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.
ADMM for Structured Fractional Minimization
Authors: Ganzhao Yuan
ICLR 2025 | Venue PDF | LLM Run Details | Input Tokens: 45,557 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,714 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 | Extensive experiments on synthetic and real-world datasets, including sparse Fisher discriminant analysis, robust Sharpe ratio minimization, and robust sparse recovery, demonstrate the effectiveness of our approach. |
| Researcher Affiliation | Academia | Ganzhao Yuan Peng Cheng Laboratory, China EMAIL |
| Pseudocode | Yes | We summarize FADMM-D and FADMM-Q in Algorithm 1, and provide the following remarks. |
| Open Source Code | No | The corresponding MATLAB code is available on the author s research webpage. |
| Open Datasets | Yes | We incorporate a set of 8 datasets into our experiments, comprising both randomly generated and publicly available real-world data. Appendix Section I describes how to generate the data used in the experiments. (https://www.csie.ntu.edu.tw/~cjlin/libsvm/). |
| Dataset Splits | No | The paper describes how the data is generated and selected, for instance, "Q R m d is constructed by randomly selecting m examples and d dimensions from the original real-world dataset," but it does not specify any training, validation, or test splits. |
| Hardware Specification | Yes | All methods are implemented in MATLAB on an Intel 2.6 GHz CPU with 64 GB RAM. |
| Software Dependencies | No | The paper states, "All methods are implemented in MATLAB," but does not provide a specific version number for MATLAB or any other software dependencies. |
| Experiment Setup | Yes | For all SPGM and FADMM, we consider the default parameter settings (ξ, θ, p, χ) = (1/2, 1.01, 1/3, 2 1 + ξ+10 14). For SPM, we use the default diminishing step size ηt = 1/βt, where βt is the same penalty parameter as in SPGM and FADMM. ... We examine two fixed small step sizes, γ (10 3, 10 4), leading to two variants: FSA-I and FSA-II. For sparse Fisher Discriminant Analysis, we set β0 = 100ρ. |