### Abstract

Let A be a real m×n matrix with full row rank m. In many algorithms in engineering and science, such as the force method in structural analysis, the dual variable method for the Navier-Stokes equations or more generally null space methods in quadratic programming, it is necessary to compute a basis matrix B for the null space of A. Here B is n×r, r=n-m, of rank r, with AB=0. In many instances A is large and sparse and often banded. The purpose of this paper is to describe and test a variation of a method originally suggested by Topcu and called the turnback algorithm for computing a banded basis matrix B. Two implementations of the algorithm are given, one using Gaussian elimination and the other using orthogonal factorization by Givens rotations. The FORTRAN software was executed on an IBM 3081 computer with an FPS-164 attached array processor at the Triangle Universities Computing Center and on a CYBER 205 vector computer. Test results on a variety of structural analysis problems including two- and three-dimensional frames, plane stress, plate bending and mixed finite element problems are discussed. These results indicate that both implementations of the algorithm yielded a well-conditioned, banded, basis matrix B when A is well-conditioned. However, the orthogonal implementation yielded a better conditioned B for large, illconditioned problems.

Original language | English |
---|---|

Pages (from-to) | 483-504 |

Number of pages | 22 |

Journal | Numerische Mathematik |

Volume | 47 |

Issue number | 4 |

DOIs | |

Publication status | Published - 1985 Dec |

Externally published | Yes |

### Fingerprint

### Keywords

- Subject Classifications: AMS(MOS), 65F30, CR: G1.3

### ASJC Scopus subject areas

- Computational Mathematics
- Applied Mathematics
- Mathematics(all)

### Cite this

*Numerische Mathematik*,

*47*(4), 483-504. https://doi.org/10.1007/BF01389453

**An algorithm to compute a sparse basis of the null space.** / Berry, M. W.; Heath, M. T.; Kaneko, I.; Lawo, M.; Plemmons, R. J.; Ward, R. C.

Research output: Contribution to journal › Article

*Numerische Mathematik*, vol. 47, no. 4, pp. 483-504. https://doi.org/10.1007/BF01389453

}

TY - JOUR

T1 - An algorithm to compute a sparse basis of the null space

AU - Berry, M. W.

AU - Heath, M. T.

AU - Kaneko, I.

AU - Lawo, M.

AU - Plemmons, R. J.

AU - Ward, R. C.

PY - 1985/12

Y1 - 1985/12

N2 - Let A be a real m×n matrix with full row rank m. In many algorithms in engineering and science, such as the force method in structural analysis, the dual variable method for the Navier-Stokes equations or more generally null space methods in quadratic programming, it is necessary to compute a basis matrix B for the null space of A. Here B is n×r, r=n-m, of rank r, with AB=0. In many instances A is large and sparse and often banded. The purpose of this paper is to describe and test a variation of a method originally suggested by Topcu and called the turnback algorithm for computing a banded basis matrix B. Two implementations of the algorithm are given, one using Gaussian elimination and the other using orthogonal factorization by Givens rotations. The FORTRAN software was executed on an IBM 3081 computer with an FPS-164 attached array processor at the Triangle Universities Computing Center and on a CYBER 205 vector computer. Test results on a variety of structural analysis problems including two- and three-dimensional frames, plane stress, plate bending and mixed finite element problems are discussed. These results indicate that both implementations of the algorithm yielded a well-conditioned, banded, basis matrix B when A is well-conditioned. However, the orthogonal implementation yielded a better conditioned B for large, illconditioned problems.

AB - Let A be a real m×n matrix with full row rank m. In many algorithms in engineering and science, such as the force method in structural analysis, the dual variable method for the Navier-Stokes equations or more generally null space methods in quadratic programming, it is necessary to compute a basis matrix B for the null space of A. Here B is n×r, r=n-m, of rank r, with AB=0. In many instances A is large and sparse and often banded. The purpose of this paper is to describe and test a variation of a method originally suggested by Topcu and called the turnback algorithm for computing a banded basis matrix B. Two implementations of the algorithm are given, one using Gaussian elimination and the other using orthogonal factorization by Givens rotations. The FORTRAN software was executed on an IBM 3081 computer with an FPS-164 attached array processor at the Triangle Universities Computing Center and on a CYBER 205 vector computer. Test results on a variety of structural analysis problems including two- and three-dimensional frames, plane stress, plate bending and mixed finite element problems are discussed. These results indicate that both implementations of the algorithm yielded a well-conditioned, banded, basis matrix B when A is well-conditioned. However, the orthogonal implementation yielded a better conditioned B for large, illconditioned problems.

KW - Subject Classifications: AMS(MOS), 65F30, CR: G1.3

UR - http://www.scopus.com/inward/record.url?scp=0001151403&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0001151403&partnerID=8YFLogxK

U2 - 10.1007/BF01389453

DO - 10.1007/BF01389453

M3 - Article

AN - SCOPUS:0001151403

VL - 47

SP - 483

EP - 504

JO - Numerische Mathematik

JF - Numerische Mathematik

SN - 0029-599X

IS - 4

ER -