Struct GradientFreeStatus 

pub struct GradientFreeStatus {
    pub message: String,
    pub x: DVector<Float>,
    pub fx: Float,
    pub n_f_evals: usize,
    pub converged: bool,
    pub hess: Option<DMatrix<Float>>,
    pub cov: Option<DMatrix<Float>>,
    pub err: Option<DVector<Float>>,
}

A status message struct containing all information about a minimization result.

Fields

message: String

A String message that can be set by Algorithms.

x: DVector<Float>

The current parameters of the minimization.

fx: Float

The current value of the minimization problem function at GradientFreeStatus::x.

n_f_evals: usize

The number of function evaluations (approximately, this is left up to individual Algorithms to correctly compute and may not be exact).

converged: bool

Flag that says whether or not the fit is in a converged state.

hess: Option<DMatrix<Float>>

The Hessian matrix at the end of the fit (None if not computed yet)

cov: Option<DMatrix<Float>>

Covariance matrix at the end of the fit (None if not computed yet)

err: Option<DVector<Float>>

Errors on parameters at the end of the fit (None if not computed yet)

Implementations

impl GradientFreeStatus

pub fn with_message(&mut self, message: &str)

Updates the GradientFreeStatus::message field.

pub fn with_position(&mut self, pos: (DVector<Float>, Float))

Updates the GradientFreeStatus::x and GradientFreeStatus::fx fields.

pub fn set_converged(&mut self)

Sets GradientFreeStatus::converged to be true.

pub fn inc_n_f_evals(&mut self)

Increments GradientFreeStatus::n_f_evals by 1.

pub fn with_cov(&mut self, covariance: Option<DMatrix<Float>>)

Updates the GradientFreeStatus::err field.

pub fn with_hess(&mut self, hessian: &DMatrix<Float>)

Updates the GradientFreeStatus::hess field and computes GradientFreeStatus::cov and GradientFreeStatus::err.

Trait Implementations

impl<P, U, E> Algorithm<P, GradientFreeStatus, U, E> for NelderMead

where P: CostFunction<U, E>,

type Summary = MinimizationSummary

A type which holds a summary of the algorithm’s ending state.

type Config = NelderMeadConfig

The configuration struct for the algorithm.

fn initialize( &mut self, problem: &P, status: &mut GradientFreeStatus, args: &U, config: &Self::Config, ) -> Result<(), E>

Any setup work done before the main steps of the algorithm should be done here.

fn step( &mut self, _current_step: usize, problem: &P, status: &mut GradientFreeStatus, args: &U, config: &Self::Config, ) -> Result<(), E>

The main “step” of an algorithm, which is repeated until termination conditions are met or the max number of steps have been taken.

fn summarize( &self, _current_step: usize, _func: &P, status: &GradientFreeStatus, _args: &U, config: &Self::Config, ) -> Result<MinimizationSummary, E>

Generates a new Algorithm::Summary from the current state of the Algorithm, which can be displayed or used elsewhere.

fn default_callbacks() -> Callbacks<Self, P, GradientFreeStatus, U, E, Self::Config>

where Self: Sized,

Provides a set of reasonable default callbacks specific to this Algorithm.

fn postprocessing( &mut self, problem: &P, status: &mut S, args: &U, config: &Self::Config, ) -> Result<(), E>

Runs any steps needed by the Algorithm after termination or convergence. This will run regardless of whether the Algorithm converged.

fn reset(&mut self)

Reset the algorithm to its initial state.

fn process<C>( &mut self, problem: &P, args: &U, config: Self::Config, callbacks: C, ) -> Result<Self::Summary, E>

where C: Into<Callbacks<Self, P, S, U, E, Self::Config>>, Self: Sized,

Process the given problem using this Algorithm.

fn process_default( &mut self, problem: &P, user_data: &U, config: Self::Config, ) -> Result<Self::Summary, E>

where Self: Sized,

Process the given problem using this Algorithm.

impl Clone for GradientFreeStatus

fn clone(&self) -> GradientFreeStatus

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for GradientFreeStatus

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Default for GradientFreeStatus

fn default() -> GradientFreeStatus

Returns the “default value” for a type.

impl<'de> Deserialize<'de> for GradientFreeStatus

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl Serialize for GradientFreeStatus

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.

impl Status for GradientFreeStatus

fn reset(&mut self)

Resets the status to its default state. This is called at the beginning of every Algorithm::process run. Only members that are not persistent between runs should be reset. For example, the initial parameters of a minimization should not be reset.

fn converged(&self) -> bool

Returns the convergence flag of the minimization.

fn message(&self) -> &str

Returns the message of the minimization.

fn update_message(&mut self, message: &str)

Sets the message of the minimization.

impl<P, U, E> Terminator<NelderMead, P, GradientFreeStatus, U, E, NelderMeadConfig> for NelderMeadFTerminator

where P: CostFunction<U, E>,

fn check_for_termination( &mut self, _current_step: usize, algorithm: &mut NelderMead, _problem: &P, status: &mut GradientFreeStatus, _args: &U, _config: &NelderMeadConfig, ) -> ControlFlow<()>

A termination check which is called on each step of an Algorithm.

impl<P, U, E> Terminator<NelderMead, P, GradientFreeStatus, U, E, NelderMeadConfig> for NelderMeadXTerminator

where P: CostFunction<U, E>,

fn check_for_termination( &mut self, _current_step: usize, algorithm: &mut NelderMead, _problem: &P, status: &mut GradientFreeStatus, _args: &U, _config: &NelderMeadConfig, ) -> ControlFlow<()>

A termination check which is called on each step of an Algorithm.