Displaying similar documents to “Augmented Lagrangian method for recourse problem of two-stage stochastic linear programming”

Approximate dynamic programming based on high dimensional model representation

Miroslav Pištěk (2013)

Kybernetika

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This article introduces an algorithm for implicit High Dimensional Model Representation (HDMR) of the Bellman equation. This approximation technique reduces memory demands of the algorithm considerably. Moreover, we show that HDMR enables fast approximate minimization which is essential for evaluation of the Bellman function. In each time step, the problem of parametrized HDMR minimization is relaxed into trust region problems, all sharing the same matrix. Finding its eigenvalue decomposition,...

A fast Lagrangian heuristic for large-scale capacitated lot-size problems with restricted cost structures

Kjetil K. Haugen, Guillaume Lanquepin-Chesnais, Asmund Olstad (2012)

Kybernetika

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In this paper, we demonstrate the computational consequences of making a simple assumption on production cost structures in capacitated lot-size problems. Our results indicate that our cost assumption of increased productivity over time has dramatic effects on the problem sizes which are solvable. Our experiments indicate that problems with more than 1000 products in more than 1000 time periods may be solved within reasonable time. The Lagrangian decomposition algorithm we use does of...

Computing minimum norm solution of a specific constrained convex nonlinear problem

Saeed Ketabchi, Hossein Moosaei (2012)

Kybernetika

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The characterization of the solution set of a convex constrained problem is a well-known attempt. In this paper, we focus on the minimum norm solution of a specific constrained convex nonlinear problem and reformulate this problem as an unconstrained minimization problem by using the alternative theorem.The objective function of this problem is piecewise quadratic, convex, and once differentiable. To minimize this function, we will provide a new Newton-type method with global convergence...