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The restrictions imposed on the sequence are mild.
Corollary 3.2 improves Theorem CKZ by relaxing the restriction imposed on the sequence {e n }.
Note that Assumption 1 is stronger than the conditions imposed on the sequence in Theorem 2.1 for (2.1).
The strong convergence of this technique is proved under certain assumptions imposed on the sequence of parameters.
It is proved in [3, 4] that, under appropriate condition imposed on, the sequence generated by (1.2) converges strongly to the unique solution in of the variational inequality: (1.3).
It is proved [18, 19] that, under certain appropriate conditions imposed on, the sequence generated by (1.28) strongly converges to the unique solution in of the variational inequality (1.29).
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The strong convergence theorems of the rules are proved under certain assumptions imposed on the sequences of parameters.
They proved, under certain appropriate conditions imposed on the sequences and, that defined by (1.33) converges strongly to a fixed point of, which solves some variational inequality.
It is worth pointing out that in our results there is no assumption of the boundedness imposed on the sequences {x n } and {y n } generated by Algorithms I or II.
We will prove that the generalized viscosity implicit rules (1.2) and (1.3) converge strongly to a fixed point of T under certain assumptions imposed on the sequences of parameters, which also solve VI (1.1).
Our data indicate that positive selection in SELEX acts independently of the neutral selective requirements imposed on the sequences.
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