Talk:Levenberg–Marquardt algorithm

message: The article talks about "the functions f(x_i,\boldsymbol \beta+\boldsymbol \delta)". This confused the heck out of me, until I realized that what is meant is the values of the function f(x,\boldsymbol \beta+\boldsymbol \delta) at x_i. Similar confused terminology is used at other points in the article, for example when calling J_i the gradient. These kind of terminology issues make the article hard to read.  — Preceding unsigned comment added by 80.65.109.181 (talk) 12:09, 13 September 2013 (UTC)
thread_title:  Functions vs. Values 

message: In the article is says that Levenberg's contribution is in introducing lambda. But at the same time in the references it says that he was the first to publish the algorithm in 1944 or something. This seems a bit contradictory to me.
Ravn-hawk (talk) 15:38, 28 September 2011 (UTC)
thread_title:  Inconsistency 

message: Could I please recommend that people interested in this article look at Sam Roweis' document on Levenberg Marquardt at <http://www.cs.toronto.edu/~roweis/notes/lm.pdf>.  It seems to be a very complete discription.  If it were simply pasted in (pdf2html?) it would answer the questions below and add detail.  This would have to be checked with the author, of course, which I will be happy to do. I am new to this, so my etiquette is probably short of the mark, but I hope this is useful.
:I suggest that if you think there is valuable insights in Roweis' article you might use the information there to improve the existing wikipedia article. The questions below are not fundamentally important, but the article can be improved. Certainly no case for wholesale replacement.Billlion 09:49, 5 September 2006 (UTC)
thread_title:  Possible replacement article? 

message: What's about the improvement Marquardt made to the algorithm? 
He replaced I by diag[H], i.e. the diagonal of the (approximated) 
Hessian, to incorporate some local curvature estimation. This makes 
the algorithm go further in directions of smaller gradient to get 
out of narrow valleys on the error surface.
last entry by G.k. 13:46, 27 April 2006 (UTC)
replies: text: A way to address this might be to add a section called (say) "Improved solution" after the "Choice of damping parameter" section, with the new formula.  The original "solution" section and this new one should probably be better referenced as well.  Thoughts?  I may give it a shot soon.  Baccyak4H (Yak!) 18:29, 25 July 2007 (UTC)
thread_title:  Improvement by Marquardt 

message: Reference to nonlinear programming seems rather less appropriate here
than reference to nonlinear regression.  However, the article 
on the latter inadequate (but developing).Dfarrar 09:08, 10 March 2007 (UTC)
thread_title:  Reference to nonlinear programming 

message: A short explanation (or a link to a page with an explanation) about the terminology f(t_i|p) would be in order here. I dont think it is common usage outside of statistical groups.  —Preceding unsigned comment added by 130.225.102.1 (talk) 15:26, 25 September 2007 (UTC)
thread_title:  explain terminology better 

message: Please see talk:least squares#A major proposal for details. Petergans (talk) 17:39, 22 January 2008 (UTC)
thread_title:  A major proposal 

message: - should discuss the approach of Moré, the standard used in most production-quality numerical libraries.  It tends to be both faster and more robust than earlier more naive methods. Jheald (talk) 22:26, 30 January 2008 (UTC)
thread_title:  Choice of damping parameter 

message: The data sequence has very high frequency components. The sparse sampling plan shown in the plots will necessarily contain huge aliasing error that is beyond any inverse algorithm. The way to correct this apparent poor performance of L-M is to sample the data train much more densely, e.g. in 0.001 step size, then the L-M will find the correct answer easily under a very wide range of initial conditions. It is really not L-M that is at fault here.  --Xzhou.lumi (talk) 22:32, 2 July 2008 (UTC)
thread_title:  Explanation of the poor performance of L-M in example 

message: There was a slight problem with the use of Notes and References. I have fixed it. --Михал Орела (talk) 20:50, 3 February 2009 (UTC)
thread_title:  Notes and references

message: Can anyone provide a section or information comparing the LMA with other algorithms such as the Conjugate-Gradient method?MxBuck (talk) 19:18, 23 September 2009 (UTC)
thread_title:  Comparison of Efficacy and Performance with other similar Algorithms 

message: I think there's a little mistake in the definition of J:
Shouldn't it be: J = S(β) / dβ ?
This would also correspond to this (from the external links):
http://www.siam.org/books/textbooks/fr18_book.pdf
We're trying to minimize the error, so taking the Jacobian of the original function wouldn't make sense.  — Preceding unsigned comment added by 129.132.154.39 (talk) 10:42, 20 October 2011 (UTC)
thread_title:  Jacobian J 

message: There should be discussion of the covariance of the solution. I think it's that if J is left-multiplied by the inverse of the input covariance, than J^\top \Sigma^{-1} J is the covariance of the output, or somesuch. —Ben FrantzDale (talk) 17:20, 26 October 2011 (UTC)
thread_title:  Covariance of the solution 

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thread_title:  External links modified 

message: Hi just a note to say that the image links on this page appear to be broken. Thanks.  — Preceding unsigned comment added by 2A01:110:8012:1012:0:0:0:82 (talk) 11:59, 17 January 2018 (UTC)
thread_title:  Broken images