Talk:Lanczos algorithm

[[1]] - an unfocused variety of Lanczos algorithm —Preceding unsigned comment added by 134.219.166.104 (talk • contribs) 21:23, 1 September 2005

This doesn't have much but it does have a reference to a book mathworld on Lanczos Algorithm]—Preceding unsigned comment added by RJFJR (talk • contribs) 23:36, 25 September 2005

I don't believe this is the Lanczos algorithm at all. It is the power method. —Preceding unsigned comment added by 130.126.55.123 (talk • contribs) 01:04, 5 August 2006

I don't know if the algorithm is correct, but it's certainly different than the power method, and presented pretty clearly. I think it's gotten me on the right track at least... Thanks. --Jjdonald (talk) 22:22, 17 December 2007 (UTC)[reply]

It is not easy to say it's wrong or correct, since quite some information is missing in order to apply it: (a) how to choose v[1], (b) how to chose m, (c) how to recognize the eigenvalues of A among those of T_mm. Unfortunately, this vagueness is by no means eliminated by the Numerical stability section. — MFH:Talk 21:57, 12 September 2008 (UTC)[reply]

It should state that "it applies to Hermitian matrices" at the start of the article and not somewhere in the middle. limweizhong (talk) 09:54, 11 November 2008 (UTC)[reply]

In Latent Semantic Indexing, for...

I really think that this sentense has nothing to do in the first paragraph! Please someone who understand anything about it should create a separate section and explain what this is about! Alain Michaud (talk) 16:51, 19 February 2010 (UTC)[reply]

Block Lanczos algorithm

I suppose that Peter Montgomery`s 1995 paper was very good, but I do not see the need to inform everyone about its existence. This topic is much too advanced to be discussed at the top of the page. Please move this (second paragraph) towards the end of the page.

Alain Michaud (talk) 16:50, 19 February 2010 (UTC)[reply]

Extracting information from tridiagonal matrix

So Lanczos gives you a tridiagonal matrix. I think a link would be helpful which explains how to extract low eigenvalues/eigenvectors from this matrix. —Preceding unsigned comment added by 209.6.144.249 (talk) 06:30, 2 March 2008 (UTC)[reply]

Agree - or largest eigenvalues: anyway, the article starts by saying that it's for calculating eigenvalues, but then stops with the tridiag. matrix.

B.t.w., the algorithm calculates up to v[m+1], I think this could be avoided. (also, "unrolling" the 1st part of the m=1 case as initialization should allow to avoid using v[0].) — MFH:Talk 03:09, 11 September 2008 (UTC)[reply]

PS: also, it should be said what is 'm'...