Mathematical Modeling 3e is a general introduction to an increasingly crucial topic for today's mathematicians. Unlike textbooks focused on one kind of mathematical model, this book covers the broad spectrum of modeling problems, from optimization to dynamical systems to stochastic processes. Mathematical modeling is the link between mathematics and the rest of the world. Meerschaert shows how to refine a question, phrasing it in precise mathematical terms. Then he encourages students to reverse the process, translating the mathematical solution back into a comprehensible, useful answer to the original question. This textbook mirrors the process professionals must follow in solving complex problems. Each chapter in this book is followed by a set of challenging exercises. These exercises require significant effort on the part of the student, as well as a certain amount of creativity. Meerschaert did not invent the problems in this book--they are real problems, not designed to illustrate the use of any particular mathematical technique. Meerschaert's emphasis on principles and general techniques offers students the mathematical background they need to model problems in a wide range of disciplines. This new edition will be accompanied by expanded and enhanced on-line support for instructors. MATLAB material will be added to complement existing support for Maple, Mathematica, and other software packages, and the solutions manual will be provided both in hard copy and on the web.

Key Features

* Increased support for instructors, including MATLAB material as well as other on-line resources * New sections on time series analysis and diffusion models * Additional problems with international focus such as whale and dolphin populations, plus updated optimization problems


Advanced undergraduate or beginning graduate students in mathematics and closely related fields. Formal prerequisites consist of the usual freshman-sophomore sequence in mathematics, including one-variable calculus, multivariable calculus, linear algebra, and differential equations. Prior exposure to computing and probability and statistics is useful, but is not required.

Table of Contents

I. OPTIMIZATION MODELS 1. One-Variable Optimization 2. Multivariable Optimization 3. Computational Methods for Optimization II. DYNAMIC MODELS 4. Introduction to Dynamic Models 5. Analysis of Dynamic Models 6. Simulation of Dynamic Models III. PROBABILITY MODELS 7. Introduction to Probability Models 8. Stochastic Models 9. Simulation of Probability Models


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© 2007
Academic Press
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"I think this is the best book in its genre. I haven't been tempted to use another. The mathematics in it is interesting, useful, and still within reach of typical undergraduates." --John E. Doner, Department of Mathematics, University of California-Santa Barbara