Displaying similar documents to “On the Implementation of Stochastic Quasigradient Methods to some Facility Location Problems”

Decomposition of large-scale stochastic optimal control problems

Kengy Barty, Pierre Carpentier, Pierre Girardeau (2010)

RAIRO - Operations Research

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In this paper, we present an Uzawa-based heuristic that is adapted to certain type of stochastic optimal control problems. More precisely, we consider dynamical systems that can be divided into small-scale subsystems linked through a static almost sure coupling constraint at each time step. This type of problem is common in production/portfolio management where subsystems are, for instance, power units, and one has to supply a stochastic power demand at each time step. We outline...

Expériences with Stochastic Algorithms fir a class of Constrained Global Optimisation Problems

Abdellah Salhi, L.G. Proll, D. Rios Insua, J.I. Martin (2010)

RAIRO - Operations Research

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The solution of a variety of classes of global optimisation problems is required in the implementation of a framework for sensitivity analysis in multicriteria decision analysis. These problems have linear constraints, some of which have a particular structure, and a variety of objective functions, which may be smooth or non-smooth. The context in which they arise implies a need for a single, robust solution method. The literature contains few experimental results relevant to such...

A Global Stochastic Optimization Method for Large Scale Problems

W. El Alem, A. El Hami, R. Ellaia (2010)

Mathematical Modelling of Natural Phenomena

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In this paper, a new hybrid simulated annealing algorithm for constrained global optimization is proposed. We have developed a stochastic algorithm called ASAPSPSA that uses Adaptive Simulated Annealing algorithm (ASA). ASA is a series of modifications to the basic simulated annealing algorithm (SA) that gives the region containing the global solution of an objective function. In addition, Simultaneous Perturbation Stochastic Approximation...

Warm-start cuts for Generalized Benders Decomposition

Jakub Kůdela, Pavel Popela (2017)

Kybernetika

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In this paper, we describe a decomposition algorithm suitable for two-stage convex stochastic programs known as Generalized Benders Decomposition. For this algorithm we propose a new reformulation that incorporates a lower bound cut that serves as a warm-start, decreasing the overall computation time. Additionally, we test the performance of the proposed reformulation on two modifications of the algorithm (bunching and multicut) using numerical examples. The numerical part is programmed...

Asymptotic normality of randomly truncated stochastic algorithms

Jérôme Lelong (2013)

ESAIM: Probability and Statistics

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We study the convergence rate of randomly truncated stochastic algorithms, which consist in the truncation of the standard Robbins–Monro procedure on an increasing sequence of compact sets. Such a truncation is often required in practice to ensure convergence when standard algorithms fail because the expected-value function grows too fast. In this work, we give a self contained proof of a central limit theorem for this algorithm under local assumptions on the expected-value function,...

Regularization method for stochastic mathematical programs with complementarity constraints

Gui-Hua Lin, Masao Fukushima (2010)

ESAIM: Control, Optimisation and Calculus of Variations

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In this paper, we consider a class of stochastic mathematical programs with equilibrium constraints (SMPECs) that has been discussed by Lin and Fukushima (2003). Based on a reformulation given therein, we propose a regularization method for solving the problems. We show that, under a weak condition, an accumulation point of the generated sequence is a feasible point of the original problem. We also show that such an accumulation point is S-stationary to the problem under additional...