Operations Research (OR) analytics play a crucial role in optimizing decision-making
processes for real-world problems. The assignment problem, with applications in supply …

This paper describes a new instance library for quadratic programming (QP), ie, the family of
continuous and (mixed)-integer optimization problems where the objective function and/or …

We consider the NP-hard problem of minimizing a convex quadratic function over the integer
lattice Z^ n Z n. We present a simple semidefinite programming (SDP) relaxation for …

We consider the global optimization of nonconvex (mixed-integer) quadratic programs. We
present a family of convex quadratic relaxations derived by convexifying nonconvex …

In recent years the use of decision diagrams within the context of discrete optimization has
proliferated. This paper continues this expansion by proposing the use of decision diagrams …

We consider the global optimization of nonconvex mixed-integer quadratic programs with
linear equality constraints. In particular, we present a new class of convex quadratic …

Solving convex quadratic integer minimization problems by a branch-and-bound algorithm
requires tight lower bounds on the optimal objective value. To obtain such dual bounds, we …

Abstract The Quadratic Convex Reformulation (QCR) method is used to solve quadratic
unconstrained binary optimization problems. In this method, the semidefinite relaxation is …

We derive and study a reformulation technique for general 0–1 quadratic programs (QP) that
uses diagonal as well as nondiagonal perturbation of the objective function. The technique …

The technique of semidefinite programming (SDP) relaxation can be used to obtain a
nontrivial bound on the optimal value of a nonconvex quadratically constrained quadratic …