Struct BrentOpt 

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

Brent’s method

A minimization algorithm combining parabolic interpolation and the golden-section method. It has the reliability of the golden-section method, but can be faster thanks to the parabolic interpolation steps.

Requirements on the optimization problem

The optimization problem is required to implement CostFunction.

Reference

“An algorithm with guaranteed convergence for finding a minimum of a function of one variable”, Algorithms for minimization without derivatives, Richard P. Brent, 1973, Prentice-Hall.

Implementations

impl<F: ArgminFloat> BrentOpt<F>

pub fn new(min: F, max: F) -> Self

Constructor

The values min and max must bracket the minimum of the function.

pub fn set_tolerance(self, eps: F, t: F) -> Self

Set the tolerance to the value required.

The algorithm will return an approximation x of a local minimum of the function, with an accuracy smaller than 3 tol, where tol = eps*abs(x) + t. It is useless to set eps to less than the square root of the machine precision (F::epsilon().sqrt()), which is its default value. The default value of t is 1e-5.

Trait Implementations

impl<F: Clone> Clone for BrentOpt<F>

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

Returns a duplicate of the value.

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

Performs copy-assignment from source.

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

where O: CostFunction<Param = F, Output = F>, F: ArgminFloat,

fn name(&self) -> &str

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

Checks whether basic termination reasons apply.

fn terminate(&mut self, _state: &I) -> TerminationStatus

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