Multi-objective Maximization of Monotone Submodular Functions with Cardinality Constraint
Authors: Rajan Udwani
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we run synthetic experiments using Kronecker graphs and find that our MWU inspired heuristic outperforms existing heuristics. |
| Researcher Affiliation | Academia | Rajan Udwani Operations Research Center, M.I.T. rudwani@alum.mit.edu |
| Pseudocode | Yes | Algorithm 2 Stage 2: MWU |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We choose synthetic experiments where we can control the parameters to see how the algorithm performs in various scenarios... Random graphs for our experiments were generated using the Kronecker graph framework introduced in [LCK+10]. These graphs exhibit several natural properties and are considered a good approximation for real networks (esp. social networks [HK16]). |
| Dataset Splits | No | The paper describes synthetic experiments and evaluation metrics but does not specify dataset splits for training, validation, or testing, as it's not a machine learning model training scenario. |
| Hardware Specification | No | The paper states "All experiments were done using MATLAB" but does not specify any hardware details like CPU/GPU models or memory. |
| Software Dependencies | No | The paper states "All experiments were done using MATLAB" but does not provide a specific version number for MATLAB or any other software dependencies. |
| Experiment Setup | Yes | We pick Kronecker graphs of sizes n {64, 512, 1024} with random initiator matrix 3 and for each n, we test for m {10, 50, 100}. ... Also, for the MWU stage, we tested δ = 0.5 or 0.2. |