Pymanopt: A Python Toolbox for Optimization on Manifolds using Automatic Differentiation

  title={Pymanopt: A Python Toolbox for Optimization on Manifolds using Automatic Differentiation},
  author={James Townsend and Niklas Koep and Sebastian Weichwald},
  journal={Journal of Machine Learning Research},
Optimization on manifolds is a class of optimization methods, for (non-convex) 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 optimization methods… CONTINUE READING
Highly Cited
This paper has 19 citations. REVIEW CITATIONS
Related Discussions
This paper has been referenced on Twitter 49 times. VIEW TWEETS


Publications citing this paper.
Showing 1-10 of 14 extracted citations

A semi-supervised method for multi-subject FMRI functional alignment

2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) • 2017
View 1 Excerpt


Publications referenced by this paper.
Showing 1-10 of 14 references

Optimization Techniques on Riemannian Manifolds

ArXiv • 2014
View 4 Excerpts
Highly Influenced

Robust estimation of a location parameter

P. J. Huber
Processing Systems, • 2015

a Matlab Toolbox for Optimization on Manifolds

B. Mishra, P.-A. Absil, R. Sepulchre. Manopt
Journal of Machine Learning Research • 2015

The Geometry of Algorithms with Orthogonality Constraints

T. A. Arias, S. T. Smith
SIAM J . Matrix Anal . & Appl . • 2014

Similar Papers

Loading similar papers…