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- 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. [[User:Jheald|Jheald]] ([[User talk:Jheald|talk]]) 22:26, 30 January 2008 (UTC)
== Explanation of the poor performance of L-M in example ==
The data sequence has very high frequency components. The sparse sampling plan shown in the plots will necessarily contain 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.
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