gsOptProblemStatic< T > Class Template Reference

Detailed Description

template<typename T>
class gismo::gsOptProblemStatic< T >

A class representing a static optimization problem.

Template Parameters

T	The coefficient type.

This class inherits from gsOptProblem and provides functionality for evaluating the objective function and its gradient for static optimization problems.

Inheritance diagram for gsOptProblemStatic< T >:

Collaboration diagram for gsOptProblemStatic< T >:

Public Member Functions
virtual void	computeJacStructure ()
	Computes the sparsity pattern of the constraint Jacobian matrix.
virtual void	evalCon_into (const gsAsConstVector< T > &, gsAsVector< T > &) const
	Returns values of the constraints at design value u.
T	evalObj (const gsAsConstVector< T > &u) const
	Evaluates the objective function.
void	gradObj_into (const gsAsConstVector< T > &u, gsAsVector< T > &result) const
	Computes the gradient of the objective function.
	gsOptProblemStatic (const typename gsStructuralAnalysisOps< T >::ALResidual_t &ALresidualFun, const T &L, index_t numDesignVars)
	Constructor for gsOptProblemStatic with arc-length residual function.
	gsOptProblemStatic (const typename gsStructuralAnalysisOps< T >::Residual_t &residualFun, const T &L, index_t numDesignVars)
	Constructor for gsOptProblemStatic.
virtual void	hessLagr_into (const gsAsConstVector< T > &, gsAsVector< T > &) const
	Returns Hessian Lagrangian of the constraints at design value.
virtual void	jacobCon_into (const gsAsConstVector< T > &, gsAsVector< T > &) const
	Returns Jacobian of the constraints at design value u. Format of result is sparse, complying to m_conJacRows and m_conJacCols.
int	numDesignVars () const
	Callback function is executed after every iteration. Returning false causes premature termination of the optimization.

Protected Attributes
gsMatrix< T >	m_lambda
	Lagrange multipliers (set in the finalize_solution method)

Private Attributes
std::vector< index_t >	m_conJacCols
	Constraint Jacobian non-zero entries columns.
std::vector< index_t >	m_conJacRows
	Constraint Jacobian non-zero entries rows.
gsVector< T >	m_conLowerBounds
	Lower bounds for the constraints.
gsVector< T >	m_conUpperBounds
	Upper bounds for the constraints.
gsMatrix< T >	m_curDesign
	Current design variables (and starting point )
gsVector< T >	m_desLowerBounds
	Lower bounds for the design variables.
gsVector< T >	m_desUpperBounds
	Upper bounds for the design variables.
int	m_numConJacNonZero
	Number of nonzero entries in the Constraint Jacobian.
int	m_numConstraints
	Number of constraints.
int	m_numDesignVars
	Number of design variables.

Constructor & Destructor Documentation

gsOptProblemStatic() [1/2]

template<typename T >

gsOptProblemStatic ( const typename gsStructuralAnalysisOps< T >::Residual_t &  residualFun, const T &  L, index_t  numDesignVars  )

inline

Constructor for gsOptProblemStatic.

Parameters

residualFun	The residual function.
L	A coefficient (unused but needs to be assigned).
numDesignVars	The number of design variables.

gsOptProblemStatic() [2/2]

template<typename T >

gsOptProblemStatic ( const typename gsStructuralAnalysisOps< T >::ALResidual_t &  ALresidualFun, const T &  L, index_t  numDesignVars  )

inline

Constructor for gsOptProblemStatic with arc-length residual function.

Parameters

ALresidualFun	The arc-length residual function.
L	A coefficient.
numDesignVars	The number of design variables.

Member Function Documentation

computeJacStructure()

template<typename T >

virtual void computeJacStructure ( )

inlinevirtualinherited

Computes the sparsity pattern of the constraint Jacobian matrix.

Number of constraints and design variables need to be set before calling this. By default the constraint Jacobian is set to full. Override this function to provide sparsity.

evalObj()

template<typename T >

T evalObj ( const gsAsConstVector< T > &  u ) const

inlinevirtual

Evaluates the objective function.

Parameters

u	The input vector.

Returns

The norm of the residual.

Reimplemented from gsOptProblem< T >.

gradObj_into()

template<typename T >

void gradObj_into ( const gsAsConstVector< T > &  u, gsAsVector< T > &  result  ) const

inlinevirtual

Computes the gradient of the objective function.

Parameters

u	The input vector.
result	The output vector for the gradient.

Reimplemented from gsOptProblem< T >.