# MatSetNullSpace#

attaches a null space to a matrix.

## Synopsis#

#include "petscmat.h"
PetscErrorCode MatSetNullSpace(Mat mat, MatNullSpace nullsp)


Logically Collective

## Input Parameters#

• mat - the matrix

• nullsp - the null space object

## Notes#

This null space is used by the KSP linear solvers to solve singular systems.

Overwrites any previous null space that may have been attached. You can remove the null space from the matrix object by calling this routine with an nullsp of NULL

For inconsistent singular systems (linear systems where the right hand side is not in the range of the operator) the KSP residuals will not converge to to zero but the linear system will still be solved in a least squares sense.

The fundamental theorem of linear algebra (Gilbert Strang, Introduction to Applied Mathematics, page 72) states that the domain of a matrix A (from R^n to R^m (m rows, n columns) R^n = the direct sum of the null space of A, n(A), + the range of A^T, R(A^T). Similarly R^m = direct sum n(A^T) + R(A). Hence the linear system A x = b has a solution only if b in R(A) (or correspondingly b is orthogonal to n(A^T)) and if x is a solution then x + alpha n(A) is a solution for any alpha. The minimum norm solution is orthogonal to n(A). For problems without a solution the solution that minimizes the norm of the residual (the least squares solution) can be obtained by solving A x = \hat{b} where \hat{b} is b orthogonalized to the n(A^T). This \hat{b} can be obtained by calling MatNullSpaceRemove() with the null space of the transpose of the matrix.

If the matrix is known to be symmetric because it is an MATSBAIJ matrix or one as called MatSetOption(mat,MAT_SYMMETRIC or possibly MAT_SYMMETRY_ETERNAL,PETSC_TRUE); this routine also automatically calls MatSetTransposeNullSpace().

The user should call MatNullSpaceDestroy().

MatCreate(), MatNullSpaceCreate(), MatSetNearNullSpace(), MatGetNullSpace(), MatSetTransposeNullSpace(), MatGetTransposeNullSpace(), MatNullSpaceRemove(), KSPSetPCSide()