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Displaying similar documents to “Coercivity properties and well-posedness in vector optimization*”

A survey on combinatorial optimization in dynamic environments

Nicolas Boria, Vangelis T. Paschos (2011)

RAIRO - Operations Research

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This survey presents major results and issues related to the study of NPO problems in dynamic environments, that is, in settings where instances are allowed to undergo some modifications over time. In particular, the survey focuses on two complementary frameworks. The first one is the reoptimization framework, where an instance that is already solved undergoes some local perturbation. The goal is then to make use of the information provided by the initial solution to compute a new solution....

A survey on combinatorial optimization in dynamic environments

Nicolas Boria, Vangelis T. Paschos (2011)

RAIRO - Operations Research

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This survey presents major results and issues related to the study of NPO problems in dynamic environments, that is, in settings where instances are allowed to undergo some modifications over time. In particular, the survey focuses on two complementary frameworks. The first one is the reoptimization framework, where an instance that is already solved undergoes some local perturbation. The goal is then to make use of the information provided by the initial solution to compute a new solution....

Coercivity properties and well-posedness in vector optimization

Sien Deng (2003)

RAIRO - Operations Research - Recherche Opérationnelle

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This paper studies the issue of well-posedness for vector optimization. It is shown that coercivity implies well-posedness without any convexity assumptions on problem data. For convex vector optimization problems, solution sets of such problems are non-convex in general, but they are highly structured. By exploring such structures carefully via convex analysis, we are able to obtain a number of positive results, including a criterion for well-posedness in terms of that of associated...

Multi-objective Optimization Problem with Bounded Parameters

Ajay Kumar Bhurjee, Geetanjali Panda (2014)

RAIRO - Operations Research - Recherche Opérationnelle

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In this paper, we propose a nonlinear multi-objective optimization problem whose parameters in the objective functions and constraints vary in between some lower and upper bounds. Existence of the efficient solution of this model is studied and gradient based as well as gradient free optimality conditions are derived. The theoretical developments are illustrated through numerical examples.