yane::SqpFortran::SqpFortran Class Reference

Wrapper class for the SQP routine NLPQLP.

Inheritance diagram for yane::SqpFortran::SqpFortran:

Collaboration diagram for yane::SqpFortran::SqpFortran:

List of all members.

Public Member Functions
virtual double	abortTime ()
	Function which returns the current abort time, after which the optimization would be stopped.
virtual void	calcMin (double *x, double *fx, double *lb, double *ub, double *rpar=0, int *ipar=0)
	Function which solves the minimization problem.
virtual yane::MinProg::NLP *	clone ()
	Function to clone the object with the current configuration.
virtual void	getLagrangeParameters (double *lambda)
	Function which returns the Lagrange parameters of the nonlinear contraints regarding to the calculated optimal solution.
virtual void	getRestrictionJacobi (double *jac)
	Function which returns the Jacobian of the Restriction.
virtual void	init (int n, yane::MinProg::T_FUNC func, yane::MinProg::T_FUNC rest=0, int nrest=0, int nrest_eq=0, yane::MinProg::T_FUNC df=0, yane::MinProg::T_FUNC dg=0)
	Function to initialize the minimization problem.
virtual yane::MinProg::T_MEMMODEL	memoryModel () const
	Function to specify the memorymodel (by-line or by-column)
virtual void	setAbortTime (double time)
	Function to set abort time, after which the optimization will be stopped.
virtual void	setAccuracy (double acc)
	Function to set the accuracy of the optimization routine.
void	setLineSearchTol (double tol=.1)
	Function to set the tolerance of the linesearch.
void	setMaxFun (int num=20)
	Function to set the maximal number of function evaluations.
virtual void	setMaxIterations (int num)
	Function to set the maximum number of iterations for the optimization routine.
void	setMinimalStepsize (double step=1E-6)
	Function to set the minimal stepsize for a single step of the optimization routine.
void	setNMLineSearchStack (int size=0)
	Function to set the maximal number of nonmonotone linesearch steps.
void	setPrintLevel (int lvl=0)
	Function to set the desired output level of the routine.
void	setQLAccuracy (double acc=DBL_EPSILON *10000.0)
	Function to set the accurracy of the QP-Solver.
	SqpFortran (int n, yane::MinProg::T_FUNC func, yane::MinProg::T_FUNC rest=0, int nrest=0, int nrest_eq=0, yane::MinProg::T_FUNC df=0, yane::MinProg::T_FUNC dg=0)
	Constructor.
	SqpFortran ()
	Default-Constructor.
virtual	~SqpFortran ()
	Destructor.
Public Attributes
T_FUNCTIONSTRUCT *	func_params
	Structure containing the dynamic of the system.
T_RESTRICTIONSTRUCT *	restr_params
	Structure containing the restrictions of the system.
Protected Attributes
double	_aborttime
	Saves the maximal allowed runtime for the optimization.
double	_accql
	The tolerance is needed for the QP solver to perform several tests, for example whether optimality conditions are satisfied or whether a number is considered as zero or not. If _accqö is less or equal to zero, then the machine precision is computed by NLPQLP and subsequently multiplied by 10000.
double	_accuracy
	The user has to specify the desired final accuracy (e.g. 1.0D-7). The termination accuracy should not be smaller than the accuracy by which gradients are computed.
int *	_active
	Logical array which shows a user the constraints, which NLPQLP considers to be active at the last computed iterate.
double *	_c
	Pointer of the last computed approximation of the Hessian.
yane::Utils::RTClock *	_clock
	Internal clock (time measurement for aborttime)
double *	_d
	Pointer to the diagonal elements of the LDL of the Quasi-Newton matrix of the constraints.
double *	_df
	Pointer of the gradient of the cost function.
double *	_dg
	Pointer of the Jacobian matrix.
int	_dimension
	Dimension of the optimization variable.
int	_dimension_rest
	Number of constriants.
int	_dimension_resteq
	Number of equality constraints.
double	_f
	Value of the cost function.
T_FUNC	_function
	Function pointer of the objective function.
T_FUNC	_function_diff
	Function pointer of the gradient of the objective function.
double	_fvalue1
	Variable to store the objective function value.
double	_fvalue2
	Auxilliary variable to store the objective function value.
double *	_g
	Pointer of the constraints.
double *	_gvalue1
	Array to store the constraint values.
double *	_gvalue2
	Auxilliary array to store the constraint values.
int	_ifail
