A Correlatively Sparse Lagrange Multiplier Expression Relaxation for Polynomial Optimization

TL;DR

This paper introduces a new sparse Lagrange multiplier relaxation method for polynomial optimization that leverages correlative sparsity to improve solution accuracy and computational efficiency.

Contribution

It proposes the CS-LME reformulation and CS-SOS relaxations, inheriting sparsity patterns and providing sharper bounds with guaranteed convergence under mild conditions.

Findings

Achieves global optimal solutions with low relaxation order.

Requires less computational time than previous methods.

Provides sharper lower bounds for polynomial optimization.

Abstract

In this paper, we consider polynomial optimization with correlative sparsity. We construct correlatively sparse Lagrange multiplier expressions (CS-LMEs) and propose CS-LME reformulations for polynomial optimization problems using the Karush-Kuhn-Tucker optimality conditions. Correlatively sparse sum-of-squares (CS-SOS) relaxations are applied to solve the CS-LME reformulation. We show that the CS-LME reformulation inherits the original correlative sparsity pattern, and the CS-SOS relaxation provides sharper lower bounds when applied to the CS-LME reformulation, compared with when it is applied to the original problem. Moreover, the convergence of our approach is guaranteed under mild conditions. In numerical experiments, our new approach usually finds the global optimal value (up to a negligible error) with a low relaxation order for cases where directly solving the problem fails to get an accurate approximation. Also, by properly exploiting the correlative sparsity, our CS-LME approach requires less computational time than the original LME approach to reach the same accuracy level.