A multi-parameter parallel ADMM for multi-block linearly constrained separable convex optimization

The alternating direction method of multipliers (ADMM) has been proved to be effective for solving two-block convex minimization model subject to linear constraints. However, the convergence of multiple-block convex minimization model with linear constraints may not be guaranteed without additional...

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Vydáno v:Applied numerical mathematics Ročník 171; s. 369 - 388
Hlavní autoři: Shen, Yuan, Gao, Qianming, Yin, Xue
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 01.01.2022
Témata:
Alternating direction method of multipliers
Convex optimization
Multi-block
Parallel computing
Proximal point algorithm
Multi-block
Alternating direction method of multipliers
Parallel computing
Proximal point algorithm
Convex optimization
ISSN:0168-9274, 1873-5460
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Shrnutí:The alternating direction method of multipliers (ADMM) has been proved to be effective for solving two-block convex minimization model subject to linear constraints. However, the convergence of multiple-block convex minimization model with linear constraints may not be guaranteed without additional assumptions. Recently, some parallel multi-block ADMM algorithms which solve the subproblems in a parallel way have been proposed. This paper is a further study on this method with the purpose of improving the parallel multi-block ADMM algorithm by introducing more parameters. We propose two multi-parameter parallel ADMM algorithms with proximal point terms attached to all subproblems. Comparing with some popular parallel ADMM-based algorithms, the parameter conditions of the new algorithms are relaxed. Experiments on both real and synthetic problems are conducted to justify the effectiveness of the proposed algorithms compared to several efficient ADMM-based algorithms for multi-block problems.
ISSN:0168-9274
1873-5460
DOI:10.1016/j.apnum.2021.09.011