Hi there,

I am trying to write a tool which involves implementing logistic
regression. With the batch gradient descent method, the convergence is
guaranteed as it is a convex problem. However, I find that with the
stochastic gradient decent method, it typically converges to some random
points (i.e., not very close to the minimum point resulted from the batch
method). I have tried different ways of decreasing the learning rate, and
different starting points of weights. However, the performance (e.g.,
accuracy, precision/recall, ...) are comparable (to the batch method).

I understand that this is possible, since SGD(stochastic gradient descent)
uses an approximation to the real cost each step. Does it matter? I guess
it does since otherwise the interpretation of the weights would not make
much sense even the accuracy is comparable. If it matters, I wonder if you
have some suggestions on how to make it converge or getting close to the
global optimal point.



Thanks!

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