preconditioned conjugate gradient

On solving large-scale multistage stochastic problems with a new specialized interior-point approach

  • Jordi Castro
  • Laureano F. Escudero
  • Juan F. Monge
    • Categories Linear, Cone and Semidefinite Programming, Stochastic Programming Tags , , , , ,

    A novel approach based on a specialized interior-point method (IPM) is presented for solving large-scale stochastic multistage continuous optimization problems, which represent the uncertainty in strategic multistage and operational two-stage scenario trees, the latter being rooted at the strategic nodes. This new solution approach considers a split-variable formulation of the strategic and operational structures, for … Read more

    On geometrical properties of preconditioners in IPMs for classes of block-angular problems

  • Jordi Castro
  • Stefano Nasini
    • Categories Linear Programming Tags , , , ,

    One of the most efficient interior-point methods for some classes of block-angular structured problems solves the normal equations by a combination of Cholesky factorizations and preconditioned conjugate gradient for, respectively, the block and linking constraints. In this work we show that the choice of a good preconditioner depends on geometrical properties of the constraints structure. … Read more

    Communication-Efficient Distributed Optimization of Self-Concordant Empirical Loss

  • Lin Xiao
  • Yuchen Zhang
    • Categories Convex Optimization, Statistics Tags , , , ,

    We consider distributed convex optimization problems originated from sample average approximation of stochastic optimization, or empirical risk minimization in machine learning. We assume that each machine in the distributed computing system has access to a local empirical loss function, constructed with i.i.d. data sampled from a common distribution. We propose a communication-efficient distributed algorithm to … Read more

    Matrix-free Interior Point Method for Compressed Sensing Problems

  • Kimon Fountoulakis
  • Jacek Gondzio
  • Pavel Zhlobich
    • Categories Applications - Science and Engineering Tags , , , ,

    We consider a class of optimization problems for sparse signal reconstruction which arise in the field of Compressed Sensing (CS). A plethora of approaches and solvers exist for such problems, for example GPSR, FPC AS, SPGL1, NestA, l1 ls, PDCO to mention a few. CS applications lead to very well conditioned optimization problems and therefore … Read more

    A Matrix-Free Approach For Solving The Gaussian Process Maximum Likelihood Problem

  • Mihai Anitescu
  • Jie Chen
  • Lei Wang
    • Categories Statistics, Unconstrained Optimization Tags , , , , , ,

    Gaussian processes are the cornerstone of statistical analysis in many application ar- eas. Nevertheless, most of the applications are limited by their need to use the Cholesky factorization in the computation of the likelihood. In this work, we present a matrix-free approach for comput- ing the solution of the maximum likelihood problem involving Gaussian processes. … Read more

    A Matrix-Free Approach For Solving The Gaussian Process Maximum Likelihood Problem

  • Mihai Anitescu
    • Categories Nonlinear Optimization, Statistics, Stochastic Programming Tags , , , , , ,

    Gaussian processes are the cornerstone of statistical analysis in many application ar- eas. Nevertheless, most of the applications are limited by their need to use the Cholesky factorization in the computation of the likelihood. In this work, we present a matrix-free approach for comput- ing the solution of the maximum likelihood problem involving Gaussian processes. … Read more

    A Matrix-Free Approach For Solving The Gaussian Process Maximum Likelihood Problem

  • Mihai Anitescu
  • Jie Chen
  • Lei Wang
    • Categories Nonlinear Optimization, Statistics, Stochastic Programming Tags , , , , , ,

    Gaussian processes are the cornerstone of statistical analysis in many application ar- eas. Nevertheless, most of the applications are limited by their need to use the Cholesky factorization in the computation of the likelihood. In this work, we present a matrix-free approach for comput- ing the solution of the maximum likelihood problem involving Gaussian processes. … Read more

    A Matrix-Free Approach For Solving The Gaussian Process Maximum Likelihood Problem

  • Mihai Anitescu
    • Categories Nonlinear Optimization, Statistics, Stochastic Programming Tags , , , , , ,

    Gaussian processes are the cornerstone of statistical analysis in many application ar- eas. Nevertheless, most of the applications are limited by their need to use the Cholesky factorization in the computation of the likelihood. In this work, we present a matrix-free approach for comput- ing the solution of the maximum likelihood problem involving Gaussian processes. … Read more

    Interior-point method for nonlinear programming with complementarity constraints

  • Ladislav Lukšan
  • Ctirad Matonoha
  • Jan Vlček
    • Categories Constrained Nonlinear Optimization, Nonlinear Optimization Tags , , , , , , ,

    In this report, we propose an algorithm for solving nonlinear programming problems with com-plementarity constraints, which is based on the interior-point approach. Main theoretical results concern direction determination and step-length selection. We use an exact penalty function to remove complementarity constraints. Thus a new indefinite linear system is defined with a tridiagonal low-right submatrix. Inexact … Read more

    Convergence Analysis of Inexact Infeasible Interior Point Method for Linear Optimization

  • Ghussoun Al-Jeiroudi
  • Jacek Gondzio
    • Categories Linear Programming Tags , , ,

    In this paper we present the convergence analysis of the inexact infeasible path-following(IIPF) interior point algorithm. In this algorithm the preconditioned conjugate gradient method is used to solve the reduced KKT system (the augmented system). The augmented system is preconditioned by using a block triangular matrix. The KKT system is solved approximately. Therefore, it becomes … Read more