	Parameter which shows the reason for terminating a solution process. Initially _ifail must be set to zero. On return _ifail could contain the following values: _ifail =-2 : Compute gradient values w.r.t. the variables stored in first column of _x, and store them in _df and _dg. Only derivatives for active constraints _active[j] == true need to be computed. Then call NLPQLP again. _ifail =-1 : Compute objective function and all constraint values subject the variables found in the first l columns of _x, and store them in _f and _g. Then call NLPQLP again. _ifail = 0 : The optimality conditions are satisfied. _ifail = 1 : The algorithm has been stopped after _maxit iterations. _ifail = 2 : The algorithm computed an uphill search direction. _ifail = 3 : Underflow occurred when determining a new approximation matrix for the Hessian of the Lagrangian. _ifail = 4 : The line search could not be terminated successfully. _ifail = 5 : Length of a working array is too short. More detailed error information is obtained with '_irpint>0'. _ifail = 6 : There are false dimensions, for example M>MMAX, N>=NMAX, or MNN2<>M+N+N+2. _ifail = 7 : The search direction is close to zero, but the current iterate is still infeasible. _ifail = 8 : The starting point violates a lower or upper bound. _ifail = 9 : Wrong input parameter, i.e., _mode, LDL decomposition in _d and _c (in case of _mode == 1), _iprint, _iout _ifail = 10 : Internal inconsistency of the quadratic subproblem, division by zero. _ifail > 100 : The solution of the quadratic programming subproblem has been terminated with an error message and _ifail is set to _ifql + 100, where _ifql denotes the index of an inconsistent constraint.
int	_iout
	Parameter for the desired output unit.
int	_iprint
	Specification of the desired output level. _iprint = 0 : No output of the program. _iprint = 1 : Only a final convergence analysis is given. _iprint = 2 : One line of intermediate results is printed in each iteration. _iprint = 3 : More detailed information is printed in each iteration step, e.g. variable, constraint and multiplier values. _iprint = 4 : In addition to '_iprint=3', merit function and steplength values are displayed during the line search. NOTE: Constraint and multiplier values are not displayed if the number of constraints is larger than 10000.
int *	_kwa
	Integer working array of length _lkwa.
int	_l
	Number of parallel systems, i.e. function calls duringline search at predetermined iterates. HINT: If less than 10 parallel function evaluations are available, it is recommended to apply the serial version by setting L=1.
int	_lactive
	Length of the logical array _active.
int	_lkwa
	Length of the integer working array _kwa.
int	_lql
	If _lql is set to true, the quadratic programming subproblem is solved with a full positive definite quasi-Newton matrix. Otherwise, a Cholesky decomposition is performed and updated, so that the subproblem matrix contains only an upper triangular factor.
int	_lwa
	Length of the real working array _lwa.
int	_max_nm
	Stack size for storing merit function values at previous iterations for non-monotone line search (e.g. 10). In case of _max_nm = 0, monotone line search is performed. _max_nm should not be greater than 50.
int	_maxfun
	The integer variable defines an upper bound for the number of function calls during the line search (e.g. 20). _maxfun is only needed in case of _l=1, and must not be greater than 50.
int	_maxit
	Maximum number of outer iterations, where one iteration corresponds to one formulation and solution of the quadratic programming subproblem, or, alternatively, one evaluation of gradients (e.g. 100).
int	_mmax
	Row dimension of the Jacobian matrix. _mmax must be at least one and greater or equal to the number of constraints.
int	_mnn2
	Auxilliary variable.
int	_mode
	Parameter for warm starts _mode = 0: Normal execution (reverse communication!). _mode = 1: The user wants to provide an initial guess for the multipliers in _u and for the Hessian of the Lagrangian function in _c. In case of lql == true, _d is ignored. Otherwise, the lower part of C has to contain the lower triangular factor of an LDL decomposition and _d the diagonal part.
int	_nmax
	Row dimension of the Hessian. _nmax must be at least two and greater than the number of optimization variables.
T_FUNC	_restrictions
	Function pointer of the constraints.
T_FUNC	_restrictions_diff
	Function pointer of the Jacobian of the constraints.
double	_stepmin
	Minimum steplength in case of L>1. Recommended is any value in the order of the accuracy by which functions are computed. The value is needed to compute a steplength reduction factor by pow(_stepmin, (1/_l-1)). If _stepmin <= 0, then _stepmin = _acc is used.
double	_tol_nm
	Relative bound for increase of merit function value, if linesearch is not successful during the very first step. Must be non-negative (e.g. 0.1).
double *	_u
	_u contains the multipliers with respect to the actual iterate stored in the first column of _x. The first locations contain the multipliers of the nonlinear constraints, the subsequent locations the multipliers of the lower bounds, and the final locations the multipliers of the upper bounds. At an optimal solution, all multipliers with respect to inequality constraints should be nonnegative.
double *	_wa
	Real working array of length _lwa.
double *	_x
	Array of the optimization variable.
double	_xold
	Auxilliary variable to save one entry of the optimization variable.

