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Distributed consensus-based solver for semi-definite programming: An optimization viewpoint
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DOI：10.1016/j.automatica.2021.109737.png)
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Abstract
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This paper aims at the distributed computation for semi-definite programming (SDP) problems over multi-agent networks. Two SDP problems, including a non-sparse case and a sparse case, are transformed into distributed optimization problems, respectively, by fully exploiting their structures and introducing consensus constraints. Inspired by primal-dual and consensus methods, we propose two distributed algorithms for the two cases with the help of projection and derivative feedback techniques. Furthermore, we prove that the algorithms converge to their optimal solutions, and moreover, their convergences rates are evaluated by the duality gap. (C) 2021 Elsevier Ltd. All rights reserved.
Keywords:
Distributed optimization
Consensus-based algorithm
Semi-definite programming
Sparsity
Journal
IF:
5.9
Papers:
1.2W
Citations:
5.2W
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