On the Convergence of Nesterov’s Accelerated Gradient Method in Stochastic Settings
Authors: Mahmoud Assran, Mike Rabbat
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Figure 1 we visualize runs of the ASG method on a least-squares regression problem for different problem condition numbers Q. ...The left plots in each sub-figure depict theoretical predictions from Theorem 1, while the right plots in each sub-figure depict empirical results. |
| Researcher Affiliation | Collaboration | 1Department of Electrical & Computer Engineering, Mc Gill University, Montreal, QC, Canada 2Facebook AI Research, Montreal, QC, Canada 3Mila Quebec Artificial Intelligence Institute, Montreal, QC, Canada. |
| Pseudocode | No | The paper describes the AG method using equations (2) and (3) but does not provide a formally structured pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., repository link, explicit statement of code release) for the source code of the methodology described. |
| Open Datasets | No | The paper uses 'least-squares regression problem' and describes 'randomly generated least-squares problems' for its numerical experiments, but it does not provide concrete access information (specific link, DOI, repository name, formal citation) for a publicly available or open dataset. |
| Dataset Splits | No | The paper conducts numerical experiments on synthetically generated problems or functions (e.g., 'worst-case quadratic function') rather than a dataset with explicitly defined training, validation, and test splits. Therefore, it does not provide specific dataset split information. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | The ASG method with constant step-size and momentum parameters. ... Stochastic gradients are sampled by adding zero-mean Gaussian noise with variance σ2 = 0.0025 to the true gradient. ... The L-smoothness parameter is 100 and the modulus of strong-convexity µ is 0.05. ... with α = 1/L and β = Q 1 Q+1. ... with step-size α = 2/(µ+L). |