machine learning

Data-driven Prediction of Relevant Scenarios for Robust Combinatorial Optimization

  • Marc Goerigk
  • Jannis Kurtz
    • Categories Optimization in Data Science, Robust Optimization Tags , , ,

    We study iterative methods for (two-stage) robust combinatorial optimization problems with discrete uncertainty. We propose a machine-learning-based heuristic to determine starting scenarios that provide strong lower bounds. To this end, we design dimension-independent features and train a Random Forest Classifier on small-dimensional instances. Experiments show that our method improves the solution process for larger instances … Read more

    Learning to Accelerate Partitioning Algorithms for the Global Optimization of Nonconvex Quadratically-Constrained Quadratic Programs

  • Rohit Kannan
  • Harsha Nagarajan
  • Deepjyoti Deka
    • Categories Global Optimization Tags , , , , , ,

    We learn optimal instance-specific heuristics to accelerate partitioning algorithms for solving nonconvex quadratically-constrained quadratic programs (QCQPs) to global optimality. Specifically, we propose the novel problem of strong partitioning to optimally partition the domains of variables participating in nonconvex terms within a QCQP without sacrificing guarantees of global optimality. We then design a local optimization method … Read more

    A mixed-integer exponential cone programming formulation for feature subset selection in logistic regression

  • Sahand Asgharieh Ahari
  • Burak Kocuk
    • Categories Optimization in Data Science Tags , , ,

    Logistic regression is one of the widely-used classification tools to construct prediction models. For datasets with a large number of features, feature subset selection methods are considered to obtain accurate and interpretable prediction models, in which irrelevant and redundant features are removed. In this paper, we address the problem of feature subset selection in logistic … Read more

    Deep learning and hyperparameter optimization for assessing one’s eligibility for a subcutaneous implantable cardioverter-defibrillator

  • Alain Zemkoho
    • Categories Applications - OR and Management Sciences, Applications - Science and Engineering, Optimization in Data Science Tags , , ,

    In cardiology, it is standard for patients suffering from ventricular arrhythmias (the leading cause of sudden cardiac death) belonging to high risk populations to be treated using Subcutaneous Implantable Cardioverter-Defibrillators (S-ICDs). S-ICDs carry a risk of so-called T Wave Over Sensing (TWOS), which can lead to inappropriate shocks with an inherent health risk. For this … Read more

    Machine Learning for K-adaptability in Two-stage Robust Optimization

  • Esther Julien
  • Krzysztof Postek
  • Ş. İlker Birbil
    • Categories Robust Optimization Tags , , , , ,

    Two-stage robust optimization problems constitute one of the hardest optimization problem classes.One of the solution approaches to this class of problems is K-adaptability. This approach simultaneously seeks the best partitioning of the uncertainty set of scenarios into K subsets, and optimizesdecisions corresponding to each of these subsets. In general case, it is solved using the … Read more

    Robust Two-Stage Optimization with Covariate Data

  • Bart Paul Gerard Van Parys
  • Amine Bennouna
    • Categories Convex Optimization, Robust Optimization, Stochastic Programming Tags , ,

    We consider a generalization of two-stage decision problems in which the second-stage decision may be a function of a predictive signal but cannot adapt fully to the realized uncertainty. We will show how such problems can be learned from sample data by considering a family of regularized sample average formulations. Furthermore, our regularized data-driven formulations … Read more

    A Machine Learning Approach to Solving Large Bilevel and Stochastic Programs: Application to Cycling Network Design

  • Bo Lin
    • Categories Integer Programming, Stochastic Programming, Transportation Tags , , , ,

    We present a novel machine learning-based approach to solving bilevel programs that involve a large number of independent followers, which as a special case include two-stage stochastic programming. We propose an optimization model that explicitly considers a sampled subset of followers and exploits a machine learning model to estimate the objective values of unsampled followers. … Read more

    Revisiting local branching with a machine learning lens

  • Defeng Liu
  • Matteo Fischetti
  • Andrea Lodi
    • Categories (Mixed) Integer Linear Programming, 0-1 Programming, Integer Programming Tags , ,

    Finding high-quality solutions to mixed-integer linear programming problems (MILPs) is of great importance for many practical applications. In this respect, the refinement heuristic local branching (LB) has been proposed to produce improving solutions and has been highly influential for the development of local search methods in MILP. The algorithm iteratively explores a sequence of solution … Read more

    Learning to Use Local Cuts

  • Timo Berthold
  • Matteo Francobaldi
  • Gregor Hendel
    • Categories (Mixed) Integer Linear Programming, Optimization Software Design Principles Tags , ,

    An essential component in modern solvers for mixed-integer (linear) programs (MIPs) is the separation of additional inequalities (cutting planes) to tighten the linear programming relaxation. Various algorithmic decisions are necessary when integrating cutting plane methods into a branch-and-bound (B&B) solver as there is always the trade-off between the efficiency of the cuts and their costs, … Read more