LEADER 03838nam 22006255 450 001 996465342403316 005 20230913171352.0 010 $a981-15-2910-8 010 $a9789811529108$b(eBook) 024 7 $a10.1007/978-981-15-2910-8 035 $a(CKB)4100000011273628 035 $a(MiAaPQ)EBC6213171 035 $a(DE-He213)978-981-15-2910-8 035 $a(PPN)248393383 035 $a(EXLCZ)994100000011273628 100 $a20200529h20202020 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAccelerated optimization for machine learning $efirst-order algorithms /$fZhouchen Lin, Huan Li, Cong Fang 210 1$aSingapore :$cSpringer,$d[2020] 210 4$dİ2020 215 $a1 online resource (286 pages) 311 08$a981-15-2909-4 311 08$a9789811529092 320 $aIncludes bibliographical references and index. 327 $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. 330 $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. 606 $aMachine learning$xMathematics 606 $aMathematical optimization 606 $aComputer mathematics 606 $aMachine Learning$3https://scigraph.springernature.com/ontologies/product-market-codes/I21010 606 $aOptimization$3https://scigraph.springernature.com/ontologies/product-market-codes/M26008 606 $aMath Applications in Computer Science$3https://scigraph.springernature.com/ontologies/product-market-codes/I17044 606 $aComputational Mathematics and Numerical Analysis$3https://scigraph.springernature.com/ontologies/product-market-codes/M1400X 615 0$aMachine learning$xMathematics. 615 0$aMathematical optimization. 615 0$aComputer mathematics. 615 14$aMachine Learning. 615 24$aOptimization. 615 24$aMath Applications in Computer Science. 615 24$aComputational Mathematics and Numerical Analysis. 676 $a006.31 700 $aLin$b Zhouchen$4aut$4http://id.loc.gov/vocabulary/relators/aut$0908694 702 $aLi$b Huan$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aFang$b Cong$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996465342403316 996 $aAccelerated Optimization for Machine Learning$92032253 997 $aUNISA