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

Conglong Xu; Hao Wu; Peiqi Yang

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

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2503.11833 v2 pith:L4ZAKD4G submitted 2025-03-14 math.OC cs.AIcs.LG

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

Peiqi Yang , Conglong Xu , Hao Wu This is my paper

classification math.OC cs.AIcs.LG

keywords adaptiveapproximationdescentsgradientlow-rankmanifoldsstochasticweighted

verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

0 comments

Share X Bluesky LinkedIn Reddit HN

read the original abstract

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.

discussion (0)

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

Convergence of Riemannian Stochastic Gradient Descents: Varying Batch Sizes And Nonstandard Batch Forming
math.OC 2026-04 unverdicted novelty 6.0

Convergence theorems are established for Riemannian SGD with iteration-varying probability spaces, applying to varying batch sizes and unbiased batch forming schemes.