Welcome to my Project Euler solutions! Project Euler
is a catalog of math problems designed to improve math, programming, and
problem solving skills. Only solutions to the first 100 problems are documented
here, per Project Euler's guidelines. All of my solutions past problem 100 are
in a private repo, though they still use this python package as the core
framework and register additional solutions via the pe.problems entry point.
It's pretty nifty.
This is a return to Project Euler for me. My original solutions were written in Java back in 2010, so I decided to restart my solutions in Python and publish them here.
On top of the Project Euler guidelines, I imposed additional constraints on my development in this project. These constraints are designed to keep my problem solving consistent, focused, and documented.
Python is a very readable open source, high level, language with a rich standard library. As I am already familiar with Python, this constraint allows me to focus more on the problem sets than learning additional languages.
Pure Python can be slow for certain kinds of problems, which means I need to be flexible in my thinking. CPython, the most popular implementation of Python, is written in C and provides a framework for extending Python with C. With CPython, I can write C extensions in the CPython ecosystem without breaking my Python-only constraint. This allows more efficient solutions and gives me a deeper knowledge of the whole Python ecosystem!
Third-party libraries can be useful for investigating or demonstrating aspects of a problem. For example, using matplotlib to plot the trend of a sequence can give useful insight to a problem. My final solutions, however, must use only the Python standard library and my own developed framework.
Using third party libraries such as numpy, sympy, etc, may make solutions easier to solve and provide faster runtimes, but could cause me to miss out on crucial understanding. My intention is to learn and deeply understand each problem, not just get the answer.
Each problem, and my core library, should be documented using sphinx and reStructured text. Sphinx is a popular option for documentation generation of Python packages. This constraint ensures consistency in how I publish explanations to my first 100 solutions, to help others learn.
Enjoy!