https://github.com/tttapa/linear-algebra

Educational linear algebra algorithms
https://github.com/tttapa/linear-algebra

cpp linear-algebra matrix numerical-linear-algebra

Last synced: 10 months ago
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Educational linear algebra algorithms

• Host: GitHub
• URL: https://github.com/tttapa/linear-algebra
• Owner: tttapa
• License: gpl-3.0
• Created: 2020-06-21T19:37:20.000Z (almost 6 years ago)
• Default Branch: master
• Last Pushed: 2021-02-20T15:44:42.000Z (about 5 years ago)
• Last Synced: 2025-04-19T18:10:09.010Z (12 months ago)
• Topics: cpp, linear-algebra, matrix, numerical-linear-algebra
• Language: C++
• Homepage: https://tttapa.github.io/Linear-Algebra
• Size: 2.01 MB
• Stars: 12
• Watchers: 2
• Forks: 2
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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# Linear Algebra

This repo aims to implement some well-known linear algebra algorithms in a 

readable and easy to understand way.  

Established libraries such as BLAS, LAPACK, Eigen etc. offer great performance

and precision, but the code is often hard to read.

## Documentation

[**Documentation**](https://tttapa.github.io/Linear-Algebra/Doxygen/index.html)

The [**modules**](https://tttapa.github.io/Linear-Algebra/Doxygen/modules.html)

page is the best place to start.

Direct links to the algorithms:

 - [LU factorization without pivoting](https://tttapa.github.io/Linear-Algebra/Doxygen/d2/d7b/classNoPivotLU.html#a756ec297e96edf953ce640744892a0cc)

 - [LU factorization with row pivoting](https://tttapa.github.io/Linear-Algebra/Doxygen/d6/d1a/classRowPivotLU.html#a756ec297e96edf953ce640744892a0cc)

 - [QR factorization using Householder reflectors](https://tttapa.github.io/Linear-Algebra/Doxygen/d1/dac/classHouseholderQR.html#a756ec297e96edf953ce640744892a0cc)

## Arduino

This library has an Arduino version as well. It's available on the 

[`arduino`](https://github.com/tttapa/Linear-Algebra/tree/arduino) branch.

## Disclaimer

While the code is definitely useful as a linear algebra library, don't use it

in critical applications. Use Eigen instead, it'll be faster, numerically more

accurate, and more reliable.