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Here, the time complexity of the proposed algorithm is O(n), compared to a naïve implementation with time complexity of O(n) for sequence length n and distances that can cover the whole sequence length.
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The simulations were also "confirmed" by our explicit Euler implementation with time-step (Delta t = 10^{-7}).
In the context of the MDGs, it amounts to comparing the time of actual implementation with the time stipulated by the schedule to the 2015 target.
In addition, the fact that the costs and time necessary for data standards implementation decrease with time should be made known to the wider research community.
Motivated by practical considerations, we analyze the algorithm implementation with discrete-time communication.
For parallel-distributed implementation with (R times C) partitioning, if we assume that the edges and vertices are distributed evenly, since the number of rows in the distributed submatrices is V / R, the required memory per node is: begin{aligned} frac{V}{R}+frac{E}{RC} end{aligned} (2).
Our focus is on implementations with a time complexity that is as fast as possible in terms of its dependence on N, the number of data.
In order to compare different implementations, this simulation was executed by each implementation with a constant time step of 20 ms.
For A ≪ R, the procedure is much faster than an alternate implementation with O NR) running time.
Külist is more than usable enough now and hopefully implementation will only improve with time.
Also, the frequency domain (FD) implementation greatly reduces the computational complexity [17] that is associated with time domain implementation in channels with long impulse responses and has many other advantages [18].
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