Truthful Mechanisms for Steiner Tree Problems
Authors: Jinshan Zhang, Zhengyang Liu, Xiaotie Deng, Jianwei Yin
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We present a truthful-in-expectation mechanism that achieves the approximation ratio ln 4 + ϵ ≈ 1.39, which matches the current best algorithmic ratio for STP. |
| Researcher Affiliation | Academia | 1 Zhejiang University 2 Beijing Institute of Technology 3 Peking University |
| Pseudocode | No | The paper describes algorithmic steps in prose (e.g., 'The mechanism M for k-DCR works as follows. 1. The allocation rule X: select the solution xℓwith the probability λℓ. 2. The payment rule P: for each edge e, the payment for e is...'), but it does not present any formal pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., a link or explicit statement of code release) for the source code of the described methodology. |
| Open Datasets | No | This is a theoretical paper focused on algorithm design and analysis; it does not involve the use of datasets for training in an empirical machine learning sense. Therefore, there is no mention of publicly available datasets or access information for them. |
| Dataset Splits | No | This is a theoretical paper focused on algorithm design and analysis, not empirical experiments. Therefore, there is no discussion of training/validation/test dataset splits. |
| Hardware Specification | No | This is a theoretical paper discussing mechanism design and approximation algorithms. It does not report on experimental setups or hardware specifications used for running experiments. |
| Software Dependencies | No | This is a theoretical paper that focuses on mathematical proofs and algorithm design. It does not mention any specific software dependencies with version numbers that would be required to replicate experiments. |
| Experiment Setup | No | This is a theoretical paper focused on algorithm design and analysis, not empirical experiments. Therefore, it does not provide details about experimental setup, hyperparameters, or training configurations. |