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Article Dans Une Revue Mathematical Programming Année : 2021

Mixing convex-optimization bounds for maximum-entropy sampling

Résumé

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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Dates et versions

hal-03016397 , version 1 (04-12-2020)

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