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On Copositive Programming and Standard Quadratic Optimization Problems

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Abstract

A standard quadratic problem consists of finding global maximizers of a quadratic form over the standard simplex. In this paper, the usual semidefinite programming relaxation is strengthened by replacing the cone of positive semidefinite matrices by the cone of completely positive matrices (the positive semidefinite matrices which allow a factorization FF T where F is some non-negative matrix). The dual of this cone is the cone of copositive matrices (i.e., those matrices which yield a non-negative quadratic form on the positive orthant). This conic formulation allows us to employ primal-dual affine-scaling directions. Furthermore, these approaches are combined with an evolutionary dynamics algorithm which generates primal-feasible paths along which the objective is monotonically improved until a local solution is reached. In particular, the primal-dual affine scaling directions are used to escape from local maxima encountered during the evolutionary dynamics phase.

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Authors and Affiliations

  1. ISDS, Universität Wien, Austria

    Immanuel M. Bomze

  2. Department of Statistics, Vienna University of Economics, Austria

    Mirjam Dür

  3. Faculty ITS/TWI/SSOR, Delft University of Technology, The Netherlands

    Etienne de Klerk, Cornelis Roos & Arie J. Quist

  4. Department of Computing and Software, McMaster University Hamilton, Ontario, Canada

    Tamás Terlaky

Authors
  1. Immanuel M. Bomze
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  2. Mirjam Dür
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  3. Etienne de Klerk
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  4. Cornelis Roos
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  5. Arie J. Quist
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  6. Tamás Terlaky
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Bomze, I.M., Dür, M., de Klerk, E. et al. On Copositive Programming and Standard Quadratic Optimization Problems. Journal of Global Optimization 18, 301–320 (2000). https://doi.org/10.1023/A:1026583532263

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