And, do you really need an inverse, or pseudo-inverse? But, no, there are really no direct utilities for this. But we could probably tell you how to do it efficiently, as long as you don't actually mean a full inverse.
On Fri, Jan 18, 2013 at 11:58 AM, Ted Dunning <[email protected]> wrote: > Left unsaid in this comment is the fact that matrix inversion of any > sizable matrix is almost always a mistake because it is (a) inaccurate, (b) > slow. > > In scalable numerics it is also commonly true that the only really scalable > problems are sparse. The reason for that is that systems whose cost grows > with O(n^2) cannot be scaled to arbitrary size n. Sparse systems with only > k items on average per row can often be handled with o(n) complexity which > a requirement for a practical system. > > On Thu, Jan 17, 2013 at 8:49 PM, Koobas <[email protected]> wrote: > >> Martix inversion, as in explicitly computing the inverse, >> e.g. computing variance / covariance, >> or matrix inversion, as in solving a linear system of equations? >> >> >> On Thu, Jan 17, 2013 at 7:49 PM, Colin Wang < >> [email protected] >> > wrote: >> >> > Hi All, >> > >> > I want to solve the matrix inversion, of course, big size, in Map/Reduce >> > way. >> > I don't know if Mahout offers this kind of utility. Could you please give >> > me some tips? >> > >> > Thank you, >> > Colin >> > >>
