Description

Book Synopsis
Reflecting the current high level of interest in the application of the principle of mathematical induction to the solution of optimization problems, this book offers a comprehensive introduction to the field.

Trade Review
"This textbook...draws on his many years of experience in teaching this topic as well as on his considerable professional expertise in the area. It is ideally suited to its stated purpose as a student text." (Short Book Reviews, Vol. 20. No. 3, December 2000)

"...I was impressed with this book..." (The Statistician, Vol.51, No.2 2002)

"...excellent for the audience to whom it is addressed, and it is to be hoped that the author will write a further textbook..." (Jnl of the Operational Research Society, Vol 54(10) 2003)

Table of Contents

Preface xi

1 Introduction 1

1.1 Mathematical Induction 1

1.2 Historical Background 2

1.3 Dynamic Programming 5

1.4 The Executioner’s Tale 8

1.5 Summary 8

Exercises 10

I Deterministic Models 11

2 Multi-Stage Decision Problems 13

2.1 Maximizing Utilities 13

2.2 A General Model 17

2.3 Applications 19

Exercises 25

3 Networks 27

3.1 Shortest Paths 27

3.2 Directed Networks 29

3.3 Critical Path Analysis 30

Exercises 37

4 Further Applications 39

4.1 Discrete Actions 39

4.2 The Knapsack Problem 39

4.3 A Simple Replacement Model 42

4.4 Scheduling Problems 44

4.5 Johnson’s Algorithm 45

Exercises 49

5 Convexity 51

5.1 Convex and Concave Functions 51

5.2 Allocation Problems 56

5.3 Concave Utility Functions 60

Exercises 64

II Stochastic Models 67

6 Markov Systems 69

6.1 Introduction 69

6.2 Stochastic Dynamic Programming 70

6.3 Applications 72

Exercises 78

7 Optimal Stopping 79

7.1 Introduction 79

7.2 Stopping Times and Stopping Sets 82

7.3 Applications 90

Exercises 94

8 Special Problems 97

8.1 Introduction 97

8.2 Selling an Asset 97

8.3 The Marriage Problem 104

8.4 Prophet Inequalities 109

Exercises 116

III Markov Decision Processes 119

9 General Theory 121

9.1 Introduction 121

9.2 Minimizing Discounted Expectations 122

9.3 Policy Improvements 130

9.4 A Machine Replacement Model 137

10 Minimizing Average Costs 145

10.1 Introduction 145

10.2 Long-Term Average Costs 148

10.3 Extension to Infinitely Many States 153

10.4 Optimal Inventory Policies 158

11 Statistical Decisions 165

11.1 Introduction 165

11.2 Testing Statistical Hypotheses 166

11.3 The Sequential Probability Ratio Test 170

Notes On the Exercises 177

Chapter 1 177

Chapter 2 177

Chapter 3 178

Chapter 4 179

Chapter 5 179

Chapter 6 180

Chapter 7 181

Chapter 8 183

References 185

Index 187

Decision Theory An Introduction to Dynamic

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    A Paperback / softback by John Bather

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      View other formats and editions of Decision Theory An Introduction to Dynamic by John Bather

      Publisher: John Wiley & Sons Inc
      Publication Date: 30/05/2000
      ISBN13: 9780471976493, 978-0471976493
      ISBN10: 0471976490

      Description

      Book Synopsis
      Reflecting the current high level of interest in the application of the principle of mathematical induction to the solution of optimization problems, this book offers a comprehensive introduction to the field.

      Trade Review
      "This textbook...draws on his many years of experience in teaching this topic as well as on his considerable professional expertise in the area. It is ideally suited to its stated purpose as a student text." (Short Book Reviews, Vol. 20. No. 3, December 2000)

      "...I was impressed with this book..." (The Statistician, Vol.51, No.2 2002)

      "...excellent for the audience to whom it is addressed, and it is to be hoped that the author will write a further textbook..." (Jnl of the Operational Research Society, Vol 54(10) 2003)

      Table of Contents

      Preface xi

      1 Introduction 1

      1.1 Mathematical Induction 1

      1.2 Historical Background 2

      1.3 Dynamic Programming 5

      1.4 The Executioner’s Tale 8

      1.5 Summary 8

      Exercises 10

      I Deterministic Models 11

      2 Multi-Stage Decision Problems 13

      2.1 Maximizing Utilities 13

      2.2 A General Model 17

      2.3 Applications 19

      Exercises 25

      3 Networks 27

      3.1 Shortest Paths 27

      3.2 Directed Networks 29

      3.3 Critical Path Analysis 30

      Exercises 37

      4 Further Applications 39

      4.1 Discrete Actions 39

      4.2 The Knapsack Problem 39

      4.3 A Simple Replacement Model 42

      4.4 Scheduling Problems 44

      4.5 Johnson’s Algorithm 45

      Exercises 49

      5 Convexity 51

      5.1 Convex and Concave Functions 51

      5.2 Allocation Problems 56

      5.3 Concave Utility Functions 60

      Exercises 64

      II Stochastic Models 67

      6 Markov Systems 69

      6.1 Introduction 69

      6.2 Stochastic Dynamic Programming 70

      6.3 Applications 72

      Exercises 78

      7 Optimal Stopping 79

      7.1 Introduction 79

      7.2 Stopping Times and Stopping Sets 82

      7.3 Applications 90

      Exercises 94

      8 Special Problems 97

      8.1 Introduction 97

      8.2 Selling an Asset 97

      8.3 The Marriage Problem 104

      8.4 Prophet Inequalities 109

      Exercises 116

      III Markov Decision Processes 119

      9 General Theory 121

      9.1 Introduction 121

      9.2 Minimizing Discounted Expectations 122

      9.3 Policy Improvements 130

      9.4 A Machine Replacement Model 137

      10 Minimizing Average Costs 145

      10.1 Introduction 145

      10.2 Long-Term Average Costs 148

      10.3 Extension to Infinitely Many States 153

      10.4 Optimal Inventory Policies 158

      11 Statistical Decisions 165

      11.1 Introduction 165

      11.2 Testing Statistical Hypotheses 166

      11.3 The Sequential Probability Ratio Test 170

      Notes On the Exercises 177

      Chapter 1 177

      Chapter 2 177

      Chapter 3 178

      Chapter 4 179

      Chapter 5 179

      Chapter 6 180

      Chapter 7 181

      Chapter 8 183

      References 185

      Index 187

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