Class NonLinearLeastSquare

Using a given optimization algorithm (default is conjugate gradient),

$$ min { r(x) : x in R^n } $$

where $ r(x) = |f(x)|^2 $ is the Euclidean norm of $ f(x) $ for some vector-valued function $ f $ from $ R^n $ to $ R^m $, $$ f = (f_1, ..., f_m) $$ with $ f_i(x) = b_i - \phi(x,t_i) $ where $ b $ is the vector of target data and $ phi $ is a scalar function.

Assuming the differentiability of $ f $, the gradient of $ r $ is defined by $$ grad r(x) = f'(x)^t.f(x) $$