Ebook: Stochastic Recursive Algorithms for Optimization: Simultaneous Perturbation Methods

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.

---

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.

---

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.
Content:
Front Matter....Pages 1-15
Front Matter....Pages 1-2
Introduction....Pages 3-12
Deterministic Algorithms for Local Search....Pages 13-15
Stochastic Approximation Algorithms....Pages 17-28
Front Matter....Pages 29-30
Kiefer-Wolfowitz Algorithm....Pages 31-39
Gradient Schemes with Simultaneous Perturbation Stochastic Approximation....Pages 41-76
Smoothed Functional Gradient Schemes....Pages 77-102
Front Matter....Pages 103-104
Newton-Based Simultaneous Perturbation Stochastic Approximation....Pages 105-131
Newton-Based Smoothed Functional Algorithms....Pages 133-148
Front Matter....Pages 149-150
Discrete Parameter Optimization....Pages 151-166
Algorithms for Constrained Optimization....Pages 167-186
Reinforcement Learning....Pages 187-220
Front Matter....Pages 221-223
Service Systems....Pages 225-241
Road Traffic Control....Pages 243-255
Communication Networks....Pages 257-280
Back Matter....Pages 0--1

---

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.
Content:
Front Matter....Pages 1-15
Front Matter....Pages 1-2
Introduction....Pages 3-12
Deterministic Algorithms for Local Search....Pages 13-15
Stochastic Approximation Algorithms....Pages 17-28
Front Matter....Pages 29-30
Kiefer-Wolfowitz Algorithm....Pages 31-39
Gradient Schemes with Simultaneous Perturbation Stochastic Approximation....Pages 41-76
Smoothed Functional Gradient Schemes....Pages 77-102
Front Matter....Pages 103-104
Newton-Based Simultaneous Perturbation Stochastic Approximation....Pages 105-131
Newton-Based Smoothed Functional Algorithms....Pages 133-148
Front Matter....Pages 149-150
Discrete Parameter Optimization....Pages 151-166
Algorithms for Constrained Optimization....Pages 167-186
Reinforcement Learning....Pages 187-220
Front Matter....Pages 221-223
Service Systems....Pages 225-241
Road Traffic Control....Pages 243-255
Communication Networks....Pages 257-280
Back Matter....Pages 0--1
....