I want to multiply B = A @ A.T
in numpy. Obviously, the answer would be a symmetric matrix (i.e. B[i, j] == B[j, i]
).
However, it is not clear to me how to leverage this easily to cut the computation time down in half (by only computing the lower triangle of B
and then using that to get the upper triangle for free).
Is there a way to perform this optimally?
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