A De-singularity Subgradient Approach for the Extended Weber Location Problem
Authors: Zhao-Rong Lai, Xiaotian Wu, Liangda Fang, Ziliang Chen
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments in a real-world machine learning scenario to show that the proposed approach solves the singularity problem, produces the same results as in the non-singularity cases, and shows a reasonable rate of linear convergence. |
| Researcher Affiliation | Academia | 1Department of Mathematics, College of Information Science and Technology, Jinan University 2Department of Computer Science, College of Information Science and Technology, Jinan University 3Research Institute of Multiple Agents and Embodied Intelligence, Peng Cheng Laboratory 4Guangdong Institute of Smart Education, Jinan University |
| Pseudocode | Yes | We summarize the whole q PWAWS in Supplementary A.2, which gives a complete procedure to deal with different situations that may occur in real applications. |
| Open Source Code | Yes | The supplementary material and codes for this paper are available at https://github.com/laizhr/q PWAWS. |
| Open Datasets | Yes | We adopt the NYSE(N) data set [Li et al., 2013] and propose a new CSI300 data set. |
| Dataset Splits | No | The paper uses a rolling window approach for data processing (e.g., 'As the observation window moves from t = 1 to t = T m+1, there are a total number of (T m + 1) sets of data points'), but it does not specify explicit train/validation/test dataset splits with percentages, counts, or predefined standard splits for reproducibility. |
| Hardware Specification | Yes | A computer with an Intel Core i7-6700 CPU and a 4GB DDR3 memory card is used to record the computational time of q PWAWS. |
| Software Dependencies | No | The paper mentions that 'codes for this paper are available', but it does not specify any particular software libraries, frameworks, or their version numbers that would be required to reproduce the experiments. |
| Experiment Setup | Yes | As for the parameters, if not specified, the observation window size is m = 5, which is consistent with previous related methods [Huang et al., 2016; Lai et al., 2018a; Lai et al., 2018b; Lai et al., 2022]. The tolerance threshold is Tol = 10 9. The reducing factor is ρ = 0.1, which is a moderate value. |