Deciding Feasibility of a Booking in the European Gas Market on a Cycle is in P for the Case of Passive Networks

  • Martine Labbé
  • Martin Schmidt
  • Johannes Thürauf
  • Fränk Plein
    • Categories Applications - OR and Management Sciences, Network Optimization Tags , , , , ,

    We show that the feasibility of a booking in the European entry-exit gas market can be decided in polynomial time on single-cycle networks that are passive, i.e., do not contain controllable elements. The feasibility of a booking can be characterized by solving polynomially many nonlinear potential-based flow models for computing so-called potential-difference maximizing load flow … Read more

    Optimal time-and-level-of-use price setting for an energy retailer

  • Miguel F. Anjos
  • Luce Brotcorne
  • Juan A. Gomez-Herrera
    • Categories (Mixed) Integer Linear Programming, Energy, Game Theory Tags , , , , ,

    This paper presents a novel price setting optimization problem for an energy retailer in the smart grid. In this framework the retailer buys energy from multiple generators via bilateral contracts, and sells it to a population of smart homes using Time-and-Level-of-Use prices (TLOU). TLOU is an energy price structure recently introduced in the literature, where … Read more

    Online matrix factorization for Markovian data and applications to Network Dictionary Learning

  • Laura Balzano
  • Hanbaek Lyu
  • Deanna Needell
    • Categories Data-Mining, Nonlinear Optimization Tags , , , ,

    Online Matrix Factorization (OMF) is a fundamental tool for dictionary learning problems, giving an approximate representation of complex data sets in terms of a reduced number of extracted features. Convergence guarantees for most of the OMF algorithms in the literature assume independence between data matrices, and the case of a dependent data stream remains largely … Read more

    The Convex Hull Heuristic for Nonlinear 0-1 Programming Problems with Linear Constraints

  • Aykut Ahlatçıoğlu
  • Monique Guignard
    • Categories Applications - OR and Management Sciences Tags , , , ,

    The Convex Hull Heuristic (CHH) is a heuristic for mixed-integer programming problems with a nonlinear objective function and linear constraints. It is a matheuristic in two ways: it is based on the mathematical programming algorithm called simplicial decomposition, or SD, and at each iteration, one solves a mixed-integer programming problem with a linear objective function … Read more

    Dynamic Portfolio Selection with Linear Control Policies for Coherent Risk Minimization

  • Jun-ya Gotoh
  • Yuichi Takano
    • Categories Finance and Economics, Robust Optimization Tags , , ,

    This paper is concerned with a linear control policy for dynamic portfolio selection. We develop this policy by incorporating time-series behaviors of asset returns on the basis of coherent risk minimization. Analyzing the dual form of our optimization model, we demonstrate that the investment performance of linear control policies is directly connected to the intertemporal … Read more

    Transmission Switching Under Wind Uncertainty Using Linear Decision Rules

  • Grani A. Hanasusanto
  • Yuqi Zhou
  • Hao Zhu
    • Categories Applications - Science and Engineering Tags , , ,

    Increasing penetration of wind and renewable generation poses significant challenges to the power system operations and reliability. This paper considers the real-time optimal transmission switching (OTS) problem for determining the generation dispatch and network topology that can account for uncertain energy resources. To efficiently solve the resultant two-stage stochastic program, we propose a tractable linear … Read more

    Bridging Bayesian and Minimax Mean Square Error Estimation via Wasserstein Distributionally Robust Optimization

  • Daniel Kuhn
  • Peyman Mohajerin Esfahani
  • Viet Anh Nguyen
  • Soroosh Shafieezadeh-Abadeh
    • Categories Infinite Dimensional Optimization, Statistics, Stochastic Programming Tags , ,

    We introduce a distributionally robust minimium mean square error estimation model with a Wasserstein ambiguity set to recover an unknown signal from a noisy observation. The proposed model can be viewed as a zero-sum game between a statistician choosing an estimator—that is, a measurable function of the observation—and a fictitious adversary choosing a prior—that is, … Read more

    Convergence Analysis and a DC Approximation Method for Data-driven Mathematical Programs with Distributionally Robust Chance Constraints

  • Hailin Sun
  • Chen Yannan
  • Dali Zhang
    • Categories Robust Optimization, Stochastic Programming Tags , , , ,

    In this paper, we consider the convergence analysis of data-driven mathematical programs with distributionally robust chance constraints (MPDRCC) under weaker conditions without continuity assumption of distributionally robust probability functions. Moreover, combining with the data-driven approximation, we propose a DC approximation method to MPDRCC without some special tractable structures. We also give the convergence analysis of … Read more

    A Framework for Mathematical Optimization in Microservice Architectures

  • Andrea Cassioli
  • Stefan Guericke
    • Categories Applications - OR and Management Sciences, Optimization Software and Modeling Systems, Optimization Software Design Principles Tags , ,

    In the last years, the gap between solution methods in literature and optimization running in production has increased. Agile development practices, DevOps and modern cloud-based infrastructure call for a revisit of how optimization software is developed. We review the state-of-the-art, propose a development framework that can be applied across different programming languages and modeling frameworks … Read more

    Supermodularity in Two-Stage Distributionally Robust Optimization

  • Daniel Zhuoyu Long
  • Jin Qi
  • Aiqi Zhang
    • Categories Applications - OR and Management Sciences, Linear Programming, Robust Optimization

    In this paper, we solve a class of two-stage distributionally robust optimization problems which have the property of supermodularity. We exploit the explicit upper bounds on the expectation of supermodular functions and derive the worst-case distribution for the robust counterpart. This enables us to develop an efficient method to derive an exact optimal solution of … Read more