Continuous DR-submodular Maximization: Structure and Algorithms
Authors: An Bian, Kfir Levy, Andreas Krause, Joachim M. Buhmann
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our theoretical findings are validated on synthetic and real-world problem instances. |
| Researcher Affiliation | Academia | An Bian ETH Zurich ybian@inf.ethz.ch Kfir Y. Levy ETH Zurich yehuda.levy@inf.ethz.ch Andreas Krause ETH Zurich krausea@ethz.ch Joachim M. Buhmann ETH Zurich jbuhmann@inf.ethz.ch |
| Pseudocode | Yes | Algorithm 1: TWO-PHASE FRANK-WOLFE for non-monotone DR-submodular maximization |
| Open Source Code | Yes | All experiments were implemented using MATLAB. Source code can be found at: https://github.com/bianan/non-monotone-dr-submodular. |
| Open Datasets | Yes | In our experiments, we used a similar setting to the one in [20]. We experimented on the 2012 US Republican debates data, which consists of 8 candidates: Bachman, Gingrich, Huntsman, Paul, Perry, Romney and Santorum. |
| Dataset Splits | No | The paper mentions using synthetic and real-world data but does not provide explicit details on train/validation/test splits, such as percentages, sample counts, or references to predefined splits. |
| Hardware Specification | No | No specific hardware details (e.g., CPU, GPU models, memory, or cloud instance types) used for running the experiments are mentioned in the paper. |
| Software Dependencies | No | The paper states 'All experiments were implemented using MATLAB.' and refers to 'QUADPROGIP2 [39]' and 'IBM CPLEX optimization studio' as subroutines. However, specific version numbers for MATLAB or CPLEX are not provided, preventing full reproducibility of the software environment. |
| Experiment Setup | Yes | We run all the algorithms for 100 iterations. For the subroutine (Algorithm 3) of TWO-PHASE FRANK-WOLFE, we set 1 = 2 = 10 6, K1 = K2 = 100. ... In order to make f non-monotone, we set h = 0.2 H> u. To make sure that f is non-negative, we first of all solve the problem minx2P 1 2x>Hx + h>x using QUADPROGIP, let the solution to be ˆx, then set c = f(ˆx) + 0.1 |f(ˆx)|. |