Alternating Direction Method of Multipliers for Machine Learning (Record no. 178813)

MARC details
000 -LEADER
fixed length control field 03304nam a22005655i 4500
001 - CONTROL NUMBER
control field 978-981-16-9840-8
003 - CONTROL NUMBER IDENTIFIER
control field DE-He213
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20240423125522.0
007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION
fixed length control field cr nn 008mamaa
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
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020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9789811698408
-- 978-981-16-9840-8
024 7# - OTHER STANDARD IDENTIFIER
Standard number or code 10.1007/978-981-16-9840-8
Source of number or code doi
050 #4 - LIBRARY OF CONGRESS CALL NUMBER
Classification number Q325.5-.7
072 #7 - SUBJECT CATEGORY CODE
Subject category code UYQM
Source bicssc
072 #7 - SUBJECT CATEGORY CODE
Subject category code MAT029000
Source bisacsh
072 #7 - SUBJECT CATEGORY CODE
Subject category code UYQM
Source thema
082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 006.31
Edition number 23
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Lin, Zhouchen.
Relator term author.
Relator code aut
-- http://id.loc.gov/vocabulary/relators/aut
245 10 - TITLE STATEMENT
Title Alternating Direction Method of Multipliers for Machine Learning
Medium [electronic resource] /
Statement of responsibility, etc by Zhouchen Lin, Huan Li, Cong Fang.
250 ## - EDITION STATEMENT
Edition statement 1st ed. 2022.
264 #1 -
-- Singapore :
-- Springer Nature Singapore :
-- Imprint: Springer,
-- 2022.
300 ## - PHYSICAL DESCRIPTION
Extent XXIII, 263 p. 1 illus.
Other physical details online resource.
336 ## -
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-- computer
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338 ## -
-- online resource
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347 ## -
-- text file
-- PDF
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505 0# - FORMATTED CONTENTS NOTE
Formatted contents note Chapter 1. Introduction -- Chapter 2. Derivations of ADMM -- Chapter 3. ADMM for Deterministic and Convex Optimization -- Chapter 4. ADMM for Nonconvex Optimization -- Chapter 5. ADMM for Stochastic Optimization -- Chapter 6. ADMM for Distributed Optimization -- Chapter 7. Practical Issues and Conclusions.
520 ## - SUMMARY, ETC.
Summary, etc Machine learning heavily relies on optimization algorithms to solve its learning models. Constrained problems constitute a major type of optimization problem, and the alternating direction method of multipliers (ADMM) is a commonly used algorithm to solve constrained problems, especially linearly constrained ones. Written by experts in machine learning and optimization, this is the first book providing a state-of-the-art review on ADMM under various scenarios, including deterministic and convex optimization, nonconvex optimization, stochastic optimization, and distributed optimization. Offering a rich blend of ideas, theories and proofs, the book is up-to-date and self-contained. It is an excellent reference book for users who are seeking a relatively universal algorithm for constrained problems. Graduate students or researchers can read it to grasp the frontiers of ADMM in machine learning in a short period of time.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Machine learning.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Mathematical optimization.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Computer science
General subdivision Mathematics.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Mathematics
General subdivision Data processing.
650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Machine Learning.
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Optimization.
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Mathematical Applications in Computer Science.
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Computational Mathematics and Numerical Analysis.
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Li, Huan.
Relator term author.
Relator code aut
-- http://id.loc.gov/vocabulary/relators/aut
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Fang, Cong.
Relator term author.
Relator code aut
-- http://id.loc.gov/vocabulary/relators/aut
710 2# - ADDED ENTRY--CORPORATE NAME
Corporate name or jurisdiction name as entry element SpringerLink (Online service)
773 0# - HOST ITEM ENTRY
Title Springer Nature eBook
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Display text Printed edition:
International Standard Book Number 9789811698392
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Display text Printed edition:
International Standard Book Number 9789811698415
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Display text Printed edition:
International Standard Book Number 9789811698422
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier <a href="https://doi.org/10.1007/978-981-16-9840-8">https://doi.org/10.1007/978-981-16-9840-8</a>
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942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type eBooks-CSE-Springer

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