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→problematic matrix decomposition: Rebuttal. |
→Extracting information from tridiagonal matrix: Yes, this needs elaborating. |
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:PS: also, it should be said what is 'm'...
*'''Seconded''' — the article spends a lot of ink on the Lanczos iteration (but could do a better job at explaining it) for producing a tridiagonal matrix, says various other algorithms can be used for calculating eigenvalues and eigenvectors of that tridiagonal matrix, but is almost silent on how the two are related. As far as I can tell, early steps of the iteration tends to put most of the weight on the extreme eigenvalues (largest and smallest both, ''regardless of their absolute values''), meaning those are fairly accurately reproduced in the tridiagonal matrix, and the algorithm proceeds towards less extreme eigenvalues the longer it is run; it's 'tends' because the initial weight distribution depends on the initial vector, which is chosen at random. What is not clear from mere thought experiments is how concentrated the distribution is … [[Special:Contributions/130.243.68.202|130.243.68.202]] ([[User talk:130.243.68.202|talk]]) 14:22, 2 May 2017 (UTC)
== Define variables ==
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