In simple kriging, the error variance is minimized without any additional constraint. The kriging system is then:

or, if secondary variables are accounted for:

`SK_constraints` computes the kriging system size, resizes the kriging matrix and the second member, and returns the system size.

`SK_constraints::SK_constraints()`Default constructor.

`template<class InputIterator, class Location, class Matrix, class Vector>`Function call operator.

unsigned int

SK_constraints::operator()(Matrix& A, Vector& b,

const Location& u

InputIterator first_neigh, InputIterator last_neigh)

`first_neigh,last_neigh)`is a range of Neighborhood, and`Location`is a model of Location.`A`is the kriging matrix and`b`the right hand side of the kriging system.`u`is the location being estimated. The function returns the total number of neighbors, i.e. the sum of the number of neighbors in each neighborhoods. The requirements on concepts Matrix and Vector are fully described in 2.4.