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Eigenvalue techniques for proving bounds for convex objective, nonconvex programs

Daniel Bienstock (dano***at***columbia.edu)

Abstract: We describe techniques combining the S-lemma and computation of projected quadratics which experimentally yield strong bounds on the value of convex quadratic programs with nonconvex constraints

Keywords: nonconvex optimization, integer programming

Category 1: Convex and Nonsmooth Optimization

Category 2: Combinatorial Optimization

Citation: unpublished report, Columbia University, March 2009

Download: [PDF]

Entry Submitted: 05/26/2009
Entry Accepted: 05/26/2009
Entry Last Modified: 09/13/2009

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