Non-Monotone DR-Submodular Function Maximization

Authors: Tasuku Soma, Yuichi Yoshida

AAAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental We also conduct numerical experiments on the revenue maximization problem using real-world networks. The experimental results show that the solution quality of our algorithm is comparable to other algorithms. Furthermore, our algorithm runs several orders of magnitude faster than other algorithms when B is large.
Researcher Affiliation Collaboration Tasuku Soma The University of Tokyo tasuku soma@mist.i.u-tokyo.ac.jp Yuichi Yoshida National Institute of Informatics, and Preferred Infrastructure, Inc. yyoshida@nii.ac.jp
Pseudocode Yes Algorithm 1 Pseudopolynomial-time algorithm
Open Source Code No The paper states: "All the algorithms were implemented in C# and were run using Mono 4.2.3." However, it does not provide any link or explicit statement about the public availability of the source code for the methodology described.
Open Datasets Yes In our experiment, we used three networks, Adolescent health (2,539 vertices and 12,969 edges), Advogato (6,541 vertices and 61,127 edges), and Twitter lists (23,370 vertices and 33,101 edges), all taken from (Kunegis 2013).
Dataset Splits No The paper mentions using specific datasets but does not provide any details about training, validation, or test splits (e.g., percentages or specific subsets).
Hardware Specification Yes We conducted experiments on a Linux server with an Intel Xeon E5-2690 (2.90 GHz) processor and 256 GB of main memory.
Software Dependencies Yes All the algorithms were implemented in C# and were run using Mono 4.2.3.
Experiment Setup Yes We set p = 0.0001, and set wij = 1 when an edge exists between i and j and wij = 0 otherwise. We imposed the constraint 0 x(e) B for every e E, where B is chosen from {102, . . . , 106}.