Research map / Optimization / Nonconvex and smooth optimization
Escaping from saddle points on Riemannian manifolds
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add perturbations when the Riemannian gradient is small | Riemannian gradient descent · Nonconvex smooth Riemannian optimization | iterations to second-order stationarity: O\Bigg(\frac{\beta(f(x_0) - f(x^*))}{\epsilon^2}\log^4\bigg(\frac{\beta d(f(x_0) - f(x^*))}{\epsilon^2\delta}\bigg)\Bigg | Helped |
Closest papers by meaning
- Finite-Time Analysis of Stochastic Nonconvex Nonsmooth Optimization on the Riemannian Manifolds (NeurIPS 2025 · similarity 0.89)
- Convergence and Complexity Guarantee for Inexact First-order Riemannian Optimization Algorithms (ICML 2024 · similarity 0.89)
- Convergence and Trade-Offs in Riemannian Gradient Descent and Riemannian Proximal Point (ICML 2024 · similarity 0.89)
- An Adaptive Algorithm for Bilevel Optimization on Riemannian Manifolds (NeurIPS 2025 · similarity 0.88)
- A Framework for Bilevel Optimization on Riemannian Manifolds (NeurIPS 2024 · similarity 0.88)
- Learning-Rate-Free Stochastic Optimization over Riemannian Manifolds (ICML 2024 · similarity 0.88)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.