Struct argmin::solver::quasinewton::SR1TrustRegion

pub struct SR1TrustRegion<R, F> { /* private fields */ }

SR1 Trust region method

A Quasi-Newton method which uses symmetric rank 1 (SR1) updating of the Hessian in a trust region framework. An initial parameter vector must be provided, initial cost, gradient and Hessian are optional and will be computed if not provided. Requires a trust region sub problem.

Requirements on the optimization problem

The optimization problem is required to implement CostFunction, Gradient and Hessian.

Reference

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

Implementations

impl<R, F> SR1TrustRegion<R, F>where F: ArgminFloat,

pub fn new(subproblem: R) -> Self

Construct a new instance of SR1TrustRegion

Example

let subproblem = argmin::solver::trustregion::Steihaug::new().with_max_iters(20);
let sr1: SR1TrustRegion<_, f64> = SR1TrustRegion::new(subproblem);

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

Set denominator factor

If the denominator of the update is below the denominator_factor (scaled with other factors derived from the parameter vectors and the gradients), then the update of the inverse Hessian will be skipped.

Must be in (0, 1) and defaults to 1e-8.

Example

let sr1: SR1TrustRegion<_, f64> = SR1TrustRegion::new(subproblem).with_denominator_factor(1e-7)?;

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

Set initial radius

Defaults to 1.0.

Example

let sr1: SR1TrustRegion<_, f64> = SR1TrustRegion::new(subproblem).with_radius(2.0);

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

Set eta

A step is taken if the actual reduction over the predicted reduction exceeds eta. Must be in (0, 10^-3) and defaults to 0.5 * 10^-3.

Example

let sr1: SR1TrustRegion<_, f64> = SR1TrustRegion::new(subproblem).with_eta(10e-4)?;

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

The algorithm stops if the norm of the gradient is below tol_grad.

The provided value must be non-negative. Defaults to 10^-3.

Example

let sr1: SR1TrustRegion<_, f64> = SR1TrustRegion::new(subproblem).with_tolerance_grad(1e-6)?;

Trait Implementations

impl<R: Clone, F: Clone> Clone for SR1TrustRegion<R, F>

fn clone(&self) -> SR1TrustRegion<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 SR1TrustRegion<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 SR1TrustRegion<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, P, G, B, F> Solver<O, IterState<P, G, (), B, F>> for SR1TrustRegion<R, F>where O: CostFunction<Param = P, Output = F> + Gradient<Param = P, Gradient = G> + Hessian<Param = P, Hessian = B>, P: Clone + SerializeAlias + DeserializeOwnedAlias + ArgminSub<P, P> + ArgminAdd<P, P> + ArgminDot<P, F> + ArgminDot<P, B> + ArgminL2Norm<F> + ArgminZeroLike, G: Clone + SerializeAlias + DeserializeOwnedAlias + ArgminL2Norm<F> + ArgminDot<P, F> + ArgminSub<G, P>, B: Clone + SerializeAlias + DeserializeOwnedAlias + ArgminDot<P, P> + ArgminAdd<B, B> + ArgminMul<F, B>, R: Clone + TrustRegionRadius<F> + Solver<O, IterState<P, G, (), B, F>>, F: ArgminFloat + ArgminL2Norm<F>,

const NAME: &'static str = "SR1 trust region"

Name of the solver. Mainly used in Observers.

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

Initializes the algorithm.

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