Mathematical Modeling / Edition 3

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Overview

The second edition of this popular text offers a unique approach to mathematical modeling. Meerschaert offers an inviting introduction and applies a problem-solving methodology in the three major areas of optimization, dynamical systems, and stochastic processes. This edition takes a practical approach toward the solution of a variety of real problems including docking two vehicles in space, the growth rate of an infectious disease, and wildlife management. Rigorous mathematical techniques required for a reasonable solution are introduced as necessary.

Audience: College level courses in mathematical modeling.

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Editorial Reviews

Booknews
The author introduces a five-step approach to mathematical modeling, which he demonstrates throughout the text. A variety of techniques and programs, such as Mathematica, MAPLE, and MINITAB, are used to solve a spectrum of realistic applications, from optimization to dynamical systems to stochastic processes. The book is divided into sections on each major type of mathematical model: optimization models, dynamic models, and probability models. Each chapter includes exercises. Annotation c. Book News, Inc., Portland, OR (booknews.com)
From the Publisher
"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
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Product Details

  • ISBN-13: 9780123708571
  • Publisher: Elsevier Science
  • Publication date: 6/18/2007
  • Edition description: New Edition
  • Edition number: 3
  • Pages: 352
  • Product dimensions: 6.10 (w) x 9.00 (h) x 0.90 (d)

Meet the Author

Mark M. Meerschaert is Chairperson of the Department of Statistics and Probability at Michigan State University and an Adjunct Professor in the Department of Physics at the University of Nevada. Professor Meerschaert has professional experience in the areas of probability, statistics, statistical physics, mathematical modeling, operations research, partial differential equations, ground water and surface water hydrology. He started his professional career in 1979 as a systems analyst at Vector Research, Inc. of Ann Arbor and Washington D.C., where he worked on a wide variety of modeling projects for government and industry. Meerschaert earned his doctorate in Mathematics from the University of Michigan in 1984. He has taught at the University of Michigan, Albion College, Michigan State University, the University of Nevada in Reno, and the University of Otago in Dunedin, New Zealand. His current research interests include limit theorems and parameter estimation for infinite variance probability models, heavy tail models in finance, modeling river flows with heavy tails and periodic covariance structure, anomalous diffusion, continuous time random walks, fractional derivatives and fractional partial differential equations, and ground water flow and transport. For more details, see his personal web page http://www.stt.msu.edu/~mcubed

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Table of Contents

Preface to the Second Edition
Preface
Pt. 1 Optimization Models 1
1 One-Variable Optimization 3
2 Multivariable Optimization 21
3 Computational Methods for Optimization 61
Pt. 2 Dynamic Models 125
4 Introduction to Dynamic Models 127
5 Analysis of Dynamic Models 153
6 Simulation of Dynamic Models 191
Pt. 3 Probability Models 247
7 Introduction to Probability Models 249
8 Stochastic Models 272
9 Simulation of Probability Models 313
Afterword 345
Index 349
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