Tight SDP relaxations for cardinality-constrained problems

TL;DR

Experimental results show that a relaxation using semidefinite programming generates tight lower bounds and even achieves optimality on many instances from the literature, underlines the modeling power of semidefinite programming for mixed-integer quadratic problems.

Abstract

We model the cardinality-constrained portfolio problem using semidefinite matrices and investigate a relaxation using semidefinite programming. Experimental results show that this relaxation generates tight lower bounds and even achieves optimality on many instances from the literature. This underlines the modeling power of semidefinite programming for mixed-integer quadratic problems.