A Masked Spectral Bound for Maximum-Entropy Sampling

Abstract

We introduce a new “masked spectral bound” for the maximum-entropy sampling problem. This bound is a continuous generalization of the very effective “spectral partition bound”. Optimization of the masked spectral bound requires the minimization of a nonconvex, nondifferentiable objective over a semidef-initeness constraint. We describe a nonlinear affine scaling algorithm to approximately minimize the bound. Implementation of the procedure obtains excellent bounds at modest computational expense.

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