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..
Non-Ergodic Alternating Proximal Augmented Lagrangian Algorithms with Optimal Rates
Authors: Quoc Tran Dinh
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We verify our algorithms on different numerical examples and compare them with some state-of-the-art methods. |
| Researcher Affiliation | Academia | Quoc Tran-Dinh Department of Statistics and Operations Research, University of North Carolina at Chapel Hill Address: Hanes Hall 333, UNC-Chapel Hill, NC27599, USA. Email: EMAIL |
| Pseudocode | Yes | Algorithm 1 (Non-Ergodic Alternating Proximal Augmented Lagrangian Algorithm (NEAPAL)) |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code or a link to a code repository for the methodology described. |
| Open Datasets | No | The paper describes generating synthetic data for Square-root LASSO and pre-processing logo images for low-rank matrix recovery, but does not provide concrete access information (link, DOI, formal citation with author/year) to a publicly available or open dataset used in their experiments. |
| Dataset Splits | No | The paper does not provide specific details on dataset splits (e.g., percentages, sample counts for training, validation, or test sets) or mention cross-validation setup. |
| Hardware Specification | Yes | All the experiments are implemented in Matlab R2014b, running on a Mac Book Pro. Retina, 2.7GHz Intel Core i5 with 16Gb RAM. |
| Software Dependencies | Yes | All the experiments are implemented in Matlab R2014b, running on a Mac Book Pro. |
| Experiment Setup | Yes | For ASGARD, we use the same setting as in [23], and for Chambolle-Pock s (CP) method, we use step-sizes σ = = k Bk 1 and = 1. In Algorithm 1, we choose 0 := kλ?k k Bkky0 y?k as suggested by Theorem 3.1 to trade-off the objective residual and feasibility gap... In Algorithm 2, we set 0 := µg 4k Bk2 as suggested by our theory, where µg := 0.1 σmin(B) as a guess for the restricted strong convexity parameter. |