Eigenvalue techniques for proving bounds for convex objective, nonconvex programs

  • Daniel Bienstock
    • Categories Combinatorial Optimization, Convex and Nonsmooth Optimization Tags , Short URL: https://optimization-online.org/?p=10752

    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

    Citation

    unpublished report, Columbia University, March 2009

    Article

    Download

    View Eigenvalue techniques for proving bounds for convex objective, nonconvex programs