Extended-variable relaxations for the constrained generalized maximum-entropy sampling problem

TL;DR

This paper introduces novel extended-variable relaxations for the constrained generalized maximum-entropy sampling problem, providing new bounds, techniques, and numerical results to improve solution approaches.

Contribution

It develops new non-convex and convex relaxations for CGMESP, analyzes their relationships, and proposes methods for bound improvement and branching in branch-and-bound algorithms.

Findings

New non-convex and convex relaxations for CGMESP

Relations between various upper bounds including new bounds

Numerical experiments demonstrating effectiveness of the methods

Abstract

The constrained generalized maximum-entropy sampling problem (CGMESP) is to select an order-s principal submatrix from an order-n covariance matrix, subject to some linear side constraints, so as to maximize the product of its t greatest eigenvalues, 0 < t <= s <n. GMESP refers to the version with no side constraints. Introduced more than 25 years ago, CGMESP is a natural generalization of two fundamental problems in statistical design theory: (i) constrained maximum-entropy sampling problem (CMESP); (ii) binary D-optimality (D-Opt). In the general case, it can be motivated by a selection problem in the context of principal component analysis (PCA). We present novel non-convex extended variable formulations for CGMESP. Using these formulations as points of departure, we present, first non-convex and then convex, continuous relaxations for CGMESP. We demonstrate many relations between different upper bounds for CGMESP, including upper bounds from the literature and our new upper bounds. We investigate the behavior of our relaxations related to the constraints linking the natural variables with the extended variables. We propose and investigate a generalized scaling technique for bound improvement. In the context of branch-and-bound, we determine the better of two natural branching techniques for fixing variables to zero. Finally, we present numerical experiments illustrating the value of our methods.