Parallel Restarted Search
Authors: Andre Cire, Serdar Kadioglu, Meinolf Sellmann
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We develop a simple technique for parallelizing restarted search deterministically and demonstrate experimentally that we can achieve near-linear speed-ups in practice. |
| Researcher Affiliation | Collaboration | Andre A. Cire Carnegie Mellon University Pittsburgh, PA 15213, USA acire@andrew.cmu.edu Serdar Kadioglu Oracle America Inc. Burlington, MA 01803, USA serdar.kadioglu@oracle.com Meinolf Sellmann IBM Research Yorktown Heights, NY 10598, USA meinolf@us.ibm.com |
| Pseudocode | No | The paper describes the Luby strategy and its parallelization in narrative text and figures, but does not include formal pseudocode blocks or algorithms. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing their source code or links to a code repository. |
| Open Datasets | Yes | We first evaluated our proposed approach on 3 distinct problem domains: Quasi-Group Completion (QCP), Magic Squares, and Costas Arrays (Costas 1984). Our last experiment was performed on the instances from the Minizinc Challenge using Gecode solver. |
| Dataset Splits | No | The paper mentions generating instances and using instances from the Minizinc Challenge, but does not provide specific details on train/validation/test splits, percentages, or explicit methodologies for data partitioning. |
| Hardware Specification | Yes | All experiments were conducted on Intel Xeon 2.4 GHz computers with varying number of cores as required by each run. |
| Software Dependencies | No | The paper states "We employed Gecode solver" and refers to "Gecode (Schulte, Tack, and Lagerkvist 2010)" in the text, but does not provide a specific version number for the Gecode solver or any other software dependency. |
| Experiment Setup | Yes | We employed Gecode solver with 2,000 second timeout and all experiments presented use a Luby scaling factor of 64. This solver uses a search strategy based on active failure count with a decay of 0.99 and geometric restarts (scale factor 1,000 and base 2). We use a static failure count reset at the beginning of each restart. To randomize the restarts, again, the first five branching variables are selected uniformly at random. |