Displaying similar documents to “A finite dimensional linear programming approximation of Mather's variational problem”

Linear programming interpretations of Mather’s variational principle

L. C. Evans, D. Gomes (2002)

ESAIM: Control, Optimisation and Calculus of Variations

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We discuss some implications of linear programming for Mather theory [13, 14, 15] and its finite dimensional approximations. We find that the complementary slackness condition of duality theory formally implies that the Mather set lies in an n -dimensional graph and as well predicts the relevant nonlinear PDE for the “weak KAM” theory of Fathi [6, 7, 8, 5].

Discrete limit theorems for general Dirichlet series. III

A. Laurinčikas, R. Macaitienė (2004)

Open Mathematics

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Here we prove a limit theorem in the sense of the weak convergence of probability measures in the space of meromorphic functions for a general Dirichlet series. The explicit form of the limit measure in this theorem is given.

Comparison between different duals in multiobjective fractional programming

Radu Boţ, Robert Chares, Gert Wanka (2007)

Open Mathematics

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The present paper is a continuation of [2] where we deal with the duality for a multiobjective fractional optimization problem. The basic idea in [2] consists in attaching an intermediate multiobjective convex optimization problem to the primal fractional problem, using an approach due to Dinkelbach ([6]), for which we construct then a dual problem expressed in terms of the conjugates of the functions involved. The weak, strong and converse duality statements for the intermediate problems...

Saddle points criteria via a second order η -approximation approach for nonlinear mathematical programming involving second order invex functions

Tadeusz Antczak (2011)

Kybernetika

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In this paper, by using the second order η -approximation method introduced by Antczak [3], new saddle point results are obtained for a nonlinear mathematical programming problem involving second order invex functions with respect to the same function η . Moreover, a second order η -saddle point and a second order η -Lagrange function are defined for the so-called second order η -approximated optimization problem constructed in this method. Then, the equivalence between an optimal solution...