Efficient Solution of Maximum-Entropy Sampling Problems

Kurt M. Anstreicher ()
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Kurt M. Anstreicher: Department of Business Analytics, University of Iowa, Iowa City, Iowa 52242

Operations Research, 2020, vol. 68, issue 6, 1826-1835

Abstract: We consider a new approach for the maximum-entropy sampling problem (MESP) that is based on bounds obtained by maximizing a function of the form ldet M ( x ) over linear constraints, where M ( x ) is linear in the n -vector x . These bounds can be computed very efficiently and are superior to all previously known bounds for MESP on most benchmark test problems. A branch-and-bound algorithm using the new bounds solves challenging instances of MESP to optimality for the first time.

Keywords: maximum-entropy sampling; convex programming; nonlinear integer programming (search for similar items in EconPapers)
Date: 2020
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