Function bounded_least_squares 

pub fn bounded_least_squares<F, J, D, S1, S2>(
    residuals: F,
    x0: &ArrayBase<S1, Ix1>,
    bounds: Option<Bounds>,
    jacobian: Option<J>,
    data: &ArrayBase<S2, Ix1>,
    options: Option<BoundedOptions>,
) -> OptimizeResult<OptimizeResults<f64>>
where
    F: Fn(&[f64], &[D]) -> Array1<f64>,
    J: Fn(&[f64], &[D]) -> Array2<f64>,
    D: Clone,
    S1: Data<Elem = f64>,
    S2: Data<Elem = D>,

Solve a bounded least squares problem

This function minimizes the sum of squares of residuals subject to box constraints on the variables.

Arguments

• residuals - Function that returns the residuals
• x0 - Initial guess for the parameters
• bounds - Optional bounds on variables
• jacobian - Optional Jacobian function
• data - Additional data to pass to residuals and jacobian
• options - Options for the optimization