Core problems in linear algebraic systems

被引:28
|
作者
Paige, CC [1 ]
Strakos, Z
机构
[1] McGill Univ, Sch Comp Sci, Montreal, PQ H3A 2A7, Canada
[2] Acad Sci Czech Republic, Inst Comp Sci, Prague 18207 8, Czech Republic
关键词
scaled total least squares; least squares; data least squares; orthogonal regression; core problem; orthogonal reduction; minimum 2-norm solutions; bidiagonalization; singular value decomposition; ill-posed problems;
D O I
10.1137/040616991
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
For any linear system Ax approximate to b we define a set of core problems and show that the orthogonal upper bidiagonalization of [b, A] gives such a core problem. In particular we show that these core problems have desirable properties such as minimal dimensions. When a total least squares problem is solved by first finding a core problem, we show the resulting theory is consistent with earlier generalizations, but much simpler and clearer. The approach is important for other related solutions and leads, for example, to an elegant solution to the data least squares problem. The ideas could be useful for solving ill-posed problems.
引用
收藏
页码:861 / 875
页数:15
相关论文
共 50 条