109 for( run1 = 0; run1 < rhs.
ny; run1++ )
211 for( run1 = 0; run1 <
ny; run1++ )
291 for( run1 = 0; run1 <
nx; run1++ )
294 for( run1 = 0; run1 <
na; run1++ )
297 for( run1 = 0; run1 <
np; run1++ )
300 for( run1 = 0; run1 <
nu; run1++ )
303 for( run1 = 0; run1 <
nw; run1++ )
448 objectiveValue =
obj;
Data class for storing generic optimization variables.
returnValue init(const OCPiterate &x)
Implements a very rudimentary block sparse matrix class.
Base class for all kind of objective function terms within optimal control problems.
virtual returnValue getBackwardSensitivities(BlockMatrix *D, int order)
Allows to pass back messages to the calling function.
Allows to conveniently handle (one-dimensional) grids consisting of time points.
#define CLOSE_NAMESPACE_ACADO
returnValue init(const Function &f, uint nx_=0, uint na_=0, uint np_=0, uint nu_=0, uint nw_=0, uint nd_=0, uint N_=0)
virtual ~ObjectiveElement()
int index(VariableType variableType_, int index_) const
void rhs(const real_t *x, real_t *f)
virtual returnValue getObjectiveValue(double &objectiveValue)
#define ACADOWARNING(retval)
#define BEGIN_NAMESPACE_ACADO
virtual returnValue setForwardSeed(BlockMatrix *xSeed_, BlockMatrix *xaSeed_, BlockMatrix *pSeed_, BlockMatrix *uSeed_, BlockMatrix *wSeed_, int order)
virtual returnValue setBackwardSeed(BlockMatrix *seed, int order)
uint getNumValues() const
virtual returnValue getForwardSensitivities(BlockMatrix *D, int order)
ObjectiveElement & operator=(const ObjectiveElement &rhs)
#define ACADOERROR(retval)