Methods › General › Optimization › SAGA

SAGA

81 papers tagged archive 2025-07-28

Introduced by Aaron Defazio et al. in SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

SAGA is a method in the spirit of SAG, SDCA, MISO and SVRG, a set of recently proposed incremental gradient algorithms with fast linear convergence rates. SAGA improves on the theory behind SAG and SVRG, with better theoretical convergence rates, and has support for composite objectives where a proximal operator is used on the regulariser. Unlike SDCA, SAGA supports non-strongly convex problems directly, and is adaptive to any inherent strong convexity of the problem.

PaperSource

Papers archive 2025-07-28

30 shown of 81, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

20 shown of 39 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Stochastic Optimization11
BIG-bench Machine Learning7
Distributed Optimization3
Federated Learning3
Bilevel Optimization2
Segmentation2
3DGS1
Adversarial Attack1
Attribute1
Autonomous Driving1
Clustering1
Code Generation1
Computational Efficiency1
Data Augmentation1
Distributed Computing1
Domain Adaptation1
Dynamic Reconstruction1
Entity Linking1
Fact Verification1
Graph Representation Learning1

Usage over time archive 2025-07-28

Papers per year tagged with SAGA: 2014 to 2025, peak 13 13 0 2014: 1 paper 2014 2015: 5 papers 2015 2016: 4 papers 2016 2017: 13 papers 2017 2018: 12 papers 2018 2019: 12 papers 2019 2020: 7 papers 2020 2021: 7 papers 2021 2022: 5 papers 2022 2023: 4 papers 2023 2024: 5 papers 2024 2025: 6 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (81 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Optimization

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