Month: May 2002

The Penalty Interior Point Method fails to converge for Mathematical Programs with Equilibrium Constraints

  • Sven Leyffer
    • Categories Complementarity and Variational Inequalities, Constrained Nonlinear Optimization Tags , , , , ,

    This paper presents a small example for which the Penalty Interior Point Method converges to a non-stationary point. The reasons for this adverse behaviour are discussed. Citation Numerical Analysis Report NA/208, Department of Mathematics, University of Dundee, February 2002. Article Download View The Penalty Interior Point Method fails to converge for Mathematical Programs with Equilibrium … Read more

    The NEOS Server for Optimization: Version 4 and Beyond

  • Elizabeth D. Dolan
  • Robert Fourer
  • Jorge MorĂ©
  • Todd S. Munson
    • Categories Problem Solving Environments Tags , , ,

    We describe developments associated with Version 4 of the NEOS Server and note that these developments have led to an exponential growth in the number of job submissions. We also provide an overview of some of the research and educational uses for the NEOS Server and discuss future research challenges. Citation Preprint ANL/MCS-P947-0202, Argonne National … Read more

    Relating Homogeneous Cones and Positive Definite Cones via hBcalgebras

  • Chek Beng Chua
    • Categories Convex Optimization, Linear, Cone and Semidefinite Programming Tags , , ,

    $T$-algebras are non-associative algebras defined by Vinberg in the early 1960’s for the purpose of studying homogeneous cones. Vinberg defined a cone $K(\mathcal A)$ for each $T$-algebra $\mathcal A$ and proved that every homogeneous cone is isomorphic to one such $K(\mathcal A)$. We relate each $T$-algebra $\mathcal A$ with a space of linear operators in … Read more

    Semidefinite Programming in the Space of Partial Positive Semidefinite Matrices

  • Samuel Burer
    • Categories Semi-definite Programming Tags , ,

    We build upon the work of Fukuda et al.\ \cite{FuKoMuNa01} and Nakata et al.\ \cite{NaFuFuKoMu01}, in which the theory of partial positive semidefinite matrices has been applied to the semidefinite programming (SDP) problem as a technique for exploiting sparsity in the data. In contrast to their work, which improves an existing algorithm that is based … Read more