Robust Optimization

Combinatorial Robust Optimization with Decision-Dependent Information Discovery and Polyhedral Uncertainty

  • Jérémy Omer
  • Michael Poss
    • Categories Combinatorial Optimization, Robust Optimization Tags , , ,

    Given a nominal combinatorial optimization problem, we consider a robust two-stages variant with polyhedral cost uncertainty, called Decision-Dependent Information Discovery (DDID). In the first stage, DDID selects a subset of uncertain cost coefficients to be observed, and in the second-stage, DDID selects a solution to the nominal problem, where the remaining cost coefficients are still … Read more

    The Blessing of Strategic Customers in Personalized Pricing

  • Zhi Chen
  • Bradley Sturt
  • Weijun Xie
    • Categories Infinite Dimensional Optimization, Robust Optimization, Stochastic Programming

    We consider a feature-based personalized pricing problem in which the buyer is strategic: given the seller’s pricing policy, the buyer can augment the features that they reveal to the seller to obtain a low price for the product. We model the seller’s pricing problem as a stochastic program over an infinite-dimensional space of pricing policies … Read more

    On the Trade-Off Between Distributional Belief and Ambiguity: Conservatism, Finite-Sample Guarantees, and Asymptotic Properties

  • Man Yiu Tsang
  • Karmel S. Shehadeh
    • Categories Robust Optimization, Stochastic Programming Tags , , , ,

    We propose and analyze a new data-driven trade-off (TRO) approach for modeling uncertainty that serves as a middle ground between the optimistic approach, which adopts a distributional belief, and the pessimistic distributionally robust optimization approach, which hedges against distributional ambiguity. We equip the TRO model with a TRO ambiguity set characterized by a size parameter … Read more

    An exact method for a class of robust nonlinear optimization problems

  • Danique de Moor
  • Dick den Hertog
  • Jianzhe Zhen
  • Dimitris Bertsimas
  • Thodoris Koukouvinos
    • Categories Nonlinear Optimization, Robust Optimization Tags , , ,

    We introduce a novel exact approach for addressing a broad spectrum of optimization problems with robust nonlinear constraints. These constraints are defined as sums of products of linear times concave (SLC) functions with respect to the uncertain parameters. Our approach synergizes a cutting set method with reformulation-perspectification techniques and branch and bound. We further extend … Read more

    Integer Programming Approaches for Distributionally Robust Chance Constraints with Adjustable Risks

  • Yiling Zhang
    • Categories Integer Programming, Robust Optimization, Stochastic Programming

    We study distributionally robust chance-constrained programs (DRCCPs) with individual chance constraints under a Wasserstein ambiguity. The DRCCPs treat the risk tolerances associated with the distributionally robust chance constraints (DRCCs) as decision variables to trade off between the system cost and risk of violations by penalizing the risk tolerances in the objective function. The introduction of … Read more

    Distributionally and Adversarially Robust Logistic Regression via Intersecting Wasserstein Balls

  • Aras Selvi
    • Categories Data Science Theory, Robust Optimization

    Adversarially robust optimization (ARO) has emerged as the *de facto* standard for training models that hedge against adversarial attacks in the test stage. While these models are robust against adversarial attacks, they tend to suffer severely from overfitting. To address this issue, some successful methods replace the empirical distribution in the training stage with alternatives … Read more

    Wasserstein Distributionally Robust Optimization with Heterogeneous Data Sources

  • Adrian Esteban Perez
  • Yves Rychener
  • Daniel Kuhn
  • Juan M. Morales
    • Categories Robust Optimization Tags , ,

    We study decision problems under uncertainty, where the decision-maker has access to K data sources that carry biased information about the underlying risk factors. The biases are measured by the mismatch between the risk factor distribution and the K data-generating distributions with respect to an optimal transport (OT) distance. In this situation the decision-maker can … Read more

    Toll Setting with Robust Wardrop Equilibrium Conditions Under Budgeted Uncertainty

  • Yasmine Beck
  • Martine Labbé
  • Martin Schmidt
    • Categories (Mixed) Integer Nonlinear Programming, Robust Optimization, Transportation Tags , , , ,

    We consider two variants of the toll-setting problem in which a traffic authority uses tolls either to maximize revenue or to alleviate bottlenecks in the traffic network. The users of the network are assumed to act according to Wardrop’s user equilibrium so that the overall toll-setting problems are modeled as mathematical problems with equilibrium constraints. … Read more

    k-Submodular Interdiction Problems under Distributional Risk-Receptiveness and Robustness: Application to Machine Learning

  • Seonghun Park
  • Manish Bansal
    • Categories Game Theory, Robust Optimization, Stochastic Programming

    We study submodular optimization in adversarial context, applicable to machine learning problems such as feature selection using data susceptible to uncertainties and attacks. We focus on Stackelberg games between an attacker (or interdictor) and a defender where the attacker aims to minimize the defender’s objective of maximizing a $k$-submodular function. We allow uncertainties arising from … Read more

    Robustness Analysis for Adaptive Optimization With Application to Industrial Decarbonization in the Netherlands

  • Justin Starreveld
  • Dick den Hertog
  • Jaron Davelaar
  • gregorbrandt
  • Nort Thijssen
    • Categories Applications - OR and Management Sciences, Energy, Robust Optimization Tags , , ,

    Robustness analysis assesses the performance of a particular solution under variation in the input data. This is distinct from sensitivity analysis, which assesses how variation in the input data changes a model’s optimal solution. For risk assessment purposes, robustness analysis has more practical value than sensitivity analysis. This is because sensitivity analysis, when applied to … Read more