Mixing convex-optimization bounds for maximum-entropy sampling
Optimization and Control
2020-02-03 v1
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- principal submatrix of an order- 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.
Cite
@article{arxiv.2001.11896,
title = {Mixing convex-optimization bounds for maximum-entropy sampling},
author = {Zhongzhu Chen and Marcia Fampa and Amélie Lambert and Jon Lee},
journal= {arXiv preprint arXiv:2001.11896},
year = {2020}
}