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A masked spectral bound for maximum-entropy sampling

Kurt Anstreicher (kurt-anstreicher***at***uiowa.edu)
Jon Lee (jonlee***at***us.ibm.com)

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.

Download: [PDF]

Entry Submitted: 09/16/2003
Entry Accepted: 09/16/2003
Entry Last Modified: 05/28/2004

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