Struct argmin::solver::goldensectionsearch::GoldenSectionSearch

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

Golden-section search

The golden-section search is a technique for finding an extremum (minimum or maximum) of a function inside a specified interval.

The method operates by successively narrowing the range of values on the specified interval, which makes it relatively slow, but very robust. The technique derives its name from the fact that the algorithm maintains the function values for four points whose three interval widths are in the ratio 2-φ:2φ-3:2-φ where φ is the golden ratio. These ratios are maintained for each iteration and are maximally efficient.

The min_bound and max_bound arguments define values that bracket the expected minimum.

Requires an initial guess which is to be provided via Executors configure method.

Requirements on the optimization problem

The optimization problem is required to implement CostFunction.

Reference

https://en.wikipedia.org/wiki/Golden-section_search

Implementations

impl<F> GoldenSectionSearch<F>where F: ArgminFloat,

pub fn new(min_bound: F, max_bound: F) -> Result<Self, Error>

Construct a new instance of GoldenSectionSearch.

The min_bound and max_bound arguments define values that bracket the expected minimum.

Example

let gss = GoldenSectionSearch::new(-2.5f64, 3.0f64)?;

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

Set tolerance.

Must be larger than 0 and defaults to 0.01.

Example

let gss = GoldenSectionSearch::new(-2.5f64, 3.0f64)?.with_tolerance(0.0001)?;

Trait Implementations

impl<F: Clone> Clone for GoldenSectionSearch<F>

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

Returns a copy of the value.

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

Performs copy-assignment from source.

impl<'de, F> Deserialize<'de> for GoldenSectionSearch<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 GoldenSectionSearch<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, F> Solver<O, IterState<F, (), (), (), F>> for GoldenSectionSearch<F>where O: CostFunction<Param = F, Output = F>, F: ArgminFloat,

const NAME: &'static str = "Golden-section search"

Name of the solver. Mainly used in Observers.

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

Initializes the algorithm.

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