pkgsrc-wip/spai/distinfo

$NetBSD: distinfo,v 1.1.1.1 2011/05/09 21:29:38 jihbed Exp $

SHA1 (spai-3.2.tar.bz2) = 723ece1f65538111cffccc5c90691fe1f89256ba
RMD160 (spai-3.2.tar.bz2) = dee77a66e04e0515cac47a3840b0d06899fa70bf
Size (spai-3.2.tar.bz2) = 565871 bytes
Import spai-3.2 as wip/spai. Given a sparse matrix A the SPAI Algorithm computes a sparse approximate inverse M by minimizing \|\| AM - I \|\| in the Frobenius norm. The approximate inverse is computed explicitly and can then be applied as a preconditioner to an iterative method.The sparsity pattern of the approximate inverse is either fixed a priori or captured automatically: * Fixed sparsity: The sparsity pattern of M is either banded or a subset of the sparsity pattern of A. * Adaptive sparsity: The algorithm proceeds until the 2-norm of each column of AM-I is less than eps. By varying eps the user controls the quality and the cost of computing the preconditioner. Usually the optimal eps lies between 0.5 and 0.7. A very sparse preconditioner is very cheap to compute but may not lead to much improvement, while if M becomes rather dense it becomes too expensive to compute. The optimal preconditioner lies between these two extremes and is problem and computer architecture dependent. The approximate inverse M can also be used as a robust (parallel) smoother for (algebraic) multi-grid methods 2011-05-09 23:29:38 +02:00			$NetBSD: distinfo,v 1.1.1.1 2011/05/09 21:29:38 jihbed Exp $

			`SHA1 (spai-3.2.tar.bz2) = 723ece1f65538111cffccc5c90691fe1f89256ba`
			`RMD160 (spai-3.2.tar.bz2) = dee77a66e04e0515cac47a3840b0d06899fa70bf`
			`Size (spai-3.2.tar.bz2) = 565871 bytes`