Sobre a convergência de métodos de descida em otimização não-suave: aplicações à ciência comportamental
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| Hlavní autor: | |
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| Datum vydání: | 2017 |
| Médium: | Doctoral thesis |
| Jazyk: | por |
| Zdroj: | Repositório Institucional da UFG |
| Download full: | http://repositorio.bc.ufg.br/tede/handle/tede/6864 |
Shrnutí: | In this work, we investigate four different types of descent methods: a dual descent method in the scalar context and a multiobjective proximal point methods (one exact and two inexact versions). The first one is restricted to functions that satisfy the Kurdyka-Lojasiewicz property, where it is used a quasi-distance as a regularization function. In the next three methods, the objective is to study the convergence of a multiobjective proximal methods (exact an inexact) for a particular class of multiobjective functions that are not necessarily differentiable. For the inexact methods, we choose a proximal distance as the regularization term. Such a well-known distance allows us to analyze the convergence of the method under various settings. Applications in behavioral sciences are analyzed in the sense of the variational rationality approach. |
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