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amd in Matlab |
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amd
Approximate minimum degree permutation Syntax P = amd(A) P = amd(A,opts) Description P = amd(A) returns the approximate minimum degree permutation vector for the sparse matrix C = A + A'. The Cholesky factorization of C(P,P) or A(P,P) tends to be sparser than that of C or A. The amd function tends to be faster than symamd, and also tends to return better orderings than symamd. Matrix A must be square. If A is a full matrix, then amd(A) is equivalent to amd(sparse(A)). P = amd(A,opts) allows additional options for the reordering. The opts input is a structure with the two fields shown below. You only need to set the fields of interest: dense — A nonnegative scalar value that indicates what is considered to be dense. If A is n-by-n, then rows and columns with more than max(16,(dense*sqrt(n))) entries in A + A' are considered to be "dense" and are ignored during the ordering. MATLAB places these rows and columns last in the output permutation. The default value for this field is 10.0 if this option is not present. aggressive — A scalar value controlling aggressive absorption. If this field is set to a nonzero value, then aggressive absorption is performed. This is the default if this option is not present. MATLAB performs an assembly tree post-ordering, which is typically the same as an elimination tree post-ordering. It is not always identical because of the approximate degree update used, and because "dense" rows and columns do not take part in the post-order. It well-suited for a subsequent chol operation, however, If you require a precise elimination tree post-ordering, you can use the following code: P = amd(S); C = spones(S)+spones(S'); % Skip this line if S is already symmetric [ignore, Q] = etree(C(P,P)); P = P(Q); Examples This example constructs a sparse matrix and computes a two Cholesky factors: one of the original matrix and one of the original matrix preordered by amd. Note how much sparser the Cholesky factor of the preordered matrix is compared to the factor of the matrix in its natural ordering: A = gallery('wathen',50,50); p = amd(A); L = chol(A,'lower'); Lp = chol(A(p,p),'lower'); figure; subplot(2,2,1); spy(A); title('Sparsity structure of A'); subplot(2,2,2); spy(A(p,p)); title('Sparsity structure of AMD ordered A'); subplot(2,2,3); spy(L); title('Sparsity structure of Cholesky factor of A'); subplot(2,2,4); spy(Lp); title('Sparsity structure of Cholesky factor of AMD ordered A'); set(gcf,'Position',[100 100 800 700]); See Also colamd, colperm, symamd, symrcm, / References AMD Version 1.2 is written and copyrighted by Timothy A. Davis, Patrick R. Amestoy, and Iain S. Duff. It is available at http://www.cise.ufl.edu/research/sparse/amd. The authors of the code for symamd are Stefan I. Larimore and Timothy A. Davis (davis@cise.ufl.edu), University of Florida. The algorithm was developed in collaboration with John Gilbert, Xerox PARC, and Esmond Ng, Oak Ridge National Laboratory. Sparse Matrix Algorithms Research at the University of Florida: http://www.cise.ufl.edu/research/sparse/ Add by Pancho You can edit it here |
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Using the model organism Ciona intestinalis, commonly known as the sea squirt, researchers at the University of California, Berkeley, have uncovered the origins of the second heart field in vertebrates. Sea squirts are bag-like gelatinous creatures whose full genome has been sequenced--one that shares 80 percent of its genes with humans. Though its body is clearly more primitive than creatures with backbones and spinal columns, the sea squirt nevertheless offers a valuable ...
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