To whomever is interested in deflated Krylov solvers: Deflation is very useful technique for accelerating the convergence of Krylov iterations. It seems to me that deflation is not supported in PETSc, except for the special case DGMRES, where eigenvectors are used to deflate GMRES. As a matter of fact, one does not need to use eigenvectors for deflation, nor deflation is dependent on a specific choice of the iterative method.
Coincidentally, Kees Vuik pointed to me an earlier paper of his: http://ta.twi.tudelft.nl/nw/users/vuik/papers/Vui01F.pdf which claims that the convergence of using block Jacobi/ILU(0) [the default preconditioner for PETSc] becomes in general better if deflation is included. In his paper, deflation means "coarse grid acceleration"; see page 935, the paragraph of equation (2). So I think (including my personal experience), deflation is a general enough technique that may benefit every user of the KSP solver. Implementation-wise, my personal take is that the library needs to change the A-multiply to (A-AZ(Z'AZ)^{-1}Z'A)-multiply, where Z is the deflation matrix: tall, skinny, and with a small number of columns. PETSc can ask the user to define Z, and it can also predefine several choices of Z, one example choice is from the above mentioned paper. In principle, deflation should be independent of the KSP types, but I don't see how exactly it can be implemented without touching the code of every krylov solvers. Perhaps adding one more layer of abstraction is needed. So for anyone who is interested in implementing deflation, I can provide a more detailed tutorial. The design of PETSc is becoming more and more complicated for me to digest, thus I think it may be unrealistic for me to implement deflation myself for the moment. Jie -- Jie Chen Mathematics and Computer Science Division Argonne National Laboratory Address: 9700 S Cass Ave, Bldg 240, Lemont, IL 60439 Phone: (630) 252-3313 Email: jiechen at mcs.anl.gov Homepage: http://www.mcs.anl.gov/~jiechen
