mlsanityData to 7 Oct 2026

A Simple Practical Accelerated Method for Finite Sums

arXiv 1602.02442 ↗First posted 8 Feb 2016stat.ML cs.LGcited by 18

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Abstract (arXiv)

We describe a novel optimization method for finite sums (such as empirical risk minimization problems) building on the recently introduced SAGA method. Our method achieves an accelerated convergence rate on strongly convex smooth problems. Our method has only one parameter (a step size), and is radically simpler than other accelerated methods for finite sums. Additionally it can be applied when the terms are non-smooth, yielding a method applicable in many areas where operator splitting methods would traditionally be applied.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Feb 2016.

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Citing papers per year: 2021: 4; 2022: 7; 2023: 5; 2024: 2.

18 papers.

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  1. 2021Lower Complexity Bounds of Finite-Sum Optimization Problems: The Results and Constructioncited by 22
  2. 2022Can 5th Generation Local Training Methods Support Client Sampling? Yes!cited by 22
  3. 2022Faster federated optimization under second-order similaritycited by 18
  4. 2021AI-SARAH: Adaptive and Implicit Stochastic Recursive Gradient Methodscited by 15
  5. 2022Sharper Rates for Separable Minimax and Finite Sum Optimization via Primal-Dual Extragradient Methodscited by 14
  6. 2022On the fast convergence of minibatch heavy ball momentumcited by 11
  7. 2023Stochastic Distributed Optimization under Average Second-order Similarity: Algorithms and Analysiscited by 11
  8. 2022PEPit: computer-assisted worst-case analyses of first-order optimization methods in Pythoncited by 10
  9. 2021Adaptive Importance Sampling for Finite-Sum Optimization and Sampling with Decreasing Step-Sizescited by 9
  10. 2021Stochastic Reweighted Gradient Descentcited by 7
  11. 2023A Coefficient Makes SVRG Effectivecited by 7
  12. 2024A Unified Theory of Stochastic Proximal Point Methods without Smoothnesscited by 4
6 more
  1. 2023TAMUNA: Doubly Accelerated Distributed Optimization under Partial Participationcited by 3
  2. 2023Variance reduction techniques for stochastic proximal point algorithmscited by 3
  3. 2022Efficient algorithms for implementing incremental proximal-point methodscited by 2
  4. 2024On the SAGA algorithm with decreasing stepcited by 1
  5. 2022An Adaptive Incremental Gradient Method With Support for Non-Euclidean Normscited by 0
  6. 2023Unified analysis of SGD-type methodscited by 0