Low-Rank Semidefinite Programming for the MAX2SAT Problem
Authors: Po-Wei Wang, J. Zico Kolter1641-1649
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments show that the approach is faster (sometimes by orders of magnitude) than existing state-of-the-art complete and incomplete solvers, representing a substantial advance in search methods specialized for MAX2SAT problems.In this section we present the experimental results highlighting the performance of the MIXSAT method on the relevant problems from the MAXSAT 2016 competition |
| Researcher Affiliation | Collaboration | Po-Wei Wang Machine Learning Department Carnegie Mellon University Pittsburgh, PA 15213 poweiw@cs.cmu.edu J. Zico Kolter School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213, and Bosch Center for Artificial Intelligence Pittsburgh, PA 15222 zkolter@cs.cmu.edu |
| Pseudocode | Yes | Algorithm 1: The Mixing method for MAXSAT relaxation |
| Open Source Code | Yes | For the full implementation and more technical details, please see the source code in https://github.com/locuslab/mixsat . |
| Open Datasets | Yes | Specifically, we test our solvers on all 228 the MAX2SAT instances (s2v instances) from the unweighted random categories. (Argelich et al., 2016) |
| Dataset Splits | No | The paper refers to instances from the MAXSAT 2016 competition and other benchmarks but does not provide specific train/validation/test split percentages, sample counts, or explicit details of how the data was partitioned for training, validation, or testing beyond simply stating the total number of instances used. |
| Hardware Specification | Yes | For fair comparison, we replicate the experimental setup of the 2016 environment, with exactly the same CPU (Intel Xeon E5-2620) in single core mode and a 3.5 GB memory limit. |
| Software Dependencies | No | The paper mentions the use of 'Gurobi' for LP solving but does not provide a specific version number for it or any other software dependencies crucial for replication. |
| Experiment Setup | Yes | For fair comparison, we replicate the experimental setup of the 2016 environment, with exactly the same CPU (Intel Xeon E5-2620) in single core mode and a 3.5 GB memory limit. We have empirically found that taking rounding time proportion to the square root of the remaining variables works best. |