Abstract
We consider a new nonlinear relaxation for the Constrained Maximum Entropy Sampling Problem — the problem of choosing the s × s principal submatrix with maximal determinant from a given n × n positive definite matrix, subject to linear constraints. We implement a branch-and-bound algorithm for the problem, using the new relaxation. The performance on test problems is far superior to a previous implementation using an eigenvalue-based relaxation.
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Visiting the Dept. of Management Sciences, University of Iowa, supported by a Research Fellowship from CNPq-Brasilia-Brazil.
Supported in part by NSF grant DMI-9401424.
Supported in part by NSF grant DMI-9401424 and by the U. K. Center for Computational Sciences.
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© 1996 Springer-Verlag Berlin Heidelberg
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Anstreicher, K.M., Fampa, M., Lee, J., Williams, J. (1996). Continuous relaxations for Constrained Maximum-Entropy Sampling. In: Cunningham, W.H., McCormick, S.T., Queyranne, M. (eds) Integer Programming and Combinatorial Optimization. IPCO 1996. Lecture Notes in Computer Science, vol 1084. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61310-2_18
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DOI: https://doi.org/10.1007/3-540-61310-2_18
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