Struct argmin::solver::trustregion::trustregion_method::TrustRegion

pub struct TrustRegion<R, F> { /* private fields */ }

The trust region method approximates the cost function within a certain region around the current point in parameter space. Depending on the quality of this approximation, the region is either expanded or contracted.

The calculation of the actual step length and direction is done by one of the following methods:

• Cauchy point
• Dogleg method
• Steihaug method

This subproblem can be set via set_subproblem(...). If this is not provided, it will default to the Steihaug method.

References:

[0] Jorge Nocedal and Stephen J. Wright (2006). Numerical Optimization. Springer. ISBN 0-387-30303-0.

Implementations

impl<R, F> TrustRegion<R, F> where
    F: ArgminFloat, 

pub fn new(subproblem: R) -> Self

Constructor

pub fn radius(self, radius: F) -> Self

set radius

pub fn max_radius(self, max_radius: F) -> Self

Set maximum radius

pub fn eta(self, eta: F) -> Result<Self, Error>

Set eta

Trait Implementations

impl<R: Clone, F: Clone> Clone for TrustRegion<R, F>

fn clone(&self) -> TrustRegion<R, F>

Returns a copy of the value.

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

Performs copy-assignment from source.

impl<'de, R, F> Deserialize<'de> for TrustRegion<R, F> where
    R: 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<R, F> Serialize for TrustRegion<R, F> where
    R: 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, R, F, P, G, H> Solver<O, IterState<P, G, (), H, F>> for TrustRegion<R, F> where
    O: CostFunction<Param = P, Output = F> + Gradient<Param = P, Gradient = G> + Hessian<Param = P, Hessian = H>,
    P: Clone + SerializeAlias + DeserializeOwnedAlias + ArgminNorm<F> + ArgminDot<P, F> + ArgminDot<G, F> + ArgminAdd<P, P>,
    G: Clone + SerializeAlias + DeserializeOwnedAlias,
    H: Clone + SerializeAlias + DeserializeOwnedAlias + ArgminDot<P, P>,
    R: Clone + TrustRegionRadius<F> + Solver<O, IterState<P, G, (), H, F>>,
    F: ArgminFloat, 

const NAME: &'static str

Name of the solver. Mainly used in Observers.

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

Initializes the algorithm.

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

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

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

Checks whether basic termination reasons apply.