Introduction to Dynamic Programming

International Series in Modern Applied Mathematics and Computer Science, Volume 1

1st Edition - January 1, 1981
Authors: Leon Cooper, Mary W. Cooper
Editor: E. Y. Rodin
Language: English
eBook ISBN:
9 7 8 - 1 - 4 8 3 1 - 6 1 5 8 - 7

Introduction to Dynamic Programming provides information pertinent to the fundamental aspects of dynamic programming. This book considers problems that can be quantitatively… Read more

Purchase options

LIMITED OFFER

Save 50% on book bundles

Immediately download your ebook while waiting for your print delivery. No promo code is needed.

Institutional subscription on ScienceDirect

Request a sales quote

Introduction to Dynamic Programming provides information pertinent to the fundamental aspects of dynamic programming. This book considers problems that can be quantitatively formulated and deals with mathematical models of situations or phenomena that exists in the real world. Organized into 10 chapters, this book begins with an overview of the fundamental components of any mathematical optimization model. This text then presents the details of the application of dynamic programming to variational problems. Other chapters consider the application of dynamic programming to inventory theory, Markov processes, chemical engineering, optimal control theory, calculus of variations, and economics. This book discusses as well the approach to problem solving that is typical of dynamic programming. The final chapter deals with a number of actual applications of dynamic programming to practical problems. This book is a valuable resource for .graduate level students of mathematics, economics, statistics, business, operations research, industrial engineering, or other engineering fields.

Chapter 1. Introduction

1.1. Optimization

1.2. Separable Functions

1.3. Convex and Concave Functions

1.4. Optima of Convex and Concave Functions

1.5. Dynamic Programming

1.6. Dynamic Programming: Advantages and Limitations

1.7. The Development of Dynamic Programming

Exercises—Chapter 1

Chapter 2. Some Simple Examples

2.1. Introduction

2.2. The Wandering Applied Mathematician

2.3. The Wandering Applied Mathematician (Continued)

2.4. A Problem in "Division"

2.5. A Simple Equipment Replacement Problem

2.6. Summary

Exercises—Chapter 2

Chapter 5. Functional Equations: Basic Theory

3.1. Introduction

3.2. Sequential Decision Processes

3.3. Functional Equations and the Principle of Optimality

3.4. The Principle of Optimality—^Necessary and Sufficient Conditions

Exercise—Chapter 3

Chapter 4. One-dimensional Dynamic Programming: Analytic Solutions

4.1. Introduction

4.2. A Prototype Problem

4.3. Some Variations of the Prototype Problem

4.4. Some Generalizations of the Prototype Problem

4.5. Some Generalizations

4.6. A Problem in Renewable Resources

4.7. Multiplicative Constraints and Functions

4.8. Some Variations on State Functions

4.9. A Minimax Objective Function

Exercises—Chapter 4

Chapter 5. One-Dimensional Dynamic Programming: Computational Solutions

5.1. Introduction

5.2. A Prototype Problem

5.3. An Example of the Computational Process

5.4. The Computational Eflfectiveness of Dynamic Programming

5.5. An Integer Nonlinear Programming Problem

5.6. Computation with Continuous Variables

5.7. Convex and Concave

5.8. Equipment Replacement Problems

5.9. Some Integer Constrained Problems

5.10. A Deterministic Inventory Problem—Forward and Backward Recursion

Exercises—Chapter 5

Chapter 6. Multidimensional Problems

6.1. Introduction

6.2. A Nonlinear Allocation Problem

6.3. A Nonlinear Allocation Problem with Several Decision Variables

6.4. An Equipment Replacement Problem

6.5. Some Investment Problems

6.6. A Stochastic Decision Problem

6.7. The Traveling Salesman Problem

6.8. A Multicomponent Reliability Problem

6.9. A Problem in Product Development and Planning

6.10. A Smoothing Problem

6.11. Operation of a Chemical Reactor

Exercises—Chapter 6

Chapter 7. Reduction of State Dimensionality and Approximations

7.1. Introduction

7.2. Lagrange Multipliers and Reduction of State Variables

7.3. Method of Successive Approximations

7.4. Approximation in Policy and Function Space

7.5. Polynomial Approximation in Dynamic Programming

7.6. Reduction of Dimensionality and Expanding Grids

7.7. A New Method for Reduction of Dimensionality

Exercises—Chapter 7

Chapter 8. Stochastic Processes and Dynamic Programming

8.1. Introduction

8.2. A Stochastic Allocation Problem—Discrete Case

8.3. A Stochastic Allocation Problem—Continuous Case

8.4. A General Stochastic Inventory Model

8.5. A Stochastic Production Scheduling and Inventory Control Problem

8.6. Markov Processes

8.7. Markovian Sequential Decision Processes

8.8. The Policy Iteration Method of Howard

Exercises—Chapter

Chapter 9. Dynamic Programming and the Calculus of Variations

9.1. Introduction

9.2. Necessary and Sufficient Conditions for Optimality

9.3. Boundary Conditions and Constraints

9.4. Practical Difficulties of the Calculus of Variations

9.5. Dynamic Programming in Variational Problems

9.6. Computational Solution of Variational Problems by Dynamic Programming

9.7. A Computational Example

9.8. Additional Variational Problems

Exercises—Chapter 9

Chapter 10. Applications of Dynamic Programming

10.1. Introduction

10.2. Municipal Bond Coupon Schedules

10.3. Expansion of Electric Power Systems

10.4. The Design of a Hospital Ward

10.5. Optimal Scheduling of Excess Cash Investment

10.6. Animal Feedlot Optimization

10.7. Optimal Investment in Human Capital

10.8. Optimal Crop Supply

10.9. A Style Goods Inventory Model

Appendix. Sets, Convexity, and n-Dimensional Geometry

A.1. Sets and Set Notation

A.2. n-Dimensional Geometry and Sets

A.3. Convex Sets

References

Index