Fast Combinatorial Algorithm for Optimizing the Spread of Cascades
Authors: Xiaojian Wu, Daniel Sheldon, Shlomo Zilberstein
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | On the Red-cockaded Woodpecker data, our algorithm produces near optimal solutions and runs significantly faster than a standard mixed integer program solver. Compared with a greedy baseline, the solution quality is comparable or better, but our algorithm is 10 30 times faster. On synthetic problems that do not exhibit submodularity, our algorithm significantly outperforms the greedy baseline. Experimental results are reported in Section 6. |
| Researcher Affiliation | Academia | Xiaojian Wu Daniel Sheldon Shlomo Zilberstein College of Information and Computer Sciences University of Massachusetts, Amherst, MA 01003 {xiaojian,sheldon,shlomo}@cs.umass.edu |
| Pseudocode | Yes | Algorithm 1 Primal-Dual Algorithm for PCSW-DSG |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described. |
| Open Datasets | Yes | We used the data set for the RCW problem introduced by Sheldon et al. [2010]. |
| Dataset Splits | No | The paper does not specify exact split percentages or absolute sample counts for training, validation, or test sets. It mentions 'samples' in the context of cascade scenarios, not dataset splits. |
| Hardware Specification | Yes | All the experiments were run on a 2.2GHz Intel Core i7 CPU with 16GB of RAM. |
| Software Dependencies | No | The paper mentions using 'the Gurobi Optimzer as the MIP solver [Gurobi, 2015]' but does not provide a specific version number for Gurobi or any other key software dependencies. |
| Experiment Setup | No | The paper describes some aspects of the experimental setup, such as applying forward/backward methods, local search, and greedy padding, and iterating 10-20 times for local search. However, it does not provide specific hyperparameter values or detailed system-level training settings required for full reproducibility. |