On Discrete Truthful Heterogeneous Two-Facility Location
Authors: Panagiotis Kanellopoulos, Alexandros A. Voudouris, Rongsen Zhang
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we focus exclusively on deterministic mechanisms, and improve upon the bounds of Serafino and Ventre for both the social and the maximum cost. Our goal is to design strategyproof mechanisms with an as low f-approximation ratio as possible (close to 1). The paper presents theorems, lemmas, and approximation ratios, which are characteristics of theoretical research without empirical data analysis. |
| Researcher Affiliation | Academia | Panagiotis Kanellopoulos , Alexandros A. Voudouris , Rongsen Zhang School of Computer Science and Electronic Engineering, University of Essex, UK {panagiotis.kanellopoulos, alexandros.voudouris, rz19109}@essex.ac.uk |
| Pseudocode | Yes | Mechanism 1: FIXED-OR-MEDIAN-NEAREST-EMPTY (FMNE) Mechanism 2: PRIORITY-DICTATORSHIP Mechanism 3: α-LEFT-RIGHT |
| Open Source Code | No | The paper does not provide any explicit statements or links indicating that source code for the described methodology is publicly available. |
| Open Datasets | No | As a theoretical paper, it does not use empirical datasets for training. It discusses abstract problem instances for theoretical analysis, not real-world datasets. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments, so there are no dataset splits for training, validation, or testing mentioned. |
| Hardware Specification | No | The paper is theoretical and does not conduct experiments requiring specific hardware, so no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not conduct experiments requiring specific software, so no software dependencies with version numbers are mentioned. |
| Experiment Setup | No | The paper is theoretical and does not describe empirical experiments with hyperparameters or system-level training settings. |