Computational Methods for General Sparse Matrices by Zahari Zlatev (auth.)

By Zahari Zlatev (auth.)

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This could be done by an algorithm described in [341]. If the is required (as in the NN ~ NZ is used, then [341] algorithm from beginning of this section), but let us repeat that in practice NN as a rule should be considerably larger than 2*NZ (this will be demonstrated by many numerical examples in the end of this chapter). 2. 1, sparsity is in use. 11), or for some other iterative process. Therefore these arrays cannot be overwritten by ALU, RNLU and CNLU as in the case where the classical manner of exploiting sparsity is in use.

Beginning of the (i=s,s+l, ... ,N) that are stored between numbers of the elements of row i than s, while the column numbers of the smaller are HA(i,l) and HA(i,2)-l elements that are stored between HA(i,2) and HA(i,3) are larger than or equal to s. This means that, while the order of the elements within a row some ordering is performed in the GE, is arbitrary at the beginning of course of GE. The reason for this action will become clear later, when the is described. In this performance of the GE transformations at stage s chapter the order of the elements within a row will only be used to explain how a new element, fill-in, can be stored by the use of such a structure (this 14 Chapter 2 will be done in the next section).

Some other examples that demonstrate this statement will be given at the end of this chapter. • Remark 2. 3. The dynamic storage scheme described in this section consists of two groups of arrays. In the first group of arrays the elements of matrix A together with their column numbers are ordered by rows in ALU and CNLU; some pointers concerning this structure are kept in the first three columns of HA. It is said that ALU, CNLU and the first three columns of HA form Chapter 2 16 the row ordered list or the row-oriented structure.

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