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 <koo...@gmail.com> 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 < > colin.bin.wang.mah...@gmail.com > > 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 > > >
