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This isnt an issue per se. I did want to figure out if I could use a similar approach for a simple LASSO regression in pytorch. Working with proximal operators with SGD is straightforward (but then SGD has step size issues). ADAM requires memory for past gradients - but isn't meant for non-differentiable convex problems (even though L1 regularization does improve results a fair bit). I wanted tos ee if AdaProx improves results.
The text was updated successfully, but these errors were encountered:
This isnt an issue per se. I did want to figure out if I could use a similar approach for a simple LASSO regression in pytorch. Working with proximal operators with SGD is straightforward (but then SGD has step size issues). ADAM requires memory for past gradients - but isn't meant for non-differentiable convex problems (even though L1 regularization does improve results a fair bit). I wanted tos ee if AdaProx improves results.
The text was updated successfully, but these errors were encountered: