Results 231 to 240 of about 109,672 (263)

Extended Well-Posedness of Quasiconvex Vector Optimization Problems

Journal of Optimization Theory and Applications, 2008
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Crespi G. P., Papalia M., ROCCA, MATTEO
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Optimality Conditions for Vector Optimization Problems

2014
In this chapter we provide subdifferential information for the scalarization functionals introduced in Chap. 6. Based on that we are able to formulate necessary and sufficient optimality conditions of Fermat and Lagrange type for unconstrained and constrained vector optimization problems with (set-valued) objective maps mapping in a real linear space ...
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Pareto-optimality conditions in discrete vector optimization problems

Discrete Mathematics and Applications, 1997
Summary: For the vector optimization problem \[ F=(f_1,f_2, \dots, f_n):X\to \mathbb{R}^n,\quad n\geq 2, \] \[ f_i(x) \to\min_X \quad\forall i\in N_n =\{1,2, \dots,n\}, \] with a finite set of vector estimators \(F(X)\) we give a wide class of efficiency (Pareto-optimality) criteria in terms of linear convolutions of transformed partial criteria.
Emelichev, V. A., Yanushkevich, O. A.
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Optimality theorems for convex semidefinite vector optimization problems

Nonlinear Analysis: Theory, Methods & Applications, 2009
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Lee, Gue Myung, Lee, Kwang Baik
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On necessary optimality conditions in vector optimization problems

Journal of Optimization Theory and Applications, 1988
Necessary conditions of the multiplier rule type for vector optimization problems in Banach spaces are proved by using separation theorems and Lyusternik's theorem. The Pontryagin maximum principle for multiobjective control problems with state constraints is derived from these general conditions.
Khanh, P. Q., Nuong, T. H.
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Fuzzy necessary optimality conditions for vector optimization problems

Optimization, 2009
The primary aim of this article is to derive Lagrange multiplier rules for vector optimization problems using a non-convex separation technique and the concept of abstract subdifferential. Furthermore, we present a method of estimation of the norms of such multipliers in very general cases and for many particular subdifferentials.
Marius Durea, Christiane Tammer
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Vector Duality via Scalarization for Vector Optimization Problems

2014
As seen in Chap. 3, one can consider different minimality notions for sets and these can be employed in different situations in order to serve various purposes, among which one can find the theory of vector optimization. Solving a vector optimization problem amounts of determining its feasible elements where the value of its objective function ...
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C1,1 vector optimization problems and Riemann derivatives [PDF]

, 2002
In this paper we introduce a generalized second-order Riemann-type derivative for C 1,1 vector functions and use it to establish necessary and sufficient optimality conditions for vector optimization problems. We show that these conditions are stronger than those obtained by means of the second-order sub-differential in Clarke's sense, considered e.g ...
IVANOV, IVAN GINCHEV, GUERRAGGIO, ANGELO, ROCCA, MATTEO   +2 more
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Vector Optimization Problems

2005
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Nonsmooth Vector Optimization Problems

1989
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