Unconstrained Minimization
Mostrando 1-12 de 14 artigos, teses e dissertações.
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1. STOCHASTIC GRADIENT METHODS FOR UNCONSTRAINED OPTIMIZATION
This papers presents an overview of gradient based methods for minimization of noisy functions. It is assumed that the objective functions is either given with error terms of stochastic nature or given as the mathematical expectation. Such problems arise in the context of simulation based optimization. The focus of this presentation is on the gradient based
Pesqui. Oper.. Publicado em: 2014-12
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2. TRUST-REGION-BASED METHODS FOR NONLINEAR PROGRAMMING: RECENT ADVANCES AND PERSPECTIVES
The aim of this text is to highlight recent advances of trust-region-based methods for nonlinear programming and to put them into perspective. An algorithmic framework provides a ground with the main ideas of these methods and the related notation. Specific approaches concerned with handling the trust-region subproblem are recalled, particularly for the larg
Pesqui. Oper.. Publicado em: 2014-12
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3. Comparative study of spectral steplengths and nonmonotone linear searches / Estudo comparativo de passos espectrais e buscas lineares não monótonas
The Spectral Gradient method, introduced by Barzilai and Borwein and analized by Raydan for unconstrained minimization, is a simple method whose performance is comparable to traditional methods, such as conjugate gradients. Since the introduction of method, as well as its extension to minimization of convex sets, there were introduced various combinations of
Publicado em: 2008
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4. A new algorithm of nonlinear conjugate gradient method with strong convergence
The nonlinear conjugate gradient method is a very useful technique for solving large scale minimization problems and has wide applications in many fields. In this paper, we present a new algorithm of nonlinear conjugate gradient method with strong convergence for unconstrained minimization problems. The new algorithm can generate an adequate trust region rad
Computational & Applied Mathematics. Publicado em: 2008
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5. Otimização de colunas de destilação : uma abordagem aplicada dos multiplicadores de Lagrange / Optimization of distillation comumns : an applied approach of the Lagrange multipliers
This work tackles the optimization of a distillation process of a binary mixture in a column with plates, which came from the methanol distillation in the production process of the biodiesel. More specifically, it considers the minimization of a cost objective function that encompass the heat rate supplied to the reboiler and the feed temperature, subject to
Publicado em: 2008
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6. Aplicações de computação paralela em otimização contínua / Applications of parallel computing in continuous optimization
No presente trabalho, estudamos alguns conceitos relacionados ao desenvolvimento de programas paralelos, algumas formas de aplicar computação paralela em métodos de otimização contínua e dois métodos que envolvem o uso de otimização. O primeiro método que apresentamos, chamado PUMA (Pointwise Unconstrained Minimization Approach), recupera constante
Publicado em: 2008
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7. Um metodo de região de confiança para minimização irrestrita sem derivadas / On the region method for unconstrained minimization without derivatives
Neste trabalho apresentamos métodos de minimização irrestrita, de uma função objetivo F de várias variáveis, que não fazem uso nem do gradiente da função objetivo - métodos derivative-free, nem de aproximações do mesmo. Nosso objetivo básico foi estudar e comparar o desempenho de métodos desse tipo propostos por M. J. D. Powell, que consistem
Publicado em: 2008
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8. Accelerating the Levenberg-Marquardt method for the minimization of the square of functions with box constraints / Acelerando o metodo de Levenberg-Marquardt para a minimização da soma de quadrados de funções com restrições de caixa
In this work, we present an active set algorithm for minimizing the sum of squares of smooth functions, with box constraints. The algorithm is highly inspired in the work of Birgin and Mart´inez [4]. The differences are concentrated on the chosen search direction and on the use of an acceleration technique to update the step. At each iteration, we define an
Publicado em: 2008
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9. Sobre um metodo de busca direta sem derivada, com decrescimo fortalecido / About a derivative-free direct search method with fortified-descent strategy
Neste trabalho, tratamos de métodos de busca direta para minimização irrestrita de uma função de n variáveis a valores reais. Alem de serem derivative-free, métodos que não calculam derivadas, os métodos de busca direta não fazem uso de aproximações das derivadas nem do valor expl?cito da função nas suas operações. Nesta classe, abordamos um
Publicado em: 2008
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10. Step-size estimation for unconstrained optimization methods
Some computable schemes for descent methods without line search are proposed. Convergence properties are presented. Numerical experiments concerning large scale unconstrained minimization problems are reported.
Computational & Applied Mathematics. Publicado em: 2005-12
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11. Sobre o desempenho de metodos de busca direta para minimização irrestrita / About the performance of direct search methods for unconstrained minimization
Neste trabalho, voltamos nossa atenção para estratégias de busca direta, que são métodos de minimização que não fazem uso de derivadas ou de suas aproximações. Abordamos um algoritmo proposto por Lucidi e Sciandrone para problemas irrestritos, que usa um critério de decréscimo suficiente para garantir convergência global, no sentido que todo pon
Publicado em: 2005
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12. On the convergence properties of the projected gradient method for convex optimization
When applied to an unconstrained minimization problem with a convex objective, the steepest descent method has stronger convergence properties than in the noncovex case: the whole sequence converges to an optimal solution under the only hypothesis of existence of minimizers (i.e. without assuming e.g. boundedness of the level sets). In this paper we look at
Computational & Applied Mathematics. Publicado em: 2003