**Abstract** |
Consider applying the Conjugate Residual (CR) method, which is a Krylov subspace type iterative solver, to systems of linear
equations $ A {\bf x} = {\bf b} $ or least squares problems
$ {\displaystyle \min_{ {\bf x} \in \rn}
\| {\bf b} - A {\bf x} \|_2 } $,
where $ A $ is singular and nonsymmetric.
We will show that when $R(A)^\perp=\ker A$, the CR method can be decomposed into the $ R(A) $ and $ \ker A $ components, and the necessary and sufficient condition for the CR method to converge to the least squares solution without breaking down for arbitrary $ {\bf b} $ and initial approximate solution $ {\bf x}_0, $ is that the symmetric part $ M(A) $ of $ A $ is semi-definite and $ \rank \, M(A) = \rank A $. Furthermore, when ${\bf x}_0 \in R(A), $ the approximate solution converges to the pseudo inverse solution.
Next, we will also derive the necessary and sufficient condition for the CR method to converge to the least squares solution without breaking down for arbitrary initial approximate solutions, for the case when
$ R(A) \oplus \ker A = \rn $ and $ {\bf b} \in R(A). $ |