Efficient Solution of Maximum-Entropy Sampling Problems
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
Category 1: Integer Programming ((Mixed) Integer Nonlinear Programming )
Category 2: Convex and Nonsmooth Optimization (Convex Optimization )
Category 3: Applications -- Science and Engineering (Statistics )
Citation: Department of Management Sciences, University of Iowa, Iowa City, IA 52242 USA, June 2018.
Entry Submitted: 07/10/2018
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