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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
An Efficient Semismooth Newton based Algorithm for Convex Clustering
Authors: Yancheng Yuan, Defeng Sun, Kim-Chuan Toh
ICML 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive numerical experiments on both simulated and real data demonstrate that our algorithm is highly efficient and robust for solving large-scale problems. |
| Researcher Affiliation | Academia | 1Department of Mathematics, National University of Singapore 2Department of Applied Mathematics, Hong Kong Polytechnic University 3Department of Mathematics, National University of Singapore. |
| Pseudocode | Yes | Algorithm 1 SSNAL for (P), Algorithm 2 SSNCG for (9), Algorithm 3 IADMM for (P) |
| Open Source Code | No | The paper mentions using open source software CVXCLUSTR, but does not provide concrete access to their own implementation code (written in MATLAB). |
| Open Datasets | Yes | MNIST, Fisher Iris, WINE, Yale Face B(10Train subset)., Fisher. Fisher iris dataset, 1936. UCI Machine Learning Repository https://archive.ics.uci.edu/ml/datasets/iris. |
| Dataset Splits | No | The paper discusses solving the convex clustering model for a range of gamma values to generate a clustering path and evaluates performance, but does not specify train/validation/test splits or cross-validation methodology for data partitioning. |
| Hardware Specification | Yes | All our computational results are obtained from a desktop having 16 cores with 32 Intel Xeon E5-2650 processors at 2.6 GHz and 64 GB memory. |
| Software Dependencies | No | The paper states, 'We write our code in MATLAB without any dedicated C functions.' and mentions using 'CVXCLUSTR' which is 'an R package', but it does not provide specific version numbers for MATLAB, R, or any other software dependencies. |
| Experiment Setup | Yes | In the experiments, we choose k = 10, φ = 0.5 (for the weights wij) and γ [0.2 : 0.2 : 10] to generate the clustering path. In our experiments, we set ϵ = 10 6 unless specified otherwise. When we generate the clustering path for the first parameter value of γ, we first run the IADMM introduced in Algorithm 3 for 100 iterations to generate an initial point, then we use SSNAL to solve (2). |