Branch-and-cut-and-price for the Cardinality-constrained Multi-cycle Problem in Kidney Exchange

  • Edward Lam
  • Vicky Mak-Hau
    • Categories Biomedical Applications, Branch and Cut Algorithms, Graphs and Matroids Tags , , , , , , ,

    The establishment of kidney exchange programs has dramatically improved rates for kidney transplants by matching donors to compatible patients who would otherwise fail to receive a kidney for transplant. Rather than simply swapping kidneys between two patient-donor pairs, having multiple patient-donors pairs simultaneously donate kidneys in a cyclic manner enables all participants to receive a … Read more

    Dual-density-based reweighted $\ell_{1}hBcalgorithms for a class of $\ell_{0}hBcminimization problems

  • Yun-Bin Zhao
    • Categories Convex and Nonsmooth Optimization Tags , , , , ,

    The optimization problem with sparsity arises in many areas of science and engineering such as compressed sensing, image processing, statistical learning and data sparse approximation. In this paper, we study the dual-density-based reweighted $\ell_{1}$-algorithms for a class of $\ell_{0}$-minimization models which can be used to model a wide range of practical problems. This class of … Read more

    Stochastic mesh adaptive direct search for blackbox optimization using probabilistic estimates

  • Charles Audet
  • Kwassi Joseph Dzahini
  • Michael Kokkolaras
  • Sébastien Le Digabel
    • Categories Nonlinear Optimization, Other Topics, Unconstrained Optimization Tags , , , ,

    We present a stochastic extension of the mesh adaptive direct search (MADS) algorithm originally developed for deterministic blackbox optimization. The algorithm, called StoMADS, considers the unconstrained optimization of an objective function f whose values can be computed only through a blackbox corrupted by some random noise following an unknown distribution. The proposed method is based … Read more

    The Outcome Range Problem in Interval Linear Programming

  • Monica Gentili
  • Mohsen Mohammadi
    • Categories Applications - OR and Management Sciences, Linear, Cone and Semidefinite Programming Tags , , , ,

    Quantifying extra functions, herein referred to as outcome functions, over optimal solutions of an optimization problem can provide decision makers with additional information on a system. This bears more importance when the optimization problem is subject to uncertainty in input parameters. In this paper, we consider linear programming problems in which input parameters are described … Read more

    Reformulations for integrated planning of railway traffic and network maintenance

  • Tomas Lidén
  • Hamish Waterer
    • Categories Transportation

    This paper addresses the scheduling problem of coordinating train services and network maintenance windows for a railway system. We present model reformulations, for a mixed integer linear optimization model, which give a mathematically stronger model and substantial improvements in solving performance – as demonstrated with computational experiments on a set of synthetic test instances. As … Read more

    Optimal Crashing of an Activity Network with Disruptions

  • David P. Morton
  • Haoxiang Yang
    • Categories Stochastic Programming Tags , , ,

    In this paper, we consider an optimization problem involving crashing an activity network under a single disruption. A disruption is an event whose magnitude and timing are random. When a disruption occurs the duration of an activity, which has not yet started, can change. We formulate a two-stage stochastic mixed integer program, in which the … Read more

    A sparse semismooth Newton based augmented Lagrangian method for large-scale support vector machines

  • Dunbiao Niu
  • Enbin Song
  • Peipei Tang
  • Qingsong Wang
  • Chengjing Wang
    • Categories Convex and Nonsmooth Optimization, Data-Mining Tags , ,

    Support vector machines (SVMs) are successful modeling and prediction tools with a variety of applications. Previous work has demonstrated the superiority of the SVMs in dealing with the high dimensional, low sample size problems. However, the numerical difficulties of the SVMs will become severe with the increase of the sample size. Although there exist many … Read more

    Dynamic Optimization with Complementarity Constraints: Smoothing for Direct Shooting

  • Lorenz T. Biegler
  • Adrian Caspari
  • Alexander Mitsos
  • Pascal Schäfer
  • Lukas Lüken
  • Adel Mhamdi
  • Yannic Vaupel
    • Categories Complementarity and Variational Inequalities, Nonlinear Optimization, Systems governed by Differential Equations Optimization Tags , , ,

    We consider optimization of differential-algebraic equations (DAEs) with complementarity constraints (CCs) of algebraic state pairs. Formulating the CCs as smoothed nonlinear complementarity problem (NCP) functions leads to a smooth DAE, allowing for the solution in direct shooting. We provide sufficient conditions for well-posedness. Thus, we can prove that with the smoothing parameter going to zero, … Read more

    Objective Selection for Cancer Treatment: An Inverse Optimization Approach

  • Temitayo Ajayi
  • Andrew J. Schaefer
  • Taewoo Lee
    • Categories Biomedical Applications, Convex and Nonsmooth Optimization, Multi-Criteria Optimization Tags , , , , , ,

    In radiation therapy treatment-plan optimization, selecting a set of clinical objectives that are tractable and parsimonious yet effective is a challenging task. In clinical practice, this is typically done by trial and error based on the treatment planner’s subjective assessment, which often makes the planning process inefficient and inconsistent. We develop the objective selection problem … Read more

    Stochastic Dynamic Linear Programming: A Sequential Sampling Algorithm for Multistage Stochastic Linear Programming

  • Harsha Gangammanavar
  • Suvrajeet Sen
    • Categories Stochastic Programming Tags , , ,

    Multistage stochastic programming deals with operational and planning problems that involve a sequence of decisions over time while responding to realizations that are uncertain. Algorithms designed to address multistage stochastic linear programming (MSLP) problems often rely upon scenario trees to represent the underlying stochastic process. When this process exhibits stagewise independence, sampling-based techniques, particularly the … Read more