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There are many papers on how Adagrad is used in SGD, but I have not seen any where it is applied in batch descent.

I have a situation wherein batch gradient descent is faster than SGD (unique to my problem).

So far I am simply using a optimization package that does LBFGS optimization. This works ok, but LBFGS only does line search for a scalar learning rate. With Adagrad i could get learning rates per dimension of my parameter vector which seems better than a scalar learning rate.

My question is - is there any reason NOT to use Adagrad in batch gradient descent?

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  • $\begingroup$ I am not completely clear by 'batch' descent, but in principle you can apply it. Any reason you think why not? $\endgroup$ – Daniel May 20 '15 at 3:48

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