Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
First-Order Algorithms Converge Faster than $O(1/k)$ on Convex Problems
Authors: Ching-Pei Lee, Stephen Wright
ICML 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this work, we improve this rate to o(1/k). We extend the result to proximal gradient and proximal coordinate descent on regularized problems to show similar o(1/k) convergence rates. The result is tight in the sense that a rate of O(1/k1+ϵ) is not generally attainable for any ϵ > 0, for any of these methods. |
| Researcher Affiliation | Academia | 1Department of Computer Sciences and Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, Wisconsin, USA. Correspondence to: Ching-pei Lee <EMAIL>, Stephen J. Wright <EMAIL>. |
| Pseudocode | No | The paper describes algorithms mathematically and in prose, but does not contain structured pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link regarding the public availability of its source code for the described methodology. |
| Open Datasets | No | This is a theoretical paper that does not use or reference any datasets for training. |
| Dataset Splits | No | This is a theoretical paper that does not use or reference any datasets, thus no dataset split information is provided. |
| Hardware Specification | No | As a theoretical paper, no specific hardware used for running experiments is mentioned. |
| Software Dependencies | No | As a theoretical paper, no specific software dependencies with version numbers are mentioned. |
| Experiment Setup | No | As a theoretical paper, no experimental setup details such as hyperparameters or training settings are provided. |