De-singularity Subgradient for the q-th-Powered lₚ-Norm Weber Location Problem
Authors: Zhao-Rong Lai, Xiaotian Wu, Liangda Fang, Ziliang Chen, Cheng Li
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on six real-world data sets demonstrate that q Pp NWAWS successfully solves the singularity problem and achieves a linear computational convergence rate in practical scenarios. |
| Researcher Affiliation | Academia | 1College of Cyber Security, Jinan University 2Department of Mathematics, College of Information Science and Technology, Jinan University 3Department of Computer Science, College of Information Science and Technology, Jinan University 4Pazhou Laboratory 5Research Institute of Multiple Agents and Embodied Intelligence, Peng Cheng Laboratory EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The whole procedure of q Pp NWAWS is illustrated in Supplementary A. The main text does not contain a clearly labeled pseudocode or algorithm block. |
| Open Source Code | Yes | The supplementary material and codes for this paper are available at https://github.com/laizhr/q Pp NWAWS. |
| Open Datasets | Yes | Experiments are conducted on six data sets: CSI300 (Lai et al. 2024), NYSE(N) (Li et al. 2013), FTSE100, NASDAQ100 (Bruni et al. 2016), FF100, and FF100MEOP (Lin et al. 2024). ... CSI300 is extracted by Lai et al. (2024) from the CSI300 constituents1 of Shanghai Stock Exchange and Shenzhen Stock Exchange in China, while FF100 and FF100MEOP are extracted by Lin et al. (2024) from the Kenneth R. French s Data Library2. ... 1http://www.csindex.com.cn 2http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data library.html |
| Dataset Splits | No | The paper mentions 'The time window size m is set as 5 by following previous methods (Huang et al. 2016; Lai et al. 2018a,b, 2022).', but does not provide specific training/test/validation dataset splits or percentages. |
| Hardware Specification | Yes | The experiments are carried out on a desktop workstation with an Intel Core i9-14900KF CPU, 64-GB DDR5 6000-MHz memory cards, and an Nvidia RTX 4080 graphics card with 16-GB independent memory. |
| Software Dependencies | No | The paper does not explicitly state any software dependencies with specific version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | The time window size m is set as 5 by following previous methods (Huang et al. 2016; Lai et al. 2018a,b, 2022). The convergence tolerance thresholds are set as Tol = 10 4 and Tol 2 = 10 14, and the reducing factor ρ in the line search is set as 0.1. |