class HCL_LeastSquaresFcnlGNProductDomain_d : public HCL_FunctionalProductDomain_d

HCL_LeastSquaresFcnl_ creates least squares objective functions from operators and data

Inheritance:

Public Methods

HCL_LeastSquaresFcnlGNProductDomain_d ( HCL_OpProductDomain_d * Operator, HCL_Vector_d * Data)
Usual constructor. Pointers to the operator and data are required.
virtual HCL_VectorSpace_d& Domain () const
Domain space access
virtual HCL_EvaluateFunctional_d* Evaluate ( const HCL_Vector_d & x ) const
Evaluate creates an "evaluation" object, which knows how to compute the function value, gradient,and Hessian at the given x
virtual ostream& Write ( ostream & str ) const
Write invokes the Write methods of the operator and data.

Inherited from HCL_FunctionalProductDomain_d:

Public Methods

virtual HCL_ProductSpace_d& ProductDomain() const
virtual void PartialGradient( int i, const HCL_Vector_d & x, HCL_Vector_d & g ) const
virtual HCL_LinearOp_d* HessianBlock( int i, int j, const HCL_Vector_d & x ) const
int CheckHessianBlock( const HCL_Vector_d &, const HCL_Vector_d &, const HCL_Vector_d &, int i, int j, ostream & str, int n=10, double hmin=0.1, double hmax=1.0 ) const
int CheckHessianBlock( const HCL_Vector_d &, const HCL_Vector_d &, int ind, ostream & str, int n=10, double hmin=0.1, double hmax=1.0 ) const
int CheckHessianBlockAdj( const HCL_Vector_d &, const HCL_Vector_d &, const HCL_Vector_d &, int i, int j, ostream & str, int n=10, double hmin=0.1, double hmax=1.0 ) const

Protected Methods

virtual double Value1( const HCL_Vector_d & x ) const
virtual void PartialGradient1( int i, const HCL_Vector_d & x, HCL_Vector_d & y ) const
virtual HCL_LinearOp_d* HessianBlock1( int i, int j, const HCL_Vector_d & x ) const
virtual void HessianBlockImage( int i, int j, const HCL_Vector_d & x, const HCL_Vector_d & dx, HCL_Vector_d & dy ) const

Inherited from HCL_Functional_d:

Public Methods

virtual double MaxStep( const HCL_Vector_d & x, const HCL_Vector_d & dir) const
virtual double Value( const HCL_Vector_d & x ) const
virtual void Gradient( const HCL_Vector_d & x, HCL_Vector_d & g ) const
virtual HCL_LinearOp_d* Hessian( const HCL_Vector_d & x ) const
void Scan( const HCL_Vector_d & x, const HCL_Vector_d & dx, int N, double hmin, double hmax, char * fname = NULL )
int CheckGrad( const HCL_Vector_d & x, const HCL_Vector_d & y, ostream & str, int n=10, double hmin=0.1, double hmax=1.0 )
int CheckHess( const HCL_Vector_d &, const HCL_Vector_d &, ostream & str, int n=10, double hmin=0.1, double hmax=1.0 )

Protected Methods

virtual void Gradient1( const HCL_Vector_d & x, HCL_Vector_d & y ) const
virtual void HessianImage( const HCL_Vector_d & x, const HCL_Vector_d & dx, HCL_Vector_d & dy ) const
virtual void HessianInvImage( const HCL_Vector_d & x, const HCL_Vector_d & dy, HCL_Vector_d & dx ) const
virtual HCL_LinearOp_d* Hessian1( const HCL_Vector_d & x ) const

Inherited from HCL_Base:

Public Methods

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

Documentation

HCL_LeastSquaresFcnl_ creates least squares objective functions from operators and data. The term "ProductDomain" indicates the operator (and therefore the functional) is defined on a product space. This is a so called bridge class, i.e. it exists merely to make objects of some class behave like objects of another class. In this case, the input objects are a nonlinear operator (an instance of HCL_OpProductDomain_d) and a data vector d. The interface to these objects constructed by the class is the least squares functional its gradient and the Gauss-Newton approximation to its Hessian operator expressed as an HCL_Functional_d, an input class for optimization methods.

So this class can be used to solve nonlinear least squares problems straight from the implementation of the underlying operator, without constructing by hand the least squares function, as follows: If you have a nonlinear operator N instead (an instance of HCL_OpProductDomain_d), use 

  HCL_LeastSquaresFcnl_d F(N, d);

The HCL_Functional_d F so constructed is suitable for submission to an optimization algorithm using second derivative information, such as Steihaug-Toint (HCL_UMinTR_d), or to any of the algorithms using first derivative information, such as BFGS (HCL_UMin_lbfgs_d) or nonlinear conjugate gradients (HCL_UMinNLCG_d), as well as those algorithms specifically recognizing the product space structure, such as HCL_UMinAlt_d and HCL_UMinSub_d.

HCL_LeastSquaresFcnlGNProductDomain_d( HCL_OpProductDomain_d * Operator, HCL_Vector_d * Data)
Usual constructor. Pointers to the operator and data are required.

virtual HCL_VectorSpace_d& Domain() const
Domain space access. Returns a reference to the vector space defining the domain of the functional.

virtual HCL_EvaluateFunctional_d* Evaluate( const HCL_Vector_d & x ) const
Evaluate creates an "evaluation" object, which knows how to compute the function value, gradient,and Hessian at the given x

virtual ostream& Write( ostream & str ) const
Write invokes the Write methods of the operator and data.

This class has no child classes.

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