Book Image

Applying Math with Python - Second Edition

By : Sam Morley
Book Image

Applying Math with Python - Second Edition

By: Sam Morley

Overview of this book

The updated edition of Applying Math with Python will help you solve complex problems in a wide variety of mathematical fields in simple and efficient ways. Old recipes have been revised for new libraries and several recipes have been added to demonstrate new tools such as JAX. You'll start by refreshing your knowledge of several core mathematical fields and learn about packages covered in Python's scientific stack, including NumPy, SciPy, and Matplotlib. As you progress, you'll gradually get to grips with more advanced topics of calculus, probability, and networks (graph theory). Once you’ve developed a solid base in these topics, you’ll have the confidence to set out on math adventures with Python as you explore Python's applications in data science and statistics, forecasting, geometry, and optimization. The final chapters will take you through a collection of miscellaneous problems, including working with specific data formats and accelerating code. By the end of this book, you'll have an arsenal of practical coding solutions that can be used and modified to solve a wide range of practical problems in computational mathematics and data science.
Table of Contents (13 chapters)

Solving equations

Many mathematical problems eventually reduce to solving an equation of the form , where is a function of a single variable. Here, we try to find a value of for which the equation holds. The values of for which the equation holds are sometimes called roots of the equation. There are numerous algorithms for finding solutions to equations of this form. In this recipe, we will use the Newton-Raphson and secant methods to solve an equation of the form .

The Newton-Raphson method (Newton’s method) and the secant method are good, standard root-finding algorithms that can be applied in almost any situation. These are iterative methods that start with an approximation of the root and iteratively improve this approximation until it lies within a given tolerance.

To demonstrate these techniques, we will use the function from the Differentiating and integrating symbolically using SymPy recipe, defined by the following formula:

This is...