The Levenberg-Marquardt 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.
Optional box- and linear constraints can be given. Both single and double precision floating point types are supported.
This library depends on bindings-levmar which is bundled together with a C library which falls under the GPL. Please be aware of this when distributing programs linked with this library. For details see the description and license of bindings-levmar.