Struct GaussNewtonLS 

pub struct GaussNewtonLS<L, F> { /* private fields */ }

Gauss-Newton method with line search

Gauss-Newton method where an appropriate step length is obtained by a line search.

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<L, F: ArgminFloat> GaussNewtonLS<L, F>

pub fn new(linesearch: L) -> Self

Construct a new instance of GaussNewtonLS.

Example

let gauss_newton_ls: GaussNewtonLS<_, f64> = GaussNewtonLS::new(linesearch);

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_ls = GaussNewtonLS::new(linesearch).with_tolerance(1e-4f64)?;

Trait Implementations

impl<L: Clone, F: Clone> Clone for GaussNewtonLS<L, F>

fn clone(&self) -> GaussNewtonLS<L, F>

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<'de, L, F> Deserialize<'de> for GaussNewtonLS<L, F>

where L: Deserialize<'de>, 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<L, F> Serialize for GaussNewtonLS<L, F>

where L: Serialize, 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, L, F, P, G, J, U, R> Solver<O, IterState<P, G, J, (), R, F>> for GaussNewtonLS<L, F>

where O: Operator<Param = P, Output = U> + Jacobian<Param = P, Jacobian = J>, P: Clone + ArgminMul<F, P>, G: Clone, U: ArgminL2Norm<F>, J: Clone + ArgminTranspose<J> + ArgminInv<J> + ArgminDot<J, J> + ArgminDot<G, P> + ArgminDot<U, G>, L: Clone + LineSearch<P, F> + Solver<LineSearchProblem<O, F>, IterState<P, G, (), (), R, F>>, F: ArgminFloat, R: Clone,

fn name(&self) -> &str

Name of the solver. Mainly used in Observers.

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

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

fn init( &mut self, _problem: &mut Problem<O>, state: I, ) -> Result<(I, Option<KV>), Error>

Initializes the algorithm.

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

Checks whether basic termination reasons apply.