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)

Finding Optimal Solutions

In this chapter, we’ll address various methods for finding the best outcome in a given situation. This is called optimization and usually involves either minimizing or maximizing an objective function. An objective function is a function with one or more arguments that returns a single scalar value, representing the cost or payoff for a given choice of parameters. The problems regarding minimizing and maximizing functions are actually equivalent to one another, so we’ll only discuss minimizing object functions in this chapter. Minimizing a function, , is equivalent to maximizing the function. More details on this will be provided when we discuss the first recipe.

The algorithms available to us for minimizing a given function depend on the nature of the function. For instance, a simple linear function containing one or more variables has different algorithms available compared to a non-linear function with many variables. The minimization...