# Adaptive Stochastic Gradient Descents on Manifolds with an Application on Weighted Low-Rank Approximation

arXiv: https://arxiv.org/abs/2503.11833 · HTML: https://mlsanity.com/paper/2503.11833/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 18 references that are in the map.
- First posted on arXiv 14 Mar 2025.

## Builds on (most cited first, 5 of 5)

- [On the Convergence of Stochastic Gradient Descent with Adaptive Stepsizes](https://mlsanity.com/paper/1805.08114/) (2018)
- [Stochastic Majorization-Minimization Algorithms for Large-Scale Optimization](https://mlsanity.com/paper/1306.4650/) (2013)
- [Regularized Weighted Low Rank Approximation](https://mlsanity.com/paper/1911.06958/) (2019)
- [Matrix Completion in Almost-Verification Time](https://mlsanity.com/paper/2308.03661/) (2023)
- [Disjunctive Branch-and-Bound for Certifiably Optimal Low-Rank Matrix Completion](https://mlsanity.com/paper/2305.12292/) (2023)

## Abstract (arXiv)

We prove a convergence theorem for stochastic gradient descents on manifolds with adaptive learning rate and apply it to the weighted low-rank approximation problem.