Further Results on Approximating Nonconvex Quadratic Optimizationby Semidefinite Programming Relaxation
We study approximation bounds for the SDP relaxation of quadratically constrained quadratic optimization: min f^0(x) subject to f^k(x)
approximation algorithms
Solving standard quadratic optimization problems via linear, semidefinite and copositive programming
The problem of minimizing a (non-convex) quadratic function over the simplex (the standard quadratic optimization problem) has an exact convex reformulation as a copositive programming problem. In this paper we show how to approximate the optimal solution by approximating the cone of copositive matrices via systems of linear inequalities, and, more refined, linear matrix inequalities … Read more