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They proved that the algorithm (1.14) converges weakly to some element of A-10.
Meanwhile, in [8], the authors Combettes and Wajs also proved that the algorithm converged weakly.
It is proved that the algorithm we propose can obtain strong convergence.
It was proved that the algorithm converged to the feasible pure strategy Nash equilibrium under specific conditions.
Finally, it has been proved that the algorithm can decrease the objective function value monotonically and any limit point is a stationary point.
Finally, by proving the algorithm through learning experience of documents data, they proved that the algorithm can effectively cope with the problem of mass data.
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It is proved that the algorithms converge monotonically to the solution of the problem in a finite number of steps.
The research proved that the algorithms put forward in this paper were effective in processing data in short time and with high precision.
We prove that the algorithm also works in those cases.
We prove that the algorithm achieves constant message and linear time complexity.
We will theoretically prove that the algorithm converges for a relevantly chosen initialization value.
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