LEADER 04625nam 2200613L 450 001 9910146070403321 005 20211213133032.0 010 $a1-280-25282-0 010 $a9786610252824 010 $a0-470-34845-3 010 $a0-471-44190-2 010 $a0-471-72213-8 035 $a(CKB)1000000000019003 035 $a(EBL)226531 035 $a(OCoLC)181839360 035 $a(SSID)ssj0000182942 035 $a(PQKBManifestationID)11196822 035 $a(PQKBTitleCode)TC0000182942 035 $a(PQKBWorkID)10172460 035 $a(PQKB)10763308 035 $a(MiAaPQ)EBC226531 035 $a(PPN)223411191 035 $a(EXLCZ)991000000000019003 100 $a20021003d2003 uy 0 101 0 $aeng 135 $aurcn|---||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aIntroduction to stochastic search and optimization $eestimation, simulation, and control /$fJames C. Spall 210 4$aHoboken, N.J. :$cWiley-Interscience,$d2003. 215 $a1 online resource (620 pages) 225 1 $aWiley-Interscience series in discrete mathematics and optimization 300 $aDescription based upon print version of record. 311 1 $a0-471-33052-3 320 $aIncludes bibliographical references (p. 558-579) and index. 327 $a1. Stochastic Search and Optimization: Motivation and Supporting Results -- 2. Direct Methods for Stochastic Search -- 3. Recursive Estimation for Linear Models -- 4. Stochastic Approximation for Nonlinear Root-Finding -- 5. Stochastic Gradient Form of Stochastic Approximation -- 6. Stochastic Approximation and the Finite-Difference Method -- 7. Simultaneous Perturbation Stochastic Approximation -- 8. Annealing-Type Algorithms -- 9. Evolutionary Computation I: Genetic Algorithms -- 10. Evolutionary Computation II: General Methods and Theory -- 11. Reinforcement Learning via Temporal Differences -- 12. Statistical Methods for Optimization in Discrete Problems -- 13. Model Selection and Statistical Information -- 14. Simulation-Based Optimization I: Regeneration, Common Random Numbers, and Selection Methods -- 15. Simulation-Based Optimization II: Stochastic Gradient and Sample Path Methods -- 16. Markov Chain Monte Carlo -- 17. Optimal Design for Experimental Inputs. 330 $aA unique interdisciplinary foundation for real-world problem solving Stochastic search and optimization techniques are used in a vast number of areas, including aerospace, medicine, transportation, and finance, to name but a few. Whether the goal is refining the design of a missile or aircraft, determining the effectiveness of a new drug, developing the most efficient timing strategies for traffic signals, or making investment decisions in order to increase profits, stochastic algorithms can help researchers and practitioners devise optimal solutions to countless real-world problems. Introduction to Stochastic Search and Optimization: Estimation, Simulation, and Control is a graduate-level introduction to the principles, algorithms, and practical aspects of stochastic optimization, including applications drawn from engineering, statistics, and computer science. The treatment is both rigorous and broadly accessible, distinguishing this text from much of the current literature and providing students, researchers, and practitioners with a strong foundation for the often-daunting task of solving real-world problems. The text covers a broad range of today?s most widely used stochastic algorithms, including: Random search Recursive linear estimation Stochastic approximation Simulated annealing Genetic and evolutionary methods Machine (reinforcement) learning Model selection Simulation-based optimization Markov chain Monte Carlo Optimal experimental design. The book includes over 130 examples, Web links to software and data sets, more than 250 exercises for the reader, and an extensive list of references. These features help make the text an invaluable resource for those interested in the theory or practice of stochastic search and optimization. 606 $aStochastic processes 606 $aSearch theory 606 $aMathematical optimization 615 0$aStochastic processes. 615 0$aSearch theory. 615 0$aMathematical optimization. 676 $a519.23 700 $aSpall$b James C.$0248352 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 801 2$b6680 906 $aBOOK 912 $a9910146070403321 996 $aIntroduction to stochastic search and optimization$92246800 997 $aUNINA