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].
ADMM for Structured Fractional Minimization
Authors: Ganzhao Yuan
ICLR 2025 | Venue PDF | LLM Run Details
| 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. |