Struct levenberg_marquardt::LevenbergMarquardt
source · [−]pub struct LevenbergMarquardt<F> { /* private fields */ }Expand description
Levenberg-Marquardt optimization algorithm.
See the module documentation for a usage example.
The runtime and termination behavior can be controlled by various hyperparameters.
Implementations
Set the relative error desired in the objective function $f$.
Termination occurs when both the actual and
predicted relative reductions for $f$ are at most ftol.
Panics
Panics if $\mathtt{ftol} < 0$.
Set relative error between last two approximations.
Termination occurs when the relative error between
two consecutive iterates is at most xtol.
Panics
Panics if $\mathtt{xtol} < 0$.
Set orthogonality desired between the residual vector and its derivative.
Termination occurs when the cosine of the angle
between the residual vector $\vec{r}$ and any column of the Jacobian $\mathbf{J}$ is at
most gtol in absolute value.
With other words, the algorithm will terminate if
\cos\bigl(\sphericalangle (\mathbf{J}\vec{e}_i, \vec{r})\bigr) =
\frac{|(\mathbf{J}^\top \vec{r})_i|}{\|\mathbf{J}\vec{e}_i\|\|\vec{r}\|} \leq \texttt{gtol}
\quad\text{for all }i=1,\ldots,n.This is based on the fact that those vectors are orthognal near the optimum (gradient is zero).
The angle check is scale invariant, whereas checking that
$\nabla f(\vec{x})\approx \vec{0}$ is not.
Panics
Panics if $\mathtt{gtol} < 0$.
Shortcut to set tol as in MINPACK LMDER1.
Sets ftol = xtol = tol and gtol = 0.
Panics
Panics if $\mathtt{tol} \leq 0$.
Set factor for the initial step bound.
This bound is set to $\mathtt{stepbound}\cdot\|\mathbf{D}\vec{x}\|$
if nonzero, or else to stepbound itself. In most cases stepbound should lie
in the interval $[0.1,100]$.
Panics
Panics if $\mathtt{stepbound} \leq 0$.
Set factor for the maximal number of function evaluations.
The maximal number of function evaluations is set to
$\texttt{patience}\cdot(n + 1)$.
Panics
Panics if $\mathtt{patience} \leq 0$.
Enable or disable whether the variables will be rescaled internally.
pub fn minimize<N, M, O>(&self, target: O) -> (O, MinimizationReport<F>) where
N: Dim,
M: DimMin<N> + DimMax<N>,
O: LeastSquaresProblem<F, M, N>,
DefaultAllocator: Allocator<F, N> + Reallocator<F, M, N, DimMaximum<M, N>, N> + Allocator<usize, N>,
pub fn minimize<N, M, O>(&self, target: O) -> (O, MinimizationReport<F>) where
N: Dim,
M: DimMin<N> + DimMax<N>,
O: LeastSquaresProblem<F, M, N>,
DefaultAllocator: Allocator<F, N> + Reallocator<F, M, N, DimMaximum<M, N>, N> + Allocator<usize, N>,
Try to solve the given least squares problem.
The paramters of the problem which are set when this function is called
are used as the initial guess for $\vec{x}$.
Trait Implementations
This method tests for self and other values to be equal, and is used
by ==. Read more
This method tests for !=.
Auto Trait Implementations
impl<F> RefUnwindSafe for LevenbergMarquardt<F> where
F: RefUnwindSafe,
impl<F> Send for LevenbergMarquardt<F> where
F: Send,
impl<F> Sync for LevenbergMarquardt<F> where
F: Sync,
impl<F> Unpin for LevenbergMarquardt<F> where
F: Unpin,
impl<F> UnwindSafe for LevenbergMarquardt<F> where
F: UnwindSafe,
Blanket Implementations
Mutably borrows from an owned value. Read more
The inverse inclusion map: attempts to construct self from the equivalent element of its
superset. Read more
pub fn is_in_subset(&self) -> bool
pub fn is_in_subset(&self) -> bool
Checks if self is actually part of its subset T (and can be converted to it).
pub fn to_subset_unchecked(&self) -> SS
pub fn to_subset_unchecked(&self) -> SS
Use with care! Same as self.to_subset but without any property checks. Always succeeds.
pub fn from_subset(element: &SS) -> SP
pub fn from_subset(element: &SS) -> SP
The inclusion map: converts self to the equivalent element of its superset.