Linear, Cone and Semidefinite Programming

Ideal formulations for constrained convex optimization problems with indicator variables.

  • Andrés Gómez
  • Simge Küçükyavuz
  • Linchuan Wei
    • Categories (Mixed) Integer Nonlinear Programming, Semi-definite Programming Tags , , ,

    Motivated by modern regression applications, in this paper, we study the convexification of a class of convex optimization problems with indicator variables and combinatorial constraints on the indicators. Unlike most of the previous work on convexification of sparse regression problems, we simultaneously consider the nonlinear non-separable objective, indicator variables, and combinatorial constraints. Specifically, we give … Read more

    Memory-efficient structured convex optimization via extreme point sampling

  • Vishnu Narayanan
  • James Saunderson
  • Nimita Shinde
    • Categories Convex Optimization, Linear, Cone and Semidefinite Programming

    Memory is a key computational bottleneck when solving large-scale convex optimization problems such as semidefinite programs (SDPs). In this paper, we focus on the regime in which storing an n × n matrix decision variable is prohibitive. To solve SDPs in this regime, we develop a randomized algorithm that returns a random vector whose covariance … Read more

    A simplified treatment of Ramana’s exact dual for semidefinite programming

  • Bruno F. Lourenço
  • Gabor Pataki
    • Categories Convex Optimization, Nonlinear Optimization, Semi-definite Programming Tags , , , ,

    In semidefinite programming the dual may fail to attain its optimal value and there could be a duality gap, i.e., the primal and dual optimal values may differ. In a striking paper, Ramana proposed a polynomial size extended dual that does not have these deficiencies and yields a number of fundamental results in complexity theory. … Read more

    A Restricted Dual Peaceman-Rachford Splitting Method for QAP

  • Naomi Graham
  • Hao Hu
  • Xinxin Li
  • Henry Wolkowicz
  • Haesol Im
    • Categories (Mixed) Integer Nonlinear Programming, Linear, Cone and Semidefinite Programming, Optimization Software Benchmark Tags , , , ,

    We revisit and strengthen splitting methods for solving doubly nonnegative, DNN, relaxations of the quadratic assignment problem, QAP. We use a modified restricted contractive splitting method, rPRSM, approach. Our strengthened bounds and new dual multiplier estimates improve on the bounds and convergence results in the literature. Citation Department of Combinatorics & Optimization, University of Waterloo, … Read more

    The Equivalence of Fourier-based and Wasserstein Metrics on Imaging Problems

  • Gennaro Auricchio
  • Stefano Gualandi
  • Giuseppe Toscani
  • Andrea Codegoni
  • Marco Veneroni
    • Categories Convex and Nonsmooth Optimization, Linear Programming, Network Optimization

    We investigate properties of some extensions of a class of Fourier-based probability metrics, originally introduced to study convergence to equilibrium for the solution to the spatially homogeneous Boltzmann equation. At difference with the original one, the new Fourier-based metrics are well-defined also for probability distributions with different centers of mass, and for discrete probability measures … Read more

    Inexact and Stochastic Generalized Conditional Gradient with Augmented Lagrangian and Proximal Step

  • Jalal Fadili
  • Antonio Silveti-Falls
  • Cesare Molinari
    • Categories Convex and Nonsmooth Optimization, Semi-definite Programming, Stochastic Programming

    In this paper we propose and analyze inexact and stochastic versions of the CGALP algorithm developed in the authors’ previous paper, which we denote ICGALP, that allows for errors in the computation of several important quantities. In particular this allows one to compute some gradients, proximal terms, and/or linear minimization oracles in an inexact fashion … Read more

    Solving Large-Scale Sparse PCA to Certifiable (Near) Optimality

  • Dimitris Bertsimas
  • Ryan Cory-Wright
  • Jean Pauphilet
    • Categories (Mixed) Integer Nonlinear Programming, Semi-definite Programming, Statistics Tags , , ,

    Sparse principal component analysis (PCA) is a popular dimensionality reduction technique for obtaining principal components which are linear combinations of a small subset of the original features. Existing approaches cannot supply certifiably optimal principal components with more than $p=100s$ of variables. By reformulating sparse PCA as a convex mixed-integer semidefinite optimization problem, we design a … Read more

    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

    Parametric analysis of conic linear optimization

  • Guo Jinhai
  • Yan Zizong
  • Li Xiangjun
    • Categories Linear, Cone and Semidefinite Programming, Semi-definite Programming Tags , , , ,

    This paper focuses on the parametric analysis of a conic linear optimization problem with respect to the perturbation of the objective function along many fixed directions. We introduce the concept of the primal and dual conic linear inequality representable sets, which is very helpful for converting the correlation of the parametric conic linear optimization problems … Read more

    Towards practical generic conic optimization

  • Chris Coey
  • Juan Pablo Vielma
  • Lea Kapelevich
    • Categories Convex Optimization, Linear, Cone and Semidefinite Programming, Optimization Software and Modeling Systems Tags , , ,

    Many convex optimization problems can be represented through conic extended formulations with auxiliary variables and constraints using only the small number of standard cones recognized by advanced conic solvers such as MOSEK 9. Such extended formulations are often significantly larger and more complex than equivalent conic natural formulations, which can use a much broader class … Read more