Convex and Nonsmooth Optimization

BBCPOP: A Sparse Doubly Nonnegative Relaxation of Polynomial Optimization Problems with Binary, Box and Complementarity Constraints

  • Naoki Ito
  • Sunyoung Kim
  • Masakazu Kojima
  • Kim-Chuan Toh
  • Akiko Takeda
    • Categories Convex Optimization, Global Optimization, Linear, Cone and Semidefinite Programming Tags , , , , , , ,

    The software package BBCPOP is a MATLAB implementation of a hierarchy of sparse doubly nonnegative (DNN) relaxations of a class of polynomial optimization (minimization) problems (POPs) with binary, box and complementarity (BBC) constraints. Given a POP in the class and a relaxation order, BBCPOP constructs a simple conic optimization problem (COP), which serves as a … Read more

    User Manual for BBCPOP: A Sparse Doubly Nonnegative Relaxation of Polynomial Optimization Problems with Binary, Box and Complementarity Constraints

  • Naoki Ito
  • Sunyoung Kim
  • Masakazu Kojima
  • Kim-Chuan Toh
  • Akiko Takeda
    • Categories Convex Optimization, Global Optimization, Linear, Cone and Semidefinite Programming Tags , , , ,

    BBCPOP proposed in [4] is a MATLAB implementation of a hierarchy of sparse doubly nonnegative (DNN) relaxations of a class of polynomial optimization (minimization) problems (POPs) with binary, box and complementarity constraints. Given a POP in the class and a relaxation order (or a hierarchy level), BBCPOP constructs a simple conic optimization problem (COP), which … Read more

    Derivative-Free Superiorization With Component-Wise Perturbations

  • Yair Censor
  • Howard Heaton
  • Reinhard W. Schulte
    • Categories Biomedical Applications, Constrained Nonlinear Optimization, Nonsmooth Optimization Tags , , , , ,

    Superiorization reduces, not necessarily minimizes, the value of a target function while seeking constraints-compatibility. This is done by taking a solely feasibility-seeking algorithm, analyzing its perturbations resilience, and proactively perturbing its iterates accordingly to steer them toward a feasible point with reduced value of the target function. When the perturbation steps are computationally efficient, this … Read more

    Stochastic model-based minimization of weakly convex functions

  • Damek Davis
  • Dmitriy Drusvyatskiy
    • Categories Convex and Nonsmooth Optimization Tags , , , , ,

    We consider an algorithm that successively samples and minimizes stochastic models of the objective function. We show that under weak-convexity and Lipschitz conditions, the algorithm drives the expected norm of the gradient of the Moreau envelope to zero at the rate $O(k^{-1/4})$. Our result yields the first complexity guarantees for the stochastic proximal point algorithm … Read more

    Entropic proximal operators for nonnegative trigonometric polynomials

  • Hsiao-Han Chao
  • Lieven Vandenberghe
    • Categories Convex and Nonsmooth Optimization, Linear, Cone and Semidefinite Programming

    Signal processing applications of semidefinite optimization are often rooted in sum-of-squares representations of nonnegative trigonometric polynomials. Interior-point solvers for semidefinite optimization can handle constraints of this form with a per-iteration-complexity that is cubic in the degree of the trigonometric polynomial. The purpose of this paper is to discuss first-order methods with a lower complexity per … Read more

    Projective Splitting with Forward Steps: Asynchronous and Block-Iterative Operator Splitting

  • Jonathan Eckstein
  • Patrick R. Johnstone
    • Categories Convex and Nonsmooth Optimization Tags ,

    This work is concerned with the classical problem of finding a zero of a sum of maximal monotone operators. For the projective splitting framework recently proposed by Combettes and Eckstein, we show how to replace the fundamental subproblem calculation using a backward step with one based on two forward steps. The resulting algorithms have the … Read more

    A Stochastic Semismooth Newton Method for Nonsmooth Nonconvex Optimization

  • Shicong Cen
  • Andre Milzarek
  • Michael Ulbrich
  • Zaiwen Wen
  • Xiantao Xiao
    • Categories Nonsmooth Optimization Tags , , , ,

    In this work, we present a globalized stochastic semismooth Newton method for solving stochastic optimization problems involving smooth nonconvex and nonsmooth convex terms in the objective function. We assume that only noisy gradient and Hessian information of the smooth part of the objective function is available via calling stochastic first and second order oracles. The … Read more

    SOS-Convex Lyapunov Functions and Stability of Difference Inclusions

  • Amir Ali Ahmadi
  • Raphaël Jungers
    • Categories Control Applications, Convex Optimization, Semi-definite Programming Tags , , , , ,

    We introduce the concept of sos-convex Lyapunov functions for stability analysis of both linear and nonlinear difference inclusions (also known as discrete-time switched systems). These are polynomial Lyapunov functions that have an algebraic certificate of convexity and that can be efficiently found via semidefinite programming. We prove that sos-convex Lyapunov functions are universal (i.e., necessary … Read more

    An Improved Method of Total Variation Superiorization Applied to Reconstruction in Proton Computed Tomography

  • Yair Censor
  • Paniz Karbasi
  • Keith E. Schubert
  • Reinhard W. Schulte
  • Blake Schultze
    • Categories Biomedical Applications, Constrained Nonlinear Optimization, Convex Optimization Tags , , , , ,

    Previous work showed that total variation superiorization (TVS) improves reconstructed image quality in proton computed tomography (pCT). The structure of the TVS algorithm has evolved since then and this work investigated if this new algorithmic structure provides additional benefits to pCT image quality. Structural and parametric changes introduced to the original TVS algorithm included: (1) … Read more

    Inexact Successive Quadratic Approximation for Regularized Optimization

  • Stephen Wright
  • Ching-pei Lee
    • Categories Nonlinear Optimization, Nonsmooth Optimization Tags , , , , , ,

    Successive quadratic approximations, or second-order proximal methods, are useful for minimizing functions that are a sum of a smooth part and a convex, possibly nonsmooth part that promotes regularization. Most analyses of iteration complexity focus on the special case of proximal gradient method, or accelerated variants thereof. There have been only a few studies of … Read more