Convex optimization algorithms

By:

Bertsekas, Dimitri P

Material type: Text

TextPublication details: Massachusetts Athena Scientific 2015Description: xii, 564pISBN:

9781886529281 (hb.)

Subject(s):

DDC classification:

519.6 BER

Summary: This book aims at an up-to-date and accessible development of algorithms for solving convex optimization problems. The book covers almost all the major classes of convex optimization algorithms. Principal among these are gradient, subgradient, polyhedral approximation, proximal, and interior point methods. Most of these methods rely on convexity (but not necessarily differentiability) in the cost and constraint functions, and are often connected in various ways to duality. The book contains numerous examples describing in detail applications to specially structured problems.

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Holdings
Item type	Current library	Collection	Call number	Status	Date due	Barcode
Book	Plaksha University Library	Mathematics	519.6 BER (Browse shelf(Opens below))	Available		002994

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This book aims at an up-to-date and accessible development of algorithms for solving convex optimization problems. The book covers almost all the major classes of convex optimization algorithms. Principal among these are gradient, subgradient, polyhedral approximation, proximal, and interior point methods. Most of these methods rely on convexity (but not necessarily differentiability) in the cost and constraint functions, and are often connected in various ways to duality. The book contains numerous examples describing in detail applications to specially structured problems.

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