Introduction to derivative-free optimization / Andrew R. Conn, Katya Scheinberg, Luis N. Vicente
Tipo de material: TextoSeries MPS-SIAM series on optimization ; 8.Detalles de publicación: Philadelphia : Society for Industrial and Applied Mathematics 2009 Descripción: XII, 277 p. : il. ; 26 cmISBN: 9780898716689Tema(s): Optimización matemáticaResumen: The absence of derivatives, often combined with the presence of noise or lack of smoothness, is a major challenge for optimization. This book explains how sampling and model techniques are used in derivative-free methods and how these methods are designed to efficiently and rigorously solve optimization problems. Although readily accessible to readers with a modest background in computational mathematics, it is also intended to be of interest to researchers in the field. Introduction to Derivative-Free Optimization is the first contemporary comprehensive treatment of optimization without derivatives. This book covers most of the relevant classes of algorithms from direct search to model-based approaches. It contains a comprehensive description of the sampling and modeling tools needed for derivative-free optimization; these tools allow the reader to better analyze the convergent properties of the algorithms and identify their differences and similarities.Tipo de ítem | Biblioteca de origen | Signatura | Estado | Fecha de vencimiento | Código de barras | Reserva de ítems |
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Monografías | 03. BIBLIOTECA INGENIERÍA PUERTO REAL | 519.86/CON/int (Navegar estantería(Abre debajo)) | Prestado | 31/01/2025 | 3744438955 |
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Bibliografía: p. 255-270
The absence of derivatives, often combined with the presence of noise or lack of smoothness, is a major challenge for optimization. This book explains how sampling and model techniques are used in derivative-free methods and how these methods are designed to efficiently and rigorously solve optimization problems. Although readily accessible to readers with a modest background in computational mathematics, it is also intended to be of interest to researchers in the field. Introduction to Derivative-Free Optimization is the first contemporary comprehensive treatment of optimization without derivatives. This book covers most of the relevant classes of algorithms from direct search to model-based approaches. It contains a comprehensive description of the sampling and modeling tools needed for derivative-free optimization; these tools allow the reader to better analyze the convergent properties of the algorithms and identify their differences and similarities.
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