https://github.com/mlpack/ensmallen

A header-only C++ library for numerical optimization --
https://github.com/mlpack/ensmallen

deep-learning function-optimization machine-learning minimization optimization optimization-algorithms

Last synced: 2 days ago
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A header-only C++ library for numerical optimization --

• Host: GitHub
• URL: https://github.com/mlpack/ensmallen
• Owner: mlpack
• License: other
• Created: 2018-10-03T01:53:06.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2024-12-12T19:21:22.000Z (23 days ago)
• Last Synced: 2024-12-27T09:02:50.529Z (9 days ago)
• Topics: deep-learning, function-optimization, machine-learning, minimization, optimization, optimization-algorithms
• Language: C++
• Homepage: http://ensmallen.org
• Size: 5.09 MB
• Stars: 758
• Watchers: 35
• Forks: 122
• Open Issues: 13
• Metadata Files:
  • Readme: README.md
  • Changelog: HISTORY.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE.txt

Awesome Lists containing this project

README

        


  



**ensmallen** is a high-quality C++ library for non-linear numerical optimization.

ensmallen provides many types of optimizers that can be used

for virtually any numerical optimization task.

This includes gradient descent techniques, gradient-free optimizers,

and constrained optimization.

Examples include L-BFGS, SGD, CMAES and Simulated Annealing.

ensmallen also allows optional callbacks to customize the optimization process.

Documentation and downloads: https://ensmallen.org

### Requirements

* C++ compiler with C++14 support

* Armadillo (https://arma.sourceforge.net), version 10.8.2 or later

* OpenBLAS or Intel MKL or LAPACK (see Armadillo site for details)

### Installation

ensmallen can be installed in several ways: either manually or via cmake, 

with or without root access.

The cmake based installation will check the requirements 

and optionally build the tests. If cmake 3.3 (or a later version) 

is not already available on your system, it can be obtained 

from [cmake.org](https://cmake.org).

Example cmake based installation with root access:

```

mkdir build

cd build

cmake ..

sudo make install

```

Example cmake based installation without root access, 

installing into `/home/blah/` (adapt as required):

```

mkdir build

cd build

cmake .. -DCMAKE_INSTALL_PREFIX:PATH=/home/blah/

make install

```

The above will create a directory named `/home/blah/include/` 

and place all ensmallen headers there.

To optionally build and run the tests

(after running cmake as above),

use the following additional commands:

```

make ensmallen_tests

./ensmallen_tests --durations yes

```

Manual installation involves simply copying the `include/ensmallen.hpp` header 

***and*** the associated `include/ensmallen_bits` directory to a location 

such as `/usr/include/` which is searched by your C++ compiler.

If you can't use `sudo` or don't have write access to `/usr/include/`, 

use a directory within your own home directory (eg. `/home/blah/include/`).

### Example Compilation

If you have installed ensmallen in a standard location such as `/usr/include/`:

    g++ prog.cpp -o prog -O2 -larmadillo

    

If you have installed ensmallen in a non-standard location, 

such as `/home/blah/include/`, you will need to make sure 

that your C++ compiler searches `/home/blah/include/` 

by explicitly specifying the directory as an argument/option. 

For example, using the `-I` switch in gcc and clang:

    g++ prog.cpp -o prog -O2 -I /home/blah/include/ -larmadillo

### Example Optimization

See [`example.cpp`](example.cpp) for example usage of the L-BFGS optimizer 

in a linear regression setting.

### License

Unless stated otherwise, the source code for **ensmallen** is licensed under the

3-clause BSD license (the "License").  A copy of the License is included in the

"LICENSE.txt" file.  You may also obtain a copy of the License at

http://opensource.org/licenses/BSD-3-Clause

### Citation

Please cite the following paper if you use ensmallen in your research and/or

software. Citations are useful for the continued development and maintenance of

the library.

* Ryan R. Curtin, Marcus Edel, Rahul Ganesh Prabhu, Suryoday Basak, Zhihao Lou, Conrad Sanderson.  

  [The ensmallen library for flexible numerical optimization](https://jmlr.org/papers/volume22/20-416/20-416.pdf).  

  Journal of Machine Learning Research, Vol. 22, No. 166, 2021.

```

@article{ensmallen_JMLR_2021,

  author  = {Ryan R. Curtin and Marcus Edel and Rahul Ganesh Prabhu and Suryoday Basak and Zhihao Lou and Conrad Sanderson},

  title   = {The ensmallen library for flexible numerical optimization},

  journal = {Journal of Machine Learning Research},

  year    = {2021},

  volume  = {22},

  number  = {166},

  pages   = {1--6},

  url     = {http://jmlr.org/papers/v22/20-416.html}

}

```

### Developers and Contributors

* Ryan Curtin

* Dongryeol Lee

* Marcus Edel

* Sumedh Ghaisas

* Siddharth Agrawal

* Stephen Tu

* Shikhar Bhardwaj

* Vivek Pal

* Sourabh Varshney

* Chenzhe Diao

* Abhinav Moudgil

* Konstantin Sidorov

* Kirill Mishchenko

* Kartik Nighania

* Haritha Nair

* Moksh Jain

* Abhishek Laddha

* Arun Reddy

* Nishant Mehta

* Trironk Kiatkungwanglai

* Vasanth Kalingeri

* Zhihao Lou

* Conrad Sanderson

* Dan Timson

* N Rajiv Vaidyanathan

* Roberto Hueso

* Sayan Goswami