CGAL::Vector_d<Kernel>

Definition

An instance of data type Vector_d<Kernel> is a vector of Euclidean space in dimension $$d. A vector $$r = (r₀,...,r _{d - 1}) can be represented in homogeneous coordinates $$(h₀,...,h_d) of number type RT, such that $$r_i = h_i/h_d which is of type FT. We call the $$r_i's the Cartesian coordinates of the vector. The homogenizing coordinate $$h_d is positive.

This data type is meant for use in computational geometry. It realizes free vectors as opposed to position vectors (type Point_d). The main difference between position vectors and free vectors is their behavior under affine transformations, e.g., free vectors are invariant under translations.

Types

Creation

Vector_d<Kernel> v;
	introduces a variable v of type Vector_d<Kernel>.
Vector_d<Kernel> v ( int d, Null_vector);
	introduces the zero vector v of type Vector_d<Kernel> in $$d-dimensional space. For the creation flag CGAL::NULL_VECTOR can be used.
template <class InputIterator>
Vector_d<Kernel> v ( int d, InputIterator first, InputIterator last);
	introduces a variable v of type Vector_d<Kernel> in dimension d. If size [first,last) == d this creates a vector with Cartesian coordinates set [first,last). If size [first,last) == p+1 the range specifies the homogeneous coordinates $$H = set [first,last) = ( ± h0, ± h1, ..., ± hd) where the sign chosen is the sign of $$hd. Precondition:  d is nonnegative, [first,last) has d or d+1 elements where the last has to be non-zero. Requirement:  The value type of InputIterator is RT.
template <class InputIterator>
Vector_d<Kernel> v ( int d, InputIterator first, InputIterator last, RT D);
	introduces a variable v of type Vector_d<Kernel> in dimension d initialized to the vector with homogeneous coordinates as defined by H = set [first,last) and D: $$( ± H[0], ± H[1], ..., ± H[d-1], ± D). The sign chosen is the sign of $$D. Precondition:  D is non-zero, the iterator range defines a $$d-tuple of RT. Requirement:  The value type of InputIterator is RT.
Vector_d<Kernel> v ( int d, Base_vector, int i);
	returns a variable v of type Vector_d<Kernel> initialized to the $$i-th base vector of dimension $$d. Precondition:  $$0 i < d.
Vector_d<Kernel> v ( RT x, RT y, RT w = 1);
	introduces a variable v of type Vector_d<Kernel> in $$2-dimensional space. Precondition:  $$w 0.
Vector_d<Kernel> v ( RT x, RT y, RT z, RT w);
	introduces a variable v of type Vector_d<Kernel> in $$3-dimensional space. Precondition:  $$w 0.

Operations

int	v.dimension ()	returns the dimension of v.
FT	v.cartesian ( int i)	returns the $$i-th Cartesian coordinate of v. Precondition:  $$0 i < d.
FT	v [ int i ]	returns the $$i-th Cartesian coordinate of v. Precondition:  $$0 i < d.
RT	v.homogeneous ( int i)	returns the $$i-th homogeneous coordinate of v. Precondition:  $$0 i d.
FT	v.squared_length ()	returns the square of the length of v.
Cartesian_const_iterator	v.cartesian_begin ()	returns an iterator pointing to the zeroth Cartesian coordinate of v.
Cartesian_const_iterator	v.cartesian_end ()	returns an iterator pointing beyond the last Cartesian coordinate of v.
Homogeneous_const_iterator	v.homogeneous_begin ()	returns an iterator pointing to the zeroth homogeneous coordinate of v.
Homogeneous_const_iterator	v.homogeneous_end ()	returns an iterator pointing beyond the last homogeneous coordinate of v.
Direction_d<Kernel>	v.direction ()	returns the direction of v.
Vector_d<Kernel>	v.transform ( Aff_transformation_d<Kernel> t)
		returns $$t(v).

Arithmetic Operators, Tests and IO

Vector_d<Kernel>&	v *= RT n	multiplies all Cartesian coordinates by n.
Vector_d<Kernel>&	v *= FT r	multiplies all Cartesian coordinates by r.
Vector_d<Kernel>	v / RT n	returns the vector with Cartesian coordinates $$vi/n, 0 i < d.
Vector_d<Kernel>	v / FT r	returns the vector with Cartesian coordinates $$vi/r, 0 i < d.
Vector_d<Kernel>&	v /= RT n	divides all Cartesian coordinates by n.
Vector_d<Kernel>&	v /= FT r	divides all Cartesian coordinates by r.
FT	v * w	inner product, i.e., $$ 0 i < d vi wi, where $$vi and $$wi are the Cartesian coordinates of $$v and $$w respectively.
Vector_d<Kernel>	v + w	returns the vector with Cartesian coordinates $$vi+wi, 0 i < d.
Vector_d<Kernel>&	v += w	addition plus assignment.
Vector_d<Kernel>	v - w	returns the vector with Cartesian coordinates $$vi-wi, 0 i < d.
Vector_d<Kernel>&	v -= w	subtraction plus assignment.
Vector_d<Kernel>	- v	returns the vector in opposite direction.
bool	v.is_zero ()	returns true if v is the zero vector.

Downward compatibility

Vector_d<Kernel>	RT n * v	returns the vector with Cartesian coordinates $$n vi.
Vector_d<Kernel>	FT r * v	returns the vector with Cartesian coordinates $$r vi, 0 i < d.

Implementation

Vectors are implemented by arrays of variables of type RT. All operations like creation, initialization, tests, vector arithmetic, input and output on a vector $$v take time $$O(v.dimension()). coordinate access, dimension() and conversions take constant time. The space requirement of a vector is $$O(v.dimension()).