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Convergence proof of the stochastic gradient algorithm is derived making mild assumptions.
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Moreover, this convergence is uniform if we make the mild assumption that the mean membrane potential decreases monotonically with the adaptation current: Given any interval ([I_{star}, I^{star}]) on which (langle x rangle) is a monotonically decreasing function of z (cf. Fig. 5), (A'(I) rightarrow1) uniformly as (s rightarrowinfty).
First, near-optimal controls may exist under mild assumptions.
Under mild assumptions, the proposed algorithm possesses the global convergence.
Under mild assumptions, some strong convergence theorems are obtained.
Strong convergence theorem is given under some mild assumptions.
Weak convergence result under mild assumptions will be established.
Algorithm 1.9 has strong convergence under some mild assumptions.
But you are making certain assumptions.
Why is everyone making these assumptions?
Quit making those assumptions.
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