Pymanopt

Pymanopt: a python toolbox for optimization on manifolds using automatic differentiation. 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.par We introduce Pymanopt (available at url{ pymanopt.github.io}), 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.


References in zbMATH (referenced in 24 articles , 1 standard article )

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  1. Ronny Bergmann: Manopt.jl: Optimization on Manifolds in Julia (2022) not zbMATH
  2. Sembach, Lena; Burgard, Jan Pablo; Schulz, Volker: A Riemannian Newton trust-region method for fitting Gaussian mixture models (2022)
  3. Yamakawa, Yuya; Sato, Hiroyuki: Sequential optimality conditions for nonlinear optimization on Riemannian manifolds and a globally convergent augmented Lagrangian method (2022)
  4. Huusari, Riikka; Kadri, Hachem: Entangled kernels -- beyond separability (2021)
  5. Sakai, Hiroyuki; Iiduka, Hideaki: Sufficient descent Riemannian conjugate gradient methods (2021)
  6. Seth D. Axen, Mateusz Baran, Ronny Bergmann, Krzysztof Rzecki: Manifolds.jl: An Extensible Julia Framework for Data Analysis on Manifolds (2021) arXiv
  7. Absil, Pierre-Antoine (ed.); Herzog, Roland (ed.); Steidl, Gabriele (ed.): Mini-workshop: Computational optimization on manifolds. Abstracts from the mini-workshop held November 15--21, 2020 (online meeting) (2020)
  8. Liu, Changshuo; Boumal, Nicolas: Simple algorithms for optimization on Riemannian manifolds with constraints (2020)
  9. Miolane, Nina; Guigui, Nicolas; Le Brigant, Alice; Mathe, Johan; Hou, Benjamin; Thanwerdas, Yann; Heyder, Stefan; Peltre, Olivier; Koep, Niklas; Zaatiti, Hadi; Hajri, Hatem; Cabanes, Yann; Gerald, Thomas; Chauchat, Paul; Shewmake, Christian; Brooks, Daniel; Kainz, Bernhard; Donnat, Claire; Holmes, Susan; Pennec, Xavier: Geomstats: a Python package for Riemannian geometry in machine learning (2020)
  10. Richards, Donald: Comments on: “Tests for multivariate normality -- a critical review with emphasis on weighted (L^2)-statistics” (2020)
  11. Sakai, Hiroyuki; Iiduka, Hideaki: Hybrid Riemannian conjugate gradient methods with global convergence properties (2020)
  12. Kühnel, Line; Sommer, Stefan; Arnaudon, Alexis: Differential geometry and stochastic dynamics with deep learning numerics (2019)
  13. Petrosyan, Armenak; Tran, Hoang; Webster, Clayton: Reconstruction of jointly sparse vectors via manifold optimization (2019)
  14. Scott, C. B.; Mjolsness, Eric: Multilevel artificial neural network training for spatially correlated learning (2019)
  15. Flamary, Rémi; Cuturi, Marco; Courty, Nicolas; Rakotomamonjy, Alain: Wasserstein discriminant analysis (2018)
  16. Giraldi, Loïc; Le Maître, Olivier P.; Hoteit, Ibrahim; Knio, Omar M.: Optimal projection of observations in a Bayesian setting (2018)
  17. Hokanson, Jeffrey M.; Constantine, Paul G.: Data-driven polynomial ridge approximation using variable projection (2018)
  18. Huang, Wen; Absil, P.-A.; Gallivan, Kyle A.; Hand, Paul: ROPTLIB: An object-oriented C++ library for optimization on Riemannian manifolds (2018)
  19. Khrulkov, Valentin; Oseledets, Ivan: Desingularization of bounded-rank matrix sets (2018)
  20. Nina Miolane, Johan Mathe, Claire Donnat, Mikael Jorda, Xavier Pennec: geomstats: a Python Package for Riemannian Geometry in Machine Learning (2018) arXiv

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