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https://github.com/dragondive/queen

My analysis and solutions to the N-Queen problem
https://github.com/dragondive/queen

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My analysis and solutions to the N-Queen problem

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README 

        
Queen Problem Solution and Analysis

===================================



This repository contains the solutions to the `Queen problem`_ in multiple

languages, and compares their execution runtimes. Contributions to improve these

solutions or provide solutions in other languages are welcome.



.. contents:: **Table of Contents**



Motivation and Background

-------------------------



I have not (yet?) formally studied Computer Science. I gained knowledge, skills, and

experience through self-learning during my professional career. I discovered the Queen

problem about 15 years ago. However, for several years, I struggled to understand the

backtracking mentioned in the solutions.



That changed one fateful day in 2018 when I found Abdul Bari's YouTube video

`6.1 N Queens Problem using Backtracking`_. His clear explanation helped me easily grasp

the "backtracking" solution, and I quickly prototyped it in C++.

[side-note: I used the online compiler `ideone`_ at the time to compile `my solution`_.

Interestingly, it ran successfully back then. However, when I accidentally submitted it

again recently without any code changes, it failed to compile due to the ``constexpr``

in the template parameter!]



I did not revisit the problem until mid-2024. In the interim, I gained more experience

with Python. I also self-learned CI/CD pipelines: Github Actions and Gitlab CI/CD. A few

weeks ago, I also started learning Rust. Putting together all these skills seemed like a

fun project, and that's how this project began.



Solution Structure

------------------



* Solutions are developed on the ``develop/queen`` branch, with a separate directory

  for each language.

* Github Actions workflows are developed directly on the ``main`` branch for now.

  This will change when this project has other contributors.

* Artifacts from the solution runs are stored on the ``artifacts`` branch in the

  ``artifacts`` directory.

* `Conventional commits`_ and squashed commits are used for a clean commit history.

* Tagging the solution versions and running the pipeline jobs is done manually for now.



Working Plan

------------



* **Step 1**: Move my 2018 C++ prototype here, fix bugs, reworked the solution to accept

  ``N`` as a command-line input, and write code documentation. :white_check_mark:

* **Step 2**: Translate the solution into Python and Rust. For uniformity of comparison,

  use the same algorithm, with language-specific adaptations as

  needed. :white_check_mark:

* **Step 3**: Develop the scripts to run and plot the solutions. This includes the

  pipeline build script and a script to run locally for convenience. :white_check_mark:

* **Step 4**: Share the first version of solutions and results

  publicly. :white_check_mark:

* **Step 5**: *To be revealed shortly!*

* **Step 6**: Optimize the solutions and the build scripts.

* ...

* **Step N**: *To be decided.*



Live Results

------------



**Plot of average execution time** (log scale)



|plot_log_scale|



**Plot of average execution time** (linear scale)



|plot_linear_scale|



.. _`Queen problem`: https://en.wikipedia.org/wiki/Eight_queens_puzzle

.. _`6.1 N Queens Problem using Backtracking`: https://www.youtube.com/watch?v=xFv_Hl4B83A

.. _`ideone`: https://ideone.com

.. _`my solution`: https://ideone.com/YU11ym

.. _`Conventional commits`: https://gist.github.com/qoomon/5dfcdf8eec66a051ecd85625518cfd13



.. |plot_log_scale| image:: https://github.com/dragondive/queen/blob/artifacts/artifacts/queen_log_scale_plot.svg

.. |plot_linear_scale| image:: https://github.com/dragondive/queen/blob/artifacts/artifacts/queen_linear_scale_plot.svg