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Proximal Algorithms (Paperback)
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Proximal Algorithms (Paperback)
Series: Foundations and Trends (R) in Optimization
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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.
General
Imprint: |
Now Publishers Inc
|
Country of origin: |
United States |
Series: |
Foundations and Trends (R) in Optimization |
Release date: |
2014 |
First published: |
2014 |
Authors: |
Neal Parikh
• Stephen Boyd
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Dimensions: |
234 x 156 x 7mm (L x W x T) |
Format: |
Paperback
|
Pages: |
130 |
ISBN-13: |
978-1-60198-716-7 |
Categories: |
Books >
Science & Mathematics >
Mathematics >
Optimization >
General
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LSN: |
1-60198-716-1 |
Barcode: |
9781601987167 |
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