A First-Order Interior-Point Method for Linearly Constrained Smooth Optimization

  • Immanuel M. Bomze
  • Paul Tseng
  • Werner Schachinger
    • Categories Nonlinear Optimization, Quadratic Programming Short URL: https://optimization-online.org/?p=10289

    We propose a first-order interior-point method for linearly constrained smooth optimization that unifies and extends first-order affine-scaling method and replicator dynamics method for standard quadratic programming. Global convergence and, in the case of quadratic programs, (sub)linear convergence rate and iterate convergence results are derived. Numerical experience on simplex constrained problems with 1000 variables is reported.

    Citation

    Technical Report TR-ISDS {\bf 2007-17}, University of Vienna (2007).

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