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https://github.com/yuki-koyama/mathtoolbox
Mathematical tools (interpolation, dimensionality reduction, optimization, etc.) written in C++11 with Eigen
https://github.com/yuki-koyama/mathtoolbox
algorithm bfgs dimensionality-reduction eigen gpr interpolation mds optimization rbf
Last synced: 25 days ago
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Mathematical tools (interpolation, dimensionality reduction, optimization, etc.) written in C++11 with Eigen
- Host: GitHub
- URL: https://github.com/yuki-koyama/mathtoolbox
- Owner: yuki-koyama
- License: mit
- Created: 2018-04-14T14:30:58.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2022-04-10T06:16:13.000Z (over 2 years ago)
- Last Synced: 2024-10-13T01:27:47.950Z (2 months ago)
- Topics: algorithm, bfgs, dimensionality-reduction, eigen, gpr, interpolation, mds, optimization, rbf
- Language: C++
- Homepage: https://yuki-koyama.github.io/mathtoolbox/
- Size: 8.94 MB
- Stars: 261
- Watchers: 17
- Forks: 35
- Open Issues: 15
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- AwesomeCppGameDev - mathtoolbox
README
# mathtoolbox
Mathematical tools (interpolation, dimensionality reduction, optimization, etc.) written in C++11 and [Eigen](http://eigen.tuxfamily.org/).
## Algorithms
### Scattered Data Interpolation and Function Approximation
- [`rbf-interpolation`: Radial basis function (RBF) interpolation](https://yuki-koyama.github.io/mathtoolbox/rbf-interpolation/)
- [`gaussian-process-regression`: Gaussian process regression (GPR)](https://yuki-koyama.github.io/mathtoolbox/gaussian-process-regression/)
### Dimensionality Reduction and Low-Dimensional Embedding
- [`classical-mds`: Classical multi-dimensional scaling (MDS)](https://yuki-koyama.github.io/mathtoolbox/classical-mds/)
- [`som`: Self-organizing map (SOM)](https://yuki-koyama.github.io/mathtoolbox/som/)
### Numerical Optimization
- [`backtracking-line-search`: Backtracking line search](https://yuki-koyama.github.io/mathtoolbox/backtracking-line-search/)
- [`bayesian-optimization`: Bayesian optimization](https://yuki-koyama.github.io/mathtoolbox/bayesian-optimization/)
- [`bfgs`: BFGS method](https://yuki-koyama.github.io/mathtoolbox/bfgs/)
- [`gradient-descent`: Gradient descent method](https://yuki-koyama.github.io/mathtoolbox/gradient-descent/)
- [`l-bfgs`: Limited-memory BFGS method](https://yuki-koyama.github.io/mathtoolbox/l-bfgs/)
- [`strong-wolfe-conditions-line-search`: Strong Wolfe conditions line search](https://yuki-koyama.github.io/mathtoolbox/strong-wolfe-conditions-line-search/)
### Linear Algebra
- [`log-determinant`: Log-determinant](https://yuki-koyama.github.io/mathtoolbox/log-determinant/)
- [`matrix-inversion`: Matrix inversion techniques](https://yuki-koyama.github.io/mathtoolbox/matrix-inversion/)
### Utilities
- [`acquisition-functions`: Acquisition functions](https://yuki-koyama.github.io/mathtoolbox/acquisition-functions/)
- [`constants`: Constants](https://yuki-koyama.github.io/mathtoolbox/constants/)
- [`data-normalization`: Data normalization](https://yuki-koyama.github.io/mathtoolbox/data-normalization/)
- [`kernel-functions`: Kernel functions](https://yuki-koyama.github.io/mathtoolbox/kernel-functions/)
- [`probability-distributions`: Probability distributions](https://yuki-koyama.github.io/mathtoolbox/probability-distributions/)
## Dependencies
### Main Library
- Eigen (`brew install eigen` / `sudo apt install libeigen3-dev`)
### Python Bindings
- pybind11 (included as gitsubmodule)
### Examples
- optimization-test-function (included as gitsubmodule)
- timer (included as gitsubmodule)
## Use as a C++ Library
mathtoolbox uses CMake for building source codes. This library can be built, for example, by
```
git clone https://github.com/yuki-koyama/mathtoolbox.git --recursive
cd mathtoolbox
mkdir build
cd build
cmake ../
make
```
and optionally it can be installed to the system by
```
make install
```
When the CMake parameter `MATHTOOLBOX_BUILD_EXAMPLES` is set `ON`, the example applications are also built. (The default setting is `OFF`.) This is done by
```
cmake ../ -DMATHTOOLBOX_BUILD_EXAMPLES=ON
make
```
When the CMake parameter `MATHTOOLBOX_PYTHON_BINDINGS` is set `ON`, the example applications are also built. (The default setting is `OFF`.) This is done by
```
cmake ../ -DMATHTOOLBOX_PYTHON_BINDINGS=ON
make
```
### Prerequisites
macOS:
```
brew install eigen
```
Ubuntu:
```
sudo apt install libeigen3-dev
```
## Use as a Python Library
pymathtoolbox is a (sub)set of Python bindings of mathtoolbox. Tested on Python `3.8` and `3.9`.
It can be installed via PyPI:
```
pip install git+https://github.com/yuki-koyama/mathtoolbox
```
### Prerequisites
macOS
```
brew install cmake eigen
```
Ubuntu 16.04/18.04
```
sudo apt install cmake libeigen3-dev
```
### Examples
See [python-examples](https://github.com/yuki-koyama/mathtoolbox/tree/master/python-examples).
## Gallery
__Bayesian optimization__ (`bayesian-optimization`) solves a one-dimensional optimization problem using only a small number of function-evaluation queries.
__Classical multi-dimensional scaling__ (`classical-mds`) is applied to pixel RGB values of a target image to embed them into a two-dimensional space.
__Self-organizing map__ (`som`) is also applicable to pixel RGB values of a target image to learn a two-dimensional color manifold.
## Projects Using mathtoolbox
- SelPh [CHI 2016]
- `classical-mds`
- Sequential Line Search [SIGGRAPH 2017]
- `acquisition-functions`, `data-normalization`, `kernel-functions`, `log-determinant`, and `probability-distributions`
- Sequential Gallery [SIGGRAPH 2020]
- `acquisition-functions` and `probability-distributions`
- elasty
- `l-bfgs`
## Contributing
Bug reports, suggestions, feature requests, and PRs are highly welcomed.
## Licensing
The MIT License.