Month: December 2021

Joint Routing of Conventional and Range-Extended Electric Vehicles in a Large Metropolitan Network

  • Anirudh Subramanyam
  • Taner Cokyasar
  • Jeffrey Larson
  • Monique Stinson
    • Categories Applications - OR and Management Sciences Tags , , ,

    Range-extended electric vehicles combine the higher efficiency and environmental benefits of battery-powered electric motors with the longer mileage and autonomy of conventional internal combustion engines. This combination is particularly advantageous for time-constrained delivery routing in dense urban areas, where battery recharging along routes can be too time-consuming to economically justify the use of all-electric vehicles. … Read more

    A sequential adaptive regularisation using cubics algorithm for solving nonlinear equality constrained optimization

  • Yonggang Pei
  • Shaofang Song
  • Zhu Detong
    • Categories Constrained Nonlinear Optimization Tags , , ,

    The adaptive regularisation algorithm using cubics (ARC) is initially proposed for unconstrained optimization. ARC has excellent convergence properties and complexity. In this paper, we extend ARC to solve nonlinear equality constrained optimization and propose a sequential adaptive regularisation using cubics algorithm inspired by sequential quadratic programming (SQP) methods. In each iteration of our method, the … Read more

    Targeted Multiobjective Dijkstra Algorithm

  • Pedro Maristany de las Casas
  • Luitgard Kraus
  • Antonio Sedeño-Noda
  • Ralf Borndœrfer
    • Categories Multi-Criteria Optimization, Network Optimization Tags , , , , ,

    In this paper, we introduce the Targeted Multiobjective Dijkstra Algorithm (T-MDA), a label setting algorithm for the One-to-One Multiobjective Shortest Path (MOSP) Problem. The T-MDA is based on the recently published Multiobjective Dijkstra Algorithm (MDA) and equips it with A*-like techniques. The resulting speedup is comparable to the speedup that the original A* algorithm achieves … Read more

    A Stochastic Bregman Primal-Dual Splitting Algorithm for Composite Optimization

  • Antonio Silveti-Falls
  • Cesare Molinari
  • Jalal Fadili
    • Categories Convex and Nonsmooth Optimization, Infinite Dimensional Optimization, Stochastic Programming Tags , , , , , , , ,

    We study a stochastic first order primal-dual method for solving convex-concave saddle point problems over real reflexive Banach spaces using Bregman divergences and relative smoothness assumptions, in which we allow for stochastic error in the computation of gradient terms within the algorithm. We show ergodic convergence in expectation of the Lagrangian optimality gap with a … Read more

    On solving large-scale multistage stochastic problems with a new specialized interior-point approach

  • Jordi Castro
  • Laureano F. Escudero
  • Juan F. Monge
    • Categories Linear, Cone and Semidefinite Programming, Stochastic Programming Tags , , , , ,

    A novel approach based on a specialized interior-point method (IPM) is presented for solving large-scale stochastic multistage continuous optimization problems, which represent the uncertainty in strategic multistage and operational two-stage scenario trees, the latter being rooted at the strategic nodes. This new solution approach considers a split-variable formulation of the strategic and operational structures, for … Read more

    A Globally Convergent Distributed Jacobi Scheme for Block-Structured Nonconvex Constrained Optimization Problems

  • Anirudh Subramanyam
  • Youngdae Kim
  • Michel Schanen
  • François Pacaud
  • Mihai Anitescu
    • Categories Constrained Nonlinear Optimization Tags , ,

    Motivated by the increasing availability of high-performance parallel computing, we design a distributed parallel algorithm for linearly-coupled block-structured nonconvex constrained optimization problems. Our algorithm performs Jacobi-type proximal updates of the augmented Lagrangian function, requiring only local solutions of separable block nonlinear programming (NLP) problems. We provide a cheap and explicitly computable Lyapunov function that allows … Read more

    Hub Network Design Problem with Capacity, Congestion and Stochastic Demand Considerations

  • Vedat Bayram
  • Barış Yıldız
  • M. Saleh Farham
    • Categories Second-Order Cone Programming, Stochastic Programming, Transportation Tags , , , , , , ,

    We introduce the hub network design problem with congestion, capacity, and stochastic demand considerations (HNDC), which generalizes the classical hub location problem in several directions. In particular, we extend state-of-the-art by integrating capacity acquisition decision and congestion cost effect into the problem and allowing dynamic routing for origin-destination pairs. Connecting strategic and operational level decisions, … Read more

    Mean-Covariance Robust Risk Measurement

  • Viet Anh Nguyen
  • Soroosh Shafieezadeh-Abadeh
  • Damir Filipovic
  • Daniel Kuhn
    • Categories Finance and Economics, Robust Optimization, Stochastic Programming Tags ,

    We introduce a universal framework for mean-covariance robust risk measurement and portfolio optimization. We model uncertainty in terms of the Gelbrich distance on the mean-covariance space, along with prior structural information about the population distribution. Our approach is related to the theory of optimal transport and exhibits superior statistical and computational properties than existing models. … Read more

    Risk-Averse Stochastic Optimal Control: an efficiently computable statistical upper bound

  • Vincent Guigues
  • Alexander Shapiro
  • Yi Cheng
    • Categories Stochastic Programming Tags , , , , ,

    In this paper, we discuss an application of the SDDP type algorithm to nested risk-averse formulations of Stochastic Optimal Control (SOC) problems. We propose a construction of a statistical upper bound for the optimal value of risk-averse SOC problems. This outlines an approach to a solution of a long standing problem in that area of … Read more

    New interior-point approach for one- and two-class linear support vector machines using multiple variable splitting

  • Jordi Castro
    • Categories Data-Mining, Linear, Cone and Semidefinite Programming Tags , , , ,

    Multiple variable splitting is a general technique for decomposing problems by using copies of variables and additional linking constraints that equate their values. The resulting large optimization problem can be solved with a specialized interior-point method that exploits the problem structure and computes the Newton direction with a combination of direct and iterative solvers (i.e., … Read more