Sentence examples for strongly convergence of from inspiring English sources

Then, under weaker hypotheses on coefficients, he proved the strongly convergence of the proposed iterative algorithm to the unique solution of the variational inequality.

Therefore, Noor et al. [44] proved the strongly convergence of the iterative sequence {z n } generated by Algorithm 7.5, under the condition strongly monotonicity of the operators T and g, not under the mild condition relaxed cocoercivity.

Later, Kumam et al. [28] proved a strongly convergence theorem of the iterative sequence generated by the shrinking projection method for finding a common element of the set of solutions of generalized mixed equilibrium problems, the set of fixed points of a finite family of quasinonexpansive mappings, and the set of solutions of variational inclusion problems.

We then prove that the sequence generated by the algorithm converges strongly (convergence in metric) to a minimizer of convex objective functions.

Throughout this paper, let ℕ be the set of positive integers and let ℝ be the set of real numbers, H 1 be a (real) Hilbert space with inner product 〈 ⋅, ⋅ 〉 and norm ∥ ⋅ ∥, respectively, and C be a nonempty closed convex subset of H 1. We denote the strongly convergence and the weak convergence of { x n } to x ∈ H 1 by x n → x and x n ⇀ x, respectively.

We denote the strongly convergence and the weak convergence of {x n } to x ∈ H by x n → x and x n ⇀ x, respectively.

We denote the strongly convergence and the weak convergence of ({x_{n}}) to (xin H) by (x_{n}rightarrow x) and (x_{n}rightharpoonup x), respectively.

From (H2), all the growth of is subcritical, so the standard argument shows that admits a strongly convergence subsequence.

with strongly convergence as.

Now we prove the strongly global convergence of TMPRP1 method for uniformly convex functions.

Bilgin [31] introduced the definition of lacunary strongly Δ-convergence of fuzzy numbers.