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

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

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Article ID: iaor20051152
Country: Netherlands
Volume: 1
Issue: 2
Start Page Number: 99
End Page Number: 120
Publication Date: Nov 2004
Journal: Discrete Optimization
Authors: , ,
Keywords: programming: linear
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 Langrangian 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.

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