Preface

Part I: Optimization Models

Chapter 1. One Variable Optimization

1.1 The five-step Method

1.2 Sensitivity Analysis

1.3 Sensitivity and Robustness

1.4 Exercises

Further Reading

Chapter 2. Multivariable Optimization

2.1 Unconstrained Optimization

2.2 Lagrange Multipliers

2.3 Sensitivity Analysis and Shadow Prices

2.4 Exercises

Further Reading

Chapter 3. Computational Methods for Optimization

3.1 One Variable Optimization

3.2 Multivariable Optimization

3.3 Linear Programming

3.4 Discrete Optimization

3.5 Exercises

Further Reading

Part II: Dynamic Models

Chapter 4. Introduction to Dynamic Models

4.1 Steady State Analysis

4.2 Dynamical Systems

4.3 Discrete Time Dynamical Systems

4.4 Exercises

Further Reading

Chapter 5. Analysis of Dynamic Models

5.1 Eigenvalue Methods

5.2 Eigenvalue Methods for Discrete Systems

5.3 Phase Portraits

5.4 Exercises

Further Reading

Chapter 6. Simulation of Dynamic Models

6.1 Introduction to Simulation

6.2 Continuous-Time Models

6.3 The Euler Method

6.4 Chaos and Fractals

6.5 Exercises

Further Reading

Part III: Probability Models

Chapter 7. Introduction to Probability Models

7.1 Discrete Probability Models

7.2 Continuous Probability Models

7.3 Introduction to Statistics

7.4 Diffusion

7.5 Exercises

Further Reading

Chapter 8. Stochastic Models

8.1 Markov Chains

8.2 Markov Processes

8.3 Linear Regression

8.4 Time Series

8.5 Exercises

Further Reading

Chapter 9. Simulation of Probability Models

9.1 Monte Carlo Simulation

9.2 The Markov Property

9.3 Analytic Simulation

9.4 Particle Tracking

9.5 Fractional Diffusion

9.6 Exercises

Further Reading

Afterword

Further Reading

Index