Struct argmin::solver::gaussnewton::GaussNewton

pub struct GaussNewton<F> { /* private fields */ }

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

impl<F: ArgminFloat> GaussNewton<F>

pub fn new() -> Self

Construct a new instance of GaussNewton.

Example

let gauss_newton: GaussNewton<f64> = GaussNewton::new();

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)?;

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

impl<F: Clone> Clone for GaussNewton<F>

fn clone(&self) -> GaussNewton<F>

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl<F: ArgminFloat> Default for GaussNewton<F>

fn default() -> GaussNewton<F>

Returns the “default value” for a type.

impl<'de, F> Deserialize<'de> for GaussNewton<F>

where F: Deserialize<'de>,

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl<F> Serialize for GaussNewton<F>

where F: Serialize,

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.

impl<O, P, J, R, F> Solver<O, IterState<P, (), J, (), R, F>> for GaussNewton<F>

where O: Operator<Param = P, Output = R> + Jacobian<Param = P, Jacobian = J>, P: Clone + ArgminSub<P, P> + ArgminMul<F, P>, R: ArgminL2Norm<F>, J: Clone + ArgminTranspose<J> + ArgminInv<J> + ArgminDot<J, J> + ArgminDot<R, P> + ArgminDot<P, P>, F: ArgminFloat,

fn name(&self) -> &str

Name of the solver. Mainly used in Observers.

fn init( &mut self, problem: &mut Problem<O>, state: IterState<P, (), J, (), R, F>, ) -> Result<(IterState<P, (), J, (), R, F>, Option<KV>), Error>

Initializes the algorithm.

fn next_iter( &mut self, problem: &mut Problem<O>, state: IterState<P, (), J, (), R, F>, ) -> Result<(IterState<P, (), J, (), R, 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.

fn terminate( &mut self, state: &IterState<P, (), J, (), R, F>, ) -> TerminationStatus

Used to implement stopping criteria, in particular criteria which are not covered by (terminate_internal.

fn terminate_internal(&mut self, state: &I) -> TerminationStatus

Checks whether basic termination reasons apply.