large-scale optimization

Compact Representations of Structured BFGS Matrices

  • Johannes Brust
  • Sven Leyffer
  • Zichao (Wendy) Di
  • Cosmin G. Petra
    • Categories Nonlinear Optimization Tags , , , ,

    For general large-scale optimization problems compact representations exist in which recursive quasi-Newton update formulas are represented as compact matrix factorizations. For problems in which the objective function contains additional structure, so-called structured quasi-Newton methods exploit available second-derivative information and approximate unavailable second derivatives. This article develops the compact representations of two structured Broyden-Fletcher-Goldfarb-Shanno update formulas. … Read more

    Solving Large Scale Cubic Regularization by a Generalized Eigenvalue Problem

  • Felix Lieder
    • Categories Nonlinear Optimization, Optimization Software and Modeling Systems, Unconstrained Optimization Tags , ,

    Cubic Regularization methods have several favorable properties. In particular under mild assumptions, they are globally convergent towards critical points with second order necessary conditions satisfied. Their adoption among practitioners, however, does not yet match the strong theoretical results. One of the reasons for this discrepancy may be additional implementation complexity needed to solve the occurring … Read more

    Random projections for quadratic programs

  • Claudia D'Ambrosio
  • Leo Liberti
  • Pierre-Louis Poirion
  • Ky Vu
    • Categories Quadratic Programming Tags , , , ,

    Random projections map a set of points in a high dimensional space to a lower dimen- sional one while approximately preserving all pairwise Euclidean distances. While random projections are usually applied to numerical data, we show they can be successfully applied to quadratic programming formulations over a set of linear inequality constraints. Instead of solving … Read more

    An Inexact Primal-Dual Smoothing Framework for Large-Scale Non-Bilinear Saddle Point Problems

  • William Haskell
  • Le Thi Khanh Hien
  • Renbo Zhao
    • Categories Convex Optimization Tags , , , ,

    We develop an inexact primal-dual first-order smoothing framework to solve a class of non-bilinear saddle point problems with primal strong convexity. Compared with existing methods, our framework yields a significant improvement over the primal oracle complexity, while it has competitive dual oracle complexity. In addition, we consider the situation where the primal-dual coupling term has … Read more

    On Electricity Market Equilibria with Storage: Modeling, Uniqueness, and a Distributed ADMM

  • Julia Grübel
  • Thomas Kleinert
  • Vanessa Krebs
  • Lars Schewe
  • Martin Schmidt
  • Johannes Thürauf
  • Galina Orlinskaya
    • Categories Convex Optimization, Parallel Algorithms, Quadratic Programming Tags , , , , , ,

    We consider spot-market trading of electricity including storage operators as additional agents besides producers and consumers. Storages allow for shifting produced electricity from one time period to a later one. Due to this, multiple market equilibria may occur even if classical uniqueness assumptions for the case without storages are satisfied. For models containing storage operators, … Read more

    Machine learning approach to chance-constrained problems: An algorithm based on the stochastic gradient descent

  • Lukáš Adam
  • Martin Branda
    • Categories Stochastic Programming Tags , , , , ,

    We consider chance-constrained problems with discrete random distribution. We aim for problems with a large number of scenarios. We propose a novel method based on the stochastic gradient descent method which performs updates of the decision variable based only on looking at a few scenarios. We modify it to handle the non-separable objective. A complexity … 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

    Relative-Continuity” for Non-Lipschitz Non-Smooth Convex Optimization using Stochastic (or Deterministic) Mirror Descent

  • Haihao Lu
    • Categories Convex Optimization, Nonsmooth Optimization Tags , , ,

    The usual approach to developing and analyzing first-order methods for non-smooth (stochastic or deterministic) convex optimization assumes that the objective function is uniformly Lipschitz continuous with parameter $M_f$. However, in many settings the non-differentiable convex function $f(\cdot)$ is not uniformly Lipschitz continuous — for example (i) the classical support vector machine (SVM) problem, (ii) the … Read more

    A Sigmoidal Approximation for Chance-constrained Nonlinear Programs

  • Yankai Cao
  • Victor M. Zavala
    • Categories Stochastic Programming Tags , , ,

    We propose a sigmoidal approximation (SigVaR) for the value-at-risk (VaR) and we use this approximation to tackle nonlinear programming problems (NLPs) with chance constraints. We prove that the approximation is conservative and that the level of conservatism can be made arbitrarily small for limiting parameter values. The SigVar approximation brings computational benefits over exact mixed-integer … Read more

    Electric Power Infrastructure Planning: Mixed-Integer Programming Model and Nested Decomposition Algorithm

  • Ignacio E. Grossmann
  • Dimitri J. Papageorgiou
  • Cristiana L. Lara
  • D. Mallapragada
  • A. Venkatesh
    • Categories (Mixed) Integer Linear Programming, Applications - Science and Engineering Tags , , , , ,

    This paper addresses the long-term planning of electric power infrastructures considering high renewable penetration. To capture the intermittency of these sources, we propose a deterministic multi-scale Mixed-Integer Linear Programming (MILP) formulation that simultaneously considers annual generation investment decisions and hourly operational decisions. We adopt judicious approximations and aggregations to improve its tractability. Moreover, to overcome … Read more