A Dynamical Systems Perspective on Nesterov Acceleration
Authors: Michael Muehlebach, Michael Jordan
ICML 2019 | Conference PDF | Archive PDF | Plain Text | 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 κ 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. |