## Numerical Methods in Engineering with Python 3This book is an introduction to numerical methods for students in engineering. It covers the usual topics found in an engineering course: solution of equations, interpolation and data fitting, solution of differential equations, eigenvalue problems, and optimization. The algorithms are implemented in Python 3, a high-level programming language that rivals MATLABŪ in readability and ease of use. All methods include programs showing how the computer code is utilized in the solution of problems. The book is based on Numerical Methods in Engineering with Python, which used Python 2. This new text demonstrates the use of Python 3 and includes an introduction to the Python plotting package Matplotlib. This comprehensive book is enhanced by the addition of numerous examples and problems throughout. |

### What people are saying - Write a review

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

### Contents

Preface ix | |

Systems of Linear Algebraic Equations 31 | |

Interpolation and Curve Fitting 104 | 21 |

Problem Set 3 2 | 141 |

Numerical Differentiation | 183 |

Numerical Integration | 199 |

Initial Value Problems | 246 |

TwoPoint Boundary Value Problems | 293 |

Symmetric Matrix Eigenvalue Problems | 321 |

Introduction to Optimization | 374 |

Appendices | 407 |

List of Program Modules by Chapter | 417 |

### Other editions - View all

### Common terms and phrases

algorithm array back substitution bisection boundary conditions boundary value problem bracketed coefﬁcient matrix column Compute constraints convergence cubic spline data points deﬁned deﬁnite derivatives Determine diagonal differential equation displacement efﬁcient eigenvalue problem eigenvectors elements equations Ax evaluate EXAM PLE example ﬁgure ﬁle ﬁnd ﬁnding ﬁnite difference approximations ﬁrst ﬁrst-order ﬁt ﬁtting formula Gauss elimination Gauss-Legendre quadrature Gauss-Seidel method I/usr/bin/python import math import numpy initial conditions initial value problem input("\nPress return interpolation inverse power method linear loop LU decomposition midpoint minimization module Newton-Raphson method nodes numpy as np numpy import obtained output panels pivot plot polynomial polynomial interpolation Prob PROBLEM SET Python range(n result return to exit Richardson extrapolation Romberg integration root Runge-Kutta method shown solution Solve the equations speciﬁed symmetric Table Taylor series tion transformation trapezoidal rule triangular tridiagonal tridiagonal matrix truncation error vector Write a program XStop yields zero