Continuous Submodular Maximization: Beyond DR-Submodularity
Authors: Moran Feldman, Amin Karbasi
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we propose the first continuous optimization algorithms that achieve a constant factor approximation guarantee for the problem of monotone continuous submodular maximization subject to a linear constraint. We first prove that a simple variant of the vanilla coordinate ascent, called COORDINATE-ASCENT+, achieves a ( e 1 2e 1 ε)-approximation guarantee while performing O(n/ε) iterations... |
| Researcher Affiliation | Academia | Moran Feldman Department of Computer Science University of Haifa Haifa 3498838, Israel moranfe@cs.haifa.ac.il Amin Karbasi School of Engineering and Applied Science Yale University New Haven, CT 06520 amin.karbasi@yale.edu |
| Pseudocode | Yes | Algorithm 1: COORDINATE-ASCENT (ε) |
| Open Source Code | No | The paper does not provide any explicit statement about releasing source code for the described methodology, nor does it include links to a code repository. |
| Open Datasets | No | The paper does not conduct empirical studies, use datasets, or describe any training process. Therefore, there is no information about public dataset availability. |
| Dataset Splits | No | The paper is theoretical and does not describe experiments or dataset usage, so there is no mention of training, validation, or test dataset splits. |
| Hardware Specification | No | The paper focuses on theoretical algorithm design and analysis, and therefore does not specify any hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and focuses on algorithm design and proofs, hence no software dependencies with specific version numbers are mentioned. |
| Experiment Setup | No | The paper is theoretical and describes algorithms and their approximation guarantees, but it does not detail any experimental setup, hyperparameters, or system-level training settings. |