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..
Parameterized Approximation Algorithms for Sum of Radii Clustering and Variants
Authors: Xianrun Chen, Dachuan Xu, Yicheng Xu, Yong Zhang
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We propose a general technical framework to overcome the challenge posed by varying radii in So R, which yields fixed-parameter tractable (fpt) algorithms with respect to k (i.e., whose running time is f(k)ploy(n) for some f). Our framework is versatile and obtains fpt approximation algorithms with constant approximation ratios for So R as well as its variants in general metrics, such as Fair So R and Matroid So R, which significantly improve the previous results. Our main result is a unified framework that returns (2+ε)-approximation for So R and (3+ε)-approximation for both Fair So R and Mat So R respectively that all run in times of 2k log(k/ε)n O(1). |
| Researcher Affiliation | Academia | 1 Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China 2 University of Chinese Academy of Sciences, Beijing, China 3 Beijing University of Technology, Beijing, China |
| Pseudocode | Yes | Algorithm 1: Iterative covering. Algorithm 2: Fair exchange via matching. Algorithm 3: Exchange via matroid intersetcion. |
| Open Source Code | No | The paper does not contain any statement about releasing source code for the described methodology, nor does it provide any links to a code repository. |
| Open Datasets | No | This is a theoretical paper focused on algorithm design and analysis. It does not use or reference any specific datasets for training or evaluation. |
| Dataset Splits | No | This is a theoretical paper. No dataset splits (e.g., training, validation, test) are mentioned or discussed, as it does not involve empirical validation. |
| Hardware Specification | No | This is a theoretical paper focused on algorithm design and analysis; therefore, no specific hardware specifications for running experiments are mentioned. |
| Software Dependencies | No | This is a theoretical paper. It does not list any specific software dependencies with version numbers (e.g., libraries, frameworks, or solvers). |
| Experiment Setup | No | This is a theoretical paper focused on algorithm design and analysis, and thus does not include details on experimental setup such as hyperparameters or training settings. |