Computational · Paper · 2026

Convergence Analysis of Gradient Descent Optimization

Zenodo

Catalog Row121
Citation KeyFriedman2026ConvergenceAnalysisGradientDescent121
Paper FolderAvailable
DOI10.5281/zenodo.20417136
Platform availability

Overview

Extracted from the local paper documentation when available.

This paper presents a convergence study of fixed-step gradient descent on a convex quadratic, framed as the computational exemplar of the Research Project Template (https://github.com/docxology/template). The implementation lives in projects/templates/template code project/src/optimizer.py; experiments and figures are orchestrated by projects/templates/template code project/scripts/optimization an...

optimization algorithmsgradient descentconvergence analysisnumerical methodsmathematical programmingreproducible researchinfrastructure automation

Use Notes

Concise findings and methods pulled from README/SKILL documentation.

Findings / Concepts
  • This paper presents a convergence study of fixed-step gradient descent on a convex quadratic, framed as the computational exemplar of the Research Project Template (https://github.com/docxology/templa
  • The implementation lives in projects/templates/template code project/src/optimizer.py; experiments and figures are orchestrated by projects/templates/template code project/scripts/optimization analysi
Methods / Techniques
  • Software pipeline design
  • Data-driven analysis

Citation

Plain-text citation for quick reuse.

Friedman, Daniel Ari. 2026. Convergence Analysis of Gradient Descent Optimization. Zenodo. DOI: 10.5281/zenodo.20417136. URL: https://doi.org/10.5281/zenodo.20417136.

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