class HCL_TRCG_d : public HCL_TrustRegionSolver_d

The class HCL_TRCG_d is an implentation of the Steihaug-Toint algorithm for approximately solving the trust region subproblem

Inheritance:

Public Methods

virtual Table& Parameters ()
The method Parameters returns the parameter table of the algorithm
virtual void SetScaling ( HCL_LinearOpAdjInv_d * S )
Alternate version of SetScaling.
virtual void SetScaling ( HCL_LinearOpAdj_d * S, HCL_LinearSolver_d * lsolver )
SetScaling defines a new inner product in terms of a symmetric, positive definite operator S: <x,y> = (x,Sy)
virtual void Solve ( const HCL_LinearOp_d & B, const HCL_Vector_d & g, HCL_Vector_d & p )
The method Solve attempts to compute an approximate solution of the trust region subproblem defined by the inputs of the method
virtual void UnSetScaling ()
UnSetScaling returns the inner product to the default.

Inherited from HCL_TrustRegionSolver_d:

Private

Input parameters

double Delta
Initial trust region radius (1.0).
double MinStep
minimum step length, that is, minimum Delta (1.0e-6).
double MaxStep
maximum step length, that is, maximum Delta (1.0e6).

Required output parameters

int TermCode
Termination code
double Multiplier
Lagrange multiplier.
double Reduction
Reduction in the quadratic model.
double StepLength
Length of step taken.

Inherited from HCL_Base:

Public Methods

int Count()
void DecCount()
void IncCount()

Private Fields

int ReferenceCount

Documentation

The class HCL_TRCG_d is an implentation of the Steihaug-Toint algorithm for approximately solving the trust region subproblem. This algorithm is based on using the conjugate gradient method to compute the Newton step, with modifications for handling the case in which the approximate step, as computed by CG, leaves the trust region, and the case in which negative curvature is encountered. Note, in particular, that in spite of the use of CG, the linear operator defining the quadratic model is not assumed to be positive definite.

For more information about the underlying algorithm, see

"The conjugate gradient method and trust regions in large scale optimization" by Trond Steihaug

SIAM J. Numer. Anal. Vol. 20, No. 3, 626--637

virtual Table& Parameters()
The method Parameters returns the parameter table of the algorithm. Algorithmic parameters can be accessed via Parameters().GetValue( "name",val ) and changed via Parameters().PutValue( "name",val ).

virtual void SetScaling( HCL_LinearOpAdj_d * S, HCL_LinearSolver_d * lsolver )
SetScaling defines a new inner product in terms of a symmetric, positive definite operator S: <x,y> = (x,Sy)

virtual void SetScaling( HCL_LinearOpAdjInv_d * S )
Alternate version of SetScaling.

virtual void UnSetScaling()
UnSetScaling returns the inner product to the default.

virtual void Solve( const HCL_LinearOp_d & B, const HCL_Vector_d & g, HCL_Vector_d & p )
The method Solve attempts to compute an approximate solution of the trust region subproblem defined by the inputs of the method. Note that the trust region radius Delta must defined through the Parameter method.

This class has no child classes.

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