levmar is a robust and efficient C/C++ implementation of the Levenberg - Marquardt (LM) optimization algorithm. LM solves the non-linear least squares problem, i.e. fitting a set of m observations with a model that is non-linear in n unknown parameters (m>=n). levmar includes double and single precision LM variants, both with analytic and finite difference approximated Jacobians. It also has some support for constrained non-linear least squares, allowing linear equation and box constraints to be imposed. For problems with sparse Jacobians, the sparseLM project might be more efficient.
