Tridiagonal Maximum-Entropy Sampling and Tridiagonal Masks

Abstract

The NP-hard maximum-entropy sampling problem (MESP) seeks a maximum (log-)determinant principal submatrix, of a given order, from an input covariance matrix $C$. We give an efficient dynamic-programming algorithm for MESP when $C$ (or its inverse) is tridiagonal and generalize it to the situation where the support graph of $C$ (or its inverse) is a spider graph with a constant number of legs (and beyond). We give a class of arrowhead covariance matrices $C$ for which a natural greedy algorithm solves MESP. A \emph{mask} $M$ for MESP is a correlation matrix with which we pre-process $C$, by taking the Hadamard product $M\circ C$. Upper bounds on MESP with $M\circ C$ give upper bounds on MESP with $C$. Most upper-bounding methods are much faster to apply, when the input matrix is tridiagonal, so we consider tridiagonal masks $M$ (which yield tridiagonal $M\circ C$). We make a detailed analysis of such tridiagonal masks, and develop a combinatorial local-search based upper-bounding method that takes advantage of fast computations on tridiagonal matrices.