## Mathematical ModelingMathematical Modeling, Second Edition, offers a unique approach to mathematical modeling by providing an inviting overview, and applying problem-solving methodology throughout concerning three major areas: optimization, dynamical systems, and stochastic processes. Providing a thorough revision, the author takes a practical approach toward the solution of a variety of real problems such as docking two vehicles in space, growth rate of an infectious disease, and wildlife management. Rigorous mathematical techniques required for reasonable solutions are introduced as necessary.* A large collection of real-world problems * An integration of computer outputs from the latest versions of Mathematica, Maple, Lindo, Minitab* A systematic five-step modeling method * Applies calculus, differential equations, linear algebra, and probability New To This Edition: * Material on discrete modeling, including integer programming * Extended treatment on chaos and fractals * Additional material on linear programming, including the use of spreadsheet tools * More applications in probability and statistics |

### From inside the book

18 pages matching **Monte Carlo simulation** in this book

Where's the rest of this book?

Results 1-3 of 18

### What people are saying - Write a review

We haven't found any reviews in the usual places.

### Contents

Optimization Models | 1 |

Multivariate Optimization | 21 |

Computational Methods for Optimization | 61 |

Copyright | |

5 other sections not shown

### Common terms and phrases

19-inch sets algorithm analytic assume assumptions average behavior blue whales bombers calculate Chapter Color TV Problem complete phase portrait computer algebra system computer implementation constraint cost cubic yards decision variables denote Determine diodes discrete discrete-time dynamical system distribution docking problem dynamic models eigenvalues equilibrium point estimate Euler method Examine the sensitivity Exercise feasible region Figure fin whales five-step method Graph growth rate initial condition integer Lagrange multiplier linear programming linear system Markov chain Markov process maximize maximum modeling approach Monte Carlo simulation Newton's method nonlinear objective function obtain optimal solution optimization problem parameter Perform a sensitivity phase portrait Pig Problem plot population levels problem of Example profit programming problem random variable Reconsider regression represents results of step RLC circuit RLC Circuit Problem robustness sell sensitivity analysis shadow prices solution curve solve species stable equilibrium steady-state Suppose UMAP module vector field week Whale Problem x2 versus