SDNA: Stochastic Dual Newton Ascent for Empirical Risk Minimization
Authors: Zheng Qu, Peter Richtarik, Martin Takac, Olivier Fercoq
ICML 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 7. Numerical Experiments In our first experiment (Figure 1) we compare SDNA and our new minibatch version of SDCA on two real (mushrooms: d = 112, n = 8, 124; cov: d = 54, n = 522, 911) and one synthetic (d = 1, 024, n = 2, 048) dataset. |
| Researcher Affiliation | Academia | Zheng Qu ZHENGQU@HKU.HK Department of Mathematics, The University of Hong Kong, Hong Kong Peter Richt arik PETER.RICHTARIK@ED.AC.UK School of Mathematics, The University of Edinburgh, UK Martin Tak aˇc TAKAC.MT@GMAIL.COM Industrial and Systems Engineering, Lehigh University, USA Olivier Fercoq OLIVIER.FERCOQ@TELECOM-PARISTECH.FR LTCI, CNRS, T el ecom Paris-Tech, Universit e Paris-Saclay, France |
| Pseudocode | Yes | Algorithm 1 Proximal Overlapping-Block CD |
| Open Source Code | No | The paper does not contain any statements about making the source code for their proposed method publicly available, nor does it provide a link to a code repository. |
| Open Datasets | Yes | In our first experiment (Figure 1) we compare SDNA and our new minibatch version of SDCA on two real (mushrooms: d = 112, n = 8, 124; cov: d = 54, n = 522, 911) and one synthetic (d = 1, 024, n = 2, 048) dataset. |
| Dataset Splits | No | The paper states the use of 'mushrooms', 'cov', and a 'synthetic' dataset, but it does not specify any training, validation, or test dataset splits (e.g., percentages or sample counts). |
| Hardware Specification | No | The paper mentions that 'all experiments are done using a single core' but does not provide any specific details about the hardware used, such as GPU models, CPU models, or memory specifications. |
| Software Dependencies | No | The paper does not provide any specific software dependency details, such as library names with version numbers (e.g., Python 3.x, TensorFlow x.x, PyTorch x.x). |
| Experiment Setup | Yes | In both cases, we used λ = 1/n as the regularization parameter and g(w) = 1/2 ||w||^2_2. Comparison of SDNA and SDCA for minibatch sizes τ = 1, 32, 256. Runtime of SDNA for minibatch sizes τ = 1, 4, 16, 32, 64. |