Wolfram Wiesemann

An MILP-Based Solution Scheme for Factored and Robust Factored Markov Decision Processes

  • Huikang Liu
  • Wolfram Wiesemann
  • Man-Chung Yue
    • Categories (Mixed) Integer Linear Programming, Robust Optimization Tags ,

    Factored Markov decision processes (MDPs) are a prominent paradigm within the artificial intelligence community for modeling and solving large-scale MDPs whose rewards and dynamics decompose into smaller, loosely interacting components. Through the use of dynamic Bayesian networks and context-specific independence, factored MDPs can achieve an exponential reduction in the state space of an MDP and … Read more

    It’s All in the Mix: Wasserstein Machine Learning with Mixed Features

  • Mohammad Reza Belbasi
  • Aras Selvi
  • Wolfram Wiesemann
    • Categories Optimization in Data Science, Robust Optimization Tags , , ,

    Citation Belbasi R., Selvi A., Wiesemann W. (December 2023) It’s all in the mix: Wasserstein machine learning with mixed features. Preprint. Article Download View It's All in the Mix: Wasserstein Machine Learning with Mixed Features

    Differential Privacy via Distributionally Robust Optimization

  • Aras Selvi
  • Huikang Liu
  • Wolfram Wiesemann
    • Categories Robust Optimization, Stochastic Programming Tags , ,

    In recent years, differential privacy has emerged as the de facto standard for sharing statistics of datasets while limiting the disclosure of private information about the involved individuals. This is achieved by randomly perturbing the statistics to be published, which in turn leads to a privacy-accuracy trade-off: larger perturbations provide stronger privacy guarantees, but they … Read more

    Multi-Stage Robust Mixed-Integer Programming

  • Krzysztof Postek
  • Ward Romeijnders
  • Wolfram Wiesemann
    • Categories (Mixed) Integer Linear Programming, Dynamic Programming, Robust Optimization Tags , ,

    Multi-stage robust optimization, in which decisions are taken sequentially as new information becomes available about the uncertain problem parameters, is a very versatile yet computationally challenging paradigm for decision-making under uncertainty. In this paper, we propose a new model and solution approach for multi-stage robust mixed-integer programs, which may contain both continuous and discrete decisions … Read more

    On Approximations of Data-Driven Chance Constrained Programs over Wasserstein Balls

  • Zhi Chen
  • Daniel Kuhn
  • Wolfram Wiesemann
    • Categories Robust Optimization Tags , , , ,

    Distributionally robust chance constrained programs minimize a deterministic cost function subject to the satisfaction of one or more safety conditions with high probability, given that the probability distribution of the uncertain problem parameters affecting the safety condition(s) is only known to belong to some ambiguity set. We study two popular approximation schemes for distributionally robust … Read more

    Robust Phi-Divergence MDPs

  • Chin Pang Ho
  • Marek Petrik
  • Wolfram Wiesemann
    • Categories Dynamic Programming, Robust Optimization Tags , ,

    In recent years, robust Markov decision processes (MDPs) have emerged as a prominent modeling framework for dynamic decision problems affected by uncertainty. In contrast to classical MDPs, which only account for stochasticity by modeling the dynamics through a stochastic process with a known transition kernel, robust MDPs additionally account for ambiguity by optimizing in view … Read more

    Wasserstein Logistic Regression with Mixed Features

  • Aras Selvi
  • Mohammad Reza Belbasi
  • Martin Haugh
  • Wolfram Wiesemann
    • Categories Convex Optimization, Linear, Cone and Semidefinite Programming, Robust Optimization Tags , ,

    Recent work has leveraged the popular distributionally robust optimization paradigm to combat overfitting in classical logistic regression. While the resulting classification scheme displays a promising performance in numerical experiments, it is inherently limited to numerical features. In this paper, we show that distributionally robust logistic regression with mixed (i.e., numerical and categorical) features, despite amounting … Read more

    A Unifying Framework for the Capacitated Vehicle Routing Problem under Risk and Ambiguity

  • Shubhechyya Ghosal
  • Chin Pang Ho
  • Wolfram Wiesemann
    • Categories Applications - OR and Management Sciences, Robust Optimization, Stochastic Programming Tags , ,

    We propose a generic model for the capacitated vehicle routing problem (CVRP) under demand uncertainty. By combining risk measures, satisficing measures or disutility functions with complete or partial characterizations of the probability distribution governing the demands, our formulation bridges the popular but often independently studied paradigms of stochastic programming and distributionally robust optimization. We characterize … Read more

    On the Optimality of Affine Decision Rules in Robust and Distributionally Robust Optimization

  • Angelos Georghiou
  • Angelos Tsoukalas
  • Wolfram Wiesemann
    • Categories Robust Optimization Tags ,

    We propose tight conditions under which two-stage robust and distributionally robust optimization problems are optimally solved in affine decision rules. Contrary to previous work, our conditions do not impose any structure on the support of the uncertain problem parameters, and they ensure point-wise (as opposed to worst-case) optimality of affine decision rules. The absence of … Read more

    Mathematical Foundations of Robust and Distributionally Robust Optimization

  • Daniel Kuhn
  • Wolfram Wiesemann
  • Jianzhe Zhen
    • Categories Robust Optimization Tags , ,

    Robust and distributionally robust optimization are modeling paradigms for decision-making under uncertainty where the uncertain parameters are only known to reside in an uncertainty set or are governed by any probability distribution from within an ambiguity set, respectively, and a decision is sought that minimizes a cost function under the most adverse outcome of the … Read more