An Inexact Dual Fast Gradient-Projection Method for Separable Convex Optimization with Linear Coupled Constraints

© 2015 Springer Science+Business Media New York In this paper, a class of separable convex optimization problems with linear coupled constraints is studied. According to the Lagrangian duality, the linear coupled constraints are appended to the objective function. Then, a fast gradient-projection me...

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Main Authors: Li, J., Wu, Z., Wu, Changzhi, Long, Q., Wang, X.
Format: Journal Article
Published: Springer New York LLC 2015
Online Access:http://purl.org/au-research/grants/arc/LP130100451
http://hdl.handle.net/20.500.11937/36258
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author Li, J.
Wu, Z.
Wu, Changzhi
Long, Q.
Wang, X.
author_facet Li, J.
Wu, Z.
Wu, Changzhi
Long, Q.
Wang, X.
author_sort Li, J.
building Curtin Institutional Repository
collection Online Access
description © 2015 Springer Science+Business Media New York In this paper, a class of separable convex optimization problems with linear coupled constraints is studied. According to the Lagrangian duality, the linear coupled constraints are appended to the objective function. Then, a fast gradient-projection method is introduced to update the Lagrangian multiplier, and an inexact solution method is proposed to solve the inner problems. The advantage of our proposed method is that the inner problems can be solved in an inexact and parallel manner. The established convergence results show that our proposed algorithm still achieves optimal convergence rate even though the inner problems are solved inexactly. Finally, several numerical experiments are presented to illustrate the efficiency and effectiveness of our proposed algorithm.
first_indexed 2025-11-14T08:44:52Z
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institution Curtin University Malaysia
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last_indexed 2025-11-14T08:44:52Z
publishDate 2015
publisher Springer New York LLC
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spelling curtin-20.500.11937-362582023-02-02T03:24:11Z An Inexact Dual Fast Gradient-Projection Method for Separable Convex Optimization with Linear Coupled Constraints Li, J. Wu, Z. Wu, Changzhi Long, Q. Wang, X. © 2015 Springer Science+Business Media New York In this paper, a class of separable convex optimization problems with linear coupled constraints is studied. According to the Lagrangian duality, the linear coupled constraints are appended to the objective function. Then, a fast gradient-projection method is introduced to update the Lagrangian multiplier, and an inexact solution method is proposed to solve the inner problems. The advantage of our proposed method is that the inner problems can be solved in an inexact and parallel manner. The established convergence results show that our proposed algorithm still achieves optimal convergence rate even though the inner problems are solved inexactly. Finally, several numerical experiments are presented to illustrate the efficiency and effectiveness of our proposed algorithm. 2015 Journal Article http://hdl.handle.net/20.500.11937/36258 10.1007/s10957-015-0757-1 http://purl.org/au-research/grants/arc/LP130100451 Springer New York LLC restricted
spellingShingle Li, J.
Wu, Z.
Wu, Changzhi
Long, Q.
Wang, X.
An Inexact Dual Fast Gradient-Projection Method for Separable Convex Optimization with Linear Coupled Constraints
title An Inexact Dual Fast Gradient-Projection Method for Separable Convex Optimization with Linear Coupled Constraints
title_full An Inexact Dual Fast Gradient-Projection Method for Separable Convex Optimization with Linear Coupled Constraints
title_fullStr An Inexact Dual Fast Gradient-Projection Method for Separable Convex Optimization with Linear Coupled Constraints
title_full_unstemmed An Inexact Dual Fast Gradient-Projection Method for Separable Convex Optimization with Linear Coupled Constraints
title_short An Inexact Dual Fast Gradient-Projection Method for Separable Convex Optimization with Linear Coupled Constraints
title_sort inexact dual fast gradient-projection method for separable convex optimization with linear coupled constraints
url http://purl.org/au-research/grants/arc/LP130100451
http://hdl.handle.net/20.500.11937/36258