[gajomi]

I agree. Native access to legacy linear algebra functionality exposed by Numpy is the #1 reason this project seems exciting to me. I don't really care if the bulk of the code is O(10) slower so long as the numerical bits are O(1000) faster, which is a typical comparison of LAPACK for the JVM equivalents.

[mreid]

(Pedant warning)

I think you may be abusing Big-O notation a little here since, technically O(10) is equivalent to O(1000)—and both are O(1).

[gajomi]

Haha, thanks for the warning. I am reminded of another time I ended up abusing the Landau notation in a numerical analysis class ( was it probability?) some years ago. The homework was to get and N^2log(N) algorithm of some kind, but I became concerned about certain large constant factors which I could determine exactly. However, these constant factor could be written as a series in another parameter, which I couldn't approximate in a certain limit and... well, you see where I'm going. There where lots of O's with slightly different semantics on the same paper.

[Anderkent]

It's fairly clear in the context that he's using O(x) in the informal 'order of' meaning: 'order of 10', 'order of 1000' etc.

[mreid]

I agree, and had no problem understanding his intended meaning but I was irked by the unnecessary misuse of technical notation to attempt to get across an idea that would have been perfectly easy to write without it (e.g., "10 times" and "1000 times").