Book Image

Scientific Computing with Python - Second Edition

By : Claus Führer, Jan Erik Solem, Olivier Verdier
Book Image

Scientific Computing with Python - Second Edition

By: Claus Führer, Jan Erik Solem, Olivier Verdier

Overview of this book

Python has tremendous potential within the scientific computing domain. This updated edition of Scientific Computing with Python features new chapters on graphical user interfaces, efficient data processing, and parallel computing to help you perform mathematical and scientific computing efficiently using Python. This book will help you to explore new Python syntax features and create different models using scientific computing principles. The book presents Python alongside mathematical applications and demonstrates how to apply Python concepts in computing with the help of examples involving Python 3.8. You'll use pandas for basic data analysis to understand the modern needs of scientific computing, and cover data module improvements and built-in features. You'll also explore numerical computation modules such as NumPy and SciPy, which enable fast access to highly efficient numerical algorithms. By learning to use the plotting module Matplotlib, you will be able to represent your computational results in talks and publications. A special chapter is devoted to SymPy, a tool for bridging symbolic and numerical computations. By the end of this Python book, you'll have gained a solid understanding of task automation and how to implement and test mathematical algorithms within the realm of scientific computing.
Table of Contents (23 chapters)
20
About Packt
22
References

16.5.1 Example: A study on the convergence order of Newton's method

An iterative method that iterates  is said to converge with order  with , if there exists a positive constant  such that

Newton's method, when started with a good initial value, has order , and for certain problems, even . Newton's method when applied to the problem  gives the following iteration scheme:

Which converges cubically; that is, q = 3.

This implies that the number of correct digits triples from iteration to iteration. To demonstrate cubic convergence and to numerically determine the constant  is hardly possible with the standard 16-digit float data type.

The following code uses SymPy together with high-precision evaluation instead and takes the study on cubic convergence to the extreme:

import sympy as sym
x = sym.Rational(1,2)
xns=[x]

for i in range(1,9):
x = (x - sym.atan(x)*(1+x**2)).evalf(3000)
xns.append(x)

The result...