KSPGLTR#
Code to run conjugate gradient method subject to a constraint on the solution norm.
Options Database Keys#
-ksp_cg_radius
- Trust Region Radius
Notes#
Uses 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 trust region constraint
|| s || <= delta,
where
delta is the trust region radius, g is the gradient vector, H is the Hessian approximation, M is the positive definite preconditioner matrix.
KSPConvergedReason
may have the additional values
KSP_CONVERGED_NEG_CURVE if convergence is reached along a negative curvature direction,
KSP_CONVERGED_STEP_LENGTH if convergence is reached along a constrained step.
The operator and the preconditioner supplied must be symmetric and positive definite.
This is rarely used directly, it is used in Trust Region methods for nonlinear equations, SNESNEWTONTR
Reference#
**** -*** Gould, N. and Lucidi, S. and Roma, M. and Toint, P., Solving the Trust-Region Subproblem using the Lanczos Method, SIAM Journal on Optimization, volume 9, number 2, 1999, 504-525
See Also#
KSP: Linear System Solvers, KSPQCG
, KSPNASH
, KSPSTCG
, KSPCreate()
, KSPSetType()
, KSPType
, KSP
, KSPCGSetRadius()
, KSPCGGetNormD()
, KSPCGGetObjFcn()
, KSPGLTRGetMinEig()
, KSPGLTRGetLambda()
, KSPCG
Level#
developer
Location#
src/ksp/ksp/impls/cg/gltr/gltr.c
Index of all KSP routines
Table of Contents for all manual pages
Index of all manual pages