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Dear all,<br>
<br>
I have a system that I would like to solve for multiple rhs,
represented in block notation as<br>
<br>
[ A C ] x1 b1 <br>
= <br>
[ C^T B ] x2 b2<br>
<br>
I could solve the system <br>
<br>
(B - C^TA^{-1}C)x2 = bupdated<br>
<br>
with Minres algorithm in MATLAB by using the Incomplete
Factorization of B in decent iteration counts, like 43. The problem
is that B is not SPD and it has one negative eigenvalue. That is the
reason to use MINRES.<br>
<br>
Just as a try, I saved the matrix represented by (B - C^TA^{-1}C) in
sparse format and used the hypre euclid preconditioner in PETSc
which resulted in 25 iterations to convergence. But since for large
problems, this approach is not viable, I was wondering if that is
possible to use the complete cholesky factorization of
B+alpha*diag(B) where alpha is given as
<pre class="programlisting"><font face="sans-serif">alpha = max(sum(abs(A),2)./diag(A))-2
as a preconditioner for the above schur complement. Or in general use an external preconditioner
for the matrix operator.
Any pointers are appreciated.
Best,
Umut
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