A New Extension of Chubanov's Method to Symmetric Cones

TL;DR

This paper introduces an extended Chubanov's method for symmetric cone feasibility problems, improving computational bounds and demonstrating superior performance over existing methods through numerical experiments.

Contribution

It extends Roos's approach to symmetric cones, providing a unified method with better bounds and efficiency across various cone types.

Findings

Computational bounds are improved and comparable to existing methods.

The proposed method outperforms others in accuracy and execution time.

Numerical experiments validate the method's efficiency on different problem instances.

Abstract

We propose a new variant of Chubanov's method for solving the feasibility problem over the symmetric cone by extending Roos's method (2018) of solving the feasibility problem over the nonnegative orthant. The proposed method considers a feasibility problem associated with a norm induced by the maximum eigenvalue of an element and uses a rescaling focusing on the upper bound for the sum of eigenvalues of any feasible solution to the problem. Its computational bound is (i) equivalent to that of Roos's original method (2018) and superior to that of Louren\c{c}o et al.'s method (2019) when the symmetric cone is the nonnegative orthant, (ii) superior to that of Louren\c{c}o et al.'s method (2019) when the symmetric cone is a Cartesian product of second-order cones, (iii) equivalent to that of Louren\c{c}o et al.'s method (2019) when the symmetric cone is the simple positive semidefinite cone, and (iv) superior to that of Pena and Soheili's method (2017) for any simple symmetric cones under the feasibility assumption of the problem imposed in Pena and Soheili's method (2017). We also conduct numerical experiments that compare the performance of our method with existing methods by generating instances in three types: strongly (but ill-conditioned) feasible instances, weakly feasible instances, and infeasible instances. For any of these instances, the proposed method is rather more efficient than the existing methods in terms of accuracy and execution time.