A Knowledge Compilation Map for Ordered Real-Valued Decision Diagrams
Authors: Hélène Fargier, Pierre Marquis, Alexandre Niveau, Nicolas Schmidt
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our results show that many tasks that are hard on valued CSPs are actually tractable on VDDs. This paper contributes to filling this gap and completing previous results about the time and space efficiency of VDD languages, thus leading to a knowledge compilation map for real-valued functions. For space reasons, proofs are omitted; a full version of the paper, completed with proofs, can be found at hurl: https://niveau. users.greyc.fr/pub/AAAI14 FMNS.pdfi. |
| Researcher Affiliation | Academia | 1 IRIT-CNRS, Univ. Paul Sabatier, Toulouse, France 2 CRIL-CNRS, Univ. Artois, Lens, France 3 GREYC-CNRS, Univ. Caen, France |
| Pseudocode | No | The paper describes algorithms verbally (e.g., 'shortest (resp. longest) path algorithm') but does not contain any structured pseudocode or algorithm blocks. |
| 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 and does not describe experiments involving datasets. Therefore, no information about publicly available datasets is provided. |
| Dataset Splits | No | The paper is theoretical and does not describe experiments involving dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not describe any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup details such as hyperparameters or training configurations. |