Pymanopt: A Python Toolbox for Optimization on Manifolds using Automatic Differentiation
From MaRDI portal
Publication:2834452
zbMath1416.65580arXiv1603.03236MaRDI QIDQ2834452
No author found.
Publication date: 22 November 2016
Full work available at URL: https://arxiv.org/abs/1603.03236
positive definite matricesnonconvex optimizationsymmetric matricesprojection matricesRiemannian optimizationrotation matricesmanifold optimization
Symbolic computation and algebraic computation (68W30) Nonconvex programming, global optimization (90C26) Nonlinear programming (90C30) Numerical optimization and variational techniques (65K10) Packaged methods for numerical algorithms (65Y15)
Related Items (34)
Comments on: ``Tests for multivariate normality -- a critical review with emphasis on weighted \(L^2\)-statistics ⋮ Introduction to Riemannian Geometry and Geometric Statistics: From Basic Theory to Implementation with Geomstats ⋮ Riemannian Conjugate Gradient Methods: General Framework and Specific Algorithms with Convergence Analyses ⋮ Motor parameterization ⋮ Optimal projection of observations in a Bayesian setting ⋮ Sparsifying the resolvent forcing mode via gradient-based optimisation ⋮ Automatic Differentiation for Riemannian Optimization on Low-Rank Matrix and Tensor-Train Manifolds ⋮ A machine learning approach to portfolio pricing and risk management for high‐dimensional problems ⋮ Operator-valued formulas for Riemannian gradient and Hessian and families of tractable metrics in Riemannian optimization ⋮ Riemannian Hamiltonian Methods for Min-Max Optimization on Manifolds ⋮ Unnamed Item ⋮ A robust, discrete-gradient descent procedure for optimisation with time-dependent PDE and norm constraints ⋮ Faster Riemannian Newton-type optimization by subsampling and cubic regularization ⋮ Learning with symmetric positive definite matrices via generalized Bures-Wasserstein geometry ⋮ A geometric approach to linear cryptanalysis ⋮ Memoryless quasi-Newton methods based on the spectral-scaling Broyden family for Riemannian optimization ⋮ A hybrid Riemannian conjugate gradient method for nonconvex optimization problems ⋮ Desingularization of Bounded-Rank Matrix Sets ⋮ Wasserstein discriminant analysis ⋮ Mini-workshop: Computational optimization on manifolds. Abstracts from the mini-workshop held November 15--21, 2020 (online meeting) ⋮ Unnamed Item ⋮ Differential geometry and stochastic dynamics with deep learning numerics ⋮ Data-Driven Polynomial Ridge Approximation Using Variable Projection ⋮ Simple algorithms for optimization on Riemannian manifolds with constraints ⋮ Hybrid Riemannian conjugate gradient methods with global convergence properties ⋮ Sufficient descent Riemannian conjugate gradient methods ⋮ Pymanopt ⋮ A near-stationary subspace for ridge approximation ⋮ Reconstruction of jointly sparse vectors via manifold optimization ⋮ A Riemannian Newton trust-region method for fitting Gaussian mixture models ⋮ Multilevel Artificial Neural Network Training for Spatially Correlated Learning ⋮ Unnamed Item ⋮ Nonlinear matrix recovery using optimization on the Grassmann manifold ⋮ Sequential optimality conditions for nonlinear optimization on Riemannian manifolds and a globally convergent augmented Lagrangian method
Uses Software
This page was built for publication: Pymanopt: A Python Toolbox for Optimization on Manifolds using Automatic Differentiation