Javier F. Peña

An easily computable upper bound on the Hoffman constant for homogeneous inequality systems

  • Javier F. Peña
    • Categories Linear Programming, Polyhedra, Second-Order Cone Programming Tags , ,

    \(\)Let $A\in \mathbb{R}^{m\times n}\setminus \{0\}$ and $P:=\{x:Ax\le 0\}$. This paper provides a procedure to compute an upper bound on the following {\em homogeneous Hoffman constant} \[ H_0(A) := \sup_{u\in \mathbb{R}^n \setminus P} \frac{\text{dist}(u,P)}{\text{dist}(Au, \mathbb{R}^m_-)}. \] In sharp contrast to the intractability of computing more general Hoffman constants, the procedure described in this paper is entirely … Read more

    Affine invariant convergence rates of the conditional gradient method

  • Javier F. Peña
    • Categories Convex Optimization Tags , , ,

    We show that the conditional gradient method for the convex composite problem \[\min_x\{f(x) + \Psi(x)\}\] generates primal and dual iterates with a duality gap converging to zero provided a suitable growth property holds and the algorithm makes a judicious choice of stepsizes. The rate of convergence of the duality gap to zero ranges from sublinear … Read more

    Projection and rescaling algorithm for finding most interior solutions to polyhedral conic systems

  • Javier F. Peña
  • Negar Soheili
    • Categories Convex Optimization, Linear Programming Tags , , , ,

    We propose a simple projection and rescaling algorithm that finds {\em most interior} solutions to the pair of feasibility problems \[ \text{find} x\in L\cap \R^n_{+} \text{ and } \text{find} \; \hat x\in L^\perp\cap\R^n_{+}, \] where $L$ is a linear subspace of $\R^n$ and $L^\perp$ is its orthogonal complement. The algorithm complements a basic procedure that … Read more

    Equivalences among the chi measure, Hoffman constant, and Renegar’s distance to ill-posedness

  • Javier F. Peña
  • Juan C. Vera
  • Luis F. Zuluaga
    • Categories Convex Optimization, Linear Programming, Polyhedra Tags , , , ,

    We show the equivalence among the following three condition measures of a full column rank matrix $A$: the chi measure, the signed Hoffman constant, and the signed distance to ill-posedness. The latter two measures are constructed via suitable collections of matrices obtained by flipping the signs of some rows of $A$. Our results provide a … Read more

    New characterizations of Hoffman constants for systems of linear constraints

  • Javier F. Peña
  • Juan C. Vera
  • Luis F. Zuluaga
    • Categories Convex Optimization, Polyhedra Tags , ,

    We give a characterization of the Hoffman constant of a system of linear constraints in $\R^n$ relative to a reference polyhedron $R\subseteq\R^n$. The reference polyhedron $R$ represents constraints that are easy to satisfy such as box constraints. In the special case $R = \R^n$, we obtain a novel characterization of the classical Hoffman constant. More … Read more

    Generalized conditional subgradient and generalized mirror descent: duality, convergence, and symmetry

  • Javier F. Peña
    • Categories Convex Optimization Tags , , ,

    We provide new insight into a generalized conditional subgradient algorithm and a generalized mirror descent algorithm for the convex minimization problem \[\min_x \; \{f(Ax) + h(x)\}.\] As Bach showed in [SIAM J. Optim., 25 (2015), pp. 115–129], applying either of these two algorithms to this problem is equivalent to applying the other one to its … Read more

    The condition number of a function relative to a set

  • David H. Gutman
  • Javier F. Peña
    • Categories Convex Optimization Tags , , ,

    The condition number of a differentiable convex function, namely the ratio of its smoothness to strong convexity constants, is closely tied to fundamental properties of the function. In particular, the condition number of a quadratic convex function is the square of the aspect ratio of a canonical ellipsoid associated to the function. Furthermore, the condition … Read more

    A unified framework for Bregman proximal methods: subgradient, gradient, and accelerated gradient schemes

  • David H. Gutman
  • Javier F. Peña
    • Categories Convex Optimization Tags , , , ,

    We provide a unified framework for analyzing the convergence of Bregman proximal first-order algorithms for convex minimization. Our framework hinges on properties of the convex conjugate and gives novel proofs of the convergence rates of the Bregman proximal subgradient, Bregman proximal gradient, and a new accelerated Bregman proximal gradient algorithm under fairly general and mild … Read more

    A data-independent distance to infeasibility for linear conic systems

  • Javier F. Peña
  • Vera Roshchina
    • Categories Convex and Nonsmooth Optimization Tags , , ,

    We offer a unified treatment of distinct measures of well-posedness for homogeneous conic systems. To that end, we introduce a distance to infeasibility based entirely on geometric considerations of the elements defining the conic system. Our approach sheds new light into and connects several well-known condition measures for conic systems, including {\em Renegar’s} distance to … Read more

    An algorithm to compute the Hoffman constant of a system of linear constraints

  • Javier F. Peña
  • Juan C. Vera
  • Luis F. Zuluaga
    • Categories Convex Optimization, Polyhedra Tags , ,

    We propose a combinatorial algorithm to compute the Hoffman constant of a system of linear equations and inequalities. The algorithm is based on a characterization of the Hoffman constant as the largest of a finite canonical collection of easy-to-compute Hoffman constants. Our algorithm and characterization extend to the more general context where some of the … Read more