Revision as of 19:15, 5 December 2004 edit Billlion (talk \| contribs) Autopatrolled, Extended confirmed users, Pending changes reviewers 8,036 edits m →References: DuPont ← Previous edit		Revision as of 19:18, 5 December 2004 edit undo Billlion (talk \| contribs) Autopatrolled, Extended confirmed users, Pending changes reviewers 8,036 edits robuster -> more robust Next edit →
Line 3: This minimization problem arises especially in [[least squares]] [[curve fitting]] (see also: [[nonlinear programming]]). The Levenberg-Marquardt algorithm (LMA) interpolates between the [[Gauss-Newton algorithm]] (GNA) and the method of [[gradient descent]]. The LMA is more ''~~robuster~~robust'' than the GNA, which means that in many cases it finds a solution even if it starts very far off the final minimum. On the other hand, for well-behaved functions and reasonable starting parameters, the LMA tends to be a bit slower than the GNA. The LMA is the most popular curve-fitting algorithm; it is used in almost any software that provides a generic curve-fitting tool; few users will ever need another curve-fitting algorithm. == The problem ==

Levenberg–Marquardt algorithm: Difference between revisions