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 semidefiniteness constraint.We describe a onlinear affine scaling algorithm to approximately minimize the bound. Implementation of the procedure obtains excellent bounds at modest computational expense.
By: Kurt Anstreicher, Jon Lee
Published in: RC22892 in 2003
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