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Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak-Łojasiewicz Condition
Why is this work in the frame?
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Canadian affiliationAn author listed a Canadian institution. This is the only route the usual frame has.
Machine scores (provisional)
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Opus teacher head0.024
GPT teacher head0.298
- Teacher spread
- 0.274 · how far apart the two teachers sit on this one work
- Validation status
score_only:v0-immature-baseline· verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it
Abstract
No abstract. This is not a gap in this database — OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.
The record
- Venue
- Lecture notes in computer science
- Topic
- Stochastic Gradient Optimization Techniques
- Field
- Computer Science
- Canadian institutions
- University of British Columbia
- Funders
- —
- Keywords
- ConvexityRate of convergenceApplied mathematicsMathematicsStochastic gradient descentConvergence (economics)Gradient descentMathematical proofGeneralizationSimple (philosophy)Convex functionRegular polygonMathematical optimizationComputer scienceMathematical analysisArtificial neural networkArtificial intelligenceGeometry
- Has abstract in OpenAlex
- no