Levenberg-Marquardt nonlinear least squares algorithm

levmar is a native ANSI C implementation of the Levenberg-Marquardt optimization algorithm. Both unconstrained and constrained (under linear equations, inequality and box constraints) Levenberg-Marquardt variants are included. The 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 on, 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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levmar-2.6.tgz 0000081143 79.2 KB over 5 years
levmar.changes 0000000918 918 Bytes over 5 years
levmar.spec 0000004429 4.33 KB over 2 years
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