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arXiv 1909.005692019-09math.OCmath.AG

Sparse Noncommutative Polynomial Optimization

Igor Klep, Victor Magron, Janez Povh

备注：: 37 pages, 3 figures, 3 tables
发表信息：: Math. Program. 193 (2022) 789-829
DOI：: 10.1007/s10107-020-01610-1

英文摘要：

This article focuses on optimization of polynomials in noncommuting variables, while taking into account sparsity in the input data. A converging hierarchy of semidefinite relaxations for eigenvalue and trace optimization is provided. This hierarchy is a noncommutative analogue of results due to Lasserre [SIAM J. Optim. 17(3) (2006), pp. 822--843] and Waki et al. [SIAM J. Optim. 17(1) (2006), pp. 218--242]. The Gelfand-Naimark-Segal (GNS) construction is applied to extract optimizers if flatness and irreducibility conditions are satisfied. Among the main techniques used are amalgamation results from operator algebra. The theoretical results are utilized to compute lower bounds on minimal eigenvalue and trace of noncommutative polynomials from the literature.

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