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].
A Dynamical Systems Perspective on Nesterov Acceleration
Authors: Michael Muehlebach, Michael Jordan
ICML 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Sec. 5 presents a simulation example that illustrates the properties of the continuous-time dynamics and the discretization. |
| Researcher Affiliation | Academia | 1Electrical Engineering and Computer Science Department, UC Berkeley, Berkeley, California, USA. |
| Pseudocode | No | The paper provides mathematical equations for discretization (e.g., equations 3, 4, 10, 11, 12, 13) but does not include a formal pseudocode block or algorithm environment. |
| Open Source Code | No | The paper does not contain any statements about releasing open-source code for the described methodology, nor does it provide any links to a code repository. |
| Open Datasets | No | The paper's simulation example uses a custom function 'f' defined by its gradient, rather than a publicly available or open dataset. 'We choose a function f with the following gradient'. |
| Dataset Splits | No | The paper describes a simulation example using a custom function, and therefore, does not provide dataset split information (e.g., percentages, sample counts, or citations to predefined splits) typically associated with machine learning datasets. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU models, CPU specifications, or cloud computing instances used for running the simulations. |
| Software Dependencies | No | The paper does not list any specific software dependencies or their version numbers (e.g., Python version, library names, or solver versions) used for the simulations. |
| Experiment Setup | Yes | We choose a function f with the following gradient... where the condition number Îș is set to 5. The integration algorithm given by (3) and (4) is applied to the initial conditions (q0, 0), where q0 is varied from 2 to 5 in steps of 0.2. The step size Ts is successively increased from 0.1 to 1.2. |