convergence problem with torch.svd

[king-zark]

Hi:
I'm impressed by your paper and try to apply the BNM loss for domain adaptation problem. However, the torch.svd() function usually crush down, my batch-size and vector size is 128*1000, I wonder how do you figure out the torch.svd() problem?
Best wishes!

[cuishuhao]

Actually, we did not meet the problem. I guess four changes might help.

1. To calculate svd, it need to formulate a matrix of A^T \times A. If the category number is large such as 1000, it is better to calculate torch.svd(A^T).
2. If the svd does not converge, you might add a small value to A.
3. The parameter lambda before svd might be changed.
4. It is better for "compute_uv=False" in svd or directly utilizing the nuclear norm by "torch.norm(X,'nuc')", Since the computation of U and V is complicated.

[king-zark]

Thanks a lot! I will try to apply your suggestions. For now, I simply employ try-except to avoid the program crushing down. Best wishes!

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2020年4月27日 下午10:34，崔书豪 ***@***.***> 写道： Actually, we did not meet the problem. I guess four changes might help. To calculate svd, it need to formulate a matrix of A^T \times A. If the category number is large such as 1000, it is better to calculate torch.svd(A^T). If the svd does not converge, you might add a small value to A. The parameter lambda before svd might be changed. It is better for "compute_uv=False" in svd or directly utilizing the nuclear norm by "torch.norm(X,'nuc')", Since the computation of U and V is complicated. — You are receiving this because you authored the thread. Reply to this email directly, view it on GitHub <#3 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AFOAGCLAO66PYUAOCUEOF3TROWJX3ANCNFSM4MR3YZVA>.

[cuishuhao]

@king-zark Hello, the problem is solved by approximation.