Acquiring Integer Programs from Data
Authors: Mohit Kumar, Stefano Teso, Luc De Raedt
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our empirical evaluation shows that ARNOLD can acquire models for a number of realistic benchmark problems. (3) An extensive empirical analysis on a number of integer programs, showing that ARNOLD can acquire good quality programs from a handful of examples. |
| Researcher Affiliation | Academia | Mohit Kumar , Stefano Teso and Luc De Raedt KU Leuven {mohit.kumar,stefano.teso,luc.deraedt}@cs.kuleuven.be |
| Pseudocode | Yes | Algorithm 1 The ARNOLD search algorithm. |
| Open Source Code | Yes | Our code is available at github.com/mohit KULeuven/arnold |
| Open Datasets | Yes | To this end, we used ARNOLD for learning 10 satisfaction/satisficing Mini Zinc [Nethercote et al., 2007] benchmark integer programs1, detailed in Table 2. 1From github.com/Mini Zinc/benchmarks and from hakank.org/minizinc. |
| Dataset Splits | Yes | Next, we split the dataset into five folds of 25 solutions each and fed ARNOLD with n {1, 2, 10, 25} random solutions from one fold as training set, while using the union of the other four folds for performance evaluation. |
| Hardware Specification | No | The paper does not provide specific hardware details such as CPU/GPU models, memory, or cloud instance types used for running experiments. It mentions 'Runtime' but not the underlying hardware. |
| Software Dependencies | No | The paper mentions using 'Mini Zinc [Nethercote et al., 2007]' and 'Gecode solver [Schulte et al., 2006]' but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | The parameters used were (s, p) = (1, 1), (1, 2), (1, 3), (2, 1), (3, 1), which are large enough to capture the majority of the benchmark problems, and n = 1, 5, 10, 25. |