Threshold-Based Responsive Simulated Annealing for Directed Feedback Vertex Set Problem

Authors: Qingyun Zhang, Yuming Du, Zhouxing Su, Chu-Min Li, Junzhou Xu, Zhihuai Chen, Zhipeng Lü

AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental Computational experiments on 140 benchmark instances show that TRSA is highly competitive compared to the state-of-the-art methods.
Researcher Affiliation Collaboration 1School of Computer Science and Technology, Huazhong University of Science and Technology, China 2MIS, University of Picardie Jules Verne, France 3TCS Lab, Huawei Technologies Co., Ltd., China
Pseudocode Yes Algorithm 1: The main framework of the TRSA algorithm
Open Source Code Yes (The detailed results are available at https://github.com/Zhang-qingyun/DFVSP-TRSA.)
Open Datasets Yes The first set consists of 40 public classical instances, which were generated in Pardalos, Qian, and Resende (1998)... The second set consists of 100 instances introduced in the PACE 2022 competition on the heuristic track... https://pacechallenge.org/2022/01/12/public-instances
Dataset Splits No No explicit training/test/validation dataset splits are provided. The paper evaluates the algorithm on benchmark instances for an optimization problem rather than training a machine learning model on a dataset with distinct splits.
Hardware Specification Yes All experiments are carried out on Server 2012 x64 with Intel Xeon E5-2609v2 2.5 GHz CPU
Software Dependencies No The paper states 'Our proposed TRSA is programmed in C++.' but does not provide specific version numbers for compilers, libraries, or other software dependencies.
Experiment Setup Yes Table 2 shows the parameter settings of our TRSA, which can be considered as the default setting of the algorithm. Unless otherwise specified, this default setting is consistently used throughout all the experiments presented in this study.