A slightly lifted convex relaxation for nonconvex quadratic programming with ball constraints

Abstract

Globally optimizing a nonconvex quadratic over the intersection of m balls in $\mathbb {R}^n$ is known to be polynomial-time solvable for fixed m. Moreover, when $m=1$, the standard semidefinite relaxation is exact. When $m=2$, it has been shown recently that an exact relaxation can be constructed using a disjunctive semidefinite formulation based essentially on two copies of the $m=1$ case. However, there is no known explicit, tractable, exact convex representation for $m \ge 3$. In this paper, we construct a new, polynomially sized semidefinite relaxation for all m, which does not employ a disjunctive approach. We show that our relaxation is exact for $m=2$. Then, for $m \ge 3$, we demonstrate empirically that it is fast and strong compared to existing relaxations. The key idea of the relaxation is a simple lifting of the original problem into dimension $n\, +\, 1$. Extending this construction: (i) we show that nonconvex quadratic programming over $\Vert x\Vert \le \min \{ 1, g + h^T x \}$ has an exact semidefinite representation; and (ii) we construct a new relaxation for quadratic programming over the intersection of two ellipsoids, which globally solves all instances of a benchmark collection from the literature.