Function forward_difference_jacobian 

pub fn forward_difference_jacobian<P, V>(
    problem: &P,
    x: &V,
    function_precision: f64,
    fixed_step: Option<f64>,
) -> Result<<V as DenseMatrixFromFn>::Matrix, P::Error>
where
    P: Residual<Param = V, Output = V> + MaybeSync,
    V: Clone + VectorLen + VectorIndex + DenseMatrixFromFn + MaybeSync + MaybeSend,
    P::Error: MaybeSend,

Forward-difference Jacobian, column j ≈ (r(x+hⱼeⱼ) − r(x)) / hⱼ.

Reproduces MINPACK fdjac2: eps = sqrt(function_precision), hⱼ = eps·|xⱼ|, hⱼ = eps when xⱼ = 0. n+1 residual evaluations. The result is an m × n matrix (m = r(x).len(), n = x.len()). Returns Err if any probe’s residual does.