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The bandwidth of a matrix A={aij} is defined as the maximum absolute difference between i and j for which aij≠0.
The problem of reducing the bandwidth of a matrix consists of finding a permutation of the rows and columns that keeps the nonzero elements in a band that is as close as possible to the main diagonal of the matrix.
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The bandwidth of the matrix m is equal to (k1 + k2 + 1).
In contrast, high-order difference schemes formulated with non-compact stencils yield a higher bandwidth of the iteration matrix, and this involves large arithmetic operations.
Deciding copositivity of a matrix.
Table 1: Effective bandwidth of a transoceanic link.
The 3 dB bandwidth of n identical stages, as a fraction of the bandwidth of a single stage, is given approximately by, : \sqrt[4] {2^{1/n} - 1} This expression applies only to small fractional bandwidths.
A gradient-echo sequence will be used with a flip angle of 20°, a matrix of 90 256 pixels, a field of view of 40 cm, and a sampling bandwidth of 814 Hz per pixel.
Finite representation of infinite bodies, node numbering schemes to reduce the bandwidth of the resulting matrices, and automatic mesh generation methods are also discussed.
In the conventional MMSE-FDE method for the CP-OFDM [10], the CFR matrix is approximated by the banded matrix with the block size (Q 1+1)×(Q 1+1), where Q 1 is the lower and upper bandwidth of approximated CFR banded matrix.
Besides, the equalizer in[14] approximates the channel matrix to be diagonal (i.e., using a bandwidth of one for the banded matrices), and thus its performance becomes inferior in the presence of higher scale differences among the multipath as in the tested channel here.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com