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(c) For the convergence, the iterative sequence proposed by our algorithm converges strongly to a solution of GSEFP (1.7).

For the convergence, the iterative sequence proposed by our algorithm converges strongly to a solution of GSEFP (1.7).

We present an iterative procedure for finding a solution of the proposed problem and show that under some suitable assumptions, the sequence generated by our algorithm converges weakly to a solution of the considered problem.

It is proved that the sequence generated by our proposed algorithm converges strongly to a minimizer of (1.1), which is also a solution of a certain variational inequality.

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

We also verify that the approximate solutions obtained by the our proposed algorithm converge to the exact solution of the general nonlinear random A-maximal m-relaxed η-accretive equations with random relaxed cocoercive mappings and random fuzzy mappings in q-uniformly smooth Banach spaces.

The result that the sequence generated by the algorithm converges linearly to a solution of the system with the convergence rate ∥ Ψ ∥ is proved.

The authors conducted an investigation of the conditions under which the state estimate given by this algorithm converges asymptotically to the first order minimum variance estimate given by the extended Kalman filter (EKF).

This paper investigates conditions under which the state estimate given by this algorithm converges asymptotically to the first order minimum variance estimate given by the extended Kalman filter (EKF).

Therefore, the iterative sequence generated by the algorithm converges more quickly.

We prove that the sequence generated by the algorithm converges strongly to a minimizer of convex objective functions.