The conjugate-gradient method is a well-known iterative method for
minimizing a quadratic function where the Hessian is positive definite.
In this talk, we discuss some aspect of the method that may be less
well known. In particular, we study the behavior of the
conjugate-gradient method on ill-conditioned problems, for which the
Hessian has one set of eigenvalues that are large and the remaining
are small.
Our motivation is twofold: first, interior methods, where
infinitely ill-conditioned matrices arise, and second, radiation
therapy optimization, where ill-conditioned systems arising from
discretized Fredholm equations of the first kind arise. We
characterize the behavior of the residuals associated with the large
eigenvalues throughout the iterations, and also characterize the
behavior of the residuals associated with the small eigenvalues for
the early iterations. Our results show that the residuals associated
with the large eigenvalues are made small first, without changing very
much the residuals associated with the small eigenvalues.
Subsequently, the residuals associated with the small eigenvalues are
reduced.
The motivation for this research comes from radiation therapy
optimization.
|