Preface
Ⅰ OPTIMIZATION MODELS
1 ONE VARIABLE OPTIMIZATION
1.1 The Five-Step Method
1.2 Sensitivity Analysis
1.3 Sensitivity and Robustness
1.4 Exercises
2 MULTIVARIABLE OPTIMIZATION
2.1 Unconstrained Optimization
2.2 Lagrange Multipliers
2.3 Sensitivity Analysis and Shadow Prices
2.4 Exercises
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
Ⅱ DYNAMIC MODELS
4 INTRODUCTION TO DYNAMIC MODELS
4.1 Steady State Analysis
4.2 Dynamical Systems
4.3 Discrete Time Dynamical Systems
4.4 Exercises
5 ANALYSIS OF DYNAMIC MODELS
5.1 Eigenvalue Methods
5.2 Eigenvalue Methods for Discrete Systems
5.3 Phase Portraits
5.4 Exercises
6 SIMULATION OF DYNAMIC MODELS
6.1 Introduction to Simulation
6.2 Continuous-Time Models
6.3 The Euler Method
6.4 Chaos and Fractais
6.5 Exercises
Ⅲ PROBABILITY MODELS
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
8 STOCHASTIC MODELS
8.1 Markov Chains
8.2 Markov Processes
8.3 Linear Regression
8.4 Time Series
8.5 Exercises
9 SIMULATION OF PROBABILITY MODELS
9.1 Monte Carlo Simulation
9.2 The MarkovProperty
9.3 Analytic Simulation
9.4 Exercises
Afterword
Index
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