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Then, we prove strong convergence theorems by using a shrinking projection method.
Moreover, by using a shrinking projection method, Takahashi et al. [14] introduced a new algorithm and proved strong convergence theorems for finding a common fixed point of families of nonexpansive mappings.
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The kinetics data were modeled using a shrinking core approach.
Aiming at retrieving the hidden data in encrypted domain, Zhang et al. proposed a new reversible data hiding algorithm with public key cryptosystem [26] by using a pre-procession operation to shrink histogram of original image to vacate room for data hiding.
By using a matched asymptotic expansion technique, the shrinking core model (SCM) used in non-catalytic gas solid reactions with general kinetic expression is rigorously justified in this paper as a special case of the homogeneous model when the reaction rate is much faster than that of diffusion.
The problem is solved iteratively by using a permissible source region that is shrunk by removing nodes with low probability to contribute to the source.
Developed by a company aptly called Satiety Inc., the procedure shrinks the stomach by using a stapler inserted through the mouth, rather than by cutting open a person's belly.
Shrink the plastic by using a hair blow dryer.
In this paper, we introduce a new viscosity approximation method by using the shrinking projection algorithm to approximate a common fixed point of a countable family of nonlinear mappings in a Banach space.
In this section, we apply our result to the problem of finding a common element of an equilibrium problem and the problem of finding a zero of a maximal monotone operator in a Banach space by using the shrinking projection method.
We prove a strong convergence theorem for finding a common element of the set of solutions for generalized equilibrium problems, the set of fixed points of a relatively nonexpansive mapping, and the set of fixed points of a quasi- -nonexpansive mapping in a Banach space by using the shrinking Projection method.
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