Struct NewtonRaphson

pub struct NewtonRaphson {
    pub abs_tol: Scalar,
    pub line_search: Option<LineSearch>,
    pub max_steps: usize,
}

The Newton-Raphson method.

Fields

abs_tol: Scalar

Absolute error tolerance.

line_search: Option<LineSearch>

Line search algorithm.

max_steps: usize

Maximum number of steps.

Trait Implementations

impl Debug for NewtonRaphson

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

Formats the value using the given formatter.

impl Default for NewtonRaphson

fn default() -> Self

Returns the “default value” for a type.

impl<F, J, X> FirstOrderRootFinding<F, J, X> for NewtonRaphson

where F: Jacobian, for<'a> &'a F: Div<J, Output = X> + From<&'a X>, J: Hessian, X: Solution, for<'a> &'a X: Mul<Scalar, Output = X>, for<'a> &'a Matrix: Mul<&'a X, Output = Vector>,

fn root( &self, function: impl Fn(&X) -> Result<F, OptimizeError>, jacobian: impl Fn(&X) -> Result<J, OptimizeError>, initial_guess: X, equality_constraint: EqualityConstraint, ) -> Result<X, OptimizeError>

impl<J, H, X> SecondOrderOptimization<f64, J, H, X> for NewtonRaphson

where H: Hessian, J: Jacobian, for<'a> &'a J: Div<H, Output = X> + From<&'a X>, X: Solution, for<'a> &'a X: Mul<Scalar, Output = X>, for<'a> &'a Matrix: Mul<&'a X, Output = Vector>,

fn minimize( &self, function: impl Fn(&X) -> Result<Scalar, OptimizeError>, jacobian: impl Fn(&X) -> Result<J, OptimizeError>, hessian: impl Fn(&X) -> Result<H, OptimizeError>, initial_guess: X, equality_constraint: EqualityConstraint, banded: Option<Banded>, ) -> Result<X, OptimizeError>