Sentence examples for iteration computational from inspiring English sources

Thus, since these operations are used with finite times, it is easily seen that, for each iteration, computational complexities for both AFD and AWCR are O(m 3).

Designed to allow a direct comparison of existing theory, our framework implies that, according to the best available analysis on these three algorithms, IHT requires the fewest number of compressed sensing measurements, has the best proven stability bounds, and has the lowest per iteration computational cost.

Table 1 The complexity of the proposed algorithms Case Iteration Computational   complexity complexity Spectrum efficiency O(2n 2 ln(L R/ϵ 1)) O(2s n 2 ln(L R/ϵ 1)) Energy efficiency O ( 2 n 2 ln ( LR / ε 1 ) × log 2 ( P t / ε 2 ) ) O ( 2 s n 2 ln ( LR / ε 1 ) × log 2 ( P t / ε 2 ) ). 1. Initial the searching set: P min = 0, P max = P t, P 0 ∗ = P min + P max 2 ;   2.

N_{textit {rl}}^{6}right)), which is lower than the per-iteration computational complexity of the proposed Algorithm 1.

The proposed Algorithm 1 has a better MSE and BER performance than the proposed Algorithm 2 at a higher per-iteration computational complexity.

Therefore, the per-iteration computational complexity order of the proposed algorithm is O K N d 3 + N r 3 + N b 3 + N s 2 + K + 1 3.5.

Therefore, the per-iteration computational complexity order of the proposed AO algorithm is (mathcal {O}(18K(c_{x}c_{lambda }+c_{y}c_{mu }+c_{z}c_{delta }))), which increases linearly with the number of subcarriers.

Comparing the two proposed algorithms, the first algorithm has a better MSE and bit error rate (BER) performance, while the second algorithm has a smaller per-iteration computational complexity.

One alternative is to use a constrained optimization solver, like L-BFGS-B; however, the large number of variational parameters to be optimized greatly increases the per-iteration computational cost of the inference algorithm.

This iterative method requires at each iteration the computational cost of the first-order derivative of the objective function.

As per our discussion in Section 3.3, gradient calculation in each iteration requires computational complexity of O(L), and hence, the overall computational complexity could be also approximated as O(L), which is far lighter than that of the greedy search-based RRH control algorithm, i.e., O(L 2).