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Global quadratic optimization via conic relaxation

Listed author(s):
  • NESTEROV, Yurii


    (Center for Operations Research and Econometrics (CORE), Université catholique de Louvain (UCL), Louvain la Neuve, Belgium)

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    We present a convex conic relaxation for a problem of maximizing an indefinite quadratic form over a set of convex constraints on the squared variables. We show that for all these problems we get at least 12/37-relative accuracy of the approximation. In the second part of the paper we derive the conic relaxation by another approach based on the second order optimality conditions. We show that for lp-balls, p>=2, intersected by a linear subspace, it is possible to guarantee (1- 2/p)-relative accuracy of the solution. As a consequence, we prove (1 - 1/eln-n)-relative accuracy of the conic relaxation for an indefinite quadratic maximization problem over an n-dimensional unit box with homogeneous linear equality constraints. We discuss the implications of the results for the discussion around the question P = NP.

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    Paper provided by Université catholique de Louvain, Center for Operations Research and Econometrics (CORE) in its series CORE Discussion Papers with number 1998060.

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    Date of creation: 21 Feb 1998
    Handle: RePEc:cor:louvco:1998060
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    1. NESTEROV, Yurii, 1997. "Quality of semidefinite relaxation for nonconvex quadratic optimization," CORE Discussion Papers 1997019, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    2. NESTEROV, Yurii, 1997. "Semidefinite relaxation and nonconvex quadratic optimization," CORE Discussion Papers 1997044, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
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