Month: April 2019

ProxSARAH: An Efficient Algorithmic Framework for Stochastic Composite Nonconvex Optimization

  • M. Lam Nguyen
  • Quoc Tran-Dinh
  • H. Nhan Pham
  • T. Dzung Phan
    • Categories Nonlinear Optimization, Stochastic Programming

    We propose a new stochastic first-order algorithmic framework to solve stochastic composite nonconvex optimization problems that covers both finite-sum and expectation settings. Our algorithms rely on the SARAH estimator introduced in (Nguyen et al., 2017a) and consist of two steps: a proximal gradient and an averaging step making them different from existing nonconvex proximal-type algorithms. … Read more

    Burer-Monteiro guarantees for general semidefinite programs

  • Diego Cifuentes
    • Categories Semi-definite Programming Tags , , ,

    Consider a semidefinite program (SDP) involving an $n\times n$ positive semidefinite matrix $X$. The Burer-Monteiro method consists in solving a nonconvex program in $Y$, where $Y$ is an $n\times p$ matrix such that $X = Y Y^T$. Despite nonconvexity, Boumal et al. showed that the method provably solves generic equality-constrained SDP’s when $p > \sqrt{2m}$, … Read more

    Knapsack Polytopes – A Survey

  • Tristan Gally
  • Oliver Habeck
  • Christopher Hojny
  • Frederic Matter
  • Marc E. Pfetsch
  • Andreas Schmitt
  • Hendrik Lüthen
    • Categories 0-1 Programming, Polyhedra Tags , , , , ,

    The 0/1 knapsack polytope is the convex hull of all 0/1 vectors that satisfy a given single linear inequality with non-negative coefficients. This paper provides a comprehensive overview of knapsack polytopes. We discuss basic polyhedral properties, (lifted) cover and other valid inequalities, cases for which complete linear descriptions are known, geometric properties for small dimensions, … Read more

    Lower Bounds for the Bandwidth Problem

  • Truden Christian
  • Franz Rendl
  • Renata Sotirov
    • Categories Semi-definite Programming Tags , ,

    The Bandwidth Problem asks for a simultaneous permutation of the rows and columns of the adjacency matrix of a graph such that all nonzero entries are as close as possible to the main diagonal. This work focuses on investigating novel approaches to obtain lower bounds for the bandwidth problem. In particular, we use vertex partitions … Read more

    RaBVItG:An Algorithm for Solving a Class of Multi-Players Feedback Nash Differential Games

  • Jorge Herrera de la Cruz
  • Benjamin Ivorra
  • Ángel M. Ramos
    • Categories Systems governed by Differential Equations Optimization Tags , ,

    In this work, we introduce a novel numerical algorithm, called RaBVItG (Radial Basis Value Iteration Game) to approximate feedback-Nash equilibria for deterministic differential games. More precisely, RaBVItG is an algorithm based on value iteration schemes in a meshfree context. It is used to approximate optimal feedback Nash policies for multi-players, trying to tackle the dimensionality … Read more

    Convex-Concave Backtracking for Inertial Bregman Proximal Gradient Algorithms in Non-Convex Optimization

  • Mahesh Chandra Mukkamala
  • Peter Ochs
  • Thomas Pock
  • Shoham Sabach
    • Categories Convex and Nonsmooth Optimization Tags , , , , , , ,

    Backtracking line-search is an old yet powerful strategy for finding better step size to be used in proximal gradient algorithms. The main principle is to locally find a simple convex upper bound of the objective function, which in turn controls the step size that is used. In case of inertial proximal gradient algorithms, the situation … Read more

    Normal Approximation for Stochastic Gradient Descent via Non-Asymptotic Rates of Martingale CLT

  • Andreas Anastasiou
  • Krishnakumar Balasubramanian
  • Murat A. Erdogdu
    • Categories Nonlinear Optimization, Stochastic Programming Tags , ,

    We provide non-asymptotic convergence rates of the Polyak-Ruppert averaged stochastic gradient descent (SGD) to a normal random vector for a class of twice-differentiable test functions. A crucial intermediate step is proving a non-asymptotic martingale central limit theorem (CLT), i.e., establishing the rates of convergence of a multivariate martingale difference sequence to a normal random vector, … Read more

    Distributionally robust optimization with multiple time scales: valuation of a thermal power plant

  • Daniela Escobar
  • Martin Glanzer
  • Georg Ch. Pflug
  • Wim van Ackooij
    • Categories Dynamic Programming, Stochastic Programming Tags , , , , , ,

    The valuation of a real option is preferably done with the inclusion of uncertainties in the model, since the value depends on future costs and revenues, which are not perfectly known today. The usual value of the option is defined as the maximal expected (discounted) profit one may achieve under optimal management of the operation. … Read more

    Noisy Euclidean Distance Matrix Completion with a Single Missing Node

  • Lucas Pettersson
  • Stefan Sremac
  • Henry Wolkowicz
  • Fei Wang
    • Categories Convex Optimization, Semi-definite Programming, Telecommunications Tags , , , , , ,

    We present several solution techniques for the noisy single source localization problem, i.e.,~the Euclidean distance matrix completion problem with a single missing node to locate under noisy data. For the case that the sensor locations are fixed, we show that this problem is implicitly convex, and we provide a purification algorithm along with the SDP … Read more

    A Log-Barrier Newton-CG Method for Bound Constrained Optimization with Complexity Guarantees

  • Michael J. O'Neill
  • Stephen Wright
    • Categories Bound-constrained Optimization Tags , , ,

    We describe an algorithm based on a logarithmic barrier function, Newton’s method, and linear conjugate gradients, that obtains an approximate minimizer of a smooth function over the nonnegative orthant. We develop a bound on the complexity of the approach, stated in terms of the required accuracy and the cost of a single gradient evaluation of … Read more