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.
Citation
Department of Management Sciences, University of Iowa, Iowa City, IA 52242 USA, June 2018.
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