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Now we cam apply Theorem 2.3 to conclude that the sequence given by the projection Algorithm (3.9) converges weakly to a solution of the MSSFP (1.10).
The latter is modified in order to profit from the dual mesh employed by the projection algorithm and the derivatives involved in the diffusion term are discretized using a Galerkin approach.
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In this paper, motivated by the above result, we consider the projection algorithm for treating solutions of the equilibrium problem and fixed points of generalized asymptotically quasi-ϕ-nonexpansive mappings.
We here adopt the projection algorithm in the dual framework proposed by Chambolle [29] to solve minimization problem (16).
In this paper, we also use projection update laws, which generalize the projection algorithm, to tune the adjustable parameters.
A linear discriminant analysis was applied to fluorescence intensities of samples of cheese fat and oil from cheese-like products, previously selected by the successive projection algorithm, and to the first five principal components obtained by the use of principal component analysis.
The variables were selected by the Successive Projection Algorithm (SPA) in MLR model.
The lower level problem is the user equilibrium assignment model, which is solved by the Gradient projection algorithm.
The calibration models were constructed using Partial Least Square (PLS) regression and Multiple Linear Regression (MLR), with variables selected by the Successive Projection Algorithm (SPA).
Now, we discuss the convergence analysis of iterative sequence generated by the projection iterative Algorithm 4.2.
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.
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