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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Unary Integer Linear Programming with Structural Restrictions
Authors: Eduard Eiben, Robert Ganian, Dušan Knop, Sebastian Ordyniak
IJCAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We provide new algorithms and lower bounds for such ILPs by exploiting the structure of their variable interactions, represented as a graph. Our first set of results focuses on solving ILP instances through the use of a graph parameter called clique-width... In particular, we obtain a polynomial-time algorithm for instances of bounded clique-width... and we complement this positive result by a number of algorithmic lower bounds. |
| Researcher Affiliation | Academia | Eduard Eiben1, Robert Ganian2, Duˇsan Knop1, Sebastian Ordyniak3 1 Department of Informatics, University of Bergen, Norway 2 Algorithms and Complexity group, TU Wien, Austria 3 Algorithms group, University of Sheffield, UK |
| Pseudocode | No | The algorithm in Section 3.1 is described in prose rather than as structured pseudocode or an explicitly labeled algorithm block. |
| Open Source Code | No | The paper does not provide any information or links regarding open-source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not involve datasets or training procedures. |
| Dataset Splits | No | The paper is theoretical and does not specify training/test/validation dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not specify any hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not list any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup, including hyperparameters or system-level training settings. |