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An improved linearization strategy for zero-one quadratic programming problems

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Abstract

We present a new linearized model for the zero-one quadratic programming problem, whose size is linear in terms of the number of variables in the original nonlinear problem. Our derivation yields three alternative reformulations, each varying in model size and tightness. We show that our models are at least as tight as the one recently proposed in [7], and examine the theoretical relationship of our models to a standard linearization of the zero-one quadratic programming problem. Finally, we demonstrate the efficacy of solving each of these models on a set of randomly generated test instances.

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Authors and Affiliations

  1. Grado Department of Industrial and Systems Engineering (0118), Virginia Polytechnic Institute and State University, Blacksburg, VA, 24061, USA

    Hanif D. Sherali

  2. Department of Industrial and Systems Engineering, The University of Florida, Gainesville, FL, 32611, USA

    J. Cole Smith

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  1. Hanif D. Sherali
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  2. J. Cole Smith
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Correspondence to J. Cole Smith.

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Sherali, H.D., Smith, J.C. An improved linearization strategy for zero-one quadratic programming problems. Optimization Letters 1, 33–47 (2007). https://doi.org/10.1007/s11590-006-0019-0

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