New sparse moment-SOS hierarchies for polynomial matrix optimization reduce SDP size, with term sparsity converging to PMI sign symmetry blocks and a counterexample showing correlative sparsity can fail asymptotically.
Exploiting Sign Symmetries in Minimizing Sums of Rational Functions
1 Pith paper cite this work. Polarity classification is still indexing.
abstract
This paper is devoted to the problem of minimizing a sum of rational functions over a basic semialgebraic set. We provide a hierarchy of sum of squares (SOS) relaxations that is dual to the generalized moment problem approach due to Bugarin, Henrion, and Lasserre. The investigation of the dual SOS aspect offers two benefits: 1) it allows us to conduct a convergence rate analysis for the hierarchy; 2) it leads to a sign symmetry adapted hierarchy consisting of block-diagonal semidefinite relaxations. When the problem possesses correlative sparsity as well as sign symmetries, we propose sparse semidefinite relaxations by exploiting both structures. Various numerical experiments are performed to demonstrate the efficiency of our approach. Finally, an application to maximizing sums of generalized Rayleigh quotients is presented.
fields
math.OC 1years
2024 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Sparse Polynomial Matrix Optimization
New sparse moment-SOS hierarchies for polynomial matrix optimization reduce SDP size, with term sparsity converging to PMI sign symmetry blocks and a counterexample showing correlative sparsity can fail asymptotically.