The nonlinear least squares problem without constraints can be described as follows:
where
F(x) : Rn → Rm is a twice differentiable function in Rn.
Solving a nonlinear least squares problem means searching for the best approximation to the vector y with the model function fi(x) and nonlinear variables x. The best approximation means that the sum of squares of residuals yi - fi(x) is the minimum.
See usage examples in FORTRAN and C in the examples\solver\source folder of your Intel MKL directory (ex_nlsqp_f.f and ex_nlsqp_c.c, respectively).