Struct optimization::problems::Rosenbrock

pub struct Rosenbrock { /* fields omitted */ }

Two-dimensional Rosenbrock function.

A non-convex function with its global minimum inside a long, narrow, parabolic shaped flat valley:

f(x, y) = (a - x)² + b (y - x²)²

Global minimum: f(a, a²) = 0

Methods

impl Rosenbrock

fn new(a: f64, b: f64) -> Rosenbrock

Creates a new Rosenbrock function given a and b, commonly definied with 1 and 100, respectively, which also corresponds to the default.

Trait Implementations

impl Debug for Rosenbrock

fn fmt(&self, __arg_0: &mut Formatter) -> Result

Formats the value using the given formatter.

impl Copy for Rosenbrock

impl Clone for Rosenbrock

fn clone(&self) -> Rosenbrock

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Default for Rosenbrock

fn default() -> Self

Returns the "default value" for a type.

impl Function for Rosenbrock

fn value(&self, x: &[f64]) -> f64

Computes the objective function at a given position x, i.e., f(x) = y.

impl Function1 for Rosenbrock

fn gradient(&self, x: &[f64]) -> Vec<f64>

Computes the gradient of the objective function at a given position x, i.e., ∀ᵢ ∂/∂xᵢ f(x) = ∇f(x).

impl Problem for Rosenbrock

fn dimensions(&self) -> usize

Returns the dimensionality of the input domain.

fn domain(&self) -> Vec<(f64, f64)>

Returns the input domain of the function in terms of upper and lower, respectively, for each input dimension.

fn minimum(&self) -> (Vec<f64>, f64)

Returns the position as well as the value of the global minimum.

fn random_start(&self) -> Vec<f64>

Generates a random and feasible position to start a minimization.

fn is_legal_position(&self, position: &[f64]) -> bool

Tests whether the supplied position is legal for this function.