Methods › General › Stochastic Optimization › YellowFin
YellowFin
Introduced by Jian Zhang et al. in YellowFin and the Art of Momentum Tuning
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
YellowFin is a learning rate and momentum tuner motivated by robustness properties and analysis of quadratic objectives. It stems from a known but obscure fact: the momentum operator's spectral radius is constant in a large subset of the hyperparameter space. For quadratic objectives, the optimizer tunes both the learning rate and the momentum to keep the hyperparameters within a region in which the convergence rate is a constant rate equal to the root momentum. This notion is extended empirically to non-convex objectives. On every iteration, YellowFin optimizes the hyperparameters to minimize a local quadratic optimization.
Papers archive 2025-07-28
1 shown of 1, 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.
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YellowFin and the Art of Momentum Tuning 12 Jun 2017 · 2 repositories · arXiv:1706.03471Syntology ran 0 of 1 samples · 1 unverified
Tasks archive 2025-07-28
3 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Constituency Parsing | 1 |
| Language Modeling | 1 |
| Language Modelling | 1 |
Usage over time archive 2025-07-28
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
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections