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It is an efficient iterative method which converges quickly.
When the evaluation of the function is expensive, the task is computationally challenging because the standard approach is based on a Monte Carlo method which converges slowly.
Therefore, we apply an iterative process based on the simple gradient descent method, which converges to a local maximum due to the non-concavity of the objective function.
For example, in [4, 43] it was proved that some very interesting Korpelevich-type algorithms strongly converge to a solution of V I ( C, A ). Very recently, Yao et al. [33] suggested modified Korpelevich's method which converges strongly to the minimum norm solution of variational inequality (1) in infinite-dimensional Hilbert spaces.
In Section 4 we introduce the second distributed WLS method which converges in finite time.
In Section 3 we derive the first distributed WLS method, which converges asymptotically.
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Krasnoselskij and Mann iterations were developed to obtain fixed point iteration methods which converge for some operators, such as nonexpansive ones, for which Picard iteration fails.
As special cases, we obtain two iterative methods which converge strongly to the minimum norm point of (operatorname {VI}(C,F) cap operatorname {Fix}(T)).
It employs Newton's method update rules, which converges quadratically and is free from selecting an efficient learning rate.
which converges to 2/3.
which converges to as.
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