Revision as of 23:11, 1 November 2004 edit Frau Holle (talk \| contribs) 450 edits →The problem ← Previous edit		Revision as of 23:13, 1 November 2004 edit undo Frau Holle (talk \| contribs) 450 edits →The problem Next edit →
Line 13: becomes minimal. The main application is in the least squares curve fitting problem: given a set of empirical data pairs (''xt''<sub>''i''</sub>,''y''<sub>''i''</sub>), optimize the parameters '''p''' of the model curve ''c''(''xt''\|'''p''') so that the sum of the squares of the deviations :''f''<sub>''i''</sub>('''p''')=''y''<sub>''i''</sub> - ''c''(''xt''<sub>''i''</sub>\|'''p''') becomes minimal. (A word on notation: we avoid the letter ''x'' because it is used sometimes in the place of our ''p'', sometimes in the place of our ''t''). == The solution ==

Levenberg–Marquardt algorithm: Difference between revisions