We develop a computing algorithm for SPLS by modifying the nonlinear iterative partial least-squares (NIPALS) algorithm, and illustrate the method with an analysis of a cancer dataset.
To deliver competitive performance but reduce the computational load, the proposed algorithm is developed as an iterative approach by modifying the concept of the alternative projection algorithm that was proposed by Ziskind and Wax [14].
This is accomplished by modifying an existing iterative algorithm, which is also able to account for the continuity of the vessel area, when required.
In 2014, by modifying iterative algorithm (1.8) and employing new techniques, Wei and Tan [20] presented and studied the following three-step iterative algorithm: left { textstylebegin{array}{l} x_{1}in C, u_{n} = Q_{C}[ 1-alpha_{n})(x_{n}+e_{n})], v_{n}= (1- beta_{n})x_{n} + beta_{n} S_{n} u_{n}, x_{n+1}=gamma_{n} x_{n}+(1-gamma_{n})S_{n}v_{n}, quad n geq1, end{array}displaystyle right.
We introduce a new iterative scheme by modifying Mann's iteration method to find a common element for the set of common fixed points of an infinite family of asymptotically strictly pseudocontractive mappings in the intermediate sense, the set of solutions of the cocoercive quasivariational inclusions problems, and the set of solutions of the mixed equilibrium problems in Hilbert spaces.
Our modifying algorithm does not modify the original iterative decoding algorithm, since the modified generator matrix is known both at encoder and decoder.
In this paper, we suggest two hybrid iterative algorithms by modifying Iemoto and Takahashi's iterative scheme for a countable family of nonspreading mappings and a nonexpansive mapping in Hilbert spaces.
The model is then modified and the iterative algorithm determining ( rho_{pfb}^{U} ) is presented.
Let be a sequence in generated by the modified Ishikawa iterative process: (2.16).
Later, Deepho and Kumam [11] extended the results of Xu [7] by introducing and studying the modified Halpern iterative scheme for solving the split feasibility problem (SFP) in the setting of infinite-dimensional Hilbert spaces.
