LEADER 01010nam a22002411i 4500 001 991003737769707536 005 20040602161110.0 008 040802s1930 it a||||||||||||||||ita 035 $ab13119953-39ule_inst 035 $aARCHE-107256$9ExL 040 $aBiblioteca Interfacoltà$bita$cA.t.i. Arché s.c.r.l. Pandora Sicilia s.r.l. 082 04$a823.912 100 1 $aLawrence, Thomas Edward$0110524 245 13$aLa rivolta nel deserto /$cT. E. Lawrence ; versione e prefazione di Arrigo Cajumi ; con 6 illustrazioni e una carta geografica 260 $aMilano :$bA. Mondadori Edit. Tip.,$c1930 300 $aXII, 406 p., [6] p. di tav. :$bill. ;$c22 cm 700 1 $aCajumi, Arrigo 907 $a.b13119953$b02-04-14$c05-08-04 912 $a991003737769707536 945 $aLE002 Fondo Giudici P 1313$g1$iLE002G-15041$lle002$nC. 1$o-$pE0.00$q-$rn$so $t0$u0$v0$w0$x0$y.i13755468$z05-08-04 996 $aRivolta nel deserto$9308126 997 $aUNISALENTO 998 $ale002$b05-08-04$cm$da $e-$fita$git $h3$i1 LEADER 03889nam 22006375 450 001 9910409667103321 005 20250609112032.0 010 $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(MiAaPQ)EBC6213130 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,$d[2020] 210 4$d©2020 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 $aComputer science$xMathematics 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$aComputer science$xMathematics. 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 $a9910409667103321 996 $aAccelerated Optimization for Machine Learning$92032253 997 $aUNINA