Levenberg–Marquardt algorithm: Difference between revisions

Revision as of 13:50, 12 December 2019

Revision as of 05:03, 27 December 2019

Line 1:

In [[mathematics]] and computing, the '''Levenberg–Marquardt algorithm''' ('''LMA''' or just '''LM'''), also known as the '''dampedshashank least-squares''' ('''DLS''') method, is used to solve [[non-linear least squares]] problems. These minimization problems arise especially in [[least squares]] [[curve fitting]].

The LMA is used in many software applications for solving generic curve-fitting problems. However, as with many fitting algorithms, the LMA finds only a [[local minimum]], which is not necessarily the [[global minimum]]. The LMA interpolates between the [[Gauss–Newton algorithm]] (GNA) and the method of [[gradient descent]]. The LMA is more [[Robustness (computer science)|robust]] than the GNA, which means that in many cases it finds a solution even if it starts very far off the final minimum. For well-behaved functions and reasonable starting parameters, the LMA tends to be a bit slower than the GNA. LMA can also be viewed as [[Gauss–Newton]] using a [[trust region]] approach.