Distributed consensus-based solver for semi-definite programming: An optimization viewpoint

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.

Automated summary

This paper investigates distributed computation for semi-definite programming (SDP) problems over multi-agent networks. Two SDP problems are transformed into distributed optimization problems by utilizing their structures and introducing consensus constraints. Two distributed algorithms are proposed based on primal-dual and consensus methods, with convergence proven to optimal solutions and rates evaluated by the duality gap.