000 03304nam a22005655i 4500
001 978-981-16-9840-8
003 DE-He213
005 20240423125522.0
007 cr nn 008mamaa
008 220615s2022 si | s |||| 0|eng d
020 _a9789811698408
_9978-981-16-9840-8
024 7 _a10.1007/978-981-16-9840-8
_2doi
050 4 _aQ325.5-.7
072 7 _aUYQM
_2bicssc
072 7 _aMAT029000
_2bisacsh
072 7 _aUYQM
_2thema
082 0 4 _a006.31
_223
100 1 _aLin, Zhouchen.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aAlternating Direction Method of Multipliers for Machine Learning
_h[electronic resource] /
_cby Zhouchen Lin, Huan Li, Cong Fang.
250 _a1st ed. 2022.
264 1 _aSingapore :
_bSpringer Nature Singapore :
_bImprint: Springer,
_c2022.
300 _aXXIII, 263 p. 1 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aChapter 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 _aMachine 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 _aMachine learning.
650 0 _aMathematical optimization.
650 0 _aComputer science
_xMathematics.
650 0 _aMathematics
_xData processing.
650 1 4 _aMachine Learning.
650 2 4 _aOptimization.
650 2 4 _aMathematical Applications in Computer Science.
650 2 4 _aComputational Mathematics and Numerical Analysis.
700 1 _aLi, Huan.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
700 1 _aFang, Cong.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9789811698392
776 0 8 _iPrinted edition:
_z9789811698415
776 0 8 _iPrinted edition:
_z9789811698422
856 4 0 _uhttps://doi.org/10.1007/978-981-16-9840-8
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
942 _cSPRINGER
999 _c178813
_d178813