A column approximate minimum degree ordering algorithm
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abstract
Sparse Gaussian elimination with partial pivoting computes the factorization PAQ = LU of a sparse matrix A , where the row ordering P is selected during factorization using standard partial pivoting with row interchanges. The goal is to select a column preordering, Q , based solely on the nonzero pattern of A , that limits the worst-case number of nonzeros in the factorization. The fill-in also depends on P , but Q is selected to reduce an upper bound on the fill-in for any subsequent choice of P . The choice of Q can have a dramatic impact on the number of nonzeros in L and U . One scheme for determining a good column ordering for A is to compute a symmetric ordering that reduces fill-in in the Cholesky factorization of A T A . A conventional minimum degree ordering algorithm would require the sparsity structure of A T A to be computed, which can be expensive both in terms of space and time since A T A may be much denser than A . An alternative is to compute Q directly from the sparsity structure of A ; this strategy is used by MATLAB's COLMMD preordering algorithm. A new ordering algorithm, COLAMD, is presented. It is based on the same strategy but uses a better ordering heuristic. COLAMD is faster and computes better orderings, with fewer nonzeros in the factors of the matrix.
