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..
A General Analysis of the Convergence of ADMM
Authors: Robert Nishihara, Laurent Lessard, Ben Recht, Andrew Packard, Michael Jordan
ICML 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | On a numerical example, we demonstrate that minimizing the derived bound on the convergence rate provides a practical approach to selecting algorithm parameters for particular ADMM instances. We complement our upper bound by constructing a nearly-matching lower bound on the worst-case rate of convergence. |
| Researcher Affiliation | Academia | Robert Nishihara EMAIL Laurent Lessard EMAIL Benjamin Recht EMAIL Andrew Packard EMAIL Michael I. Jordan EMAIL University of California, Berkeley, CA 94720 USA |
| Pseudocode | Yes | Algorithm 1 Alternating Direction Method of Multipliers; Algorithm 2 Over-Relaxed Alternating Direction Method of Multipliers |
| Open Source Code | No | No explicit statement or link providing access to the source code for the methodology described in the paper was found. |
| Open Datasets | No | The paper uses a synthetically generated dataset for a distributed Lasso problem: 'Each Ai is generated by populating a 600 500 matrix with independent standard normal entries and normalizing the columns. We generate each bi via bi = Aix0 + εi, where x0 is a sparse 500-dimensional vector with 250 independent standard normal entries, and εi N(0, 10 3I).' This is not a publicly available dataset with concrete access information. |
| Dataset Splits | No | The paper describes numerical examples on a synthetically generated dataset to demonstrate convergence, but it does not specify any training, validation, or test dataset splits. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory specifications) used for running the experiments are mentioned in the paper. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., programming languages, libraries, or solvers with their exact versions) used for the implementation or experiments. |
| Experiment Setup | Yes | For the distributed Lasso problem, the paper specifies experimental parameters and data generation details: 'we choose N = 5 and µ = 0.1. Each Ai is generated by populating a 600 500 matrix with independent standard normal entries and normalizing the columns. We generate each bi via bi = Aix0 + εi, where x0 is a sparse 500-dimensional vector with 250 independent standard normal entries, and εi N(0, 10 3I).' |