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RIP is not seriously considered in the greedy algorithms [12].
Therefore, the greedy algorithms cannot be applied directly.
In this paper, new algorithms are developed to improve the performance of the greedy algorithms.
We also analyze the complexity of the brute-force search and the greedy algorithms.
The results demonstrate that both CMIA-H and CMIA-O achieve matching influence blocking performance to the greedy algorithms and consistently outperform other baseline heuristics, while they are several orders of magnitude faster than the greedy algorithms.
For both coverage and capacity studies, we provide each the optimal and the greedy algorithms with computational complexity analysis.
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If the traffic became more uniform, then the greedy algorithm began to outperform honeybee.
Variants of the greedy algorithm are selective greedy and distributed greedy algorithms.
MC: The greedy algorithm with MC simulation and CELF optimization.
For the greedy algorithm, we use 10,000 Monte Carlo simulations.
As expected, the greedy algorithm fail at these regions.
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