distributionally robust optimization

On the Out-of-Sample Performance of Stochastic Dynamic Programming and Model Predictive Control

  • Dominic S. T. Keehan
  • Andrew B. Philpott
  • Edward Anderson
    • Categories Dynamic Programming, Stochastic Programming Tags , , ,

    Sample average approximation-based stochastic dynamic programming (SDP) and model predictive control (MPC) are two different methods for approaching multistage stochastic optimization. In this paper we investigate the conditions under which SDP may be outperformed by MPC. We show that, depending on the presence of concavity or convexity, MPC can be interpreted as solving a mean-constrained … Read more

    Distributionally robust optimization through the lens of submodularity

  • Karthik Natarajan
  • Divya Padmanabhan
  • Arjun Ramachandra
    • Categories Convex Optimization, Linear Programming, Linear, Cone and Semidefinite Programming Tags , , , ,

    Distributionally robust optimization is used to solve decision making problems under adversarial uncertainty where the distribution of the uncertainty is itself ambiguous. In this paper, we identify a class of these instances that is solvable in polynomial time by viewing it through the lens of submodularity. We show that the sharpest upper bound on the … Read more

    Distributionally Risk-Receptive and Robust Multistage Stochastic Integer Programs and Interdiction Models

  • Sumin Kang
  • Manish Bansal
    • Categories (Mixed) Integer Nonlinear Programming, Robust Optimization, Stochastic Programming Tags , , , , , ,

    In this paper, we study distributionally risk-receptive and distributionally robust (or risk-averse) multistage stochastic mixed-integer programs (denoted by DRR- and DRO-MSIPs). We present cutting plane-based and reformulation-based approaches for solving DRR- and DRO-MSIPs without and with decision-dependent uncertainty to optimality. We show that these approaches are finitely convergent with probability one. Furthermore, we introduce generalizations … Read more

    End-to-End Learning for Stochastic Optimization: A Bayesian Perspective

  • Yves Rychener
  • Daniel Kuhn
  • Tobias Sutter
    • Categories Stochastic Programming Tags , , ,

    We develop a principled approach to end-to-end learning in stochastic optimization. First, we show that the standard end-to-end learning algorithm admits a Bayesian interpretation and trains a posterior Bayes action map. Building on the insights of this analysis, we then propose new end-to-end learning algorithms for training decision maps that output solutions of empirical risk … Read more

    Maximum Likelihood Probability Measures over Sets and Applications to Data-Driven Optimization

  • Juan Borrero
  • Denis Saure
    • Categories Applications - OR and Management Sciences, Optimization in Data Science, Robust Optimization Tags , ,

    Motivated by data-driven approaches to sequential decision-making under uncertainty, we study maximum likelihood estimation of a distribution over a general measurable space when, unlike traditional setups, realizations of the underlying uncertainty are not directly observable but instead are known to lie within observable sets. While extant work studied the special cases when the observed sets … Read more

    Optimized Dimensionality Reduction for Moment-based Distributionally Robust Optimization

  • ShiyiJiang
  • Jianqiang Cheng
  • Kai Pan
  • Z.J. Max Shen
    • Categories Optimization in Data Science, Robust Optimization, Semi-definite Programming Tags , , , ,

    Moment-based distributionally robust optimization (DRO) provides an optimization framework to integrate statistical information with traditional optimization approaches. Under this framework, one assumes that the underlying joint distribution of random parameters runs in a distributional ambiguity set constructed by moment information and makes decisions against the worst-case distribution within the set. Although most moment-based DRO problems … Read more

    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

    A Fully Adaptive DRO Multistage Framework Based on MDR for Generation Scheduling under Uncertainty

  • Yingyang
  • Linfeng Yang
  • Zhaoyang Dong
    • Categories Applications - OR and Management Sciences Tags , , , , ,

    The growing proliferation of wind power into the power grid achieves a low-cost sustainable electricity supply while introducing technical challenges with associated intermittency. This paper proposes a fully adaptive distributionally robust multistage framework based on mixed decision rules (MDR) for generation scheduling under uncertainty to adapt wind power respecting non-anticipativity in quick-start unit status decision … Read more

    Data-Driven Stochastic Dual Dynamic Programming: Performance Guarantees and Regularization Schemes

  • Hyuk Park
  • Zhuangzhuang Jia
  • Grani A. Hanasusanto
    • Categories Stochastic Programming Tags , , , ,

    We propose a data-driven scheme for multistage stochastic linear programming with Markovian random parameters by extending the stochastic dual dynamic programming (SDDP) algorithm. In our data-driven setting, only a finite number of historical trajectories are available. The proposed SDDP scheme evaluates the cost-to-go functions only at the observed sample points, where the conditional expectations are … Read more