class HCL_TrustRegion_SS_d : public HCL_TrustRegionSolver_d

The class HCL_TrustRegion_SS_d is an implementation of the Santosa-Sorensen algorithm for 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 & A, 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_TrustRegion_SS_d is an implementation of the Santosa-Sorensen algorithm for solving the trust region subproblem. This algorithm involves transforming the subproblem into a family of parameterized eigenvalues problems, which are solving using the k-step Arnoldi method. The parameter in the eigenvalue problem is adjusted to produce a solution to the trust region subproblem, along with its Lagrange multiplier, simultaneously. For more information about the algorithm, see

"A new matrix-free algorithm for the large-scale trust-region subproblem" by Sandra A. Santos and Danny C. Sorensen

Technical report TR95-20, Department of Computational and Applied Mathematics, Rice University, Houston, TX 77251-1892.

In the initial implementation, the trust-region constraint is defined by the unscaled Euclidean norm.

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 & A, 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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