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Inégalités variationnelles non convexes

Messaoud Bounkhel, Djalel Bounkhel (2005)

ESAIM: Control, Optimisation and Calculus of Variations

Dans cet article nous proposons différents algorithmes pour résoudre une nouvelle classe de problèmes variationels non convexes. Cette classe généralise plusieurs types d’inégalités variationnelles (Cho et al. (2000), Noor (1992), Zeng (1998), Stampacchia (1964)) du cas convexe au cas non convexe. La sensibilité de cette classe de problèmes variationnels non convexes a été aussi étudiée.

Inégalités variationnelles non convexes

Messaoud Bounkhel, Djalel Bounkhel (2010)

ESAIM: Control, Optimisation and Calculus of Variations

Dans cet article nous proposons différents algorithmes pour résoudre une nouvelle classe de problèmes variationels non convexes. Cette classe généralise plusieurs types d'inégalités variationnelles (Cho et al. (2000), Noor (1992), Zeng (1998), Stampacchia (1964)) du cas convexe au cas non convexe. La sensibilité de cette classe de problèmes variationnels non convexes a été aussi étudiée.

Inf-convolution and regularization of convex functions on Riemannian manifolds of nonpositive curvature.

Daniel Azagra, Juan Ferrera (2006)

Revista Matemática Complutense

We show how an operation of inf-convolution can be used to approximate convex functions with C1 smooth convex functions on Riemannian manifolds with nonpositive curvature (in a manner that not only is explicit but also preserves some other properties of the original functions, such as ordering, symmetries, infima and sets of minimizers), and we give some applications.

Lectures on maximal monotone operators.

R. R. Phelps (1997)

Extracta Mathematicae

These lectures will focus on those properties of maximal monotone operators which are valid in arbitrary real Banach spaces.

Locally Lipschitz vector optimization with inequality and equality constraints

Ivan Ginchev, Angelo Guerraggio, Matteo Rocca (2010)

Applications of Mathematics

The present paper studies the following constrained vector optimization problem: min C f ( x ) , g ( x ) - K , h ( x ) = 0 , where f : n m , g : n p are locally Lipschitz functions, h : n q is C 1 function, and C m and K p are closed convex cones. Two types of solutions are important for the consideration, namely w -minimizers (weakly efficient points) and i -minimizers (isolated minimizers of order 1). In terms of the Dini directional derivative first-order necessary conditions for a point x 0 to be a w -minimizer and first-order sufficient conditions for x 0 ...

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