levmar
levmar : Levenberg-Marquardt nonlinear least squares algorithms in C/C++ This site provides GPL native ANSI C implementations of the Levenberg-Marquardt optimization algorithm, usable also from C++, Matlab, Perl, Python, Haskell and Tcl and explains their use. Both unconstrained and constrained (under linear equations, inequality and box constraints) Levenberg-Marquardt variants are included. The Levenberg-Marquardt (LM) algorithm is an iterative technique that finds a local minimum of a function that is expressed as the sum of squares of nonlinear functions. It has become a standard technique for nonlinear least-squares problems and can be thought of as a combination of steepest descent and the Gauss-Newton method. When the current solution is far from the correct one, the algorithm behaves like a steepest descent method: slow, but guaranteed to converge. When the current solution is close to the correct solution, it becomes a Gauss-Newton method.
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References in zbMATH (referenced in 37 articles )
Showing results 1 to 20 of 37.
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- Jin, Ping; Ling, Chen; Shen, Huifei: A smoothing Levenberg-Marquardt algorithm for semi-infinite programming (2015)
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- Behling, Roger; Fischer, Andreas: A unified local convergence analysis of inexact constrained Levenberg-Marquardt methods (2012)
- Shen, Chungen; Chen, Xiongda; Liang, Yumei: A regularized Newton method for degenerate unconstrained optimization problems (2012)
- Ueda, Kenji; Yamashita, Nobuo: Global complexity bound analysis of the Levenberg-Marquardt method for nonsmooth equations and its application to the nonlinear complementarity problem (2012)
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- Du, Shou-Qiang; Gao, Yan: Convergence analysis of nonmonotone Levenberg-Marquardt algorithms for complementarity problem (2010)