Convex Optimization

ADMM-based Unit and Time Decomposition for Price Arbitrage by Cooperative Price-Maker Electricity Storage Units

  • Miguel F. Anjos
  • James Cruise
  • Albert Solà Vilalta
    • Categories Convex Optimization, Dynamic Programming, Energy Tags , , , , , , ,

    Decarbonization via the integration of renewables poses significant challenges for electric power systems, but also creates new market opportunities. Electric energy storage can take advantage of these opportunities while providing flexibility to power systems that can help address these challenges. We propose a solution method for the optimal control of multiple price-maker electric energy storage … Read more

    Inertial-relaxed splitting for composite monotone inclusions

  • Ernesto Ore Albornoz
  • Philippe Mahey
  • Eladio Ocaña Anaya
    • Categories Convex Optimization, Nonsmooth Optimization Tags , , , , ,

    In a similar spirit of the extension of the proximal point method developed by Alves et al. \cite{alvegm20}, we propose in this work an Inertial-Relaxed primal-dual splitting method to address the problem of decomposing the minimization of the sum of three convex functions, one of them being smooth, and considering a general coupling subspace. A … Read more

    Complexity of optimizing over the integers

  • Amitabh Basu
    • Categories (Mixed) Integer Nonlinear Programming, Convex Optimization Tags , ,

    In the first part of this paper, we present a unified framework for analyzing the algorithmic complexity of any optimization problem, whether it be continuous or discrete in nature. This helps to formalize notions like “input”, “size” and “complexity” in the context of general mathematical optimization, avoiding context dependent definitions which is one of the … Read more

    Adjustable robust optimization with objective uncertainty

  • Boris Detienne
  • Henri Lefebvre
  • Enrico Malaguti
  • Michele Monaci
    • Categories Applications - OR and Management Sciences, Convex Optimization, Robust Optimization Tags , , , , ,

    In this work, we study optimization problems where some cost parameters are not known at decision time and the decision flow is modeled as a two-stage process within a robust optimization setting. We address general problems in which all constraints (including those linking the first and the second stages) are defined by convex functions and … Read more

    On the Convergence of Projected Alternating Maximization for Equitable and Optimal Transport

  • Minhui Huang
  • Shiqian Ma
  • Lifeng Lai
    • Categories Convex Optimization Tags , , , , , ,

    This paper studies the equitable and optimal transport (EOT) problem, which has many applications such as fair division problems and optimal transport with multiple agents etc. In the discrete distributions case, the EOT problem can be formulated as a linear program (LP). Since this LP is prohibitively large for general LP solvers, Scetbon \etal \cite{scetbon2021equitable} … Read more

    Log-domain interior-point methods for convex quadratic programming

  • Frank Permenter
    • Categories Convex Optimization, Linear Programming, Quadratic Programming Tags , ,

    Applying an interior-point method to the central-path conditions is a widely used approach for solving quadratic programs. Reformulating these conditions in the log-domain is a natural variation on this approach that to our knowledge is previously unstudied. In this paper, we analyze log-domain interior-point methods and prove their polynomial-time convergence. We also prove that they … Read more

    Adaptive Sampling Quasi-Newton Methods for Zeroth-Order Stochastic Optimization

  • Raghu Bollapragada
  • Stefan M. Wild
    • Categories Convex Optimization, Nonlinear Optimization, Stochastic Programming Tags , ,

    We consider unconstrained stochastic optimization problems with no available gradient information. Such problems arise in settings from derivative-free simulation optimization to reinforcement learning. We propose an adaptive sampling quasi-Newton method where we estimate the gradients of a stochastic function using finite differences within a common random number framework. We develop modified versions of a norm … Read more

    A Local MM Subspace Method for Solving Constrained Variational Problems in Image Recovery

  • Émilie Chouzenoux
  • Ségolène Martin
  • Jean-Christophe Pesquet
    • Categories Constrained Nonlinear Optimization, Convex Optimization, Nonsmooth Optimization Tags , , , , , ,

    This article introduces a new Penalized Majorization-Minimization Subspace algorithm (P-MMS) for solving smooth, constrained optimization problems. In short, our approach consists of embedding a subspace algorithm in an inexact exterior penalty procedure. The subspace strategy, combined with a Majoration-Minimization step-size search, takes great advantage of the smoothness of the penalized cost function, while the penalty … Read more

    A simple Introduction to higher order liftings for binary problems

  • Florian Jarre
    • Categories 0-1 Programming, Convex Optimization Tags ,

    \(\) A short, simple, and self-contained proof is presented showing that $n$-th lifting for the max-cut-polytope is exact. The proof re-derives the known observations that the max-cut-polytope is the projection of a higher-dimensional regular simplex and that this simplex coincides with the $n$-th semidefinite lifting. An extension to reduce the dimension of higher order liftings … Read more

    SABRINA: A Stochastic Subspace Majorization-Minimization Algorithm

  • Jean-Baptiste Fest
  • Émilie Chouzenoux
    • Categories Convex Optimization, Nonlinear Optimization Tags , , , , ,

    A wide class of problems involves the minimization of a coercive and differentiable function $F$ on $\mathbb{R}^N$ whose gradient cannot be evaluated in an exact manner. In such context, many existing convergence results from standard gradient-based optimization literature cannot be directly applied and robustness to errors in the gradient is not necessarily guaranteed. This work … Read more