Adaptive Stochastic Gradient Descents on Manifolds with an Application on Weighted Low-Rank Approximation
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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.
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.
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5 of 18 references.
References · 5
- 2018On the Convergence of Stochastic Gradient Descent with Adaptive Stepsizescited by 156
- 2013Stochastic Majorization-Minimization Algorithms for Large-Scale Optimizationcited by 28
- 2019Regularized Weighted Low Rank Approximationcited by 10
- 2023Matrix Completion in Almost-Verification Timecited by 4
- 2023Disjunctive Branch-and-Bound for Certifiably Optimal Low-Rank Matrix Completioncited by 2
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