Uncovering Hidden Structure through Parallel Problem Decomposition
Authors: Yexiang Xue, Stefano Ermon, Carla Gomes, Bart Selman
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the success of this approach on minimal set basis problem... We also show the effectiveness on a related application problem from materials discovery. ...We empirically show that a global solver... takes much less wall-clock time (typically, by several orders of magnitude) to find the exact solution. For example in table 1, it takes about 400 seconds to solve A6 with the parallel scheme, but over 18 hours sequentially. We also show our strategy greatly outperforms state-of-the-art incomplete solvers in terms of solution quality. |
| Researcher Affiliation | Academia | Yexiang Xue and Stefano Ermon and Carla P. Gomes and Bart Selman Computer Science Department, Cornell University, Ithaca, NY, 14850 {yexiang, ermonste, gomes, selman}@cs.cornell.edu |
| Pseudocode | No | The paper describes the proposed method in prose but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any statement about making the source code available or provide a link to a code repository. |
| Open Datasets | No | The paper refers to 'classic set basis problems' and a 'set of challenging benchmarks' but does not provide concrete access information (links, DOIs, specific citations with authors/year, or repository names) for publicly available datasets. |
| Dataset Splits | No | The paper does not provide specific details on dataset splits (e.g., percentages, sample counts, or cross-validation setup) for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., CPU/GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependency details, such as library or solver names with version numbers, needed to replicate the experiment. |
| Experiment Setup | No | The paper describes the overall strategy and component steps, but it does not provide specific experimental setup details such as concrete hyperparameter values, optimization settings, or training configurations. |