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Mixing convex-optimization bounds for maximum-entropy sampling

Abstract : The maximum-entropy sampling problem is a fundamental and challenging combinatorial-optimization problem, with application in spatial statistics. It asks to find a maximum-determinant order-$s$ principal submatrix of an order-$n$ covariance matrix. Exact solution methods for this NP-hard problem are based on a branch-and-bound framework. Many of the known upper bounds for the optimal value are based on convex optimization. We present a methodology for "mixing" these bounds to achieve better bounds.
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https://hal.archives-ouvertes.fr/hal-03016397
Contributor : Amélie Lambert Connect in order to contact the contributor
Submitted on : Friday, December 4, 2020 - 11:23:54 AM
Last modification on : Monday, August 23, 2021 - 10:11:26 AM
Long-term archiving on: : Friday, March 5, 2021 - 6:42:18 PM

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Zhongzhu Chen, Marcia Fampa, Amélie Lambert, Jon Lee. Mixing convex-optimization bounds for maximum-entropy sampling. Mathematical Programming, Springer Verlag, 2021, 188, pp.539-568. ⟨10.1007/s10107-020-01588-w⟩. ⟨hal-03016397⟩

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