Subdeterminants and Concave Integer Quadratic Programming

Abstract

A classic result by Cook, Gerards, Schrijver, and Tardos provides an upper bound of n∆ on the proximity of optimal solutions of an Integer Linear Programming problem and its standard linear relaxation. In this bound, n is the number of variables and ∆ denotes the maximum of the absolute values of the subdeterminants of the constraint matrix. Hochbaum and Shanthikumar, and Werman and Magagnosc showed that the same upper bound is valid if a more general convex function is minimized, instead of a linear function. No proximity result of this type is known when the objective function is nonconvex. In fact, if we minimize a concave quadratic, no upper bound can be given as a function of n and ∆. Our key observation is that, in this setting, proximity phenomena still occur, but only if we consider also approximate solutions instead of optimal solutions only. In our main result we provide upper bounds on the distance between approximate (resp., optimal) solutions to a Concave Integer Quadratic Programming problem and optimal (resp., approximate) solutions of its continuous relaxation. Our bounds are functions of n, ∆, and a parameter ǫ that controls the quality of the approximation. Furthermore, we discuss how far from optimal are our proximity bounds.

Characterizations of mixed binary convex quadratic representable sets

“…Our algorithm can also be used as a powerful subroutine for the design of algorithms with theoretical guarantees for more general mixed integer nonlinear programming problems. In particular, it is a necessary tool to generalize recent approximation algorithms for mixed integer nonconvex quadratic programming [7][8][9][10] beyond the fixed rank setting.…”