Maximum-entropy remote sampling

Kurt M. Anstreicher; Marcia Fampa; Jon Lee; Joy Williams

doi:10.1016/S0166-218X(00)00217-1

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Maximum-entropy remote sampling

Kurt M. Anstreicher, Marcia Fampa, Jon Lee and Joy Williams

Discrete Applied Mathematics, Vol.108(3), pp.211-226

03/15/2001

DOI: 10.1016/S0166-218X(00)00217-1

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Abstract

We consider the “remote-sampling” problem of choosing a subset S, with | S|= s, from a set N of observable random variables, so as to obtain as much information as possible about a set T of target random variables which are not directly observable. Our criterion is that of minimizing the entropy of T conditioned on S. We confine our attention to the case in which the random variables have a joint Gaussian distribution. We demonstrate that the problem is NP-complete. We provide two methods for calculating lower bounds on the entropy: (i) a spectral method, and (ii) a continuous nonlinear relaxation. We employ these bounds in a branch-and-bound scheme to solve problem instances to optimality.

Details

Title: Subtitle: Maximum-entropy remote sampling
Creators: Kurt M. Anstreicher - University of Iowa
Marcia Fampa - Universidade Federal do Rio de Janeiro
Jon Lee - University of Kentucky
Joy Williams - Earlham College
Resource Type: Journal article
Publication Details: Discrete Applied Mathematics, Vol.108(3), pp.211-226
DOI: 10.1016/S0166-218X(00)00217-1
ISSN: 0166-218X
eISSN: 1872-6771
Publisher: Elsevier B.V
Language: English
Date published: 03/15/2001
Academic Unit: Industrial and Systems Engineering; Computer Science; Business Analytics
Record Identifier: 9984380528602771

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