Sparse semidefinite programs with guaranteed near-linear time complexity via dualized clique tree conversion

• 成果类型: Article
• 署名作者: Zhang, Richard Y.; Lavaei, Javad
• 署名单位: University of California System; University of California Berkeley; University of Illinois System; University of Illinois Urbana-Champaign
• 刊物名称: MATHEMATICAL PROGRAMMING
• ISSN/ISSBN: 0025-5610
• DOI: 10.1007/s10107-020-01516-y
• 发表日期: 2021
• 页码: 351-393
• 关键词: interior-point methods; improved approximation algorithms; exploiting sparsity; conic optimization; implementation; relaxations; matrices; completions; sdp; cut

摘要

Clique tree conversion solves large-scale semidefinite programs by splitting an nxn matrix variable into up to n smaller matrix variables, each representing a principal submatrix of up to omega x omega. Its fundamental weakness is the need to introduce overlap constraints that enforce agreement between different matrix variables, because these can result in dense coupling. In this paper, we show that by dualizing the clique tree conversion, the coupling due to the overlap constraints is guaranteed to be sparse over dense blocks, with a block sparsity pattern that coincides with the adjacency matrix of a tree. We consider two classes of semidefinite programs with favorable sparsity patterns that encompass the MAXCUT and MAX k-CUT relaxations, the Lovasz Theta problem, and the AC optimal power flow relaxation. Assuming that omega MUCH LESS-THANn, we prove that the per-iteration cost of an interior-point method is linearO(n) time and memory, so an epsilon-accurate and epsilon-feasible iterate is obtained after O(nlog(1/epsilon)) iterations in near-linearO(n1.5log(1/epsilon)) time. We confirm our theoretical insights with numerical results on semidefinite programs as large as n=13,659.