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uninformative and not encyclopaedic in tone, this "a bit" was added to the article *17 years ago* and never once copyedited in the meantime. |
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In [[mathematics]] and computing, the '''Levenberg–Marquardt algorithm''' ('''LMA''' or just '''LM'''), also known as the '''damped 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
The algorithm was first published in 1944 by [[Kenneth Levenberg]],<ref name="Levenberg"/> while working at the [[Frankford Arsenal|Frankford Army Arsenal]]. It was rediscovered in 1963 by [[Donald Marquardt]],<ref name="Marquardt"/> who worked as a [[statistician]] at [[DuPont]], and independently by Girard,<ref name="Girard"/> Wynne<ref name="Wynne"/> and Morrison.<ref name="Morrison"/>
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