mlsanityData to 7 Oct 2026

A Short Note of PAGE: Optimal Convergence Rates for Nonconvex Optimization

arXiv 2106.09663 ↗First posted 17 Jun 2021math.OC cs.AI cs.DScited by 4

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Abstract (arXiv)

In this note, we first recall the nonconvex problem setting and introduce the optimal PAGE algorithm (Li et al., ICML'21). Then we provide a simple and clean convergence analysis of PAGE for achieving optimal convergence rates. Moreover, PAGE and its analysis can be easily adopted and generalized to other works. We hope that this note provides the insights and is helpful for future works.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 1 of its 1 references that are in the map. First posted on arXiv 17 Jun 2021.

Builds on

1 of 1 references.

References · 1

  1. 2020PAGE: A Simple and Optimal Probabilistic Gradient Estimator for Nonconvex Optimizationcited by 116

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Cited by year

Citing papers per year: 2021: 4.

4 papers.

  1. 2021CANITA: Faster Rates for Distributed Convex Optimization with Communication Compressioncited by 23
  2. 2021FedPAGE: A Fast Local Stochastic Gradient Method for Communication-Efficient Federated Learningcited by 19
  3. 2021DESTRESS: Computation-Optimal and Communication-Efficient Decentralized Nonconvex Finite-Sum Optimizationcited by 16
  4. 2021Faster Rates for Compressed Federated Learning with Client-Variance Reductioncited by 14