Displaying similar documents to “Evaluation of Clarke's generalized gradient in optimization of variational inequalities”

Optimality conditions for a class of mathematical programs with equilibrium constraints: strongly regular case

Jiří V. Outrata (1999)

Kybernetika

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The paper deals with mathematical programs, where parameter-dependent nonlinear complementarity problems arise as side constraints. Using the generalized differential calculus for nonsmooth and set-valued mappings due to B. Mordukhovich, we compute the so-called coderivative of the map assigning the parameter the (set of) solutions to the respective complementarity problem. This enables, in particular, to derive useful 1st-order necessary optimality conditions, provided the complementarity...

Random perturbation of the projected variable metric method for nonsmooth nonconvex optimization problems with linear constraints

Abdelkrim El Mouatasim, Rachid Ellaia, Eduardo Souza de Cursi (2011)

International Journal of Applied Mathematics and Computer Science

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We present a random perturbation of the projected variable metric method for solving linearly constrained nonsmooth (i.e., nondifferentiable) nonconvex optimization problems, and we establish the convergence to a global minimum for a locally Lipschitz continuous objective function which may be nondifferentiable on a countable set of points. Numerical results show the effectiveness of the proposed approach.

Augmented Lagrangian methods for variational inequality problems

Alfredo N. Iusem, Mostafa Nasri (2010)

RAIRO - Operations Research

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We introduce augmented Lagrangian methods for solving finite dimensional variational inequality problems whose feasible sets are defined by convex inequalities, generalizing the proximal augmented Lagrangian method for constrained optimization. At each iteration, primal variables are updated by solving an unconstrained variational inequality problem, and then dual variables are updated through a closed formula. A full convergence analysis is provided, allowing for inexact solution of...

On dual vector optimization and shadow prices

Letizia Pellegrini (2004)

RAIRO - Operations Research - Recherche Opérationnelle

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In this paper we present the image space analysis, based on a general separation scheme, with the aim of studying lagrangian duality and shadow prices in Vector Optimization. Two particular kinds of separation are considered; in the linear case, each of them is applied to the study of sensitivity analysis, and it is proved that the derivatives of the perturbation function can be expressed in terms of vector Lagrange multipliers or shadow prices.