








Mathematical Modeling, Third Edition












Publisher:  Elsevier (Burlington, MA) 
  






I. Optimization Models OneVariable 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






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 realworld 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.












Mathematical modeling, simulation, optimization, sensitivity analysis, dynamic models, dynamical systems, Markov Chains, Markov processes, time series, Monte Carlo, probability models, Euler method, chaos, fractals, statistics, docking problem, whale problem, tree problem, newspaper problem, state transition, multivariable optimization, diffusion






http://www.stt.msu.edu/~mcubed/modeling.html







   
 
