success: 1
file_date_updated: 2021-10-06T13:51:54Z
has_accepted_license: '1'
intvolume: ' 207'
language:
- iso: eng
month: '09'
oa: 1
oa_version: Published Version
project:
- _id: 25FBA906-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '616160'
name: 'Discrete Optimization in Computer Vision: Theory and Practice'
publication: Approximation, Randomization, and Combinatorial Optimization. Algorithms
and Techniques
publication_identifier:
isbn:
- 978-3-9597-7207-5
issn:
- 1868-8969
publication_status: published
publisher: Schloss Dagstuhl - Leibniz Zentrum für Informatik
quality_controlled: '1'
scopus_import: '1'
status: public
title: A new notion of commutativity for the algorithmic Lovász Local Lemma
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 207
year: '2021'
...
---
_id: '10552'
abstract:
- lang: eng
text: We study a class of convex-concave saddle-point problems of the form minxmaxy⟨Kx,y⟩+fP(x)−h∗(y)
where K is a linear operator, fP is the sum of a convex function f with a Lipschitz-continuous
gradient and the indicator function of a bounded convex polytope P, and h∗ is
a convex (possibly nonsmooth) function. Such problem arises, for example, as a
Lagrangian relaxation of various discrete optimization problems. Our main assumptions
are the existence of an efficient linear minimization oracle (lmo) for fP and
an efficient proximal map for h∗ which motivate the solution via a blend of proximal
primal-dual algorithms and Frank-Wolfe algorithms. In case h∗ is the indicator
function of a linear constraint and function f is quadratic, we show a O(1/n2)
convergence rate on the dual objective, requiring O(nlogn) calls of lmo. If the
problem comes from the constrained optimization problem minx∈Rd{fP(x)|Ax−b=0}
then we additionally get bound O(1/n2) both on the primal gap and on the infeasibility
gap. In the most general case, we show a O(1/n) convergence rate of the primal-dual
gap again requiring O(nlogn) calls of lmo. To the best of our knowledge, this
improves on the known convergence rates for the considered class of saddle-point
problems. We show applications to labeling problems frequently appearing in machine
learning and computer vision.
acknowledgement: Vladimir Kolmogorov was supported by the European Research Council
under the European Unions Seventh Framework Programme (FP7/2007-2013)/ERC grant
agreement no 616160. Thomas Pock acknowledges support by an ERC grant HOMOVIS, no
640156.
article_processing_charge: No
arxiv: 1
author:
- first_name: Vladimir
full_name: Kolmogorov, Vladimir
id: 3D50B0BA-F248-11E8-B48F-1D18A9856A87
last_name: Kolmogorov
- first_name: Thomas
full_name: Pock, Thomas
last_name: Pock
citation:
ama: 'Kolmogorov V, Pock T. One-sided Frank-Wolfe algorithms for saddle problems.
In: 38th International Conference on Machine Learning. ; 2021.'
apa: Kolmogorov, V., & Pock, T. (2021). One-sided Frank-Wolfe algorithms for
saddle problems. In 38th International Conference on Machine Learning.
Virtual.
chicago: Kolmogorov, Vladimir, and Thomas Pock. “One-Sided Frank-Wolfe Algorithms
for Saddle Problems.” In 38th International Conference on Machine Learning,
2021.
ieee: V. Kolmogorov and T. Pock, “One-sided Frank-Wolfe algorithms for saddle problems,”
in 38th International Conference on Machine Learning, Virtual, 2021.
ista: 'Kolmogorov V, Pock T. 2021. One-sided Frank-Wolfe algorithms for saddle problems.
38th International Conference on Machine Learning. ICML: International Conference
on Machine Learning.'
mla: Kolmogorov, Vladimir, and Thomas Pock. “One-Sided Frank-Wolfe Algorithms for
Saddle Problems.” 38th International Conference on Machine Learning, 2021.
short: V. Kolmogorov, T. Pock, in:, 38th International Conference on Machine Learning,
2021.
conference:
end_date: 2021-07-24
location: Virtual
name: 'ICML: International Conference on Machine Learning'
start_date: 2021-07-18
date_created: 2021-12-16T12:41:20Z
date_published: 2021-07-01T00:00:00Z
date_updated: 2021-12-17T09:06:46Z
day: '01'
department:
- _id: VlKo
ec_funded: 1
external_id:
arxiv:
- '2101.12617'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2101.12617
month: '07'
oa: 1
oa_version: Preprint
project:
- _id: 25FBA906-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '616160'
name: 'Discrete Optimization in Computer Vision: Theory and Practice'
publication: 38th International Conference on Machine Learning
publication_status: published
quality_controlled: '1'
status: public
title: One-sided Frank-Wolfe algorithms for saddle problems
type: conference
user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9
year: '2021'
...
---
_id: '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: '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: '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
arxiv: 1
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: '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
arxiv: 1
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
arxiv: 1
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
arxiv: 1
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: '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
arxiv: 1
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: '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
arxiv: 1
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
arxiv: 1
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'
...