Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Breaking More Composition Symmetries Using Search Heuristics
Authors: Jimmy Lee, Zichen Zhu
AAAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimentation demonstrates how the proposed heuristics and their combination can drastically reduce the solution set size, search space and runtime when compared against the state-of-the-art static and dynamic symmetry breaking methods. |
| Researcher Affiliation | Academia | Department of Computer Science and Engineering The Chinese University of Hong Kong Shatin, N.T., Hong Kong EMAIL |
| Pseudocode | No | The paper describes algorithms and heuristics in text but does not provide any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement about the release of its own source code or a link to a code repository. |
| Open Datasets | Yes | The Error Correcting Code-Lee Distance problem is prob036 in CSPLib (Gent and Walsh 1999). |
| Dataset Splits | No | The paper does not explicitly provide details about training/validation/test splits, only discussing problems and their solution sizes. |
| Hardware Specification | Yes | All experiments are conducted using Gecode Solver 4.2.0 on Intel C2D E8400 3.0Ghz (7GB). |
| Software Dependencies | Yes | All experiments are conducted using Gecode Solver 4.2.0 on Intel C2D E8400 3.0Ghz (7GB). |
| Experiment Setup | No | The paper describes the search heuristics and general problem setups (e.g., 'binary branching'), but does not provide specific hyperparameters like learning rates, batch sizes, or detailed optimizer settings. It states 'the search order is defaulted to SDF+MV' and 'Our three heuristics break ties by SDF+MV' but lacks more granular experimental setup details typically found in machine learning papers, such as hyperparameter values. |