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But x1 > sup{p ∈ E : p = Tp}, it implies that { x n } n = 1 ∞ does not converges to a fixed point of T. By using the same argument of proof as in above lemma, we get the following result.
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So log normal distribution, it does not converge.
It is a robust method, as it does not converge to a local optimum.
For such packing problems, we observe that the classical iterative Arrow–Hurwicz algorithm does not converge.
In this paper, we analyze the reason why iterative computation sometimes does not converge.
However the approximate solution does not converge to the steady state solution that is known exactly.
Due to confrontation with a large number of local minima, DNN training often does not converge.
So for all non-zero t, it does not converge for log normal distribution.
MacMillan's account of these, however, does not converge as clearly as it might have.
In the primitive-variable solutions, the penalty is that the discrete continuity equation does not converge to machine accuracy.
Nonrelatively measurable functions are such that the empirical distribution function does not converge as the data-record length approaches infinity.
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