This book provides a general introduction to an increasingly crucial topic for today's mathematicians and covers a broad spectrum of modeling problems, from optimization to dynamical systems to stochastic processes. An emphasis on principles and general techniques offers students the mathematical background they need to model real-world problems in a wide range of disciplines.
Intended for advanced undergraduate or beginning graduate students in mathematics and closely related fields, knowledge of single and multivariable calculus, linear algebra, and differential equations is expected. Prior exposure to computing and probability and statistics is useful, but is not required.
This third edition is accompanied by expanded and enhanced online support for instructors. The text includes some computer output from Mathematica and complete Mathematica implementations of all of the algorithms in the book can be downloaded from the book's companion website. The programs are written in Mathematica 5.2 but are also compatible with Mathematica 6.0. Contents
I. Optimization Models
One-Variable Optimization | Multivariable Optimization | Computational Methods for Optimization
II. Dynamic Models
Introduction to Dynamic Models | Analysis of Dynamic Models | Simulation of Dynamic Models
III. Probability Models
Introduction to Probability Models | Stochastic Models | Simulation of Probability Models Additional Resources
http://www.stt.msu.edu/~mcubed/modeling.html Related Topics