Read more about First Semester in Numerical Analysis with Python

First Semester in Numerical Analysis with Python

(1 review)

Yaning Liu, University of Colorado Denver

Publisher: Auraria Institutional Repository

Language: English

Formats Available

Conditions of Use

Attribution-NonCommercial-ShareAlike Attribution-NonCommercial-ShareAlike
CC BY-NC-SA

Reviews

Learn more about reviews.

Reviewed by Chad Westphal, Professor of Mathematics and Computer Science, Wabash College on 2/27/21

The topics covered by the book are overall appropriate and presented at a reasonable level for undergraduate students with a background in calculus, linear algebra, and programming. It gives a brief introduction to python, which would be... read more

Table of Contents

  • 1 Introduction 
  • 2 Solutions of equations: Root-finding
  • 3 Interpolation
  • 4 Numerical Quadrature and Differentiation
  • 5 Approximation Theory

Ancillary Material

  • Suggest ancillary resource
  • About the Book

    The book is based on “First semester in Numerical Analysis with Julia”, written by Giray Ökten. The contents of the original book are retained, while all the algorithms are implemented in Python (Version 3.8.0). Python is an open source (under OSI), interpreted, general-purpose programming language that has a large number of users around the world. Python is ranked the third in August 2020 by the TIOBE programming community index, a measure of popularity of programming languages, and is the top-ranked interpreted language. We hope this book will better serve readers who are interested in a first course in Numerical Analysis, but are more familiar with Python for the implementation of the algorithms.

    The first chapter of the book has a self-contained tutorial for Python, including how to set up the computer environment. Anaconda, the open-source individual edition, is recommended for an easy installation of Python and effortless management of Python packages, and the Jupyter environment, a web-based interactive development environment for Python as well as many other programming languages, was used throughout the book and is recommended to the readers for easy code development, graph visualization and reproducibility.

    About the Contributors

    Author

    Yaning Liu, Department of Mathematical and Statistical Sciences - University of Colorado Denver