Projected Chvátal–Gomory cuts for mixed integer linear programs

Authors:
Pierre Bonami;Gérard Cornuéjols;Sanjeeb Dash;Matteo Fischetti;Andrea Lodi
Affiliations:
IBM T.J. Watson Research Center, P.O. Box 218, 10598, Yorktown Heights, NY, USA;Carnegie Mellon University, Tepper School of Business, P.O. Box 218, 15213, Pittsburgh, PA, USA and Faculté des Sciences de Luminy, LIF, 13288, Marseille, France;IBM T.J. Watson Research Center, P.O. Box 218, 10598, Yorktown Heights, NY, USA;University of Padova, DEI, P.O. Box 218, via Gradenigo 6A, 35131, Padova, PA, Italy;University of Bologna, DEIS, P.O. Box 218, viale Risorgimento 2, 40136, Bologna, PA, Italy
Venue:
Mathematical Programming: Series A and B
Year:
2008

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Abstract

Recent experiments by Fischetti and Lodi show that the first Chvátal closure of a pure integer linear program (ILP) often gives a surprisingly tight approximation of the integer hull. They optimize over the first Chvátal closure by modeling the Chvátal–Gomory (CG) separation problem as a mixed integer linear program (MILP) which is then solved by a general- purpose MILP solver. Unfortunately, this approach does not extend immediately to the Gomory mixed integer (GMI) closure of an MILP, since the GMI separation problem involves the solution of a nonlinear mixed integer program or a parametric MILP. In this paper we introduce a projected version of the CG cuts, and study their practical effectiveness for MILP problems. The idea is to project first the linear programming relaxation of the MILP at hand onto the space of the integer variables, and then to derive Chvátal–Gomory cuts for the projected polyhedron. Though theoretically dominated by GMI cuts, projected CG cuts have the advantage of producing a separation model very similar to the one introduced by Fischetti and Lodi, which can typically be solved in a reasonable amount of computing time.