Struct mathru::optimization::LevenbergMarquardt

pub struct LevenbergMarquardt<T> { /* fields omitted */ }

Levenberg-Marquardt method

input: $f \colon \mathbb{R}^{n} \to \mathbb{R}$ with initial approximation $x_{0} \in \mathbb{R}^{n}$

output: $x_{k}$

1. Initialization: 0 \leq \rho^{-} < \rho^{+} \leq 1, set $k := 0$
2. Solve the equation
3. $\rho = \frac{\lvert \lvert f(x_{k}) \rvert \rvert_{2}^{2} - \lvert \lvert f(x_{k+1}) \rvert \rvert_{2}^{2}}{2d_{k}^{T}(f^{'}(x_{k}) )^T f(x_{k})}$
4. if $\rho_{k} > \rho^{-} $ than $ x_{k + 1} := x_{k} + d_{k}$, else $\Delta_{k + 1} := \Delta_{k}/2$ and go to 2.
5. if $\rho_{k} > \rho^{+} $ than $ \Delta_{k + 1} := 2\Delta_{k}$, else $\Delta_{k + 1} := \Delta_{k}$
6. set $k:= k + 1$, go to 2.

Methods

impl<T> LevenbergMarquardt<T>

pub fn new(iters: u64, rho_minus: T, rho_plus: T) -> LevenbergMarquardt<T>

impl<T> LevenbergMarquardt<T> where
    T: Real, 

pub fn minimize<F: Jacobian<T>>(
    &self,
    func: &F,
    x_0: &Vector<T>
) -> OptimResult<Vector<T>>

Minimize function func

Arguments

• 'func0': Function to be minimized
• 'x_0': Initial guess for the minimum

Return

local minimum