Nov
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## Levenberg-Marquardt nonlinear least squares algorithms

In mathematics and computing, the Levenberg–Marquardt algorithm (or LMA) provides a numerical solution to the problem of minimizing a function, generally nonlinear, over a space of parameters of the function. These minimization problems arise especially in least squares curve fitting and nonlinear programming.

The Levenberg-Marquardt algorithm has proved to be an effective and popular way to solve nonlinear least squares problems. MINPACK-1 contains Levenberg-Marquardt codes in which the Jacobian matrix may be either supplied by the user or calculated by using finite differences. IMSL , MATLAB , ODRPACK , and PROC NLP also contain Levenberg-Marquardt routines.

The algorithms in ODRPACK solve unconstrained nonlinear least squares problems and orthogonal distance regression problems, including those with implicit models and multiresponse data.

For detail about Levenberg-Marquardt nonlinear least squares algorithms introduction and code pls click http://www.ics.forth.gr/~lourakis/levmar/

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The Levenberg-Marquardt algorithm has proved to be an effective and popular way to solve nonlinear least squares problems. MINPACK-1 contains Levenberg-Marquardt codes in which the Jacobian matrix may be either supplied by the user or calculated by using finite differences. IMSL , MATLAB , ODRPACK , and PROC NLP also contain Levenberg-Marquardt routines.

The algorithms in ODRPACK solve unconstrained nonlinear least squares problems and orthogonal distance regression problems, including those with implicit models and multiresponse data.

For detail about Levenberg-Marquardt nonlinear least squares algorithms introduction and code pls click http://www.ics.forth.gr/~lourakis/levmar/

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