Comparisons and enhancement strategies for linearizing mixed 0-1 quadratic programs

Authors:
Warren P. Adams;Richard J. Forrester;Fred W. Glover
Affiliations:
Clemson University, Clemson, SC 29634, USA;Dickinson College, Carlisle, PA 17013, USA;University of Colorado, Boulder, CO 80304, USA
Venue:
Discrete Optimization
Year:
2004

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Abstract

We present a linearization strategy for mixed 0-1 quadratic programs that produces small formulations with tight relaxations. It combines constructs from a classical method of Glover and a more recent reformulation-linearization technique (RLT). By using binary identities to rewrite the objective, a variant of the first method results in a concise formulation with the level-1 RLT strength. This variant is achieved as a modified surrogate dual of a Lagrangian subproblem to the RLT. Special structures can be exploited to obtain reductions in problem size, without forfeiting strength. Preliminary computational experience demonstrates the potential of the new representations.