2019/11/17 by Filippo Fabiani, Fabiani, Filippo, Sergio Grammatico +1
Computer Science · Engineering · Mathematics · #Advanced Optimization Algorithms Research #Aggregate (composite) #Algorithm #Chordal graph #Computer science #Exploit #FOS: Computer and information sciences #FOS: Mathematics #Mathematical optimization #Mathematics #Multiagent Systems (cs.MA) #Operator (biology) #Optimization and Control (math.OC) #Optimization and Variational Analysis #Semidefinite programming #Separable space #Set (abstract data type) #Sparse and Compressive Sensing Techniques #Stochastic Gradient Optimization Techniques #Theoretical computer science #cs.MA #math.OC
paper · pdf · doi:10.48550/arxiv.1911.07213
arxiv created 2019/11/17 · openalex publication_date 2019/11/17 · arxiv updated 2019/11/19 · openalex created_date 2022/07/26 · openalex updated_date 2026/07/28
We present semi-decentralized and distributed algorithms, designed via a\npreconditioned forward-backward operator splitting, for solving large-scale,\ndecomposable semidefinite programs (SDPs). We exploit a chordal aggregate\nsparsity pattern assumption on the original SDP to obtain a set of mutually\ncoupled SDPs defined on positive semidefinite (PSD) cones of reduced\ndimensions. We show that the proposed algorithms converge to a solution of the\noriginal SDP via iterations of reasonable computational cost. Finally, we\ncompare the performances of the two proposed algorithms with respect to others\navailable in the literature.\n