Accumulation of gradient and Hessian about ada-hessian HOT 4 CLOSED

works now, thank you 👍
I open-sourced my code for experimenting with AdaHessian where I used your optimizer implementation (of course added copyright + link to your repo).

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Hi, you are absolutely right. While correct accumulation could be supported, it seems that it would make the code much more complicated and it would require more memory (for storing both and ), so I'll completely remove this feature in the next version. It doesn't seem to be worth it for the default (more common) simple usage.

On the other hand, better approximation of formula 11 doesn't seem to need accumulated gradients, all samples of should be computed from the same gradient. I'll add an option to use more samples :)

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Yes, I also did it that way: simply pass number of samples to set_hessian(num_samples), accumulate z*(H@z) values, and finally divide by that number. But good to know that this will be in the next release :-)

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That's amazing, thank you for sharing the experiments!

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