**-ksp_qcg_trustregionradius <r> ** -Trust Region Radius

### Notes

This is rarely used directly

### Notes

Use preconditioned conjugate gradient to compute
an approximate minimizer of the quadratic function
q(s) = g^T * s + .5 * s^T * H * s

subject to the Euclidean norm trust region constraint

|| D * s || <= delta,

where

delta is the trust region radius,
g is the gradient vector, and
H is Hessian matrix,
D is a scaling 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

### Notes

### Currently we allow symmetric preconditioning with the following scaling matrices

PCNONE: D = Identity matrix
PCJACOBI: D = diag [d_1, d_2, ...., d_n], where d_i = sqrt(H[i,i])
PCICC: D = L^T, implemented with forward and backward solves.
Here L is an incomplete Cholesky factor of H.

### References