12 resultados para Non-convex optimization

em Bulgarian Digital Mathematics Library at IMI-BAS


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* This work was supported by the CNR while the author was visiting the University of Milan.

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Some aspects of design of the discriminant functions that in the best way separate points of predefined final sets are considered. The concept is introduced of the nested discriminant functions which allow to separate correctly points of any of the final sets. It is proposed to apply some methods of non-smooth optimization to solve arising extremal problems efficiently.

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Using monotone bifunctions, we introduce a recession concept for general equilibrium problems relying on a variational convergence notion. The interesting purpose is to extend some results of P. L. Lions on variational problems. In the process we generalize some results by H. Brezis and H. Attouch relative to the convergence of the resolvents associated with maximal monotone operators.

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The problem of finding the optimal join ordering executing a query to a relational database management system is a combinatorial optimization problem, which makes deterministic exhaustive solution search unacceptable for queries with a great number of joined relations. In this work an adaptive genetic algorithm with dynamic population size is proposed for optimizing large join queries. The performance of the algorithm is compared with that of several classical non-deterministic optimization algorithms. Experiments have been performed optimizing several random queries against a randomly generated data dictionary. The proposed adaptive genetic algorithm with probabilistic selection operator outperforms in a number of test runs the canonical genetic algorithm with Elitist selection as well as two common random search strategies and proves to be a viable alternative to existing non-deterministic optimization approaches.

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AMS subject classification: 68Q22, 90C90

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2000 Mathematics Subject Classification: 90C46, 90C26, 26B25, 49J52.

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The general iteration method for nonexpansive mappings on a Banach space is considered. Under some assumption of fast enough convergence on the sequence of (“almost” nonexpansive) perturbed iteration mappings, if the basic method is τ−convergent for a suitable topology τ weaker than the norm topology, then the perturbed method is also τ−convergent. Application is presented to the gradient-prox method for monotone inclusions in Hilbert spaces.

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Mathematics Subject Classification: Primary 47A60, 47D06.

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2000 Mathematics Subject Classification: 90C25, 68W10, 49M37.

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2000 Mathematics Subject Classification: 90C48, 49N15, 90C25

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2000 Mathematics Subject Classification: Primary 90C29; Secondary 49K30.

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AMS subject classification: 90B60, 90B50, 90A80.