On 04/06/2011 04:50 PM, Jed Brown wrote: > On Wed, 06 Apr 2011 16:37:26 +0200, Umut Tabak <u.tabak at tudelft.nl> > wrote: >> Just curious, are not the other negative eigenvalues problematic as >> well? > > Negative eigenvalues do not pose any particular problem to Krylov > methods like GMRES. Conjugate gradients does require that the matrix > be SPD, but petsc-dev detects when a matrix is negative definite and > still does the right thing.
Also with cg type methods? if yes, how? Because I am dealing with a similar problem in a projection sense which makes some factors that are already available very good preconditioners, completely problem specific, then cg converges incredibly fast, sth like 4 to 8 iterations. However, projection is the key and at every step, in cg, I should make sure that the search directions in cg are orthogonal to the previous ones by cgs/mgs, otherwise I bump into the well know orthogonality issues of Lanczos type methods... why I am digging is to see some better options if there are any. Greetz, Umut
