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BibTeX entry
@Article{Natvig:1986:ECC,
author = "J. Natvig and B. Nour-Omid and B. N. Parlett",
title = "Effect of the {CYBER 205} on the choice of method for
solving the eigenvalue problem {$ (A - \lambda M) x = 0
$}",
journal = j-J-COMPUT-APPL-MATH,
volume = "15",
number = "2",
pages = "137--159",
month = jun,
year = "1986",
CODEN = "JCAMDI",
DOI = "https://doi.org/10.1016/0377-0427(86)90023-3",
ISSN = "0377-0427 (print), 1879-1778 (electronic)",
ISSN-L = "0377-0427",
bibdate = "Sat Feb 25 11:59:56 MST 2017",
bibsource = "http://www.math.utah.edu/pub/bibnet/authors/p/parlett-beresford-n.bib;
http://www.math.utah.edu/pub/tex/bib/jcomputapplmath1980.bib",
URL = "http://www.sciencedirect.com/science/article/pii/0377042786900233",
ZMnumber = "0635.65032",
abstract = "For the eigenvalue problem {$ A x = \lambda M x $},
{$A$}, {$B$} large, sparse, symmetric matrices, two
methods, subspace iteration and Lanczos method, are
compared when running on typical examples from
structural dynamic analysis (order of {$A$}, {$B$} up
to 8000) on a Cyber 205. A fixed number of eigenpairs
is calculated. As on serial computers it turns out on
this vector computer that the Lanczos algorithm is
considerably faster. However, on problems with
substantial overhead in reading\slash writing, a block
Lanczos method is preferable.",
acknowledgement = ack-nhfb,
classmath = "*65F15 Eigenvalues (numerical linear algebra) 65F50
Sparse matrices",
fjournal = "Journal of Computational and Applied Mathematics",
journal-URL = "http://www.sciencedirect.com/science/journal/03770427",
keywords = "comparison of methods; eigenvalue problem; Lanczos
method; large, sparse, symmetric matrices; subspace
iteration; vector computer; vectorization",
reviewer = "L. Elsner",
}
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- $A$,
18(2)249,
19(1)133
- $B$,
28(z)281
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6(2)133,
6(2)145,
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6(3)247,
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7(1)77,
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12(z)433,
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25(1)33,
25(1)111,
25(3)341,
25(3)351,
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27(1)17,
27(1)37,
27(1)53,
27(1)95,
27(1)191,
27(3)421,
28(z)231,
28(z)237
- analysis,
6(1)53,
6(3)201,
7(4)277,
8(1)63,
9(3)201,
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15(1)93,
15(2)261,
20(z)53,
20(z)219,
22(2)297,
24(1)209,
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10(3)301,
10(3)329,
12(z)3,
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27(1)215,
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- comparison,
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9(3)213,
12(z)517,
15(3)371,
16(2)237,
21(2)239,
23(3)305
- computer,
7(1)41,
7(2)129,
7(2)141,
11(2)209,
18(3)279,
18(3)353,
22(2)183,
22(2)197,
22(2)297,
24(3)z,
26(3)311,
27(1)267,
27(1)323
- dynamic,
22(2)285,
22(2)297,
22(2)319,
22(2)339,
25(2)133
- effect,
12(z)233,
23(3)305,
23(3)353
- eigenvalue,
6(1)81,
8(1)15,
8(2)135,
8(3)203,
12(z)37,
15(1)109,
20(z)211,
27(1)3,
27(1)191,
27(1)215
- example,
12(z)299,
23(3)389
- fixed,
19(1)55
- iteration,
10(3)285,
15(1)13,
21(2)239,
22(1)35,
25(1)61,
25(2)133,
28(z)119
- Lanczos,
7(4)249,
12(z)37,
20(z)137
- large,
6(1)83,
7(1)3,
10(1)45,
11(1)39,
12(z)37,
20(z)83,
24(1)277,
26(1)3,
27(1)267,
28(z)105
- matrix,
6(1)81,
7(3)225,
7(4)249,
8(1)69,
8(3)203,
8(4)285,
11(3)297,
12(z)3,
12(z)475,
12(z)551,
14(3)353,
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17(3)375,
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18(3)265,
19(1)23,
19(3)313,
21(1)27,
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23(1)127,
24(1)147,
25(3)351,
26(3)257,
26(3)289,
27(1)3,
27(1)215,
27(1)229,
27(3)421
- number,
7(4)287,
10(1)65,
19(2)223,
21(2)203,
28(z)105
- order,
6(1)37,
6(3)229,
6(4)275,
7(1)67,
7(2)111,
7(4)261,
8(1)3,
8(1)21,
8(2)101,
8(4)253,
8(4)267,
9(1)41,
9(1)81,
10(1)39,
10(1)133,
10(2)203,
11(3)277,
12(z)145,
12(z)217,
14(3)361,
14(3)455,
14(3)467,
15(2)175,
15(2)213,
15(2)257,
15(2)261,
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17(3)365,
18(1)3,
21(1)129,
21(2)251,
21(3)357,
21(3)383,
22(1)137,
25(1)61
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6(3)213,
8(1)35,
8(2)93,
9(2)167,
9(3)229,
10(1)71,
10(2)157,
11(1)39,
11(2)175,
11(2)231,
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14(1)19,
14(1)125,
14(3)371,
15(3)311,
15(3)339,
16(2)169,
16(3)343,
18(2)235,
18(2)249,
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19(3)343,
21(1)111,
21(2)161,
24(1)33,
24(1)107,
24(1)227,
24(1)241,
24(1)265,
24(3)355,
25(1)79,
25(2)237,
25(3)351,
27(1)323,
28(z)391
- sparse,
7(1)77,
9(3)287,
10(1)45,
11(1)39,
15(3)339,
18(1)17,
26(1)3,
27(1)129,
27(1)229
- structural,
22(2)369
- subspace,
18(2)249,
19(3)313
- symmetric,
7(1)77,
8(2)135,
12(z)37,
17(1)173,
18(2)249,
20(z)153,
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25(2)153,
27(1)191
- two,
6(3)201,
7(3)167,
8(3)159,
9(3)239,
10(2)179,
14(3)345,
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16(2)159,
19(3)351,
23(2)199,
24(3)403
- vector,
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18(3)331,
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- when,
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