Hankel Matrix Rank Minimization with Applications to System Identification and Realization

  • Maryam Fazel
  • Ting Kei Pong
  • Defeng Sun
  • Paul Tseng
    • Categories Applications - Science and Engineering, Convex and Nonsmooth Optimization, Linear, Cone and Semidefinite Programming Tags , , , , ,

    We introduce a flexible optimization framework for nuclear norm minimization of matrices with linear structure, including Hankel, Toeplitz and moment structures, and catalog applications from diverse fields under this framework. We discuss various first-order methods for solving the resulting optimization problem, including alternating direction methods of multipliers, proximal point algorithms and gradient projection methods. We … Read more

    Convergence Analysis of an Inexact Feasible Interior Point Method for Convex Quadratic Programming

  • Jacek Gondzio
    • Categories Linear, Cone and Semidefinite Programming Tags , , , , ,

    In this paper we will discuss two variants of an inexact feasible interior point algorithm for convex quadratic programming. We will consider two different neighbourhoods: a (small) one induced by the use of the Euclidean norm which yields a short-step algorithm and a symmetric one induced by the use of the infinity norm which yields … Read more

    Note: Optimal non-homogeneous composites for dynamic loading revisited

  • Rouhollah Tavakoli
    • Categories Mechanical Engineering Tags , , ,

    The continuous adjoint sensitivity analysis for a class of optimal design problem, formerly studied in (Turteltaub, 2005), is revisited in this note. Full details of derivation is presented. It is shown that the adjoint PDE derived in this study is not identical to one derived in (Turteltaub, 2005). ArticleDownload View PDF

    Mean squared error minimization for inverse moment problems

  • Didier Henrion
  • Jean-Bernard Lasserre
  • Martin Mevissen
    • Categories Convex Optimization, Semi-definite Programming

    We consider the problem of approximating the unknown density $u\in L^2(\Omega,\lambda)$ of a measure $\mu$ on $\Omega\subset\R^n$, absolutely continuous with respect to some given reference measure $\lambda$, from the only knowledge of finitely many moments of $\mu$. Given $d\in\N$ and moments of order $d$, we provide a polynomial $p_d$ which minimizes the mean square error … Read more

    An Outer-Inner Approximation for separable MINLPs

  • Pierre Bonami
  • Hassan L. Hijazi
  • Adam Ouorou
    • Categories (Mixed) Integer Nonlinear Programming, Optimization Software and Modeling Systems Tags , ,

    A common structure in convex mixed-integer nonlinear programs is separable nonlinear functions. In the presence of such structures, we propose three improvements to the outer approximation algorithms. The first improvement is a simple extended formulation, the second is a refined outer approximation, and the third is a heuristic inner approximation of the feasible region. These … 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

    Solving Security Constrained Optimal Power Flow Problems by a Structure Exploiting Interior Point Method

  • Naiyuan Chiang
  • Andreas Grothey
    • Categories Applications - OR and Management Sciences, Linear Programming, Stochastic Programming Tags

    The aim of this paper is to demonstrate a new approach to solve the linearized (n-1) security constrained optimal power flow (SCOPF) problem by a structure exploiting interior point solver. Firstly, we present a reformulation of the linearized SCOPF model, in which most matrices that need to be factorized are constant. Hence, most factorizations and … Read more

    Fast global convergence of gradient methods for high-dimensional statistical recovery

  • Alekh Agarwal
  • Martin J Wainwright
  • Sahand Negahban
    • Categories Convex Optimization, Statistics Tags , ,

    Many statistical $M$-estimators are based on convex optimization problems formed by the combination of a data-dependent loss function with a norm-based regularizer. We analyze the convergence rates of projected gradient and composite gradient methods for solving such problems, working within a high-dimensional framework that allows the data dimension $\pdim$ to grow with (and possibly exceed) … Read more

    Approximating the solution for the multiparametric 0-1-mixed integer linear programming problem with interval data

  • Alejandro Crema
  • Fernando Crema
  • Edgar Hugo Peraza
    • Categories (Mixed) Integer Linear Programming Tags , ,

    In this paper we present algorithms to approximate the solution for the multiparametric 0-1-mixed integer linear programming problem relative to the objective function. We consider the uncertainty for the parameters that de fine the cost vector corresponding to a subset of 0-1-variables by assuming that each parameter belongs to a known interval. We suppose that we … Read more

    Erratum to: On the set-semidefinite representation of nonconvex quadratic programs over arbitrary feasible sets” [Optim. Letters, 2012]

  • Peter J.C. Dickinson
  • Gabriele Eichfelder
  • Janez Povh
    • Categories Convex and Nonsmooth Optimization, Linear, Cone and Semidefinite Programming Tags , , , ,

    In this paper, an erratum is provided to the article “\emph{On the set-semidefinite representation of nonconvex quadratic programs over arbitrary feasible sets}”, published in Optim.\ Letters, 2012. Due to precise observation of the first author, it has been found that the proof of Lemma 9 has a nontrivial gap, and consequently the main result (Theorem … Read more