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The Levenberg-Marquard Algorithm

The problem of least squares is widespread in applications amongst almost all natural sciences. The setting is a follows: We observe input data \{x_i\}_{i=1,\ldots,n} and some output data \{y_i\}_{i=1,\ldots,n} together with a model represented by a function y=f(x,\theta) which depends on a set of parameters \theta\in\Theta. The task now is to find the parameter \hat\theta which minimizes

J=\sum_{i=1}^n|y_i-f(x,\theta)|^2\, .


The case where f is linear has been addressed first by Legendre although Gauss claimed that he have been had a solution earlier. The optimal parameter \hat\theta can be explicitly given in terms of the data.

In case that f is non-linear, it usually is not possible to solve for \hat\theta directly. The first key concept of the method is to linearize the non-linear problem through a Taylor expansion, i.e.

f(x,\theta+h)=f(x,\theta)+\partial_\theta f(x,\theta)h\, .

One can then solve the linearized problem recursively, until some condition is fulfilled. In each step, the Levenberg-Maquard algorithm "interpolates" between the gradient-descend method and the Gauss-Newton algorithm.



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