Beschreibung
Stochastic Recursive Algorithms for Optimization presents algorithms for constrained and unconstrained optimization and for reinforcement learning. Efficient perturbation approaches form a thread unifying all the algorithms considered. Simultaneous perturbation stochastic approximation and smooth fractional estimators for gradient- and Hessian-based methods are presented. These algorithms: are easily implemented; do not require an explicit system model; and work with real or simulated data. Chapters on their application in service systems, vehicular traffic control and communications networks illustrate this point. The book is self-contained with necessary mathematical results placed in an appendix. The text provides easy-to-use, off-the-shelf algorithms that are given detailed mathematical treatment so the material presented will be of significant interest to practitioners, academic researchers and graduate students alike. The breadth of applications makes the book appropriate for reader from similarly diverse backgrounds: workers in relevant areas of computer science, control engineering, management science, applied mathematics, industrial engineering and operations research will find the content of value.
Autorenporträt
InhaltsangabePart I: Introduction to Stochastic Recursive Algorithms.- Introduction.- Deterministic Algorithms for Local Search.- Stochastic Approximation Algorithms.- Part II: Gradient Estimation Schemes.- Kiefer-Wolfowitz Algorithm.- Gradient Schemes with Simultaneous Perturbation Stochastic Approximation.- Smoothed Functional Gradient Schemes.- Part III: Hessian Estimation Schemes.- Hessian Estimation with Simultaneous Perturbation Stochasti Approximation.- Smoothed Functional Hessian Schemes.- Part IV: Variations to the Basic Scheme.- Discrete Optimization.- Algorithms for Contrained Optimization.- Reinforcement Learning.- Part V: Applications.- Service Systems.- Road Traffic Control.- Communication Networks.
