Mixing convex-optimization bounds for maximum-entropy sampling - Chen, Zhongzhu; Fampa, Marcia; Lambert, Amélie; Lee, Jon (Mathematical programming, 2021)
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.