Month: August 2016

Improving an ADMM-like Splitting Method via Positive-Indefinite Proximal Regularization for Three-Block Separable Convex Minimization

  • Bingsheng He
  • Xiao-Ming Yuan
    • Categories Convex Optimization Tags , , , , ,

    The augmented Lagrangian method (ALM) is fundamental for solving convex minimization models with linear constraints. When the objective function is separable such that it can be represented as the sum of more than one function without coupled variables, various splitting versions of the ALM have been well studied in the literature such as the alternating … Read more

    On a Practical Notion of Geoffrion Proper Optimality in Multicriteria Optimization

  • Kalyanmoy Deb
  • Joydeep Dutta
  • Poonam Kesarwani
  • Pradyumn Kumar Shukla
    • Categories Multi-Criteria Optimization, Nonlinear Optimization

    Geoffrion proper optimality is a widely used optimality notion in multicriteria optimization that prevents exact solutions having unbounded trade-offs. As algorithms for multicriteria optimization usually give only approximate solutions, we analyze the notion of approximate Geoffrion proper optimality. We show that in the limit, approximate Geoffrion proper optimality may converge to solutions having unbounded trade-offs. … Read more

    Optimization Methods for Locating Heteroclinic Orbits

  • Pedro Borges de Melo
    • Categories Applications - Science and Engineering Tags , ,

    Assume we are given a system of ordinary differential equations x 0 = f(x, p) depending on a parameter p ∈ R pe . In this dissertation we consider the problem of locating a parameter p and an initial condition ξ that give rise to a heteroclinic orbit. In the case that such p and … Read more

    Dice-sion Making under Uncertainty: When Can a Random Decision Reduce Risk?

  • Erick Delage
  • Daniel Kuhn
  • Wolfram Wiesemann
    • Categories Robust Optimization, Stochastic Programming

    Stochastic programming and distributionally robust optimization seek deterministic decisions that optimize a risk measure, possibly in view of the most adverse distribution in an ambiguity set. We investigate under which circumstances such deterministic decisions are strictly outperformed by random decisions which depend on a randomization device producing uniformly distributed samples that are independent of all … Read more

    Data-Driven Risk-Averse Stochastic Program And Renewable Energy Integration

  • Chaoyue Zhao
    • Categories Applications - OR and Management Sciences Tags , ,

    With increasing penetration of renewable energy into the power grid and its intermittent nature, it is crucial and challenging for system operators to provide reliable and cost effective daily electricity generation scheduling. In this dissertation, we present our recently developed innovative modeling and solution approaches to address this challenging problem. We start with developing several … Read more

    A simple preprocessing algorithm for semidefinite programming

  • Preston Faulk
  • Gabor Pataki
  • Quoc Tran-Dinh
    • Categories Convex Optimization, Optimization Software Benchmark, Semi-definite Programming

    We propose a very simple preprocessing algorithm for semidefinite programming. Our algorithm inspects the constraints of the problem, deletes redundant rows and columns in the constraints, and reduces the size of the variable matrix. It often detects infeasibility. Our algorithm does not rely on any optimization solver: the only subroutine it needs is Cholesky factorization, … Read more

    Global Optimization in Hilbert Space

  • Benoit Chachuat
  • Boris Houska
    • Categories Global Optimization Theory, Infinite Dimensional Optimization

    This paper proposes a complete-search algorithm for solving a class of non-convex, possibly infinite-dimensional, optimization problems to global optimality. We assume that the optimization variables are in a bounded subset of a Hilbert space, and we determine worst-case run-time bounds for the algorithm under certain regularity conditions of the cost functional and the constraint set. … Read more

    Decomposition-Based Approximation Algorithms for the One-Warehouse Multi-Retailer Problem with Concave Batch Order Costs

  • Maged M. Dessouky
  • Weihong Hu
  • Alejandro Toriello
  • Zhuoting Yu
    • Categories Approximation Algorithms, Production and Logistics, Supply Chain Management Tags , ,

    We study the one-warehouse multi-retailer (OWMR) problem under deterministic dynamic demand and concave batch order costs, where order batches have an identical capacity and the order cost function for each facility is concave within the batch. Under appropriate assumptions on holding cost structure, we obtain lower bounds via a decomposition that splits the two-echelon problem … Read more

    A Benders decomposition based framework for solving cable trench problems

  • Hatice Calik
  • Markus Leitner
  • Martin Luipersbeck
    • Categories Integer Programming, Network Optimization Tags , , ,

    In this work, we present an algorithmic framework based on Benders decomposition for the Capacitated p-Cable Trench Problem with Covering. We show that our approach can be applied to most variants of the Cable Trench Problem (CTP) that have been considered in the literature. The proposed algorithm is augmented with a stabilization procedure to accelerate … Read more

    A new branch-and-bound algorithm for standard quadratic programming problems

  • Giampaolo Liuzzi
  • Marco Locatelli
  • Veronica Piccialli
    • Categories Global Optimization, Quadratic Programming

    In this paper we propose convex and LP bounds for Standard Quadratic Programming (StQP) problems and employ them within a branch-and-bound approach. We first compare different bounding strategies for StQPs in terms both of the quality of the bound and of the computation times. It turns out that the polyhedral bounding strategy is the best … Read more