Linear Satisfiability Preserving Assignments
Authors: Kei Kimura, Kazuhisa Makino
JAIR 2018 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we study several classes of satisfiability preserving assignments to the constraint satisfaction problem (CSP). ... As algorithmic results, we present a pseudo-polynomial time algorithm that computes a linear fixable assignment for a given integer linear system, which implies the well known pseudo-polynomial solvability for integer linear systems such as two-variable-per-inequality (TVPI), Horn and q-Horn systems. |
| Researcher Affiliation | Academia | Kei Kimura EMAIL Toyohashi University of Technology Toyohashi, 441-8580, Japan Kazuhisa Makino EMAIL Kyoto University Kyoto, 606-8502, Japan |
| Pseudocode | No | The paper describes algorithms conceptually and through mathematical proofs (e.g., Theorem 7, Theorem 8), but it does not include any explicitly labeled 'Pseudocode' or 'Algorithm' blocks with structured, code-like steps. |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code, nor does it provide links to any code repositories or supplementary materials containing code. |
| Open Datasets | No | The paper focuses on theoretical concepts related to the Constraint Satisfaction Problem (CSP), Boolean SAT, and integer linear systems. It does not involve empirical experiments with specific, named datasets or provide access information for any datasets. |
| Dataset Splits | No | The paper does not involve empirical experiments using specific datasets; therefore, there is no mention of dataset splits like training, validation, or test sets. |
| Hardware Specification | No | The paper focuses on theoretical algorithmic results and mathematical properties. It does not describe any computational experiments or specify any hardware used for running them. |
| Software Dependencies | No | The paper focuses on theoretical and algorithmic contributions. It does not mention any specific software, libraries, or their version numbers that would be needed to reproduce experiments or implementations. |
| Experiment Setup | No | The paper is theoretical in nature, presenting algorithmic results and mathematical proofs. It does not describe any empirical experiments, and thus no details on experimental setup, hyperparameters, or training configurations are provided. |