Displaying similar documents to “Primal interior-point method for large sparse minimax optimization”

Primal interior point method for minimization of generalized minimax functions

Ladislav Lukšan, Ctirad Matonoha, Jan Vlček (2010)

Kybernetika

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In this paper, we propose a primal interior-point method for large sparse generalized minimax optimization. After a short introduction, where the problem is stated, we introduce the basic equations of the Newton method applied to the KKT conditions and propose a primal interior-point method. (i. e. interior point method that uses explicitly computed approximations of Lagrange multipliers instead of their updates). Next we describe the basic algorithm and give more details concerning...

An accurate active set Newton algorithm for large scale bound constrained optimization

Li Sun, Guoping He, Yongli Wang, Changyin Zhou (2011)

Applications of Mathematics

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A new algorithm for solving large scale bound constrained minimization problems is proposed. The algorithm is based on an accurate identification technique of the active set proposed by Facchinei, Fischer and Kanzow in 1998. A further division of the active set yields the global convergence of the new algorithm. In particular, the convergence rate is superlinear without requiring the strict complementarity assumption. Numerical tests demonstrate the efficiency and performance of the...

An active set strategy based on the multiplier function or the gradient

Li Sun, Liang Fang, Guoping He (2010)

Applications of Mathematics

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We employ the active set strategy which was proposed by Facchinei for solving large scale bound constrained optimization problems. As the special structure of the bound constrained problem, a simple rule is used for updating the multipliers. Numerical results show that the active set identification strategy is practical and efficient.

Rescaled proximal methods for linearly constrained convex problems

Paulo J.S. Silva, Carlos Humes (2007)

RAIRO - Operations Research

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We present an inexact interior point proximal method to solve linearly constrained convex problems. In fact, we derive a primal-dual algorithm to solve the KKT conditions of the optimization problem using a modified version of the rescaled proximal method. We also present a pure primal method. The proposed proximal method has as distinctive feature the possibility of allowing inexact inner steps even for Linear Programming. This is achieved by using an error criterion that ...

Random perturbation of the variable metric method for unconstrained nonsmooth nonconvex optimization

Abdelkrim El Mouatasim, Rachid Ellaia, José Souza de Cursi (2006)

International Journal of Applied Mathematics and Computer Science

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We consider the global optimization of a nonsmooth (nondifferentiable) nonconvex real function. We introduce a variable metric descent method adapted to nonsmooth situations, which is modified by the incorporation of suitable random perturbations. Convergence to a global minimum is established and a simple method for the generation of suitable perturbations is introduced. An algorithm is proposed and numerical results are presented, showing that the method is computationally effective...