Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
A Semismooth Newton Method for Fast, Generic Convex Programming
Authors: Alnur Ali, Eric Wong, J. Zico Kolter
ICML 2017 | Venue PDF | LLM Run Details | Input Tokens: 19,477 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,587 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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. |