article · American Journal of Applied Sciences · 2004

Solving Linear Programming Problems on the Parallel Virtual Machine Environment

Cited by 6Open access

Abstract

This study developed a parallel algorithm to efficiently solve linear programming models. The proposed algorithm utilizes the Dantzig-Wolfe Decomposition Principle and can be easily implemented in a general distributed computing environment. The analytical performance of the well-known method, including the speedup upper bound and lower bound limits, was derived. Numerical experiments are also provided in order to verify the complexity of the proposed algorithm. The empirical results demonstrate that the speedup of this parallel algorithm approaches linearity, which means that it can take full advantage of the distributed computing power as the size of the problem increases.

Cite this paper

APA 7

Lyu, J., Luh, H., & Lee, M. (2004). Solving Linear Programming Problems on the Parallel Virtual Machine Environment. American Journal of Applied Sciences, 1(2), 90–94. https://doi.org/10.3844/ajassp.2004.90.94

MLA 9

Lyu, JrJung, et al. “Solving Linear Programming Problems on the Parallel Virtual Machine Environment.” American Journal of Applied Sciences, vol. 1, no. 2, 2004, pp. 90–94, https://doi.org/10.3844/ajassp.2004.90.94.

Chicago

Lyu, JrJung, Hsing Luh, and Ming‐Chang Lee. 2004. “Solving Linear Programming Problems on the Parallel Virtual Machine Environment.” American Journal of Applied Sciences 1 (2): 90–94. https://doi.org/10.3844/ajassp.2004.90.94.

Harvard

Lyu, J., Luh, H. and Lee, M. (2004) ‘Solving Linear Programming Problems on the Parallel Virtual Machine Environment’, American Journal of Applied Sciences, 1(2), pp. 90–94. Available at: https://doi.org/10.3844/ajassp.2004.90.94.

BibTeX

@article{lyu2004solving,
  title = {{Solving Linear Programming Problems on the Parallel Virtual Machine Environment}},
  author = {Lyu, JrJung and Luh, Hsing and Lee, Ming‐Chang},
  journal = {American Journal of Applied Sciences},
  volume = {1},
  number = {2},
  pages = {90--94},
  year = {2004},
  doi = {10.3844/ajassp.2004.90.94}
}

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