distributionally robust optimization (DRO)

Data-driven distributionally robust optimization: Intersecting ambiguity sets, performance analysis and tractability

  • Neda Tanoumand
  • Merve Bodur
  • Joe Naoum-Sawaya
    • Categories Robust Optimization Tags , , ,

    We consider stochastic programs in which the probability distribution of uncertain parameters is unknown and partial information about it can only be captured from limited data. We use distributionally robust optimization (DRO) to model such problems. As opposed to the commonly used approach for DRO problems that suggests creating an ambiguity set by following a specific … Read more

    Algorithms for Cameras View-Frame Placement Problems in the Presence of an Adversary and Distributional Ambiguity

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

    In this paper, we introduce cameras view-frame placement problem (denoted by CFP) in the presence an adversary whose objective is to minimize the maximum coverage by p cameras in response to input provided by n autonomous agents in a remote location. We allow uncertainty in the success of attacks, incomplete information of the probability distribution … Read more

    Decision-making with Side Information: A Causal Transport Robust Approach

  • Rui Gao
    • Categories Optimization in Data Science, Robust Optimization, Stochastic Programming Tags , , ,

    We consider stochastic optimization with side information where, prior to decision making, covariate data are available to inform better decisions. In particular, we propose to consider a distributionally robust formulation based on causal transport distance. Compared with divergence and Wasserstein metric, the causal transport distance is better at capturing the information structure revealed from the conditional distribution … Read more

    Data-driven Multistage Distributionally Robust Optimization with Nested Distance

  • Rui Gao
    • Categories Dynamic Programming, Robust Optimization, Stochastic Programming Tags , , ,

    We study multistage distributionally robust linear optimization, where the uncertainty set is a ball of distributions defined through the nested distance (Pflug and Pichler 2012) centered at a scenario tree. This choice of uncertainty set, as opposed to alternatives like the Wasserstein distance between stochastic processes, takes account of information evolution, making it hedge against … Read more