Riemannian stochastic variance-reduced cubic regularized Newton method for submanifold optimization
From MaRDI portal
Publication:2679570
DOI10.1007/s10957-022-02137-5OpenAlexW4311546540MaRDI QIDQ2679570
Sam Davanloo Tajbakhsh, Dewei Zhang
Publication date: 23 January 2023
Published in: Journal of Optimization Theory and Applications (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2010.03785
stochastic optimizationvariance reductionRiemannian optimizationcubic regularizationmanifold optimization
Stochastic programming (90C15) Methods of quasi-Newton type (90C53) Programming in abstract spaces (90C48)
Related Items
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- A new approach to the proximal point method: convergence on general Riemannian manifolds
- Adaptive cubic regularisation methods for unconstrained optimization. I: Motivation, convergence and numerical results
- Adaptive cubic regularisation methods for unconstrained optimization. II: Worst-case function- and derivative-evaluation complexity
- Complexity bounds for second-order optimality in unconstrained optimization
- Minimizing a differentiable function over a differential manifold
- Subgradient algorithm on Riemannian manifolds
- A brief introduction to manifold optimization
- A collection of nonsmooth Riemannian optimization problems
- Iteration-complexity of gradient, subgradient and proximal point methods on Riemannian manifolds
- Trust-region methods on Riemannian manifolds
- Matrix concentration inequalities via the method of exchangeable pairs
- On the complexity of finding first-order critical points in constrained nonlinear optimization
- Cubic regularization of Newton method and its global performance
- Riemannian Trust Regions with Finite-Difference Hessian Approximations are Globally Convergent
- Optimization Methods on Riemannian Manifolds and Their Application to Shape Space
- Manopt, a Matlab toolbox for optimization on manifolds
- Adaptive Quadratically Regularized Newton Method for Riemannian Optimization
- Recent Advances in Stochastic Riemannian Optimization
- An implicit trust-region method on Riemannian manifolds
- Optimization Techniques on Riemannian Manifolds
- Proximal Point Algorithm On Riemannian Manifolds
- Probability
- Introduction to Riemannian Manifolds
- Multivariate T-Distributions and Their Applications
- Proximal point method for a special class of nonconvex functions on Hadamard manifolds
- Weakly Convex Optimization over Stiefel Manifold Using Riemannian Subgradient-Type Methods
- An Introduction to Optimization on Smooth Manifolds
- Methods for Sparse and Low-Rank Recovery under Simplex Constraints
- Global rates of convergence for nonconvex optimization on manifolds
- Riemannian Optimization and Its Applications
- Proximal Gradient Method for Nonsmooth Optimization over the Stiefel Manifold
- Riemannian Stochastic Variance Reduced Gradient Algorithm with Retraction and Vector Transport
- Gradient Descent Finds the Cubic-Regularized Nonconvex Newton Step
- Gradient Method for Optimization on Riemannian Manifolds with Lower Bounded Curvature
- A new, globally convergent Riemannian conjugate gradient method
- Conic Geometric Optimization on the Manifold of Positive Definite Matrices
- Stochastic Gradient Descent on Riemannian Manifolds
- The Gradient Projection Method Along Geodesics
