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arXiv · 2001.11896

Mixing convex-optimization bounds for maximum-entropy sampling

Zhongzhu Chen·Marcia Fampa·Amélie Lambert·Jon Lee
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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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Zhongzhu Chen, Marcia Fampa, Amélie Lambert, Jon Lee. 2020-01-31. Mixing convex-optimization bounds for maximum-entropy sampling. https://arxiv.org/abs/2001.11896

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