New Upper Bounds for Maximum-Entropy Sampling

Abstract

We develop new upper bounds for the constrained maximum-entropy sampling problem. Our partition bounds are based on Fischer’s inequality. Further new bounds combine the use of Fischer’s inequality with previously developed bounds. We demonstrate this in detail by using partitioning to strengthen spectral bounds. Computations suggest that these bounds may be useful in finding optimal solutions by branch-and-bound.

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