Local monotonicity and full stability for parametric variational systems

  • Boris S. Mordukhovich
  • Tran Nghia
    • Categories Nonsmooth Optimization Tags , ,

    The paper introduces and characterizes new notions of Lipschitzian and H\”olderian full stability of solutions to general parametric variational systems described via partial subdifferential of prox-regular functions acting in finite-dimensional and Hilbert spaces. These notions, postulated certain quantitative properties of single-valued localizations of solution maps, are closely related to local strong maximal monotonicity of associated … Read more

    A Stochastic Programming Approach for Shelter Location and Evacuation Planning

  • Vedat Bayram
  • Hande Yaman
    • Categories Second-Order Cone Programming, Stochastic Programming, Transportation Tags , , , , ,

    Shelter location and traffic allocation decisions are critical for an efficient evacuation plan. In this study, we propose a scenario-based two-stage stochastic evacuation planning model that optimally locates shelter sites and that assigns evacuees to nearest shelters and to shortest paths within a tolerance degree to minimize the expected total evacuation time. Our model considers … Read more

    A new explicit iterative algorithm for solving split variational inclusion and fixed point problem for the infinite family of nonexpansive operators

  • Zhihui Xu
  • Cuijie Zhang
    • Categories Complementarity and Variational Inequalities Tags , , , ,

    In this paper, we introduce a new explicit iterative algorithm for finding a solution of split variational inclusion problem over the common fixed points set of a infinite family of nonexpansive mappings in Hilbert spaces. To reach this goal, the iterative algorithms which combine Tian’s method with some fixed point technically proving methods are utilized … Read more

    Newton-like method with diagonal correction for distributed optimization

  • Dragana Bajović
  • Dušan Jakovetić
  • Nataša Krejić
  • Nataša Krklec Jerinkić
    • Categories Nonlinear Optimization Tags , , ,

    We consider distributed optimization problems where networked nodes cooperatively minimize the sum of their locally known convex costs. A popular class of methods to solve these problems are the distributed gradient methods, which are attractive due to their inexpensive iterations, but have a drawback of slow convergence rates. This motivates the incorporation of second-order information … Read more

    Variational principles, completeness and the existence of traps in behavioral sciences

  • Stephan Cobzas
  • Antoine Soubeyran
  • Bao Q. Truong
    • Categories Applications - Science and Engineering, Nonlinear Optimization

    In this paper, driven by Behavioral applications to human dynamics, we consider the characterization of completeness in pseudo-quasimetric spaces in term of a generalization of Ekeland’s variational principle in such spaces, and provide examples illustrating significant improvements to some previously obtained results, even in complete metric spaces. At the behavioral level, we show that the … Read more

    Remark on multi-target,robust linear-quadratic control problem on semi-infinite interval

  • Leonid Faybusovich
  • Thanasak Mouktonglang
    • Categories Control Applications, Infinite Dimensional Optimization Tags ,

    We consider multi-target,robust linear-quadratic control problem on semi-infinite interval. Using functional-analytic approach developed in [2], we reduce this problem to a convex optimization problem on the simplex. Explicit procedure for the reduced optimization problem is described. Citation Preprint, University of Notre Dame, August,2015 Article Download View Remark on multi-target,robust linear-quadratic control problem on semi-infinite interval

    A State Transition MIP Formulation for the Unit Commitment Problem

  • Semih Atakan
  • Guglielmo Lulli
  • Suvrajeet Sen
    • Categories Applications - OR and Management Sciences Tags ,

    In this paper, we present the state-transition formulation for the unit commitment problem. This formulation is based on the definition of new decision variables, which, instead of indicating the on/off statuses of a generator, captures its state transitions between consecutive time periods. We show that this new approach produces a formulation which naturally includes valid … Read more

    Borwein–Preiss Vector Variational Principle

  • Alexander Y. Kruger
  • Somyot Plubtieng
  • Thidaporn Seangwattana
    • Categories Convex and Nonsmooth Optimization Tags , , ,

    This article extends to the vector setting the results of our previous work Kruger et al. (2015) which refined and slightly strengthened the metric space version of the Borwein–Preiss variational principle due to Li and Shi, J. Math. Anal. Appl. 246(1), 308–319 (2000). We introduce and characterize two seemingly new natural concepts of epsilon-minimality, one … Read more

    A new family of facet defining inequalities for the maximum edge-weighted clique problem

  • Franklin Djeumou Fomeni
    • Categories 0-1 Programming, Combinatorial Optimization, Cutting Plane Approaches Tags , , , , ,

    This paper considers a family of cutting planes, recently developed for mixed 0-1 polynomial programs and shows that they define facets for the maximum edge-weighted clique problem. There exists a polynomial time exact separation algorithm for these in- equalities. The result of this paper may contribute to the development of more efficient algorithms for the … Read more

    Integer Programming Approaches for Appointment Scheduling with Random No-shows and Service Durations

  • Ruiwei Jiang
  • Siqian Shen
  • Yiling Zhang
    • Categories (Mixed) Integer Linear Programming, Scheduling, Stochastic Programming

    We consider a single-server scheduling problem given a fixed sequence of appointment arrivals with random no-shows and service durations. The probability distribution of the uncertain parameters is assumed to be ambiguous and only the support and first moments are known. We formulate a class of distributionally robust (DR) optimization models that incorporate the worst-case expectation/conditional … Read more