Introduction to stochastic search and optimization : estimation, simulation, and control /
| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Hoboken, N.J. :
J. Wiley,
c2003
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| Series: | Wiley-Interscience series in discrete mathematics and optimization
Wiley-Interscience series in discrete mathematics Wiley-Interscience series in discrete mathematics and optimization |
| Subjects: |
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| 100 | 1 | |a Spall, James C |1 http://viaf.org/viaf/40877999 | |
| 100 | 1 | |a Spall, James C | |
| 245 | 1 | 0 | |a Introduction to stochastic search and optimization : |b estimation, simulation, and control / |c James C. Spall |
| 260 | |a Hoboken, N.J. : |b J. Wiley, |c c2003 | ||
| 300 | |a xx, 595 p. : |b ill. ; |c 26 cm | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a unmediated |b n |2 rdamedia | ||
| 338 | |a volume |b nc |2 rdacarrier | ||
| 440 | 0 | |a Wiley-Interscience series in discrete mathematics and optimization | |
| 440 | 0 | |a Wiley-Interscience series in discrete mathematics | |
| 490 | 1 | |a Wiley-Interscience series in discrete mathematics and optimization | |
| 504 | |a Includes bibliographical references (p. 558-579) and index | ||
| 504 | |a Includes bibliographical references (p. [558]-579) and index | ||
| 505 | 0 | 0 | |g 1 |t Stochastic Search and Optimization: Motivation and Supporting Results -- |g 2. |t Direct Methods for Stochastic Search -- |g 3. |t Recursive Estimation for Linear Models -- |g 4. |t Stochastic Approximation for Nonlinear Root-Finding -- |g 5. |t Stochastic Gradient Form of Stochastic Approximation -- |g 6. |t Stochastic Approximation and the Finite-Difference Method -- |g 7. |t Simultaneous Perturbation Stochastic Approximation -- |g 8. |t Annealing-Type Algorithms -- |g 9. |t Evolutionary Computation I: Genetic Algorithms -- |g 10. |t Evolutionary Computation II: General Methods and Theory -- |g 11. |t Reinforcement Learning via Temporal Differences -- |g 12. |t Statistical Methods for Optimization in Discrete Problems -- |g 13. |t Model Selection and Statistical Information -- |g 14. |t Simulation-Based Optimization I: Regeneration, Common Random Numbers, and Selection Methods -- |g 15. |t Simulation-Based Optimization II: Stochastic Gradient and Sample Path Methods -- |g 16. |t Markov Chain Monte Carlo -- |g 17. |t Optimal Design for Experimental Inputs -- |g App. A. |t Selected Results from Multivariate Analysis -- |g App. B. |t Some Basic Tests in Statistics -- |g App. C. |t Probability Theory and Convergence -- |g App. D. |t Random Number Generation -- |g App. E. |t Markov Processes. |
| 596 | |a 4 | ||
| 650 | 0 | |a Mathematical optimization | |
| 650 | 0 | |a Search theory | |
| 650 | 0 | |a Stochastic processes | |
| 650 | 7 | |a Mathematical optimization |2 fast | |
| 650 | 7 | |a Search theory |2 fast | |
| 650 | 7 | |a Stochastic processes |2 fast | |
| 830 | 0 | |a Wiley-Interscience series in discrete mathematics and optimization | |
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