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Each sub-project in rBed_bgmc uses machine learning, statistics, etc to solve NP-hard problems, like combinatorial optimization problems.

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rBed_bgmc

Asymptotic Experiments with Data Structures: Bipartite Graph Matchings and Covers

Quoted from our Abstract section:

We consider instances of bipartite graphs and a number of asymptotic performance experiments in three projects: (1) top movie lists, (2) maximum matchings, and (3) minimum set covers. Experiments are designed to measure the asymptotic runtime performance of abstract data types (ADTs) in three programming languages: Java, R, and C++. The outcomes of these experiments may be surprising. In project (1), the best ADT in R consistently outperforms all ADTs in public domain Java libraries, including the library from Google. The largest movie list has 2^20 titles. In project (2), the Ford- Fulkerson algorithm implementation in R significantly outperforms Java. The hardest instance has 88452 rows and 729 columns. In project (3), a stochastic version of a greedy algorithm in R can significantly outperform a state-of-the-art stochastic solver in C++ on instances with num rows >= 300 and num columns >= 3000.

Structure

  • _data - contains all instances grouped in steiner3, orLib, and random.
  • _data_RDS - contains all results generated from our local machine. Feel free to update RDS files once you replicate our experiments with your own results
  • _data_tiny - contains all small instances.
  • rBed_bgmc - main folder
    • bgmc_movieLib - contains all necessary codes and files for supporting the experiments in our first project: top movie lists
    • bgmc_matching - contains all necessary codes and files for supporting the experiments in our first project: maximum matchings
    • bgmc_covering - contains all necessary codes and files for supporting the experiments in our first project: minimum set covers

Contact

If you have any question, feel free to reach me at lieason715@gmail.com

Updates

arxiv link: https://arxiv.org/abs/2201.00234

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Each sub-project in rBed_bgmc uses machine learning, statistics, etc to solve NP-hard problems, like combinatorial optimization problems.

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