A masked spectral bound for maximum-entropy sampling
Kurt Anstreicher (kurt-anstreicheruiowa.edu)
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 function 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.
Keywords: maximum-entropy sampling, experimental design, semidefinite programming, spectral partition bound
Category 1: Applications -- Science and Engineering (Statistics )
Category 2: Convex and Nonsmooth Optimization (Nonsmooth Optimization )
Category 3: Linear, Cone and Semidefinite Programming (Semi-definite Programming )
Citation: Research Report RC22892, IBM T.J. Watson Research Center, Yorktown Heights NY, September 2003. To appear in "MODA 7 - Advances in Model-Oriented Design and Analysis", Contributions to Statistics, Springer, Berlin, 2004.
Entry Submitted: 09/16/2003
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