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Partial Convexification of General MIPs by Dantzig-Wolfe Reformulation

Martin Bergner(martin.bergner***at***rwth-aachen.de)
Alberto Caprara(alberto.caprara***at***unibo.it)
Fabio Furini(fabio.furini***at***unibo.it)
Marco Lübbecke(marco.luebbecke***at***rwth-aachen.de)
Enrico Malaguti(enrico.malaguti***at***unibo.it)
Emiliano Traversi(emiliano.traversi2***at***unibo.it)

Abstract: Dantzig-Wolfe decomposition is well-known to provide strong dual bounds for specially structured mixed integer programs (MIPs) in practice. However, the method is not part of any state-of-the-art MIP solver: it needs tailoring to the particular problem; the typical bordered block-diagonal matrix structure determines the decomposition; the resulting column generation subproblems need to be solved efficiently; branching and cutting decisions need special attention; etc. We perform an extensive computational study on the 0-1 dynamic knapsack problem (without block-diagonal structure) and on general MIPLIB2003 instances in order to (in-)validate such reservations against the method. We present a tool which, given an LP file, automatically detects an exploitable matrix structure, accordingly performs a Dantzig-Wolfe type reformulation of subsets of the constraints (partial convexification), and performs column generation to obtain an optimal LP relaxation. Our results strongly support that Dantzig-Wolfe decomposition holds more promise as a general-purpose tool than previously acknowledged by the research community.

Keywords: mixed integer program; Dantzig-Wolfe decomposition; experimental study

Category 1: Integer Programming ((Mixed) Integer Linear Programming )

Citation:

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Entry Submitted: 11/16/2010
Entry Accepted: 11/16/2010
Entry Last Modified: 11/16/2010

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