This is a fork of Ensmallen using Eigen as backend.

3 main goals:

1. Remove checks.
2. Rewrite traits.
3. Rewrite API calls of matrices.
4. Rewrite tests.

Requirements

1. Template library Eigen.

Important Notes

1. Use VectorXd or VectorXf as type of arguments and gradients. They are column vectors.

Finished

• DE
• L_BFGS
• GradientDescent

Developers

1. Zhou Yao

Important changes

1. Traits and SFINAE utilities are removed. Now you are forced to write at most 3 methods for function type if necessary.
2. Callbacks are removed. If you really want to use it, modify source code directly.

With these changes, Ensmallen-eigen are simply a collection of unrelated optimization algorithms. In fact, you can just copy the single folder of one optimizer without install whole library.

Optimizers that only require Evaluate()

1. DE

Optimizers that requires Evaluate(), Gradient(), EvaluateWithGradient()

1. L_BFGS
2. GradientDescent

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ensmallen is a C++ header-only library for numerical optimization.

Documentation and downloads: http://ensmallen.org

ensmallen provides a simple set of abstractions for writing an objective function to optimize. It also provides a large set of standard and cutting-edge optimizers that can be used for virtually any numerical optimization task. These include full-batch gradient descent techniques, small-batch techniques, gradient-free optimizers, and constrained optimization.

Requirements

• C++ compiler with C++11 support
• Armadillo: http://arma.sourceforge.net
• OpenBLAS or Intel MKL or LAPACK (see Armadillo site for details)

Installation

ensmallen can be installed with CMake 3.3 or later. If CMake is not already available on your system, it can be obtained from https://cmake.org

If you are using an older system such as RHEL 7 or CentOS 7, an updated version of CMake is also available via the EPEL repository via the cmake3 package.

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.

• S. Bhardwaj, R. Curtin, M. Edel, Y. Mentekidis, C. Sanderson. ensmallen: a flexible C++ library for efficient function optimization. Workshop on Systems for ML and Open Source Software at NIPS 2018.

@article{DBLP:journals/corr/abs-1810-09361,
  author    = {Shikhar Bhardwaj and
               Ryan R. Curtin and
               Marcus Edel and
               Yannis Mentekidis and
               Conrad Sanderson},
  title     = {ensmallen: a flexible {C++} library for efficient function optimization},
  journal   = {CoRR},
  volume    = {abs/1810.09361},
  doi       = {10.5281/zenodo.2008650},
  year      = {2018},
  url       = {http://arxiv.org/abs/1810.09361},
  archivePrefix = {arXiv},
  eprint    = {1810.09361},
  timestamp = {Wed, 31 Oct 2018 14:24:29 +0100},
  biburl    = {https://dblp.org/rec/bib/journals/corr/abs-1810-09361},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

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