LEADER 05601nam 22007695 450 001 9910254088003321 005 20220414213211.0 010 $a3-319-42056-9 024 7 $a10.1007/978-3-319-42056-1 035 $a(CKB)3710000000872814 035 $a(DE-He213)978-3-319-42056-1 035 $a(MiAaPQ)EBC4700022 035 $a(PPN)195511786 035 $a(EXLCZ)993710000000872814 100 $a20160929d2016 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aOptimization and its applications in control and data sciences $ein honor of Boris T. Polyak?s 80th birthday /$fedited by Boris Goldengorin 205 $a1st ed. 2016. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2016. 215 $a1 online resource (XVII, 507 p. 45 illus., 22 illus. in color.) 225 1 $aSpringer Optimization and Its Applications,$x1931-6828 ;$v115 311 $a3-319-42054-2 320 $aIncludes bibliographical references at the end of each chapters. 327 $aIntroduction: Big, Small, and Optimal Steps of Boris Polyak (Boris Goldengorin) -- A Convex Optimization Approach to Modeling of Stationary Periodic Time Series (Anders Lindquist and Giorgio Picci) -- New two-phase proximal method of solving the solving the problem of equilibrium programming (Sergey I. Lyashko and Vladimir V. Semenov) -- Minimax Control of Positive Switching Systems with Markovian Jumps (Patrizio Colaneri, José Geromel, Paolo Bolzern, Grace Deaecto) -- A modified Polak-Ribière-Polyak conjugate gradient algorithm with sufficient descent and conjugacy properties for unconstrained optimization (Neculai Andrei) -- Subgradient method with the transformation of space and Polyak's step (Petro Stetsyuk) -- Invariance Conditions for Nonlinear Dynamical Systems (Y. Song, and T. Terlaky) -- Nonparametric ellipsoidal approximation of compact sets of random points (S. I., Lyashko, V.V. Semenov D.A. Klyushin, M.V. Prysyazhna, M.P. Shlykov) -- Algorithmic Principle of the Least Excessive Revenue for finding market equilibria (Yurii Nesterov, Vladimir Shikhman) -- Matrix-Free Convex Optimization Modeling (Stephen Boyd and Steven Diamond) -- Stochastic Optimization and Statistical Learning in Reproducing Kernel Hilbert Spaces the Stochastic Quasi-Gradient Methods (Vladimir I. Norkin). . 330 $aThis book focuses on recent research in modern optimization and its implications in control and data analysis. This book is a collection of papers from the conference ?Optimization and Its Applications in Control and Data Science? dedicated to Professor Boris T. Polyak, which was held in Moscow, Russia on May 13-15, 2015. This book reflects developments in theory and applications rooted by Professor Polyak?s fundamental contributions to constrained and unconstrained optimization, differentiable and nonsmooth functions, control theory and approximation. Each paper focuses on techniques for solving complex optimization problems in different application areas and recent developments in optimization theory and methods. Open problems in optimization, game theory and control theory are included in this collection which will interest engineers and researchers working with efficient algorithms and software for solving optimization problems in market and data analysis. Theoreticians in operations research, applied mathematics, algorithm design, artificial intelligence, machine learning, and software engineering will find this book useful and graduate students will find the state-of-the-art research valuable. 410 0$aSpringer Optimization and Its Applications,$x1931-6828 ;$v115 606 $aMathematical optimization 606 $aData structures (Computer science) 606 $aSystem theory 606 $aDynamics 606 $aErgodic theory 606 $aAlgorithms 606 $aComputer science?Mathematics 606 $aComputer mathematics 606 $aOptimization$3https://scigraph.springernature.com/ontologies/product-market-codes/M26008 606 $aData Structures$3https://scigraph.springernature.com/ontologies/product-market-codes/I15017 606 $aSystems Theory, Control$3https://scigraph.springernature.com/ontologies/product-market-codes/M13070 606 $aDynamical Systems and Ergodic Theory$3https://scigraph.springernature.com/ontologies/product-market-codes/M1204X 606 $aAlgorithms$3https://scigraph.springernature.com/ontologies/product-market-codes/M14018 606 $aMathematical Applications in Computer Science$3https://scigraph.springernature.com/ontologies/product-market-codes/M13110 615 0$aMathematical optimization. 615 0$aData structures (Computer science). 615 0$aSystem theory. 615 0$aDynamics. 615 0$aErgodic theory. 615 0$aAlgorithms. 615 0$aComputer science?Mathematics. 615 0$aComputer mathematics. 615 14$aOptimization. 615 24$aData Structures. 615 24$aSystems Theory, Control. 615 24$aDynamical Systems and Ergodic Theory. 615 24$aAlgorithms. 615 24$aMathematical Applications in Computer Science. 676 $a502.85 702 $aGoldengorin$b Boris$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910254088003321 996 $aOptimization and its applications in control and data sciences$91523543 997 $aUNINA