Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Inapproximability of Treewidth and Related Problems
Authors: Y. Wu, P. Austrin, T. Pitassi, D. Liu
JAIR 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we study the approximability of a number of graph problems: Treewidth and Pathwidth of graphs, Minimum Fill-In, One Shot Black (and Black-White) pebbling costs of directed acyclic graphs, and a variety of di๏ฌerent graph layout problems such as Minimum Cut Linear Arrangement and Interval Graph Completion. We show that, assuming the recently introduced Small Set Expansion Conjecture, all of these problems are NP-hard to approximate to within any constant factor in polynomial time. |
| Researcher Affiliation | Academia | Yu (Ledell) Wu EMAIL Per Austrin EMAIL Toniann Pitassi EMAIL David Liu EMAIL Department of Computer Science University of Toronto, Ontario, Canada |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. It focuses on theoretical proofs and reductions. |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described. |
| Open Datasets | No | The paper is theoretical in nature and does not conduct experiments using datasets. Therefore, it does not provide information about open datasets. |
| Dataset Splits | No | The paper does not use any datasets for experimental evaluation, thus there is no information on dataset splits. |
| Hardware Specification | No | The paper focuses on theoretical computer science and does not describe experimental work, therefore no hardware specifications are provided. |
| Software Dependencies | No | The paper is a theoretical work and does not describe implementation details or experimental setups that would require software dependency information. |
| Experiment Setup | No | As a theoretical paper, no experimental setup details such as hyperparameters or system-level training settings are provided. |