Use preconditioned conjugate gradient to compute an approximate minimizer of the quadratic function
q(s) = g^T * s + 0.5 * s^T * H * s
subject to the trust region constraint
|| s || <= delta,
where
delta is the trust region radius, g is the gradient vector, H is the Hessian approximation, and M is the positive definite preconditioner matrix.
KSPConvergedReason may be
KSP_CONVERGED_CG_NEG_CURVE if convergence is reached along a negative curvature direction,
KSP_CONVERGED_CG_CONSTRAINED if convergence is reached along a constrained step,
other KSP converged/diverged reasons