Approximation hierarchies for copositive cone over symmetric cone and their comparison

TL;DR

This paper develops and compares new approximation hierarchies for the copositive cone over symmetric cones, enhancing the accuracy and efficiency of copositive programming solutions through SOS and semidefinite constraints.

Contribution

It introduces a generalized SOS-based hierarchy for symmetric cones and compares it with existing hierarchies, improving approximation accuracy in copositive programming.

Findings

Hierarchies can be numerically refined by increasing depth parameters.

Yildirim's hierarchy yields near-optimal solutions for small nonnegative orthants.

Combining hierarchies improves the evaluation of copositive programming problems.

Abstract

We first provide an inner-approximation hierarchy described by a sum-of-squares (SOS) constraint for the copositive (COP) cone over a general symmetric cone. The hierarchy is a generalization of that proposed by Parrilo (2000) for the usual COP cone (over a nonnegative orthant). We also discuss its dual. Second, we characterize the COP cone over a symmetric cone using the usual COP cone. By replacing the usual COP cone appearing in this characterization with the inner- or outer-approximation hierarchy provided by de Klerk and Pasechnik (2002) or Yildirim (2012), we obtain an inner- or outer-approximation hierarchy described by semidefinite but not by SOS constraints for the COP matrix cone over the direct product of a nonnegative orthant and a second-order cone. We then compare them with the existing hierarchies provided by Zuluaga et al. (2006) and Lasserre (2014). Theoretical and numerical examinations imply that we can numerically increase a depth parameter, which determines an approximation accuracy, in the approximation hierarchies derived from de Klerk and Pasechnik (2002) and Yildirim (2012), particularly when the nonnegative orthant is small. In such a case, the approximation hierarchy derived from Yildirim (2012) can yield nearly optimal values numerically. Combining the proposed approximation hierarchies with existing ones, we can evaluate the optimal value of COP programming problems more accurately and efficiently.