Learning When to Use Automatic Tabulation in Constraint Model Reformulation
Authors: Carlo Cena, Özgür Akgün, Zeynep Kiziltan, Ian Miguel, Peter Nightingale, Felix Ulrich-Oltean
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results suggest that a random forest classifier is the most robust choice, improving the performance of four different SAT and CP solvers. |
| Researcher Affiliation | Academia | Carlo Cena1 , Ozg ur Akg un2 , Zeynep Kiziltan1 , Ian Miguel2 , Peter Nightingale3 and Felix Ulrich-Oltean3 1Department of Computer Science and Engineering, University of Bologna, Italy 2School of Computer Science, University of St Andrews, U.K. 3Department of Computer Science, University of York, U.K. |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code and data are available in github.5 https://github.com/carlo98/ML_Auto_Tab_CMT |
| Open Datasets | Yes | We start with an initial dataset of 40 problem classes used in [Akg un et al., 2022]. ... We then add to this dataset 18 new problem classes, taken mainly from CSPLib6, that are not expected to benefit from tabulation according to the expert knowledge. |
| Dataset Splits | No | The paper describes training and testing splits (e.g., 90% and 70% for training) and cross-validation, but does not explicitly mention or detail a separate 'validation' dataset split for hyperparameter tuning or model selection. |
| Hardware Specification | No | The paper mentions 'computational support from the University of York HPC service, Viking' but does not specify any exact hardware details such as GPU models, CPU models, or memory specifications used for the experiments. |
| Software Dependencies | No | The paper mentions software like 'scikit-learn' and solvers such as 'Minion', 'Kissat', and 'Chuffed', but does not provide specific version numbers for any of these software dependencies. |
| Experiment Setup | Yes | We set the time gain threshold λ to 1 second, which led to 4 labeled datasets containing 75% of the instances of the original dataset for Minion, 52% for Kissat and Kissat-MDD and 61% for Chuffed. ... Each run had a 10 GB memory limit and a 60 minutes time limit. ... We use a random forest algorithm in all settings to solve our classification problem. To select a subset of the features that has substantial predictive power, we use the Sequential Feature Selector algorithm of scikit-learn on the training set by doing 5 cross-validations with 5 distinct random seeds. ... To choose the best set of hyper-parameters and to weigh the instances during training, a score function score = yp s y s is used, where score is clipped between -1 and 1, yp is the array of predicted values, s the time gains associated to each instance and y the ground truth. ... We find the best hyper-parameters using a grid search over the maximum depth of the trees and, in the case of random forests, over the number of decision trees. |