Abstract
The NP-hard maximum-entropy sampling problem (MESP) seeks a maximum (log-)determinant principal submatrix, of a given order, from a positive-semidefinite input matrix C. We give an efficient dynamic-programming algorithm for MESP when C (or its inverse) is tridiagonal. A mask M for MESP is a correlation matrix with which we pre-process C, by taking the Hadamard product M ◦C. Upper bounds on MESP with M ◦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 ◦ C). We analyze such tridiagonal masks, and develop a combinatorial local-search based upper-bounding method that takes advantage of fast computations on tridiagonal matrices.
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