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I understand that several works have noted that the L2 norm may not be the best choice for fitting certain functional spaces. Personally, I have an application where the maximum error (Linf) is more important than the mean error (L2). One may also want a sparse fit in some cases too or a lasso regression. Would you consider adding minimization (via linear programming) over other norms as a possibility?
I see this package currently is using pinv and avoiding optimization, but perhaps code from pysindy might make this feasible?
The text was updated successfully, but these errors were encountered:
Hi thank you for making this awesome package,
I understand that several works have noted that the L2 norm may not be the best choice for fitting certain functional spaces. Personally, I have an application where the maximum error (Linf) is more important than the mean error (L2). One may also want a sparse fit in some cases too or a lasso regression. Would you consider adding minimization (via linear programming) over other norms as a possibility?
I see this package currently is using pinv and avoiding optimization, but perhaps code from pysindy might make this feasible?
The text was updated successfully, but these errors were encountered: