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 [1].
Faster and Non-ergodic O(1/K) Stochastic Alternating Direction Method of Multipliers
Authors: Cong Fang, Feng Cheng, Zhouchen Lin
NeurIPS 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results demonstrate that our algorithm is faster than the existing state-of-the-art stochastic ADMM methods. We conduct experiments to show the effectiveness of our method. |
| Researcher Affiliation | Academia | Cong Fang Feng Cheng Zhouchen Lin Key Laboratory of Machine Perception (MOE), School of EECS, Peking University, P. R. China Cooperative Medianet Innovation Center, Shanghai Jiao Tong University, P. R. China EMAIL EMAIL EMAIL |
| Pseudocode | Yes | Algorithm 1 Inner loop of ACC-SADMM. Algorithm 2 ACC-SADMM. |
| Open Source Code | No | The code will be available at http://www.cis.pku.edu.cn/faculty/vision/zlin/zlin.htm. (This states future availability, not current release.) |
| Open Datasets | Yes | The experiments are performed on four benchmark data sets: a9a, covertype, mnist and dna. a9a, covertype and dna are from: http://www.csie.ntu.edu.cn/~cjlin/libsvmtools/datasets/, and mnist is from: http://yann.lecun.com/exdb/mnist/. |
| Dataset Splits | No | Table 3 provides '# training' and '# testing' columns for the datasets, but no explicit 'validation' split information is mentioned. |
| Hardware Specification | Yes | Experiments are performed on Intel(R) CPU i7-4770 @ 3.40GHz machine with 16 GB memory. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | And like [3] and [4], we ๏ฌx ยต = 10 5 and report the performance based on (xt, Axt) to satisfy the constraints of ADMM. And we set m = 2n b for all the algorithms. |