Research map / Optimization / Nonconvex and smooth optimization
A Comprehensively Tight Analysis of Gradient Descent for PCA
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.
Closest papers by meaning
- Fast Algorithms for Robust PCA via Gradient Descent (NeurIPS 2016 · similarity 0.89)
- Local Linear Convergence of Gradient Methods for Subspace Optimization via Strict Complementarity (NeurIPS 2022 · similarity 0.89)
- Stochastic PCA with $\ell_2$ and $\ell_1$ Regularization (ICML 2018 · similarity 0.89)
- Convergence of Stochastic Gradient Descent for PCA (ICML 2016 · similarity 0.88)
- How Over-Parameterization Slows Down Gradient Descent in Matrix Sensing: The Curses of Symmetry and Initialization (ICLR 2024 · similarity 0.86)
- Provable Acceleration of Nesterov's Accelerated Gradient for Asymmetric Matrix Factorization and Linear Neural Networks (NeurIPS 2024 · similarity 0.86)
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.