Titlebook: Computational Methods for General Sparse Matrices; Zahari Zlatev Book 1991 Springer Science+Business Media B.V. 1991 Mathematica.Matrix.al

燈絲

https://doi.org/10.1057/9780230613188umns (Q.Q=I, I being the identity matrix in R.), D ∈ .. is a diagonal matrix and R ∈ .. is an upper triangular matrix. Very often matrix D is the identity matrix and if this is so, then (12.1) is reduced to

gerrymander

https://doi.org/10.1057/9780230613188 However, the classical manner of exploiting sparsity (see . is in fact used in the calculations because the drop-tolerance used is so small (T=10.) that practically no non-zero elements are removed during the decomposition process.

可商量

myopia

Preconditioned Conjugate Gradients for Givens Plane Rotations,trix Q ∈ .. with orthonormal columns (see .) and if the calculations are performed without rounding errors, then C=I and, thus, the CG algorithm converges in one iteration only. Even if the orthogonalization is carried out with rounding errors, the matrix C is normally close to the identity matrix I

insurgent

https://doi.org/10.1007/978-3-319-26914-6ents) of the system by Gaussian elimination, .. This is so because the factorization process can be optimized quite well, while it is difficult to improve very much the performance of the back solver. The factorization time is by far the most expensive part when the . is used, while very often the s

軟膏

https://doi.org/10.1007/978-1-349-73900-4trix Q ∈ .. with orthonormal columns (see .) and if the calculations are performed without rounding errors, then C=I and, thus, the CG algorithm converges in one iteration only. Even if the orthogonalization is carried out with rounding errors, the matrix C is normally close to the identity matrix I

腐敗

流動才波動

Pivotal Strategies for Gaussian Elimination,sed in the solution of linear algebraic equations with general sparse matrices, then the pivotal strategy plays a very important role. The pivotal strategy is a powerful tool that can efficiently be used during the efforts to preserve as well as possible the sparsity of the original matrix and, at t

TRAWL

Use of Iterative Refinement in the GE Process, whose coefficient matrices are ., then the accuracy of the results will usually be greater than the accuracy obtained by the use of Gaussian elimination without iterative refinement (.). However, both more storage (about 100% because a copy of matrix A is needed) and more computing time (some extra

chassis