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Using the binary representation of arc capacity in a polynomial time algorithm for the constrained maximum flow problem in directed networks
by
Tlas, Muhammad
in
Algorithms
/ augmenting path method
/ Lower bounds
/ Mathematics
/ Maximum flow
/ maximum flow problem
/ network flow
/ polynomial time algorithm
/ Polynomials
/ Representations
/ scaling algorithm
/ Shortest-path problems
/ Transportation networks
/ Transportation problem
2022
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Using the binary representation of arc capacity in a polynomial time algorithm for the constrained maximum flow problem in directed networks
by
Tlas, Muhammad
in
Algorithms
/ augmenting path method
/ Lower bounds
/ Mathematics
/ Maximum flow
/ maximum flow problem
/ network flow
/ polynomial time algorithm
/ Polynomials
/ Representations
/ scaling algorithm
/ Shortest-path problems
/ Transportation networks
/ Transportation problem
2022
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Do you wish to request the book?
Using the binary representation of arc capacity in a polynomial time algorithm for the constrained maximum flow problem in directed networks
by
Tlas, Muhammad
in
Algorithms
/ augmenting path method
/ Lower bounds
/ Mathematics
/ Maximum flow
/ maximum flow problem
/ network flow
/ polynomial time algorithm
/ Polynomials
/ Representations
/ scaling algorithm
/ Shortest-path problems
/ Transportation networks
/ Transportation problem
2022
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Using the binary representation of arc capacity in a polynomial time algorithm for the constrained maximum flow problem in directed networks
Journal Article
Using the binary representation of arc capacity in a polynomial time algorithm for the constrained maximum flow problem in directed networks
2022
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Overview
In this paper, the binary representation of arc capacity has been used in developing an efficient polynomial time algorithm for the constrained maximum flow problem in directed networks. The algorithm is basically based on solving the maximum flow problem as a sequence of O(n2) shortest path problems on residual directed networks with n nodes generated during iterations. The complexity of the algorithm is estimated to be no more than O(n2mr) arithmetic operations, where m denotes the number of arcs in the network, and r is the smallest integer greater than or equal to log B (B denotes the largest arc capacity in the directed network). Generalization of the algorithm has been also performed in order to solve the maximum flow problem in a directed network subject to non-negative lower bound on the flow vector. A formulation of the simple transportation problem, as a maximal network flow problem has been also performed. Numerical example has been inserted to illustrate the use of the proposed algorithm.
Publisher
International Academy of Ecology and Environmental Sciences (IAEES),International Academy of Ecology and Environmental Sciences
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