Sentence examples for approximation to the initial from inspiring English sources

Recently, Combettes and Hirstoaga [29] introduced an iterative scheme of finding the best approximation to the initial data when is nonempty and proved a strong convergence theorem.

Combettes and Hirstoaga [4] introduced an iterative scheme for finding the best approximation to the initial data when is nonempty and proved a strong convergence theorem.

For solving the equilibrium problem, Combettes and Hirstoaga [5] introduced an iterative algorithm of finding the best approximation to the initial data and proved a strong convergence theorem.

In 1997, Combettes and Hirstoaga [14] introduced an iterative method of finding the best approximation to the initial data when is nonempty and proved a strong convergence theorem.

In 1997, Combettes and Hirstoaga [13] introduced an iterative method of finding the best approximation to the initial data and proved a strong convergence theorem.

Combettes and Hirstoaga [3] introduced an iterative scheme for finding the best approximation to the initial data when EP is nonempty and derived a strong convergence theorem.

Among others, some of them are iterative techniques, reproducing kernel methods, finite difference methods etc. Esmaeili and Shamsi [21] developed a new procedure for obtaining an approximation to the solution of initial value problems of FDEs by employing a pseudo-spectral method, and Pedas and Tamme [22] studied the application of spline functions for solving FDEs.

The time derivatives are approximated with the Crank-Nicolson method [ 32] that yields an implicit approximation to the solution of the initial value problem y′ = f x, y) with y x0) = y0 at x for a given time step h.

Marr and Hildreth [73] suggest for an involvement of higher-order Gaussian derivatives, utilizing the Laplacian of Gaussian (LoG) and its DoG approximation to model the initial retinal filtering.

Then, (widehat {Theta }) is recovered by the following successive projection and thresholding operations, assuming Θ (0) is the initial approximation to the wavelet coefficients Θ: begin{array}rcl@ widehat{Theta}^{(i)}= Theta^{(i)}+frac{1}{r}Phi^{T}left widetilde{y}-Phi^{prime}Theta^{(i)}righT}left widetilde{y}-Phi^{prime}Theta^{

Fiber microdissection techniques in post- mortem brains allow for an initial approximation to the complex architecture of the fiber tract systems closely related to the human claustrum.