Code to run conjugate gradient method subject to a constraint on the solution norm. This is used in Trust Region methods for nonlinear equations, SNESNEWTONTR

Options Database Keys

-ksp_qcg_trustregionradius <r> - Trust Region Radius


This is rarely used directly

Level: developer


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,


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


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.


1. - Trond Steihaug, The Conjugate Gradient Method and Trust Regions in Large Scale Optimization, SIAM Journal on Numerical Analysis, Vol. 20, No. 3 (Jun., 1983).

See Also

KSPCreate(), KSPSetType(), KSPType (for list of available types), KSP, KSPQCGSetTrustRegionRadius()
KSPQCGGetTrialStepNorm(), KSPQCGGetQuadratic()