| A. Bar-Noy, R. Motwani, and J. Naor. The greedy algorithm is optimal for on-line edge coloring. Information Processing Letters, pages 251--253, Dec. 1992. |
....rate matrix R has to be doubly stochastic with line and column sum 1. The results will then be easy to generalize to any given M . The following theorem provides an upper bound on the number of partial permutations used by GLJD. It derives from a more general result on greedy on line edge coloring [24], 13] Theorem 5: Let K be the number of partial permutation matrices needed in the GLJD algorithm. Then 1. We have just found that the GLJD algorithm provides a guarantee on the number of matrices that it will use. We are now interested in the worst case bandwidth that it will require. ....
....Theorem 10: For any rate matrix R, TGL D (R) JD (R) # . Corollary 11: The bandwidth guarantee competitive ratio of the GLJD algorithm is upper bounded by 2 . Itis interesting to note that it is possible to derive a doublystochastic bipartite version of the worst case matching in [24] in order to prove that this bound of 2 is actually tight for large n. We now outline the scheduling algorithm that will be used to schedule the matrices generated by the LJ decomposition. III. SCHEDULING THE LJ DECOMPOSITION Sincer ij is the desired rate from input port i to output port j, we ....
A. Bar-Noy, R. Motwani, and J. Naor, "The greedy algorithm is optimal for on-line edge coloring," Information Processing Letters, vol. 44, no. 5, pp. 251-253, 1992.
No context found.
A. Bar-Noy, R. Motwani and J. Naor. The Greedy Algorithm is Optimal for On-Line Edge Coloring. Information Processing Letters, 44 (1992), pages 251-253.
No context found.
A. Bar-Noy, R. Motwani, and J. Naor. The greedy algorithm is optimal for on-line edge coloring. Information Processing Letters, pages 251--253, Dec. 1992.
No context found.
Amotz Bar-Noy, Rajeev Motwani, and Joseph Naor. The Greedy Algorithm is Optimal for On-Line Edge Coloring. Information Processing Letters, 44(5):251-253, 1992.
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