Submodular Maximization under the Intersection of Matroid and Knapsack Constraints
Authors: Yu-Ran Gu, Chao Bian, Chao Qian
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on the applications of movie recommendation and weighted max-cut demonstrate the superiority of SPROUT++ in practice. |
| Researcher Affiliation | Academia | State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China |
| Pseudocode | Yes | Algorithm 1: SPROUT; Algorithm 2: KNAPSACKSGS: Subroutine of SPROUT; Algorithm 3: SPROUT++ |
| 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 use the set of 10473 movies from the Movie Lens Dataset... We use a classic type of random graphs, i.e., Erdos Renyi graphs (Erdos, R enyi et al. 1960). |
| Dataset Splits | No | The paper describes the datasets used and the setup for experimental evaluation, but it does not specify any training, validation, or test dataset splits (e.g., percentages or sample counts for each). |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory, or cloud resources) used to run the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | Yes | We always the same setting (i.e., tc = n/5, α = 0.5, µ = 1, ℓ= 2, δ = ϵ = 0.25, β = 5 10 4, and γ = 1 10 6) and perform the sensitivity analysis on tc and µ to show their influence, and finally compare the performance of SPROUT++ and SPROUT. |