The growth is first estimated on small subintervals by means of dispersive bounds; then it is estimated on the whole interval by an iteration process.
These curve fitting factors were finally determined by an iteration process as compared to the experimental results.
Therefore, by an iteration process, one can obtain a solution of Problem (1.1), which satisfies v ( x, t ) ≤ u ( x, t ) ≤ w ( x, t ).
The following is an iteration process given by Moudafi [25]: x n + 1 : = J λ B 1 ( x n + γ A ∗ ( J λ B 2 − I ) A x n ).
A time-dependent simulation process is controlled by an iteration procedure between these three modules until convergence is reached within each time step.
In this section, we prove some strong and Δ-convergence theorems of a sequence generated by a K iteration process for Suzuki generalized nonexpansive mappings in the setting of (mathit{CAT}(0)) space.
Then, by a designed iteration process, the higher frequency of the scenario will be calculated back and add to the detail layer.
By introducing a random iteration process with weak contraction random operator, we obtain a convergence theorem of the random iteration process to a random fixed point for nonexpansive random self-mappings.
By introducing a new iteration process with error term, we obtain sufficient and necessary conditions, as well as sufficient conditions, for the existence of a fixed point.
For p=3, we obtain the following three-step implicit iteration process: For a given x0∈K, compute the sequence {x n } by the iteration process x n = a n ′ x n − 1 + b n ′ T 1 y n 1 + c n ′ u n, y n 1 = a n 1 x n − 1 + b n 1 T 2 y n 2 + c n 1 v n 1, y n 2 = a n 2 x n − 1 + b n 2 T 3 x n + c n 2 v n 2, n ≥ 0, Open image in new window (4.2).
If T 1 = T, T 2 = I, b n 1 = 1 Open image in new window, and c n 1 = 0 Open image in new window in (4.3), we obtain the implicit Mann iteration process: [2]For any given x0∈K, compute the sequence {x n } by the iteration process x n = a n ′ x n − 1 + b n ′ T x n + c n ′ u n, n ≥ 0, Open image in new window (4.4).
