| Item type | Current library | Call number | Copy number | Status | Date due | Barcode |
|---|---|---|---|---|---|---|
Book Closed Access
|
Science and Education Library | 511.8 MEE 1 (Browse shelf(Opens below)) | 1 | Available | NAGL22021418 |
Table of Contents
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
Include Index:p. 363-365
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