From Majorization to Scaling: Advancing Convex Relaxations of Maximum Entropy Sampling Problem

TL;DR

This paper introduces a unified majorization framework and a novel double-scaling technique to improve convex relaxations for the maximum entropy sampling problem, enhancing bound quality and computational efficiency.

Contribution

It develops a systematic majorization-based approach and a new double-scaling method that dominate existing bounds for MESP relaxations, advancing theoretical understanding and practical performance.

Findings

Double-scaling strictly dominates previous bounds.

Double-scaled linx relaxation outperforms existing methods.

Proposed methods improve bound quality and efficiency.

Abstract

In this paper, we study the maximum entropy sampling problem (MESP) and its variants. MESP seeks to identify a small subset of variables that maximizes the determinant of a covariance submatrix, and is a fundamental model in optimal experimental design and information acquisition. Although MESP is combinatorial and NP-hard, continuous relaxations, most notably linx and $\Gamma$ factorization, provide tractable approximations, yet their derivation, relative strength, and potential for systematic improvement remain poorly understood. We address this gap by introducing two main ideas: a unified majorization-based framework for deriving and analyzing relaxations, and a novel scaling-based bound-enhancement technique, which we call double-scaling. Our approach is motivated by the observation that the difficulty of MESP arises from two distinct sources: the combinatorial selection structure and the lack of permutation symmetry in the spectral objective. Majorization naturally resolves the latter by symmetrizing the spectral function and yielding its convex envelope. In the log-determinant setting, we establish the main theoretical properties of double-scaling and prove that it strictly dominates previously known scaling bounds. Using our majorization-based alternative characterization of $\Gamma$ factorization relaxation, we also derive, for the first time, formal dominance relations between linx- and $\Gamma$ factorization-bounds, as well as between their scaling-strengthened variants. Our numerical results show that our double-scaled linx relaxation consistently and substantially outperforms existing scaling methods and compares quite favorably with other state-of-the-art relaxations in terms of both bound quality and computational efficiency.