This chapter describes functions for multidimensional nonlinear least-squares fitting. The library provides low level components for a variety of iterative solvers and convergence tests. These can be combined by the user to achieve the desired solution, with full access to the intermediate steps of the iteration. Each class of methods uses the same framework, so that you can switch between solvers at runtime without needing to recompile your program. Each instance of a solver keeps track of its own state, allowing the solvers to be used in multi-threaded programs.
The header file gsl_multifit_nlin.h contains prototypes for the multidimensional nonlinear fitting functions and related declarations.
|• Overview of Nonlinear Least-Squares Fitting:|
|• Initializing the Nonlinear Least-Squares Solver:|
|• Providing the Function to be Minimized:|
|• Finite Difference Jacobian:|
|• Iteration of the Minimization Algorithm:|
|• Search Stopping Parameters for Minimization Algorithms:|
|• High Level Driver:|
|• Minimization Algorithms using Derivatives:|
|• Minimization Algorithms without Derivatives:|
|• Computing the covariance matrix of best fit parameters:|
|• Example programs for Nonlinear Least-Squares Fitting:|
|• References and Further Reading for Nonlinear Least-Squares Fitting:|