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001 | 978-981-16-9840-8 | ||
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_a9789811698408 _9978-981-16-9840-8 |
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024 | 7 |
_a10.1007/978-981-16-9840-8 _2doi |
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_a006.31 _223 |
100 | 1 |
_aLin, Zhouchen. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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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. |
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300 |
_aXXIII, 263 p. 1 illus. _bonline resource. |
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_atext _btxt _2rdacontent |
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_acomputer _bc _2rdamedia |
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_aonline resource _bcr _2rdacarrier |
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_atext file _bPDF _2rda |
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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. |
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650 | 0 |
_aMathematics _xData processing. |
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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 |
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700 | 1 |
_aFang, Cong. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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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 |
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