Adagrad
Algorithms AIMech:Adagrad DRAFT
An adaptive gradient optimization method that scales each coordinate's update using accumulated squared gradients.
Identity Notes & Concept Scope
This record covers the commonly implemented diagonal Adagrad algorithm. Its cumulative accumulator differs from the exponentially decayed squared-gradient estimate in RMSprop.
Claims & Grounded Evidence
1 Claim
Mechanism
#update-rule
The diagonal variant accumulates squared gradients over iterations and divides the current gradient by the square root of that accumulator plus a numerical stabilizer.
⚠️ Curated Limitations & Negative Results
- The definition is scoped to the inspected diagonal implementation, not every full-matrix adaptive subgradient formulation.
- Initialization, learning-rate decay and regularization settings are implementation choices; no benchmark or convergence claim is made.
Artificial Intelligence Ontology (AIO) Grounding
Mapped AIO Classes
| AIO Target Class | Mapping Relation | Mapping Rationale |
|---|---|---|
AIO:TrainingStrategy
TrainingStrategy
|
related_match | This algorithm can implement parameter fitting, but the pinned TrainingStrategy definition uses a preprocessing genus. This is a contextual training relationship, not equivalence or a subclass assertion. |
Evidence Sources & Primary Literature
1 Source| Source ID | Publication / Document | Type / Access | Reference & Checksum |
|---|---|---|---|
pytorch-docs
|
PyTorch contributors
|
official documentation
documentation
|
sha256:8cdb9ea6c999…
|
Curation Provenance
Curator: Codex
Method: ai_assisted
Model: GPT-6
Created: 2026-10-11T03:25:18Z
Updated: 2026-10-11T03:25:18Z
Source File:
data/curated/Adagrad.yaml