Title: 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 semidefiniteness constraint. We describe a nonlinear affine scaling algorithm to approximately minimize the bound. Implementation of the procedure obtains excellent bounds at modest computational expense. Joint work with Kurt Anstreicher.