---

Constructor & Destructor Documentation

yane::SqpFortran::SqpFortran::SqpFortran	(	int	n,
		yane::MinProg::T_FUNC	func,
		yane::MinProg::T_FUNC	rest = 0,
		int	nrest = 0,
		int	nrest_eq = 0,
		yane::MinProg::T_FUNC	df = 0,
		yane::MinProg::T_FUNC	dg = 0
	)

Parameters:

n	Dimension of the optimization variable
func	Objective function
rest	Constraints
nrest	Number of constraints (both equality and inequality constraints)
nrest_eq	number of equality constraints These have to be listed first within the definition of the constraints
df	Gradient of the objective function
dg	Jacobian matrix of the restriction function

Exceptions:

yane::Utils::MemoryException	Is thrown, if memory allocation has failed
yane::Utils::Exception	Is thrown, if initialization of the class has failed

---

Member Function Documentation

LIB_EXPORT double yane::MinProg::NLP::abortTime

(

)

[virtual, inherited]

Returns:

Abort time in seconds

LIB_EXPORT void yane::SqpFortran::SqpFortran::calcMin	(	double *	x0,
		double *	fx,
		double *	lb,
		double *	ub,
		double *	rpar = 0,
		int *	ipar = 0
	)		[virtual]

Parameters:

x	Initial guess of the optimization variable Once the algorithm terminates successfully it contains the minimizer of the objective function
fx	Optimal objective function value
lb	Lower bounds for the optimization variable
ub	Upper bounds for the optimization variable
rpar	Vector of real-valued parameters
ipar	Vector of integer paramaters

Exceptions:

SolverWarning	Is thrown, if optimization terminates with an noncritical error
SolverError	Is thrown, if optimization terminates with a known critical error
yane::MinProg::MinProgException	Is thrown, if optimization terminates with an unknown error
yane::Utils::Exception	Is thrown, if evaluations could not be done

Implements yane::MinProg::NLP.

LIB_EXPORT yane::MinProg::NLP * yane::SqpFortran::SqpFortran::clone

(

)

[virtual]

Exceptions:

yane::Utils::MemoryException

Is thrown, if memory allocation of the clone fails

Returns:

Pointer to the cloned NLP object

Implements yane::MinProg::NLP.

