期刊名称：Department of Computer and System Sciences Antonio Ruberti Technical Reports
印刷版ISSN：2035-5750
出版年度：2012
卷号：4
期号：5
页码：29
语种：English
出版社：Department of Computer and System Sciences Antonio Ruberti. Sapienza, Università di Roma
摘要：We 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.
