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- Title
Sparse noncommutative polynomial optimization.
- Authors
Klep, Igor; Magron, Victor; Povh, Janez
- Abstract
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):822–843, 2006) and Waki et al. (SIAM J Optim 17(1):218–242, 2006). The Gelfand–Naimark–Segal 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 of noncommutative polynomials from the literature.
- Subjects
POLYNOMIALS; OPERATOR algebras; SEMIALGEBRAIC sets; SEMIDEFINITE programming; AMALGAMATION
- Publication
Mathematical Programming, 2022, Vol 193, Issue 2, p789
- ISSN
0025-5610
- Publication type
Article
- DOI
10.1007/s10107-020-01610-1