An Exact Algorithm for Quadratic Integer Minimization using Nonconvex Relaxations
AbstractWe propose a branch-and-bound algorithm for minimizing a not necessarily convex quadratic function over integer variables. The algorithm is based on lower bounds computed as continuous minima of the objective function over appropriate ellipsoids. In the nonconvex case, we use ellipsoids enclosing the feasible region of the problem. In spite of the nonconvexity, these minima can be computed quickly. We present several ideas that allow to accelerate the solution of the continuous relaxation within a branch-and-bound scheme and examine the performance of the overall algorithm by computational experiments.
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Bibliographic InfoPaper provided by Department of Computer, Control and Management Engineering, Università degli Studi di Roma "La Sapienza" in its series DIS Technical Reports with number 2012-05.
Date of creation: 2012
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