Struct GradientDescent 

pub struct GradientDescent {
    pub abs_tol: Scalar,
    pub dual: bool,
    pub line_search: LineSearch,
    pub max_steps: usize,
    pub rel_tol: Option<Scalar>,
}

The method of gradient descent.

Fields

abs_tol: Scalar

Absolute error tolerance.

dual: bool

Lagrangian dual.

line_search: LineSearch

Line search algorithm.

max_steps: usize

Maximum number of steps.

rel_tol: Option<Scalar>

Relative error tolerance.

Trait Implementations

impl Debug for GradientDescent

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

Formats the value using the given formatter.

impl Default for GradientDescent

fn default() -> Self

Returns the “default value” for a type.

impl<X> FirstOrderOptimization<f64, X> for GradientDescent

where X: Jacobian + Solution, for<'a> &'a X: Mul<Scalar, Output = X>, for<'a> &'a Matrix: Mul<&'a X, Output = Vector>,

fn minimize( &self, function: impl FnMut(&X) -> Result<Scalar, String>, jacobian: impl FnMut(&X) -> Result<X, String>, initial_guess: X, equality_constraint: EqualityConstraint, ) -> Result<X, OptimizationError>

impl<X> ZerothOrderRootFinding<X> for GradientDescent

where X: Jacobian + Solution, for<'a> &'a X: Mul<Scalar, Output = X>, for<'a> &'a Matrix: Mul<&'a X, Output = Vector>,

fn root( &self, function: impl FnMut(&X) -> Result<X, String>, initial_guess: X, equality_constraint: EqualityConstraint, ) -> Result<X, OptimizationError>