Dear Mladen, Was there a resolution to this thread? A simpler related question is that you have a graph Laplacian for a connected graph. Then you know exactly what the null space is. Using L \ (b - mean(b)) throws the error " ArgumentError: matrix has one or more zero pivots" where pinv works but is slow because it doesn't use the sparsity.
I need this for a graph structured normal means problem, which is super important. --James On Monday, April 27, 2015 at 12:27:14 PM UTC-7, Mladen Kolar wrote: > > Dear Dominique, > > I am not sure that "incomplete Cholesky decomposition" is standard > terminology. It is used in John Shawe-Taylor's book on kernel methods for > pattern analysis. > > What it means is the following, instead of using the Cholesky > decomposition A = R'R where R is upper triangular matrix, one approximates > A as P'*P where P = R[1:T, :] and T is the rank of approximation. Again, > the idea is that one does not compute full Cholesky, but greedily > approximates A. > > Thanks, Mladen > > On Tuesday, April 21, 2015 at 9:30:54 PM UTC-5, Dominique Orban wrote: >> >> What do you mean by "incomplete Cholesky"? Could you explain how that >> would solve your system? > >
