Artículos relacionados a Proximal Algorithms: 3 (Foundations and Trends® in...
Sinopsis
Proximal Algorithms discusses proximal operators and proximal algorithms, and illustrates their applicability to standard and distributed convex optimization in general and many applications of recent interest in particular. Much like Newton's method is a standard tool for solving unconstrained smooth optimization problems of modest size, proximal algorithms can be viewed as an analogous tool for nonsmooth, constrained, large-scale, or distributed versions of these problems. They are very generally applicable, but are especially well-suited to problems of substantial recent interest involving large or high-dimensional datasets. Proximal methods sit at a higher level of abstraction than classical algorithms like Newton's method: the base operation is evaluating the proximal operator of a function, which itself involves solving a small convex optimization problem. These subproblems, which generalize the problem of projecting a point onto a convex set, often admit closed-form solutions or can be solved very quickly with standard or simple specialized methods. Proximal Algorithms discusses different interpretations of proximal operators and algorithms, looks at their connections to many other topics in optimization and applied mathematics, surveys some popular algorithms, and provides a large number of examples of proximal operators that commonly arise in practice.
"Sinopsis" puede pertenecer a otra edición de este libro.
Reseña del editor
Proximal Algorithms discusses proximal operators and proximal algorithms, and illustrates their applicability to standard and distributed convex optimization in general and many applications of recent interest in particular. Much like Newton's method is a standard tool for solving unconstrained smooth optimization problems of modest size, proximal algorithms can be viewed as an analogous tool for nonsmooth, constrained, large-scale, or distributed versions of these problems. They are very generally applicable, but are especially well-suited to problems of substantial recent interest involving large or high-dimensional datasets. Proximal methods sit at a higher level of abstraction than classical algorithms like Newton's method: the base operation is evaluating the proximal operator of a function, which itself involves solving a small convex optimization problem. These subproblems, which generalize the problem of projecting a point onto a convex set, often admit closed-form solutions or can be solved very quickly with standard or simple specialized methods. Proximal Algorithms discusses different interpretations of proximal operators and algorithms, looks at their connections to many other topics in optimization and applied mathematics, surveys some popular algorithms, and provides a large number of examples of proximal operators that commonly arise in practice.
"Sobre este título" puede pertenecer a otra edición de este libro.
Comprar nuevo
Ver este artículo
EUR 81,40
EUR 0,68 gastos de envío desde Estados Unidos de America a España
Destinos, gastos y plazos de envíoResultados de la búsqueda para Proximal Algorithms: 3 (Foundations and Trends® in...
Imagen de archivo
Proximal Algorithms
Impresión bajo demandaLibrería: PBShop.store US, Wood Dale, IL, Estados Unidos de America
Calificación del vendedor: 5 de 5 estrellas
PAP. Condición: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000. Nº de ref. del artículo: L0-9781601987167
Cantidad disponible: Más de 20 disponibles
Imagen de archivo
Proximal Algorithms (Foundations and Trends(r) in Optimization)
Librería: Ria Christie Collections, Uxbridge, Reino Unido
Calificación del vendedor: 5 de 5 estrellas
Condición: New. In. Nº de ref. del artículo: ria9781601987167_new
Cantidad disponible: Más de 20 disponibles
Imagen de archivo
Proximal Algorithms
Impresión bajo demandaLibrería: PBShop.store UK, Fairford, GLOS, Reino Unido
Calificación del vendedor: 5 de 5 estrellas
PAP. Condición: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000. Nº de ref. del artículo: L0-9781601987167
Cantidad disponible: Más de 20 disponibles
Imagen de archivo
Proximal Algorithms (Foundations and Trends(r) in Optimization)
Librería: California Books, Miami, FL, Estados Unidos de America
Calificación del vendedor: 5 de 5 estrellas
Condición: New. Nº de ref. del artículo: I-9781601987167
Cantidad disponible: Más de 20 disponibles
Imagen del vendedor
Proximal Algorithms
Publicado por
