LEADER 05204nam 22006975 450 001 9910586596903321 005 20240221123613.0 010 $a3-031-00832-4 024 7 $a10.1007/978-3-031-00832-0 035 $a(MiAaPQ)EBC7069304 035 $a(Au-PeEL)EBL7069304 035 $a(CKB)24342175700041 035 $a(OCoLC)1338838524 035 $a(DE-He213)978-3-031-00832-0 035 $a(PPN)264191986 035 $a(EXLCZ)9924342175700041 100 $a20220804d2022 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aHigh-Dimensional Optimization and Probability $eWith a View Towards Data Science /$fedited by Ashkan Nikeghbali, Panos M. Pardalos, Andrei M. Raigorodskii, Michael Th. Rassias 205 $a1st ed. 2022. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2022. 215 $a1 online resource (417 pages) 225 1 $aSpringer Optimization and Its Applications,$x1931-6836 ;$v191 311 08$aPrint version: Nikeghbali, Ashkan High-Dimensional Optimization and Probability Cham : Springer International Publishing AG,c2022 9783031008313 327 $aProjection of a point onto a convex set via Charged Balls Method (E. Abbasov ) -- Towards optimal sampling for learning sparse approximations in high dimensions (Adcock) -- Recent Theoretical Advances in Non-Convex Optimization (Gasnikov) -- Higher Order Embeddings for the Composition of the Harmonic Projection and Homotopy Operators (Ding) -- Codifferentials and Quasidifferentials of the Expectation of Nonsmooth Random Integrands and Two-Stage Stochastic Programming (M.V. Dolgopolik) -- On the Expected Extinction Time for the Adjoint Circuit Chains associated with a Random Walk with Jumps in Random Environments (Ganatsiou) -- A statistical learning theory approach for the analysis of the trade-off between sample size and precision in truncated ordinary least squares (Raciti) -- Recent theoretical advances in decentralized distributed convex optimization (Gasnikov) -- On training set selection in spatial deep learning (M.T. Hendrix) -- Surrogate-Based Reduced Dimension Global Optimization in Process Systems Engineering (Xiang Li) -- A viscosity iterative method with alternated inertial terms for solving the split feasibility problem (Rassias) -- Efficient Location-Based Tracking for IoT Devices Using Compressive Sensing and Machine Learning Techniques (Aboushelbaya) -- Nonsmooth Mathematical Programs with Vanishing Constraints in Banach Spaces (Singh). 330 $aThis volume presents extensive research devoted to a broad spectrum of mathematics with emphasis on interdisciplinary aspects of Optimization and Probability. Chapters also emphasize applications to Data Science, a timely field with a high impact in our modern society. The discussion presents modern, state-of-the-art, research results and advances in areas including non-convex optimization, decentralized distributed convex optimization, topics on surrogate-based reduced dimension global optimization in process systems engineering, the projection of a point onto a convex set, optimal sampling for learning sparse approximations in high dimensions, the split feasibility problem, higher order embeddings, codifferentials and quasidifferentials of the expectation of nonsmooth random integrands, adjoint circuit chains associated with a random walk, analysis of the trade-off between sample size and precision in truncated ordinary least squares, spatial deep learning, efficient location-based tracking for IoT devices using compressive sensing and machine learning techniques, and nonsmooth mathematical programs with vanishing constraints in Banach spaces. The book is a valuable source for graduate students as well as researchers working on Optimization, Probability and their various interconnections with a variety of other areas. Chapter 12 is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com. 410 0$aSpringer Optimization and Its Applications,$x1931-6836 ;$v191 606 $aMathematical optimization 606 $aProbabilities 606 $aBusiness information services 606 $aMathematics 606 $aOptimization 606 $aApplied Probability 606 $aIT in Business 606 $aApplications of Mathematics 606 $aOptimització matemàtica$2thub 606 $aProbabilitats$2thub 608 $aLlibres electrònics$2thub 615 0$aMathematical optimization. 615 0$aProbabilities. 615 0$aBusiness information services. 615 0$aMathematics. 615 14$aOptimization. 615 24$aApplied Probability. 615 24$aIT in Business. 615 24$aApplications of Mathematics. 615 7$aOptimització matemàtica 615 7$aProbabilitats 676 $a519.3 702 $aNikeghbali$b Ashkan$f1975- 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910586596903321 996 $aHigh-dimensional optimization and probability$93000516 997 $aUNINA