Results 1 
3 of
3
Predicting Structure In Nonsymmetric Sparse Matrix Factorizations
 GRAPH THEORY AND SPARSE MATRIX COMPUTATION
, 1992
"... Many computations on sparse matrices have a phase that predicts the nonzero structure of the output, followed by a phase that actually performs the numerical computation. We study structure prediction for computations that involve nonsymmetric row and column permutations and nonsymmetric or nonsqu ..."
Abstract

Cited by 30 (8 self)
 Add to MetaCart
Many computations on sparse matrices have a phase that predicts the nonzero structure of the output, followed by a phase that actually performs the numerical computation. We study structure prediction for computations that involve nonsymmetric row and column permutations and nonsymmetric or nonsquare matrices. Our tools are bipartite graphs, matchings, and alternating paths. Our main new result concerns LU factorization with partial pivoting. We show that if a square matrix A has the strong Hall property (i.e., is fully indecomposable) then an upper bound due to George and Ng on the nonzero structure of L + U is as tight as possible. To show this, we prove a crucial result about alternating paths in strong Hall graphs. The alternatingpaths theorem seems to be of independent interest: it can also be used to prove related results about structure prediction for QR factorization that are due to Coleman, Edenbrandt, Gilbert, Hare, Johnson, Olesky, Pothen, and van den Driessche.
Multifrontal Computation with the Orthogonal Factors of Sparse Matrices
 SIAM Journal on Matrix Analysis and Applications
, 1994
"... . This paper studies the solution of the linear least squares problem for a large and sparse m by n matrix A with m n by QR factorization of A and transformation of the righthand side vector b to Q T b. A multifrontalbased method for computing Q T b using Householder factorization is presented ..."
Abstract

Cited by 9 (0 self)
 Add to MetaCart
. This paper studies the solution of the linear least squares problem for a large and sparse m by n matrix A with m n by QR factorization of A and transformation of the righthand side vector b to Q T b. A multifrontalbased method for computing Q T b using Householder factorization is presented. A theoretical operation count for the K by K unbordered grid model problem and problems defined on graphs with p nseparators shows that the proposed method requires O(NR ) storage and multiplications to compute Q T b, where NR = O(n log n) is the number of nonzeros of the upper triangular factor R of A. In order to introduce BLAS2 operations, Schreiber and Van Loan's StorageEfficientWY Representation [SIAM J. Sci. Stat. Computing, 10(1989),pp. 5557] is applied for the orthogonal factor Q i of each frontal matrix F i . If this technique is used, the bound on storage increases to O(n(logn) 2 ). Some numerical results for the grid model problems as well as HarwellBoeing problems...
Separators and Structure Prediction in Sparse Orthogonal Factorization
, 1993
"... In the factorization A = QR of a matrix A, the orthogonal matrix Q can be represented either explicitly (as a matrix) or implicitly (as a matrix H of Householder vectors). We derive both upper and lower bounds on the number of nonzeros in H and the number of nonzeros in Q, in the case where the ..."
Abstract

Cited by 4 (0 self)
 Add to MetaCart
In the factorization A = QR of a matrix A, the orthogonal matrix Q can be represented either explicitly (as a matrix) or implicitly (as a matrix H of Householder vectors). We derive both upper and lower bounds on the number of nonzeros in H and the number of nonzeros in Q, in the case where the graph of A T A has "good" separators and A need not be square. We also derive an upper bound on the number of nonzeros in the nullbasis part of Q in the case where A is the edgevertex incidence matrix of a planar graph. The significance of these results is that they both illuminate and amplify a folk theorem of sparse QR factorization, which holds that the matrix H of Householder vectors represents the orthogonal factor of A much more compactly than Q itself. To facilitate discussion of this and related issues, we review several related results which have appeared previously. Keywords: Sparse matrix algorithms, QR factorization, separators, column intersection graph, strong Hall...