now publishers Inc, US, 2014
ISBN 10: 1601987161
ISBN 13: 9781601987167
Nuevo
Paperback
Librería: Rarewaves.com UK, London, Reino Unido
Calificación del vendedor: 5 de 5 estrellas
Paperback. Condición: New. Proximal Algorithms discusses proximal operators and proximal algorithms, and illustrates their applicability to standard and distributed convex optimization in general and many applications of recent interest in particular. Much like Newton's method is a standard tool for solving unconstrained smooth optimization problems of modest size, proximal algorithms can be viewed as an analogous tool for nonsmooth, constrained, large-scale, or distributed versions of these problems. They are very generally applicable, but are especially well-suited to problems of substantial recent interest involving large or high-dimensional datasets.Proximal methods sit at a higher level of abstraction than classical algorithms like Newton's method: the base operation is evaluating the proximal operator of a function, which itself involves solving a small convex optimization problem. These subproblems, which generalize the problem of projecting a point onto a convex set, often admit closed-form solutions or can be solved very quickly with standard or simple specialized methods.Proximal Algorithms discusses different interpretations of proximal operators and algorithms, looks at their connections to many other topics in optimization and applied mathematics, surveys some popular algorithms, and provides a large number of examples of proximal operators that commonly arise in practice. Nº de ref. del artículo: LU-9781601987167
Cantidad disponible: Más de 20 disponibles
Imagen de archivo
Proximal Algorithms
Librería: Chiron Media, Wallingford, Reino Unido
Calificación del vendedor: 4 de 5 estrellas
PF. Condición: New. Nº de ref. del artículo: 6666-IUK-9781601987167
Cantidad disponible: 10 disponibles
Imagen del vendedor
Proximal Algorithms
Librería: GreatBookPricesUK, Woodford Green, Reino Unido
Calificación del vendedor: 5 de 5 estrellas
Condición: New. Nº de ref. del artículo: 20882502-n
Cantidad disponible: Más de 20 disponibles
Imagen del vendedor
Proximal Algorithms
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
Calificación del vendedor: 5 de 5 estrellas
Condición: New. Nº de ref. del artículo: 20882502-n
Cantidad disponible: Más de 20 disponibles
Imagen de archivo
Proximal Algorithms
Publicado por
now publishers Inc, 2014
ISBN 10: 1601987161
ISBN 13: 9781601987167
Nuevo
Paperback / softback
Librería: THE SAINT BOOKSTORE, Southport, Reino Unido
Calificación del vendedor: 5 de 5 estrellas
Paperback / softback. Condición: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days 221. Nº de ref. del artículo: C9781601987167
Cantidad disponible: Más de 20 disponibles
Imagen del vendedor
Proximal Algorithms
Publicado por
now publishers Inc, US, 2014
ISBN 10: 1601987161
ISBN 13: 9781601987167
Nuevo
Paperback
Librería: Rarewaves.com USA, London, LONDO, Reino Unido
Calificación del vendedor: 5 de 5 estrellas
Paperback. Condición: New. Proximal Algorithms discusses proximal operators and proximal algorithms, and illustrates their applicability to standard and distributed convex optimization in general and many applications of recent interest in particular. Much like Newton's method is a standard tool for solving unconstrained smooth optimization problems of modest size, proximal algorithms can be viewed as an analogous tool for nonsmooth, constrained, large-scale, or distributed versions of these problems. They are very generally applicable, but are especially well-suited to problems of substantial recent interest involving large or high-dimensional datasets.Proximal methods sit at a higher level of abstraction than classical algorithms like Newton's method: the base operation is evaluating the proximal operator of a function, which itself involves solving a small convex optimization problem. These subproblems, which generalize the problem of projecting a point onto a convex set, often admit closed-form solutions or can be solved very quickly with standard or simple specialized methods.Proximal Algorithms discusses different interpretations of proximal operators and algorithms, looks at their connections to many other topics in optimization and applied mathematics, surveys some popular algorithms, and provides a large number of examples of proximal operators that commonly arise in practice. Nº de ref. del artículo: LU-9781601987167
Cantidad disponible: Más de 20 disponibles