Optimization Software and Modeling Systems

A Classifier to Decide on the Linearization of Mixed-Integer Quadratic Problems in CPLEX

  • Pierre Bonami
  • Andrea Lodi
  • Giulia Zarpellon
    • Categories (Mixed) Integer Linear Programming, Integer Programming, Optimization Software Design Principles Tags , , ,

    We translate the algorithmic question of whether to linearize convex Mixed-Integer Quadratic Programming problems (MIQPs) into a classification task, and use machine learning (ML) techniques to tackle it. We represent MIQPs and the linearization decision by careful target and feature engineering. Computational experiments and evaluation metrics are designed to further incorporate the optimization knowledge in … Read more

    MathOptInterface: a data structure for mathematical optimization problems

  • Joaquim Dias Garcia
  • Oscar Dowson
  • Miles Lubin
  • Benoit Legat
    • Categories Modeling Languages and Systems, Optimization Software Design Principles

    JuMP is an open-source algebraic modeling language in the Julia language. In this work, we discuss a complete re-write of JuMP based on a novel abstract data structure, which we call \textit{MathOptInterface}, for representing instances of mathematical optimization problems. MathOptInterface is significantly more general than existing data structures in the literature, encompassing, for example, a … Read more

    Exploiting problem structure in derivative free optimization

  • Margherita Porcelli
  • Philippe L. Toint
    • Categories Nonlinear Optimization, Optimization Software and Modeling Systems Tags , , ,

    A structured version of derivative-free random pattern search optimization algorithms is introduced which is able to exploit coordinate partially separable structure (typically associated with sparsity) often present in unconstrained and bound-constrained optimization problems. This technique improves performance by orders of magnitude and makes it possible to solve large problems that otherwise are totally intractable by … Read more

    A parallel splitting ALM-based algorithm for separable convex programming

  • Bingsheng He
  • Shengjie Xu
    • Categories Convex and Nonsmooth Optimization, Convex Optimization, Parallel Algorithms Tags , , , ,

    The augmented Lagrangian method (ALM) provides a benchmark for tackling the canonical convex minimization problem with linear constraints. We consider a special case where the objective function is the sum of $m$ individual subfunctions without coupled variables. The recent study reveals that the direct extension of ALM for separable convex programming problems is not necessarily … Read more

    A Distributed Quasi-Newton Algorithm for Primal and Dual Regularized Empirical Risk Minimization

  • Stephen Wright
  • Cong Han Lim
  • Ching-pei Lee
    • Categories Nonsmooth Optimization, Parallel Algorithms Tags , , , , , , ,

    We propose a communication- and computation-efficient distributed optimization algorithm using second-order information for solving empirical risk minimization (ERM) problems with a nonsmooth regularization term. Our algorithm is applicable to both the primal and the dual ERM problem. Current second-order and quasi-Newton methods for this problem either do not work well in the distributed setting or … Read more

    Linear Programming using Limited-Precision Oracles

  • Ambros Gleixner
  • Daniel E. Steffy
    • Categories Linear Programming, Optimization Software and Modeling Systems Tags , , , , , , ,

    Since the elimination algorithm of Fourier and Motzkin, many different methods have been developed for solving linear programs. When analyzing the time complexity of LP algorithms, it is typically either assumed that calculations are performed exactly and bounds are derived on the number of elementary arithmetic operations necessary, or the cost of all arithmetic operations … Read more

    A robust method based on LOVO functions for solving least squares problems

  • E. V. Castelani
  • R. Lopes
  • W. V. I. Shirabayashi
  • Francisco N. C. Sobral
    • Categories Nonlinear Systems and Least-Squares, Nonsmooth Optimization, Parallel Algorithms Tags , , ,

    The robust adjustment of nonlinear models to data is considered in this paper. When data comes from real experiments, it is possible that measurement errors cause the appearance of discrepant values, which should be ignored when adjusting models to them. This work presents a Lower Order-value Optimization (LOVO) version of the Levenberg-Marquardt algorithm, which is … Read more

    A Framework for Mathematical Optimization in Microservice Architectures

  • Andrea Cassioli
  • Stefan Guericke
    • Categories Applications - OR and Management Sciences, Optimization Software and Modeling Systems, Optimization Software Design Principles Tags , ,

    In the last years, the gap between solution methods in literature and optimization running in production has increased. Agile development practices, DevOps and modern cloud-based infrastructure call for a revisit of how optimization software is developed. We review the state-of-the-art, propose a development framework that can be applied across different programming languages and modeling frameworks … Read more

    A Strictly Contractive Peaceman-Rachford Splitting Method for the Doubly Nonnegative Relaxation of the Minimum Cut Problem

  • Xinxin Li
  • Ting Kei Pong
  • Hao Sun
  • Henry Wolkowicz
    • Categories (Mixed) Integer Nonlinear Programming, Linear, Cone and Semidefinite Programming, Optimization Software Benchmark Tags , , , , , ,

    The minimum cut problem, MC, and the special case of the vertex separator problem, consists in partitioning the set of nodes of a graph G into k subsets of given sizes in order to minimize the number of edges cut after removing the k-th set. Previous work on this topic uses eigenvalue, semidefinite programming, SDP, … 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