Algorithm 2 Newton’s method for solving the optimization.

1:$k=0,{\mathbf{f}}^0={\mathbf{f}}^{initial}$ 2:repeat 3:    Compute the gradient and Hessian: $\nabla \mu \left({\mathbf{f}}^k\right)$ and ${\nabla }^2\mu \left({\mathbf{f}}^k\right)$ 4:    Choose a stepsize: $t^k>0$ 5:    Update ${\mathbf{f}}^{k+1}:={\mathbf{f}}^k-t^k{\left[{\nabla }^2\mu \left({\mathbf{f}}^k\right)\right]}^{-1}\nabla \mu \left({\mathbf{f}}^k\right)$ 6:$k\leftarrow k+1$ 7:until stopping criterion is satisfied 8:return${\mathbf{f}}^k$