A Semismooth Newton Method for Fast, Generic Convex Programming
Authors: Alnur Ali, Eric Wong, J. Zico Kolter
ICML 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, Newton-ADMM is significantly faster than SCS on a number of problems. The paper includes a dedicated section |
| Researcher Affiliation | Academia | 1Machine Learning Department, Carnegie Mellon University 2Computer Science Department, Carnegie Mellon University. |
| Pseudocode | Yes | Algorithm 1 Newton-ADMM for convex optimization |
| Open Source Code | No | The paper does not provide any links or explicit statements about the availability of its source code. |
| Open Datasets | No | The paper states that for its numerical examples, data was |
| Dataset Splits | No | The paper does not explicitly provide details about training/validation/test dataset splits for reproducibility. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments (e.g., CPU, GPU models, or cloud computing instances). |
| Software Dependencies | No | The paper mentions various existing software tools and frameworks (e.g., |
| Experiment Setup | Yes | The method has essentially no tuning parameters, since, for all the experiments, we just fix the maximum number of Newton iterations T = 100; the backtracking line search parameters α = 0.001, β = 0.5; and the GMRES tolerances ε(i) = 1/(i + 1), for each Newton iteration i. |