A fixed point theorem for a class of multi-valued continuously differentiable maps
Gilles Fournier, Donald Violette (1987)
Annales Polonici Mathematici
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Displaying similar documents to “Favorable classes of mappings and multimappings in nonlinear analysis and optimization.”
A fixed point theorem for a class of multi-valued continuously differentiable maps
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Marián J. Fabián (1979)
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Comments on the paper by Gordji and Baghani entitled “A generalization of Nadler's fixed point theorem".
Rhoades, B.E. (2010)
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On accretive multivalued mappings
Josef Kolomý (1986)
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Applications of the Fréchet subdifferential
Durea, M. (2003)
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2000 Mathematics Subject Classification: 46A30, 54C60, 90C26. In this paper we prove two results of nonsmooth analysis involving the Fréchet subdifferential. One of these results provides a necessary optimality condition for an optimization problem which arise naturally from a class of wide studied problems. In the second result we establish a sufficient condition for the metric regularity of a set-valued map without continuity assumptions.
On a question of Ivanov relating to nonlinear quasicontractive mappings.
Ćirić, Ljubomir B. (1987)
Publications de l'Institut Mathématique. Nouvelle Série
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A coincidence point theorem for multivalued mappings in 2-Menger spaces.
Hadžić, Olga, Ovcin, Zoran (1998)
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Abderrahim Jourani (1996)
Studia Mathematica
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We extend the open mapping theorem and inversion theorem of Robinson for convex multivalued mappings to γ-paraconvex multivalued mappings. Some questions posed by Rolewicz are also investigated. Our results are applied to obtain a generalization of the Farkas lemma for γ-paraconvex multivalued mappings.
Sufficient Second Order Optimality Conditions for C^1 Multiobjective Optimization Problems
Gadhi, N. (2003)
Serdica Mathematical Journal
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2000 Mathematics Subject Classification: Primary 90C29; Secondary 90C30. In this work, we use the notion of Approximate Hessian introduced by Jeyakumar and Luc [19], and a special scalarization to establish sufficient optimality conditions for constrained multiobjective optimization problems. Throughout this paper, the data are assumed to be of class C^1, but not necessarily of class C^(1.1).