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..
Accelerated Variance Reduced Stochastic ADMM
Authors: Yuanyuan Liu, Fanhua Shang, James Cheng
AAAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental results show the effectiveness of ASVRG-ADMM. |
| Researcher Affiliation | Academia | Yuanyuan Liu, Fanhua Shang, James Cheng Department of Computer Science and Engineering, The Chinese University of Hong Kong EMAIL |
| Pseudocode | Yes | Algorithm 1 ASVRG-ADMM for strongly-convex case; Algorithm 2 ASVRG-ADMM for general convex case |
| Open Source Code | No | No explicit statement or link to the authors' open-source code for the described methodology was found. |
| Open Datasets | Yes | We used four publicly available data sets1 in our experiments, as listed in Table 2. Note that except STOC-ADMM, all the other algorithms adopted the linearization of the penalty term β/2||Ax − y + z||2 to avoid the inversion of (1/ηkId1 + βATA) at each iteration, which can be computationally expensive for large matrices. The parameters of ASVRG-ADMM are set as follows: m = 2n/b and γ = 1 as in (Zhong and Kwok 2014b; Zheng and Kwok 2016), as well as η and β. Figure 1 shows the training error (i.e. the training objective value minus the minimum) and testing loss of all the algorithms for the general convex problem on the four data sets. SAG-ADMM could not generate experimental results on the HIGGS data set because it ran out of memory. These figures clearly indicate that the variance reduced stochastic ADMM algorithms (including SAG-ADMM, SCASADMM, SVRG-ADMM and ASVRG-ADMM) converge much faster than those without variance reduction techniques, e.g. STOC-ADMM and OPG-ADMM. Notably, ASVRG-ADMM consistently outperforms all other algorithms in terms of the convergence rate under all settings, which empirically verifies our theoretical result that ASVRG-ADMM has a faster convergence rate of O(1/T 2), as opposed to the best known rate of O(1/T). |
| Dataset Splits | No | The paper mentions 'training set' and 'test set' splits but does not explicitly describe a 'validation set' split or its details. |
| Hardware Specification | Yes | All methods were implemented in MATLAB, and the experiments were performed on a PC with an Intel i5-2400 CPU and 16GB RAM. |
| Software Dependencies | No | All methods were implemented in MATLAB. (No version number for MATLAB or other libraries is provided.) |
| Experiment Setup | Yes | The parameters of ASVRG-ADMM are set as follows: m = 2n/b and γ = 1 as in (Zhong and Kwok 2014b; Zheng and Kwok 2016), as well as η and β. |