Struct argmin::solver::gaussnewton::GaussNewton
source · pub struct GaussNewton<F> { /* private fields */ }
Expand description
Gauss-Newton method
The Gauss-Newton method is used to solve non-linear least squares problems.
Requires an initial parameter vector.
Requirements on the optimization problem
The optimization problem is required to implement Operator
and Jacobian
.
Reference
Jorge Nocedal and Stephen J. Wright (2006). Numerical Optimization. Springer. ISBN 0-387-30303-0.
Implementations§
source§impl<F: ArgminFloat> GaussNewton<F>
impl<F: ArgminFloat> GaussNewton<F>
sourcepub fn new() -> Self
pub fn new() -> Self
Construct a new instance of GaussNewton
.
Example
let gauss_newton: GaussNewton<f64> = GaussNewton::new();
sourcepub fn with_gamma(self, gamma: F) -> Result<Self, Error>
pub fn with_gamma(self, gamma: F) -> Result<Self, Error>
Set step width gamma.
Gamma must be within (0, 1]
. Defaults to 1.0
.
Example
let gauss_newton = GaussNewton::new().with_gamma(0.5f64)?;
sourcepub fn with_tolerance(self, tol: F) -> Result<Self, Error>
pub fn with_tolerance(self, tol: F) -> Result<Self, Error>
Set tolerance for the stopping criterion based on cost difference.
Tolerance must be larger than zero and defaults to sqrt(EPSILON)
.
Example
let gauss_newton = GaussNewton::new().with_tolerance(1e-4f64)?;
Trait Implementations§
source§impl<F: Clone> Clone for GaussNewton<F>
impl<F: Clone> Clone for GaussNewton<F>
source§fn clone(&self) -> GaussNewton<F>
fn clone(&self) -> GaussNewton<F>
Returns a copy of the value. Read more
1.0.0 · source§fn clone_from(&mut self, source: &Self)
fn clone_from(&mut self, source: &Self)
Performs copy-assignment from
source
. Read moresource§impl<F: ArgminFloat> Default for GaussNewton<F>
impl<F: ArgminFloat> Default for GaussNewton<F>
source§fn default() -> GaussNewton<F>
fn default() -> GaussNewton<F>
Returns the “default value” for a type. Read more
source§impl<'de, F> Deserialize<'de> for GaussNewton<F>where
F: Deserialize<'de>,
impl<'de, F> Deserialize<'de> for GaussNewton<F>where F: Deserialize<'de>,
source§fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>where
__D: Deserializer<'de>,
fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>where __D: Deserializer<'de>,
Deserialize this value from the given Serde deserializer. Read more
source§impl<F> Serialize for GaussNewton<F>where
F: Serialize,
impl<F> Serialize for GaussNewton<F>where F: Serialize,
source§impl<O, F, P, J, U> Solver<O, IterState<P, (), J, (), F>> for GaussNewton<F>where
O: Operator<Param = P, Output = U> + Jacobian<Param = P, Jacobian = J>,
P: Clone + ArgminSub<P, P> + ArgminMul<F, P>,
U: ArgminL2Norm<F>,
J: Clone + ArgminTranspose<J> + ArgminInv<J> + ArgminDot<J, J> + ArgminDot<U, P> + ArgminDot<P, P>,
F: ArgminFloat,
impl<O, F, P, J, U> Solver<O, IterState<P, (), J, (), F>> for GaussNewton<F>where O: Operator<Param = P, Output = U> + Jacobian<Param = P, Jacobian = J>, P: Clone + ArgminSub<P, P> + ArgminMul<F, P>, U: ArgminL2Norm<F>, J: Clone + ArgminTranspose<J> + ArgminInv<J> + ArgminDot<J, J> + ArgminDot<U, P> + ArgminDot<P, P>, F: ArgminFloat,
source§const NAME: &'static str = "Gauss-Newton method"
const NAME: &'static str = "Gauss-Newton method"
Name of the solver. Mainly used in Observers.
source§fn next_iter(
&mut self,
problem: &mut Problem<O>,
state: IterState<P, (), J, (), F>
) -> Result<(IterState<P, (), J, (), F>, Option<KV>), Error>
fn next_iter( &mut self, problem: &mut Problem<O>, state: IterState<P, (), J, (), F> ) -> Result<(IterState<P, (), J, (), F>, Option<KV>), Error>
Computes a single iteration of the algorithm and has access to the optimization problem
definition and the internal state of the solver.
Returns an updated
state
and optionally a KV
which holds key-value pairs used in
Observers.source§fn terminate(&mut self, state: &IterState<P, (), J, (), F>) -> TerminationStatus
fn terminate(&mut self, state: &IterState<P, (), J, (), F>) -> TerminationStatus
Used to implement stopping criteria, in particular criteria which are not covered by
(
terminate_internal
. Read moresource§fn init(
&mut self,
_problem: &mut Problem<O>,
state: I
) -> Result<(I, Option<KV>), Error>
fn init( &mut self, _problem: &mut Problem<O>, state: I ) -> Result<(I, Option<KV>), Error>
Initializes the algorithm. Read more
source§fn terminate_internal(&mut self, state: &I) -> TerminationStatus
fn terminate_internal(&mut self, state: &I) -> TerminationStatus
Checks whether basic termination reasons apply. Read more