LIB_EXPORT void yane::SqpFortran::SqpFortran::getLagrangeParameters

(

double *

lambda

)

[virtual]

Parameters:

lambda

Pointer to an array of length dimension_rest, contains the Lagrange parameters

Exceptions:

yane::Utils::MemoryException

Is thrown, if lambda is a zero-pointer

Implements yane::MinProg::NLP.

LIB_EXPORT void yane::SqpFortran::SqpFortran::getRestrictionJacobi

(

double *

jac

)

[virtual]

Parameters:

jac	Pointer to an array of length dimension * dimension_rest After termination of this function, jac contains the jacobian saved by-line or by-column

Exceptions:

yane::Utils::MemoryException	Is thrown, if jac is a zero-pointer
yane::Utils::Exception	Is thrown, if calculation fails

Implements yane::MinProg::NLP.

virtual void yane::SqpFortran::SqpFortran::init	(	int	n,
		yane::MinProg::T_FUNC	func,
		yane::MinProg::T_FUNC	rest = 0,
		int	nrest = 0,
		int	nrest_eq = 0,
		yane::MinProg::T_FUNC	df = 0,
		yane::MinProg::T_FUNC	dg = 0
	)		[virtual]

Parameters:

n	Dimension of the optimizationvariable
func	Objective function
rest	Consrraints
nrest	Number of constraints (both equality and inequality constraints)
nrest_eq	Number of equality constraints These have to be listed first within the definition of the constraints
df	Gradient of the objective function
dg	Jacobian matrix of the constraints

Exceptions:

yane::Utils::MemoryException	Is thrown, if memory allocation fails
yane::Utils::Exception	Is thrown, if initialization of the NLP object fails

Reimplemented from yane::MinProg::NLP.

LIB_EXPORT void yane::MinProg::NLP::setAbortTime

(

double

time

)

[virtual, inherited]

Parameters:

time	Abort time in seconds

Reimplemented in yane::YaneIpopt::YaneIpopt.

LIB_EXPORT void yane::SqpFortran::SqpFortran::setAccuracy

(

double

acc

)

[virtual]

Parameters:

acc	Accuracy of the minimization routine

Exceptions:

InitializingException

Is thrown, if acc <= 0.0

Implements yane::MinProg::NLP.

LIB_EXPORT void yane::SqpFortran::SqpFortran::setLineSearchTol

(

double

tol = .1

)

Parameters:

tol	Absolute tolerance value of the linesearch

LIB_EXPORT void yane::SqpFortran::SqpFortran::setMaxFun

(

int

num = 20

)

Parameters:

num	Maximal number of function evaluations

Exceptions:

InitializingException

Is thrown, if num <= 0.0

LIB_EXPORT void yane::SqpFortran::SqpFortran::setMaxIterations

(

int

num

)

[virtual]

Parameters:

num	Maximal number of iterations

Exceptions:

InitializingException

Is thrown, if num <= 0

Implements yane::MinProg::NLP.

LIB_EXPORT void yane::SqpFortran::SqpFortran::setMinimalStepsize

(

double

step = 1E-6

)

Parameters:

step	Minimal stepsize of an optimization step

LIB_EXPORT void yane::SqpFortran::SqpFortran::setNMLineSearchStack

(

int

size = 0

)

Parameters:

size	Maximal number of nonmonotone linesearch steps

LIB_EXPORT void yane::SqpFortran::SqpFortran::setPrintLevel

(

int

lvl = 0

)

Parameters:

lvl	Outlevel (should lie between 0 and 4)

LIB_EXPORT void yane::SqpFortran::SqpFortran::setQLAccuracy

(

double

acc = DBL_EPSILON * 10000.0

)

Parameters:

acc	Accuracy of the QP solver

Exceptions:

InitializingException

Is thrown, if acc <= 0.0