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The Algorithm Codex

The Algorithm Codex is a comprehensive repository of reference implementations for common and advanced algorithms in computer science. Designed as a companion for computer science students and professionals, it provides clean, modern Python 3.13+ implementations while emphasizing clarity, intuition, and algorithmic efficiency.

You can read the book online at https://matcom.github.io/codex/.

Literate Programming

This project follows a literate programming philosophy. Unlike traditional software projects, the primary source of truth is the book itself, located in the docs/ directory.

  • Book-to-Code: The Python source code in the src/ directory is automatically generated from the Markdown files in the docs/ folder.
  • Narrative Driven: Every implementation is surrounded by prose that explains the intuition behind the algorithm and provides a "back-of-the-envelope" cost analysis.
  • Tooling: We use illiterate to extract code blocks from the documentation and Quarto to render the book in HTML and PDF formats.

Project Structure

  • docs/: Contains the Markdown and Quarto files that make up the book.
  • src/codex/: The generated Python package.
  • tests/: A comprehensive test suite using pytest to ensure all implementations are correct.
  • makefile: Automation for building the source, rendering docs, and running tests.
  • pyproject.toml & uv.lock: Python project metadata and dependency management.

Roadmap

The Codex is organized to build complex ideas on top of simpler ones. The planned content includes:

  • Part I: Searching and Sorting: Basic search (linear, binary) and fundamental sorting algorithms.
  • Part II: Fundamental Data Structures: Linked lists, stacks, queues, and hashing.
  • Part III: Trees: Binary search trees, heaps, tries, and balanced trees.
  • Part IV: Graphs: Traversal, shortest paths, spanning trees, and flow networks.
  • Part V: Dynamic Programming & Greedy Algorithms: Paradigm-shifting problem-solving techniques.
  • Part VI: Specialized Domains: Computational geometry, number theory, and game theory.
  • Part VII: Advanced Complexity: NP-completeness, approximation algorithms, and randomized algorithms.

Development

To set up the development environment, ensure you have Python 3.13+ and the uv package manager installed.

  • Extract source from docs: make source
  • Run tests: make tests
  • Render book: make docs
  • Live development: make dev (watches for doc changes to rebuild source automatically)

Licenses

  • Book Content: CC-BY-NC-SA 4.0. You are free to share and adapt the material for non-commercial purposes, provided you give appropriate credit and distribute your contributions under the same license.
  • Source Code: MIT License. The implementations can be used for any purpose, including commercial software.

About the Author

The Algorithm Codex is written and maintained by Alejandro Piad Morffis, Ph.D., a Computer Science professor and researcher. For updates on the Codex and other explorations into the science of computation, subscribe to The Computist Journal.