Practical proximal primal-dual algorithms for structured saddle point problems Practical proximal primal-dual algorithms for structured saddle point problems
In this paper, we are concerned with a class of convex-concave saddle point problems, where one of the objective parts is assumed to be a convex and smooth function with Lipschitz continuous gradient. By exploiting the bilinear structure of the objective, we first propose a practical accelerated Pro...
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| Vydáno v: | Journal of global optimization Ročník 93; číslo 3; s. 803 - 831 |
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| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
New York
Springer US
01.11.2025
Springer Nature B.V |
| Témata: | |
| ISSN: | 0925-5001, 1573-2916 |
| On-line přístup: | Získat plný text |
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| Shrnutí: | In this paper, we are concerned with a class of convex-concave saddle point problems, where one of the objective parts is assumed to be a convex and smooth function with Lipschitz continuous gradient. By exploiting the bilinear structure of the objective, we first propose a practical accelerated Proximal Primal-Dual algorithm (PPD+), which possesses an
O
(
1
/
N
2
)
convergence rate measured by the residual between two successive iterates, where
N
represents the iteration counter. In some cases, considering that the underlying subproblems of PPD+ cannot be easily solved exactly or up to a high precision, we further propose two inexact versions of the PPD+ under absolute and relative error criteria. Finally, we employ a restarting technique to enhance our algorithms for the purpose of making them more robust and efficient. A series of numerical experiments demonstrate that our algorithms perform well in practice. |
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| Bibliografie: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ISSN: | 0925-5001 1573-2916 |
| DOI: | 10.1007/s10898-025-01545-x |