Tight-and-cheap conic relaxation for the AC optimal power flow problem

  • Miguel F. Anjos
  • Christian Bingane
  • Sébastien Le Digabel
    • Categories Applications - Science and Engineering, Nonlinear Optimization, Semi-definite Programming Tags , , ,

    The classical alternating current optimal power flow problem is highly nonconvex and generally hard to solve. Convex relaxations, in particular semidefinite, second-order cone, convex quadratic, and linear relaxations, have recently attracted significant interest. The semidefinite relaxation is the strongest among them and is exact for many cases. However, the computational efficiency for solving large-scale semidefinite … Read more

    Iterative weighted thresholding method for sparse solution of underdetermined linear equations

  • Jianli Chen
  • Peng Zheng
  • Wenxing Zhu
  • Zilin Huang
    • Categories Combinatorial Optimization Tags , ,

    Recently, iterative reweighted methods have attracted much interest in compressed sensing, since they perform better than unweighted ones in most cases. Currently, weights are chosen heuristically in existing iterative reweighted methods, and nding an optimal weight is an open problem since we do not know the exact support set beforehand. In this paper, we present … Read more

    A forward-backward penalty scheme with inertial effects for montone inclusions. Applications to convex bilevel programming

  • Radu Ioan Bot
  • Dang-Khoa Nguyen
    • Categories Convex Optimization Tags , , ,

    We investigate forward-backward splitting algorithm of penalty type with inertial effects for finding the zeros of the sum of a maximally monotone operator and a cocoercive one and the convex normal cone to the set of zeroes of an another cocoercive operator. Weak ergodic convergence is obtained for the iterates, provided that a condition express … Read more

    Inexact cuts in Deterministic and Stochastic Dual Dynamic Programming applied to linear optimization problems

  • Vincent Guigues
    • Categories Stochastic Programming

    We introduce an extension of Dual Dynamic Programming (DDP) to solve linear dynamic programming equations. We call this extension IDDP-LP which applies to situations where some or all primal and dual subproblems to be solved along the iterations of the method are solved with a bounded error (inexactly). We provide convergence theorems both in the … Read more

    Convergence Analysis of Sample Average Approximation of Two-stage Stochastic Generalized Equations

  • Xiaojun Chen
  • Alexander Shapiro
  • Hailin Sun
    • Categories Stochastic Programming Tags , , , ,

    A solution of two-stage stochastic generalized equations is a pair: a first stage solution which is independent of realization of the random data and a second stage solution which is a function of random variables. This paper studies convergence of the sample average approximation of two-stage stochastic nonlinear generalized equations. In particular an exponential rate … Read more

    Convergence rates of proximal gradient methods via the convex conjugate

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

    We give a novel proof of the $O(1/k)$ and $O(1/k^2)$ convergence rates of the proximal gradient and accelerated proximal gradient methods for composite convex minimization. The crux of the new proof is an upper bound constructed via the convex conjugate of the objective function. CitationTechnical Report, Carnegie Mellon University, January 2018.ArticleDownload View PDF

    Large neighbourhood Benders’ search

  • Stephen J. Maher
    • Categories (Mixed) Integer Linear Programming, Stochastic Programming Tags , , ,

    A general enhancement of the Benders’ decomposition algorithm can be achieved through the improved use of large neighbourhood search heuristics within mixed-integer programming solvers. While mixed-integer programming solvers are endowed with an array of large neighbourhood search heuristics, their use is typically limited to finding solutions to the Benders’ decomposition master problem, which may be … Read more

    Bounding and Counting Linear Regions of Deep Neural Networks

  • Srikumar Ramalingam
  • Thiago Serra
  • Christian Tjandraatmadja
    • Categories (Mixed) Integer Linear Programming, Statistics Tags , , , ,

    We investigate the complexity of deep neural networks (DNN) that represent piecewise linear (PWL) functions. In particular, we study the number of linear regions, i.e. pieces, that a PWL function represented by a DNN can attain, both theoretically and empirically. We present (i) tighter upper and lower bounds for the maximum number of linear regions … Read more

    New Constraint Qualifications with Second-Order Properties in Nonlinear Optimization

  • Gabriel Haeser
  • Alberto Ramos
    • Categories Nonlinear Optimization Tags , ,

    In this paper we present and discuss new constraint qualifications to ensure the validity of well known second-order properties in nonlinear optimization. Here, we discuss conditions related to the so-called basic second-order condition, where a new notion of polar pairing is introduced in order to replace the polar operation, useful in the first-order case. We … Read more

    Bicriteria Approximation of Chance Constrained Covering Problems

  • Shabbir Ahmed
  • Weijun Xie
    • Categories Combinatorial Optimization, Robust Optimization, Stochastic Programming

    A chance constrained optimization problem involves constraints with random data which can be violated with probability bounded above by a prespecified small risk parameter. Such constraints are used to model reliability requirements in a variety of application areas such as finance, energy, service and manufacturing. Except under very special conditions, chance constrained problems are extremely … Read more