000 | 03571nam a22005655i 4500 | ||
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001 | 978-981-15-2910-8 | ||
003 | DE-He213 | ||
005 | 20240423125323.0 | ||
007 | cr nn 008mamaa | ||
008 | 200529s2020 si | s |||| 0|eng d | ||
020 |
_a9789811529108 _9978-981-15-2910-8 |
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024 | 7 |
_a10.1007/978-981-15-2910-8 _2doi |
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050 | 4 | _aQ325.5-.7 | |
072 | 7 |
_aUYQM _2bicssc |
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_aMAT029000 _2bisacsh |
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_aUYQM _2thema |
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082 | 0 | 4 |
_a006.31 _223 |
100 | 1 |
_aLin, Zhouchen. _eauthor. _0(orcid) _10000-0003-1493-7569 _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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245 | 1 | 0 |
_aAccelerated Optimization for Machine Learning _h[electronic resource] : _bFirst-Order Algorithms / _cby Zhouchen Lin, Huan Li, Cong Fang. |
250 | _a1st ed. 2020. | ||
264 | 1 |
_aSingapore : _bSpringer Nature Singapore : _bImprint: Springer, _c2020. |
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300 |
_aXXIV, 275 p. 36 illus. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_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. Accelerated Algorithms for Unconstrained Convex Optimization -- Chapter 3. Accelerated Algorithms for Constrained Convex Optimization -- Chapter 4. Accelerated Algorithms for Nonconvex Optimization -- Chapter 5. Accelerated Stochastic Algorithms -- Chapter 6. Accelerated Paralleling Algorithms -- Chapter 7. Conclusions.-. | |
520 | _aThis book on optimization includes forewords by Michael I. Jordan, Zongben Xu and Zhi-Quan Luo. Machine learning relies heavily on optimization to solve problems with its learning models, and first-order optimization algorithms are the mainstream approaches. The acceleration of first-order optimization algorithms is crucial for the efficiency of machine learning. Written by leading experts in the field, this book provides a comprehensive introduction to, and state-of-the-art review of accelerated first-order optimization algorithms for machine learning. It discusses a variety of methods, including deterministic and stochastic algorithms, where the algorithms can be synchronous or asynchronous, for unconstrained and constrained problems, which can be convex or non-convex. Offering a rich blend of ideas, theories and proofs, the book is up-to-date and self-contained. It is an excellent reference resource for users who are seeking faster optimization algorithms, as well as for graduate students and researchers wanting to grasp the frontiers of optimization in machine learning in a short 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: _z9789811529092 |
776 | 0 | 8 |
_iPrinted edition: _z9789811529115 |
776 | 0 | 8 |
_iPrinted edition: _z9789811529122 |
856 | 4 | 0 | _uhttps://doi.org/10.1007/978-981-15-2910-8 |
912 | _aZDB-2-SCS | ||
912 | _aZDB-2-SXCS | ||
942 | _cSPRINGER | ||
999 |
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