Optimization on manifolds is a class of methods for optimization of an
objective function, subject to constraints which are smooth, in the sense that
the set of points which satisfy the constraints admits the structure of a
differentiable manifold. While many optimization problems are of the described
form, technicalities of differential geometry and the laborious calculation of
derivatives pose a significant barrier for experimenting with these methods.
We introduce Pymanopt (available at <a href="https://pymanopt.github.io">this https URL</a>), a toolbox
for optimization on manifolds, implemented in Python, that---similarly to the
Manopt Matlab toolbox---implements several manifold geometries and optimization
algorithms. Moreover, we lower the barriers to users further by using automated
differentiation for calculating derivative information, saving users time and
saving them from potential calculation and implementation errors.
%0 Generic
%1 townsend2016pymanopt
%A Townsend, James
%A Koep, Niklas
%A Weichwald, Sebastian
%D 2016
%K python optimization
%T Pymanopt: A Python Toolbox for Optimization on Manifolds using Automatic Differentiation
%U http://arxiv.org/abs/1603.03236
%X Optimization on manifolds is a class of methods for optimization of an
objective function, subject to constraints which are smooth, in the sense that
the set of points which satisfy the constraints admits the structure of a
differentiable manifold. While many optimization problems are of the described
form, technicalities of differential geometry and the laborious calculation of
derivatives pose a significant barrier for experimenting with these methods.
We introduce Pymanopt (available at <a href="https://pymanopt.github.io">this https URL</a>), a toolbox
for optimization on manifolds, implemented in Python, that---similarly to the
Manopt Matlab toolbox---implements several manifold geometries and optimization
algorithms. Moreover, we lower the barriers to users further by using automated
differentiation for calculating derivative information, saving users time and
saving them from potential calculation and implementation errors.
@misc{townsend2016pymanopt,
abstract = {{Optimization on manifolds is a class of methods for optimization of an
objective function, subject to constraints which are smooth, in the sense that
the set of points which satisfy the constraints admits the structure of a
differentiable manifold. While many optimization problems are of the described
form, technicalities of differential geometry and the laborious calculation of
derivatives pose a significant barrier for experimenting with these methods.
We introduce Pymanopt (available at <a href="https://pymanopt.github.io">this https URL</a>), a toolbox
for optimization on manifolds, implemented in Python, that---similarly to the
Manopt Matlab toolbox---implements several manifold geometries and optimization
algorithms. Moreover, we lower the barriers to users further by using automated
differentiation for calculating derivative information, saving users time and
saving them from potential calculation and implementation errors.}},
added-at = {2018-12-07T09:10:16.000+0100},
archiveprefix = {arXiv},
author = {Townsend, James and Koep, Niklas and Weichwald, Sebastian},
biburl = {https://www.bibsonomy.org/bibtex/2859002c44a854ae626ad9f07c4874d1e/jpvaldes},
citeulike-article-id = {14481873},
citeulike-linkout-0 = {http://arxiv.org/abs/1603.03236},
citeulike-linkout-1 = {http://arxiv.org/pdf/1603.03236},
day = 8,
eprint = {1603.03236},
interhash = {6063afae623876b542f9df64ddd8005b},
intrahash = {859002c44a854ae626ad9f07c4874d1e},
keywords = {python optimization},
month = sep,
posted-at = {2017-11-23 10:20:36},
priority = {2},
timestamp = {2018-12-07T09:38:19.000+0100},
title = {{Pymanopt: A Python Toolbox for Optimization on Manifolds using Automatic Differentiation}},
url = {http://arxiv.org/abs/1603.03236},
year = 2016
}