%0 Generic %1 bhardwaj2018ensmallen %A Bhardwaj, Shikhar %A Curtin, Ryan R. %A Edel, Marcus %A Mentekidis, Yannis %A Sanderson, Conrad %D 2018 %K 2018 arxiv c++ cpp deep-learning library nips paper %R 10.5281/zenodo.2008650 %T ensmallen: a flexible C++ library for efficient function optimization %U http://arxiv.org/abs/1810.09361 %X We present ensmallen, a fast and flexible C++ library for mathematical optimization of arbitrary user-supplied functions, which can be applied to many machine learning problems. Several types of optimizations are supported, including differentiable, separable, constrained, and categorical objective functions. The library provides many pre-built optimizers (including numerous variants of SGD and Quasi-Newton optimizers) as well as a flexible framework for implementing new optimizers and objective functions. Implementation of a new optimizer requires only one method and a new objective function requires typically one or two C++ functions. This can aid in the quick implementation and prototyping of new machine learning algorithms. Due to the use of C++ template metaprogramming, ensmallen is able to support compiler optimizations that provide fast runtimes. Empirical comparisons show that ensmallen is able to outperform other optimization frameworks (like Julia and SciPy), sometimes by large margins. The library is distributed under the BSD license and is ready for use in production environments.

@misc{bhardwaj2018ensmallen, abstract = {We present ensmallen, a fast and flexible C++ library for mathematical optimization of arbitrary user-supplied functions, which can be applied to many machine learning problems. Several types of optimizations are supported, including differentiable, separable, constrained, and categorical objective functions. The library provides many pre-built optimizers (including numerous variants of SGD and Quasi-Newton optimizers) as well as a flexible framework for implementing new optimizers and objective functions. Implementation of a new optimizer requires only one method and a new objective function requires typically one or two C++ functions. This can aid in the quick implementation and prototyping of new machine learning algorithms. Due to the use of C++ template metaprogramming, ensmallen is able to support compiler optimizations that provide fast runtimes. Empirical comparisons show that ensmallen is able to outperform other optimization frameworks (like Julia and SciPy), sometimes by large margins. The library is distributed under the BSD license and is ready for use in production environments.}, added-at = {2019-03-23T14:47:31.000+0100}, author = {Bhardwaj, Shikhar and Curtin, Ryan R. and Edel, Marcus and Mentekidis, Yannis and Sanderson, Conrad}, biburl = {https://www.bibsonomy.org/bibtex/272dac519b21ef748e74eda48a29d8f15/analyst}, description = {[1810.09361] ensmallen: a flexible C++ library for efficient function optimization}, doi = {10.5281/zenodo.2008650}, interhash = {136242432a0342dcd972d589c03d90a9}, intrahash = {72dac519b21ef748e74eda48a29d8f15}, keywords = {2018 arxiv c++ cpp deep-learning library nips paper}, note = {cite arxiv:1810.09361Comment: Workshop on Systems for ML and Open Source Software at NIPS / NeurIPS, 2018}, timestamp = {2019-03-23T14:47:31.000+0100}, title = {ensmallen: a flexible C++ library for efficient function optimization}, url = {http://arxiv.org/abs/1810.09361}, year = 2018 }