Thanks for the help. What I am actually doing is computing a gradient to a least squares objective. That is, X.T.dot(X.dot(beta) - Y) If X is such that X.dot(beta) is fast (i.e. matvec is fast) then am I missing a "simple" optimization here at the cost of a copy? Alternatively, if X is such that vecmat is fast, then what is the best way to do this? A copy seems easiest, and possibly applying the previous "simple" optimization. Based on my understanding of the other replies, I would guess that if X2=X.copy(), then the fastest way would be (X2.dot(beta) - Y).dot(X) This doesn't pan out in my example, the winner is X2.T.dot(X2.dot(beta) - Y) which is about the same as (X2.dot(beta)-Y).dot(X2) I made a small gist: https://gist.github.com/da7b2ef6ef109511af06a9cebbfc8ed1 One difference I see between a numpy array with the same strides and the array loaded from a MAT file is the ALIGNED flag. On Sat, Aug 26, 2017 at 10:10 AM, Stephan Hoyer <shoyer@gmail.com> wrote:
On Sat, Aug 26, 2017 at 12:09 AM, Jonathan Taylor < jonathan.taylor@stanford.edu> wrote:
So, matvec is just slower because of strides and where numpy retrieves data? Is there a simple way to do this besides a copy? I can easily afford the copy, just wondering.
No, the only way to change the strides of an array with the same data is to make a copy.
Array operations will always be fastest when the smallest strides are along the axis iterated over in the inner-most (summed) loop. So this existing strides of your matrix are not sub-optimal in general, just for this specific operation. They would be suitable, for example, in a vector-matrix multiply.
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