nonlinear semidefinite programming

On the use of Jordan Algebras for improving global convergence of an Augmented Lagrangian method in nonlinear semidefinite programming

  • Roberto Andreani
  • Ellen H. Fukuda
  • Gabriel Haeser
  • Daiana O. Santos
  • Leonardo D. Secchin
    • Categories Nonlinear Optimization, Semi-definite Programming Tags , , , ,

    Jordan Algebras are an important tool for dealing with semidefinite programming and optimization over symmetric cones in general. In this paper, a judicious use of Jordan Algebras in the context of sequential optimality conditions is done in order to generalize the global convergence theory of an Augmented Lagrangian method for nonlinear semidefinite programming. An approximate … Read more

    An Augmented Lagrangian algorithm for nonlinear semidefinite programming applied to the covering problem

  • Ernesto G. Birgin
  • Walter Gómez
  • Gabriel Haeser
  • Leonardo M. Mito
  • Daiana O. Santos
    • Categories Constrained Nonlinear Optimization, Convex Optimization, Semi-definite Programming Tags , , ,

    In this work we present an Augmented Lagrangian algorithm for nonlinear semidefinite problems (NLSDPs), which is a natural extension of its consolidated counterpart in nonlinear programming. This method works with two levels of constraints; one that is penalized and other that is kept within the subproblems. This is done in order to allow exploiting the … Read more

    Optimality conditions and global convergence for nonlinear semidefinite programming

  • Roberto Andreani
  • Gabriel Haeser
  • Daiana O. Santos
    • Categories Nonlinear Optimization, Semi-definite Programming Tags , , ,

    Sequential optimality conditions have played a major role in unifying and extending global convergence results for several classes of algorithms for general nonlinear optimization. In this paper, we extend theses concepts for nonlinear semidefinite programming. We define two sequential optimality conditions for nonlinear semidefinite programming. The first is a natural extension of the so-called Approximate-Karush-Kuhn-Tucker … Read more

    Exact augmented Lagrangian functions for nonlinear semidefinite programming

  • Ellen H. Fukuda
  • Bruno F. Lourenço
    • Categories Constrained Nonlinear Optimization, Semi-definite Programming Tags , ,

    In this paper, we study augmented Lagrangian functions for nonlinear semidefinite programming (NSDP) problems with exactness properties. The term exact is used in the sense that the penalty parameter can be taken appropriately, so a single minimization of the augmented Lagrangian recovers a solution of the original problem. This leads to reformulations of NSDP problems … Read more

    Optimality conditions for nonlinear semidefinite programming via squared slack variables

  • Ellen H. Fukuda
  • Bruno F. Lourenço
  • Masao Fukushima
    • Categories Nonlinear Optimization, Semi-definite Programming Tags , , ,

    In this work, we derive second-order optimality conditions for nonlinear semidefinite programming (NSDP) problems, by reformulating it as an ordinary nonlinear programming problem using squared slack variables. We first consider the correspondence between Karush-Kuhn-Tucker points and regularity conditions for the general NSDP and its reformulation via slack variables. Then, we obtain a pair of “no-gap” … Read more

    An interior point method with a primal-dual quadratic barrier penalty function for nonlinear semidefinite programming

  • Atsushi Kato
  • Hiroshi Yabe
  • Hiroshi Yamashita
    • Categories Constrained Nonlinear Optimization Tags , , ,

    In this paper, we consider an interior point method for nonlinear semidefinite programming. Yamashita, Yabe and Harada presented a primal-dual interior point method in which a nondifferentiable merit function was used. By using shifted barrier KKT conditions, we propose a differentiable primal-dual merit function within the framework of the line search strategy, and prove the … Read more

    On metric regularity for weakly almost piecewise smooth functions and some applications in nonlinear semidefinite programming

  • Peter Fusek
    • Categories Semi-definite Programming Tags , , ,

    The one-to-one relation between the points fulfilling the KKT conditions of an optimization problem and the zeros of the corresponding Kojima function is well-known. In the present paper we study the interplay between metric regularity and strong regularity of this a priori nonsmooth function in the context of semidefinite programming. Having in mind the topological … Read more

    Local and superlinear convergence of a primal-dual interior point method for nonlinear semidefinite programming

  • Hiroshi Yabe
  • Hiroshi Yamashita
    • Categories Constrained Nonlinear Optimization, Semi-definite Programming Tags , ,

    In this paper, we consider a primal-dual interior point method for solving nonlinear semidefinite programming problems. We propose primal-dual interior point methods based on the unscaled and scaled Newton methods, which correspond to the AHO, HRVW/KSH/M and NT search directions in linear SDP problems. We analyze local behavior of our proposed methods and show their … Read more

    A primal-dual interior point method for nonlinear semidefinite programming

  • Kouhei Harada
  • Hiroshi Yabe
  • Hiroshi Yamashita
    • Categories Constrained Nonlinear Optimization Tags , , , ,

    In this paper, we consider a primal-dual interior point method for solving nonlinear semidefinite programming problems. By combining the primal barrier penalty function and the primal-dual barrier function, a new primal-dual merit function is proposed within the framework of the line search strategy. We show the global convergence property of our method. Finally some numerical … Read more

    The Rate of Convergence of the Augmented Lagrangian Method for Nonlinear Semidefinite Programming

  • Jie Sun
  • Defeng Sun
  • Liwei Zhang
    • Categories Linear, Cone and Semidefinite Programming, Nonlinear Optimization Tags , , ,

    We analyze the rate of local convergence of the augmented Lagrangian method for nonlinear semidefinite optimization. The presence of the positive semidefinite cone constraint requires extensive tools such as the singular value decomposition of matrices, an implicit function theorem for semismooth functions, and certain variational analysis on the projection operator in the symmetric-matrix space. Without … Read more