https://github.com/zjiayao/jmt-matrix
Jiayao' Math ToolBox: Linear Algebra Library
https://github.com/zjiayao/jmt-matrix
matrix-decompositions numerical-linear-algebra
Last synced: 4 days ago
JSON representation
Jiayao' Math ToolBox: Linear Algebra Library
- Host: GitHub
- URL: https://github.com/zjiayao/jmt-matrix
- Owner: zjiayao
- License: mit
- Created: 2017-07-03T08:31:07.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2017-11-19T14:45:26.000Z (almost 8 years ago)
- Last Synced: 2023-02-28T08:31:00.181Z (over 2 years ago)
- Topics: matrix-decompositions, numerical-linear-algebra
- Language: C++
- Homepage:
- Size: 28.3 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# jmt-matrix
[](https://github.com/zjiayao/jmt-matrix/blob/master/LICENSE)
[](https://travis-ci.com/zjiayao/jmt-matrix)
[](https://ci.appveyor.com/project/zjiayao/jmt-matrix)
[](https://gitter.im/zjiayao/jmt/)
*Johnson's Math ToolBox: Linear Algebra Library*
## Introduction
JMT Linear Algebra Library is a light weighted
fast library for scientific computing with C++.
Though the author does not claim the library can outperform
the celebrated LAPCK library, it is nonetheless very
easy to use and performs pretty well with matrices sized
less than 100.
This library supports common operations for real and complex
matrices including:
- Singular Value Decomposition
Single-sided Jacobi's rotation is used. This operation is
similar to a compact SVD for thin matrices.
- QR/LQ Decomposition
Householder's Reflection is used for numerical robustness.
- Cholesky Decomposition
This directly leads to the ability for matrix inversion.
## Installation
First, clone this repo using `git`:
git clone https://github.com/zjiayao/jmt-matrix
Then `cd` to the repo and build and run the example:
cd jmt-matrix && make example && ./example
For Windows users, a `CMakeList` is prepared, and thus
one may build using `cmake`:
cd jmt-matrix
cmake -H. -Bbuild
cmake --build build
./build/example
## Usage
To use JMT Linear Algebra Library for operating on real numbers,
include `dmatrix.hpp` in the header; analogously, include `cmatrix.hpp`
for complex support.
A sample program for calling common APIs is given in the
`examples` folder.
## Features
- Zero Dependency, Platform Dependent
The whole library is self-contained, shipped
with a dedicated complex library used for comlex
manipulations.
- Template Programming
This library is written in template classes, that is,
there is no need to compile or link before use.
Directly include the
desired headers (see previous section) and compile, *voila*!
This also makes the library very easy to extend to
other data types, for examples, 16bit floats, etc.
- Intuitive API
Doing a SVD and print the result is as simple
as if in `MatLAB` or `Python`:
// a normal 5 * 3 real random matrix
jmt::dmat mat = jmt::dmat::getNRand(5, 3), u, s, v;
mat.SVD(u, s, v);
u.print();
- Portability
This library provides a handful of formats for
printing, hence it is easy to feed the results
to, for example, `Python`.
// print to file
jmt::cmat cmat = jmt::cmat::getNRand(6, 3), q, r;
cmat.QR(q, r);
q.print(fout); // fout is a FILE pointer
r.print(stdout, jmt::NUMPY);
this gives:
[[ 0.671 - 0.741i ,0.000 ,0.000 ],
[ 0.000 ,0.975 + 0.223i ,0.000 ],
[ 0.000 ,0.000 ,-0.625 - 0.781i ],
[ 0.000 ,0.000 ,0.000 ],
[ 0.000 ,0.000 ,0.000 ],
[ 0.000 ,0.000 ,0.000 ]]
Of course, directly `std::cout` and `std::cin` (yes!) are supported.
## Claimer
It is recommended **NOT** to use this library in production environment,
rather, use well-crafted libraries such as LAPACK in lieu.