Book Image

Applying Math with Python

By : Sam Morley
Book Image

Applying Math with Python

By: Sam Morley

Overview of this book

Python, one of the world's most popular programming languages, has a number of powerful packages to help you tackle complex mathematical problems in a simple and efficient way. These core capabilities help programmers pave the way for building exciting applications in various domains, such as machine learning and data science, using knowledge in the computational mathematics domain. The book teaches you how to solve problems faced in a wide variety of mathematical fields, including calculus, probability, statistics and data science, graph theory, optimization, and geometry. You'll start by developing core skills and learning about packages covered in Python’s scientific stack, including NumPy, SciPy, and Matplotlib. As you advance, you'll get to grips with more advanced topics of calculus, probability, and networks (graph theory). After you gain a solid understanding of these topics, you'll discover 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 (12 chapters)

Finding minimal spanning trees and dominating sets

Networks have applications for a wide variety of problems. Two obvious areas that see many applications are communication and distribution. For example, we might wish to find a way of distributing goods to a number of cities (nodes) in a road network that covers the smallest distance from a particular point. For problems like this, we need to look at minimal spanning trees and dominating sets.

In this recipe, we will find a minimal spanning tree and a dominating set in a network.

Getting ready

For this recipe, we need to import the NetworkX package under the name nx and the Matplotlib pyplot module as plt.

How to do it...

Follow these steps to find a minimum spanning tree and dominating set for a network:

  1. First, we will create a sample network to analyze:
G = nx.gnm_random_graph(15, 22, seed=12345)
  1. Next, as usual, we will draw the network...