Linear, Cone and Semidefinite Programming

Projection: A Unified Approach to Semi-Infinite Linear Programs and Duality in Convex Programming

  • Amitabh Basu
  • Kipp Martin
  • Christopher Thomas Ryan
    • Categories Convex Optimization, Linear, Cone and Semidefinite Programming, Semi-infinite Programming Tags , , , ,

    Fourier-Motzkin elimination is a projection algorithm for solving finite linear programs. We extend Fourier-Motzkin elimination to semi-infinite linear programs which are linear programs with finitely many variables and infinitely many constraints. Applying projection leads to new characterizations of important properties for primal-dual pairs of semi-infinite programs such as zero duality gap, feasibility, boundedness, and solvability. … Read more

    Which Nonnegative Matrices Are Slack Matrices?

  • João Gouveia
  • Volker Kaibel
  • R. Grappe
  • K. Pashkovich
  • Richard Z. Robinson
  • Rekha R. Thomas
    • Categories Linear Programming, Polyhedra Tags , , ,

    In this paper we characterize the slack matrices of cones and polytopes among all nonnegative matrices. This leads to an algorithm for deciding whether a given matrix is a slack matrix. The underlying decision problem is equivalent to the polyhedral verifi cation problem whose complexity is unknown. Citation April 2013 Article Download View Which Nonnegative Matrices … Read more

    A Perturbed Sums of Squares Theorem for Polynomial Optimization and its Applications

  • Masakazu Muramatsu
  • Levent Tunçel
  • Hayato Waki
    • Categories Convex Optimization, Quadratic Programming, Semi-definite Programming

    We consider a property of positive polynomials on a compact set with a small perturbation. When applied to a Polynomial Optimization Problem (POP), the property implies that the optimal value of the corresponding SemiDefinite Programming (SDP) relaxation with sufficiently large relaxation order is bounded from below by $(f^¥ast – ¥epsilon)$ and from above by $f^¥ast … Read more

    An Augmented Lagrangian Method for Conic Convex Programming

  • Necdet Serhat Aybat
  • Garud Iyengar
    • Categories Convex and Nonsmooth Optimization, Linear, Cone and Semidefinite Programming Tags , , , ,

    We propose a new first-order augmented Lagrangian algorithm ALCC for solving convex conic programs of the form min{rho(x)+gamma(x): Ax-b in K, x in chi}, where rho and gamma are closed convex functions, and gamma has a Lipschitz continuous gradient, A is mxn real matrix, K is a closed convex cone, and chi is a “simple” … Read more

    Computational aspects of simplex and MBU-simplex algorithms using different anti-cycling pivot rules

  • Nagy Adrienn
  • Tibor Illés
    • Categories Linear Programming, Optimization Software Benchmark Tags , , ,

    Several variations of index selection rules for simplex type algorithms for linear programming, like the Last-In-First-Out or the Most-Often-Selected-Variable are rules not only theoretically finite, but also provide significant flexibility in choosing a pivot element. Based on an implementation of the primal simplex and the monotonic build-up (MBU) simplex method, the practical benefit of the … Read more

    The s-Monotone Index Selection Rule for Criss-Cross Algorithms of Linear Complementarity Problems

  • Nagy Adrienn
  • Zsolt Csizmadia
  • Tibor Illés
    • Categories Complementarity and Variational Inequalities, Linear, Cone and Semidefinite Programming Tags , , ,

    In this paper we introduce the s-monotone index selection rules for the well-known crisscross method for solving the linear complementarity problem (LCP). Most LCP solution methods require a priori information about the properties of the input matrix. One of the most general matrix properties often required for finiteness of the pivot algorithms (or polynomial complexity … Read more

    The Trust Region Subproblem with Non-Intersecting Linear Constraints

  • Samuel Burer
  • Boshi Yang
    • Categories Linear, Cone and Semidefinite Programming, Quadratic Programming, Second-Order Cone Programming Tags , , ,

    This paper studies an extended trust region subproblem (eTRS)in which the trust region intersects the unit ball with m linear inequality constraints. When m=0, m=1, or m=2 and the linear constraints are parallel, it is known that the eTRS optimal value equals the optimal value of a particular convex relaxation, which is solvable in polynomial … Read more

    A generalization of the Lowner-John’s ellipsoid theorem

  • Jean-Bernard Lasserre
    • Categories Convex Optimization, Nonlinear Optimization, Semi-definite Programming

    We address the following generalization $P$ of the Lowner-John’s ellipsoid problem. Given a (non necessarily convex) compact set $K\subset R^n$ and an even integer $d, find an homogeneous polynomial $g$ of degree $d$ such that $K\subset G:=\{x:g(x)\leq1\}$ and $G$ has minimum volume among all such sets. We show that $P$ is a convex optimization problem … Read more

    Robust Near-Separable Nonnegative Matrix Factorization Using Linear Optimization

  • Nicolas Gillis
  • Robert Luce
    • Categories Data-Mining, Linear Programming Tags , , , , ,

    Nonnegative matrix factorization (NMF) has been shown recently to be tractable under the separability assumption, under which all the columns of the input data matrix belong to the convex cone generated by only a few of these columns. Bittorf, Recht, R\’e and Tropp (`Factoring nonnegative matrices with linear programs’, NIPS 2012) proposed a linear programming … Read more

    Novel update techniques for the revised simplex method

  • J. A. Julian Hall
  • Qi Huangfu
    • Categories Linear Programming

    This paper introduces three novel techniques for updating the invertible representation of the basis matrix when solving practical sparse linear programming (LP) problems using a high performance implementation of the dual revised simplex method, being of particular value when suboptimization is used. Two are variants of the product form update and the other permits multiple … Read more