Month: October 2016

Complete mixed integer linear programming formulations for modularity density based clustering

  • Alberto Costa
  • Lin Xuan Foo
  • Tsan Sheng Ng
    • Categories (Mixed) Integer Linear Programming, Network Optimization, Nonlinear Optimization

    Modularity density maximization is a clustering method that improves some issues of the commonly-used modularity maximization approach. Recently, some Mixed-Integer Linear Programming (MILP) reformulations have been proposed in the literature for the modularity density maximization problem, but they require as input the solution of a set of auxiliary binary Non-Linear Programs (NLPs). These can become … Read more

    Branch-and-bound for biobjective mixed-integer linear programming

  • Nathan Adelgren
  • Akshay Gupte
    • Categories (Mixed) Integer Linear Programming, Branch and Cut Algorithms, Multi-Criteria Optimization Tags , , , ,

    We present a generic branch-and-bound algorithm for finding all the Pareto solutions of a biobjective mixed-integer linear program. The main contributions are new algorithms for obtaining dual bounds at a node, checking node fathoming, presolve, and duality gap measurement. Our branch-and-bound is predominantly a decision space search method because the branching is performed on the … Read more

    Relatively-Smooth Convex Optimization by First-Order Methods, and Applications

  • Robert M. Freund
  • Haihao Lu
  • Yurii Nesterov
    • Categories Convex and Nonsmooth Optimization, Convex Optimization Tags , , , , ,

    The usual approach to developing and analyzing first-order methods for smooth convex optimization assumes that the gradient of the objective function is uniformly smooth with some Lipschitz constant L. However, in many settings the differentiable convex function f(.) is not uniformly smooth — for example in D-optimal design where f(x):=-ln det(HXH^T), or even the univariate … Read more

    Moment methods in energy minimization: New bounds for Riesz minimal energy problems

  • David de Laat
    • Categories Convex Optimization, Semi-definite Programming, Semi-infinite Programming Tags , , , , , ,

    We use moment methods to construct a converging hierarchy of optimization problems to lower bound the ground state energy of interacting particle systems. We approximate the infinite dimensional optimization problems in this hierarchy by block diagonal semidefinite programs. For this we develop the necessary harmonic analysis for spaces consisting of subsets of another space, and … Read more

    Analysis and Implementation of an Asynchronous Optimization Algorithm for the Parameter Server

  • Arda Aytekin
  • Hamid Reza Feyzmahdavian
  • Mikael Johansson
    • Categories Convex and Nonsmooth Optimization, Nonlinear Optimization, Parallel Algorithms Tags , , , ,

    This paper presents an asynchronous incremental aggregated gradient algorithm and its implementation in a parameter server framework for solving regularized optimization problems. The algorithm can handle both general convex (possibly non-smooth) regularizers and general convex constraints. When the empirical data loss is strongly convex, we establish linear convergence rate, give explicit expressions for step-size choices … Read more

    Variational Geometric Approach to Generalized Differential and Fenchel Conjugate Calculi in Convex Analysis

  • Boris S. Mordukhovich
  • Nguyen Mau Nam
  • R. Blake Rector
  • Tuyen Tran
    • Categories Convex and Nonsmooth Optimization Tags , , , , ,

    This paper develops a geometric approach of variational analysis for the case of convex objects considered in locally convex topological spaces and also in Banach space settings. Besides deriving in this way new results of convex calculus, we present an overview of some known achievements with their uni ed and simplified proofs based on the developed … Read more

    Modulation Design for MIMO-CoMP HARQ

  • Zhi Ding
  • Hans D Mittelmann
  • Wenhao Wu
    • Categories Applications - Science and Engineering, Combinatorial Optimization Tags , , , ,

    Modulation diversity (MoDiv) is a simple and practical transmission enhancement technique that utilizes different modulation mappings to reduce packet loss rate and achieve higher link throughput. MoDiv is particularly meaningful and effective in hybrid-ARQ (HARQ) systems. We study the deployment and optimization of MoDiv for HARQ in a MIMOcoordinated multi-point (MIMO-CoMP) scenario under Rician fading … Read more

    R-Linear Convergence of Limited Memory Steepest Descent

  • Frank E. Curtis
  • Wei Guo
    • Categories Nonlinear Optimization, Unconstrained Optimization

    The limited memory steepest descent method (LMSD) proposed by Fletcher is an extension of the Barzilai-Borwein “two-point step size” strategy for steepest descent methods for solving unconstrained optimization problems. It is known that the Barzilai-Borwein strategy yields a method with an R-linear rate of convergence when it is employed to minimize a strongly convex quadratic. … Read more

    A parametric programming approach to redefine the global configuration of resource constraints of 0-1-Integer Linear Programming problems.

  • Alejandro Crema
    • Categories (Mixed) Integer Linear Programming, Applications - OR and Management Sciences, Multi-Criteria Optimization Tags , , , ,

    A mathematical programming approach to deal with the global configuration of resource constraints is presented. A specialized parametric programming algorithm to obtain the pareto set for the biobjective problem that appears to deal with the global configuration for 0-1-Integer Linear Programing problems is presented and implemented. Computational results for Multiconstrained Knapsack problems and Bounded Knapsack … Read more

    Decision Rule Bounds for Two-Stage Stochastic Bilevel Programs

  • Daniel Kuhn
  • Ihsan Yanıkoğlu
    • Categories Robust Optimization, Stochastic Programming Tags , ,

    We study stochastic bilevel programs where the leader chooses a binary here-and-now decision and the follower responds with a continuous wait-and-see-decision. Using modern decision rule approximations, we construct lower bounds on an optimistic version and upper bounds on a pessimistic version of the leader’s problem. Both bounding problems are equivalent to explicit mixed-integer linear programs … Read more