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Riemannian optimization

Optimization methods on Riemannian manifolds.

Papers

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Showing 51–75 of 153 papers

TitleStatusHype
Wasserstein Gradient Flows for Optimizing Gaussian Mixture Policies—0
Structured Low-Rank Tensor Learning—0
Low-complexity subspace-descent over symmetric positive definite manifoldCode0
Infeasible Deterministic, Stochastic, and Variance-Reduction Algorithms for Optimization under Orthogonality ConstraintsCode0
Faster Riemannian Newton-type Optimization by Subsampling and Cubic Regularization—0
Riemannian Stochastic Approximation for Minimizing Tame Nonsmooth Objective Functions—0
Riemannian Optimization for Variance Estimation in Linear Mixed Models—0
Accelerated Riemannian Optimization: Handling Constraints with a Prox to Bound Geometric Penalties—0
A Riemannian ADMMCode0
Learning Graphical Factor Models with Riemannian OptimizationCode0
Rieoptax: Riemannian Optimization in JAXCode0
Neural Collapse with Normalized Features: A Geometric Analysis over the Riemannian ManifoldCode0
Manifold Free Riemannian Optimization—0
Riemannian optimization for non-centered mixture of scaled Gaussian distributionsCode0
A Variance-Reduced Stochastic Gradient Tracking Algorithm for Decentralized Optimization with Orthogonality Constraints—0
On a class of geodesically convex optimization problems solved via Euclidean MM methods—0
Tensor-on-Tensor Regression: Riemannian Optimization, Over-parameterization, Statistical-computational Gap, and Their InterplayCode0
Riemannian CUR Decompositions for Robust Principal Component Analysis—0
Riemannian stochastic approximation algorithms—0
Differentially private Riemannian optimization—0
Feedback Gradient Descent: Efficient and Stable Optimization with Orthogonality for DNNsCode0
Riemannian Hamiltonian methods for min-max optimization on manifolds—0
Riemannian block SPD coupling manifold and its application to optimal transportCode0
Manifold Optimization Methods for Hybrid beamforming in mmWave Dual-Function Radar-Communication System—0
Understanding Riemannian Acceleration via a Proximal Extragradient Framework—0
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