Unifying Core-Guided and Implicit Hitting Set Based Optimization
Authors: Hannes Ihalainen, Jeremias Berg, Matti Järvisalo
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We developed an open-source implementation (available at https://bitbucket.org/coreo-group/cgss2/src/abstcg/) of ABSTCG on top of CGSS2, a state-of-the-art C++ implementation of OLL when compared to solvers in the 2022 Max SAT Evaluations [Ihalainen, 2022]. We empirically compare the runtimes of this prototype to those of CGSS2 (employing OLL) on all 607 weighted instances from Max SAT Evaluation 2022 using 2.6-GHz Intel Xeon E5-2670 processors under per-instance 3600-s time and 32-GB memory limit. |
| Researcher Affiliation | Academia | Hannes Ihalainen , Jeremias Berg and Matti J arvisalo HIIT, Department of Computer Science, University of Helsinki, Finland {hannes.ihalainen,jeremias.berg,matti.jarvisalo}@helsinki.fi |
| Pseudocode | Yes | Algorithm 1 Core-guided approach to Max SAT Algorithm 2 IHS for Max SAT Algorithm 3 UNIMAXSAT, a unified framework for core-guided and IHS-based Max SAT algorithms. |
| Open Source Code | Yes | We developed an open-source implementation (available at https://bitbucket.org/coreo-group/cgss2/src/abstcg/) of ABSTCG on top of CGSS2 |
| Open Datasets | Yes | We empirically compare the runtimes of this prototype to those of CGSS2 (employing OLL) on all 607 weighted instances from Max SAT Evaluation 2022 |
| Dataset Splits | No | The paper evaluates on instances from Max SAT Evaluation 2022, which are problem instances for solvers, not datasets for which train/validation/test splits are typically defined and reported by the authors. The paper does not specify these splits. |
| Hardware Specification | Yes | using 2.6-GHz Intel Xeon E5-2670 processors under per-instance 3600-s time and 32-GB memory limit. |
| Software Dependencies | No | The paper mentions using 'CGSS2, a state-of-the-art C++ implementation' but does not provide specific version numbers for software components or libraries. |
| Experiment Setup | Yes | To take advantage of the special feature of ABSTCG, the implementation invokes ABSTCG when an instance includes at least three different weights on the soft clauses and the median number of soft clauses for each weight is more than 25 (as a heuristic for instances that likely to contain cores which ABSTCG can divide into weight-sets in a meaningful way), and otherwise OLL. ... under per-instance 3600-s time and 32-GB memory limit. |