coordinate gradient descent method von yun sangwoon (5 Ergebnisse)

Taschenbuch. Zustand: Neu. Neuware -Nonsmooth optimization problems are generally considered to be more difficult than smooth problems. Yet, there is an important class of nonsmooth problems that lie in between. In this book, we consider the problem of minimizing the sum of a smooth function and a (block separable) convex function with or without linear constraints. This problem includes as special cases bound-constrained optimization, smooth optimization with L_1-regularization, and linearly constrained smooth optimization such as a large-scale quadratic programming problem arising in the training of support vector machines. We propose a block coordinate gradient descent method for solving this class of structured nonsmooth problems. The method is simple, highly parallelizable, and suited for large-scale applications in signal/image denoising, regression, and data mining/classification. We establish global convergence and, under a local Lipschitzian error bound assumption, local linear rate of convergence for this method. Our numerical experiences suggest that our method is effective in practice. This book is helpful to the people who are interested in solving large-scale optimization problems.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 112 pp. Englisch.

Paperback. Zustand: Brand New. 112 pages. 8.66x5.91x0.26 inches. In Stock.

Kartoniert / Broschiert. Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Yun SangwoonSangwoon Yun: PhD in Mathematics at University of Washington. Research interest: Convex and nonsmooth optimization, variational analysis. Research Fellow at National University of Singapore.Nonsmooth optimization pr.

Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Nonsmooth optimization problems are generally considered to be more difficult than smooth problems. Yet, there is an important class of nonsmooth problems that lie in between. In this book, we consider the problem of minimizing the sum of a smooth function and a (block separable) convex function with or without linear constraints. This problem includes as special cases bound-constrained optimization, smooth optimization with L_1-regularization, and linearly constrained smooth optimization such as a large-scale quadratic programming problem arising in the training of support vector machines. We propose a block coordinate gradient descent method for solving this class of structured nonsmooth problems. The method is simple, highly parallelizable, and suited for large-scale applications in signal/image denoising, regression, and data mining/classification. We establish global convergence and, under a local Lipschitzian error bound assumption, local linear rate of convergence for this method. Our numerical experiences suggest that our method is effective in practice. This book is helpful to the people who are interested in solving large-scale optimization problems. 112 pp. Englisch.

Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Nonsmooth optimization problems are generally considered to be more difficult than smooth problems. Yet, there is an important class of nonsmooth problems that lie in between. In this book, we consider the problem of minimizing the sum of a smooth function and a (block separable) convex function with or without linear constraints. This problem includes as special cases bound-constrained optimization, smooth optimization with L_1-regularization, and linearly constrained smooth optimization such as a large-scale quadratic programming problem arising in the training of support vector machines. We propose a block coordinate gradient descent method for solving this class of structured nonsmooth problems. The method is simple, highly parallelizable, and suited for large-scale applications in signal/image denoising, regression, and data mining/classification. We establish global convergence and, under a local Lipschitzian error bound assumption, local linear rate of convergence for this method. Our numerical experiences suggest that our method is effective in practice. This book is helpful to the people who are interested in solving large-scale optimization problems.