Struct NewtonRaphson 

pub struct NewtonRaphson {
    pub abs_tol: Tolerances,
    pub error_norm: Norm,
    pub line_search: LineSearch,
    pub max_steps: usize,
    pub rel_tol: Option<Scalar>,
    pub trust_region: TrustRegion,
}

The Newton-Raphson method.

Fields

abs_tol: Tolerances

Absolute error tolerances.

error_norm: Norm

Norm type for error evaluation.

line_search: LineSearch

Line search algorithm.

max_steps: usize

Maximum number of steps.

rel_tol: Option<Scalar>

Relative error tolerance.

trust_region: TrustRegion

How far the step is trusted.

Trait Implementations

impl Clone for NewtonRaphson

fn clone(&self) -> NewtonRaphson

Returns a duplicate of the value.

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

Performs copy-assignment from source.

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

where F: Jacobian + Erase<Erased = E>, for<'a> &'a F: Div<J, Output = X>, J: Hessian, X: Erase<Erased = E> + Solution, E: Tensor, <X as Tensor>::Unit: UnitDiv<<X as Tensor>::Unit, Output = Dimensionless>, for<'a> &'a X: Mul<Quantity<Dimensionless>, Output = X> + Mul<Scalar, Output = X>, for<'a> &'a Matrix: Mul<&'a X, Output = Vector>,

fn root( &self, function: impl FnMut(&X) -> Result<F, String>, jacobian: impl FnMut(&X) -> Result<J, String>, initial_guess: X, equality_constraint: EqualityConstraint, sparse: Option<SparseSolver>, ) -> Result<X, OptimizationError>

impl<U, V, Ru, Rv, Kuu, Kvu, Kuv, Kvv> FirstOrderRootFindingBlock<U, V, Ru, Rv, Kuu, Kvu, Kuv, Kvv> for NewtonRaphson

where U: Solution, V: Solution, Ru: Jacobian, Rv: Jacobian, Kuu: HessianBlock, Kvu: HessianBlock, Kuv: HessianBlock, Kvv: HessianBlock, for<'a> &'a CscMatrix: Mul<&'a U, Output = Vector> + Mul<&'a V, Output = Vector>,

fn root_block( &self, residual_global: impl FnMut(&U, &V) -> Result<Ru, String>, residual_local: impl FnMut(&U, &V) -> Result<Rv, String>, tangents: impl FnMut(&U, &V) -> Result<(Kuu, Kvu, Kuv, Kvv), String>, initial_guess: (U, V), constraint_global: (CscMatrix, Vector), constraint_local: (CscMatrix, Vector), sparse: Option<SparseSolver>, strategy: SolveStrategy, ) -> Result<(U, V), OptimizationError>

impl<F, J, X, E> FirstOrderRootFindingIncremental<F, J, X> for NewtonRaphson

where F: Jacobian + Erase<Erased = E>, for<'a> &'a F: Div<J, Output = X>, J: Hessian, X: Erase<Erased = E> + Solution, E: Tensor, <X as Tensor>::Unit: UnitDiv<<X as Tensor>::Unit, Output = Dimensionless>, for<'a> &'a X: Mul<Quantity<Dimensionless>, Output = X> + Mul<Scalar, Output = X>, for<'a> &'a Matrix: Mul<&'a X, Output = Vector>,

fn root_incremental( &self, function: impl FnMut(&X) -> Result<F, String>, jacobian: impl FnMut(&X) -> Result<J, String>, update: impl FnMut(&X, &Vector, Scalar, bool) -> Result<(), String>, initial_guess: X, equality_constraint: EqualityConstraint, sparse: Option<SparseSolver>, ) -> Result<X, OptimizationError>

impl<F, J, H, X, E> SecondOrderOptimization<F, J, H, X> for NewtonRaphson

where F: Erase<Erased = Scalar> + Tensor, <J as Tensor>::Unit: UnitMul<<X as Tensor>::Unit>, <<J as Tensor>::Unit as UnitMul<<X as Tensor>::Unit>>::Output: UnitSum, <<<J as Tensor>::Unit as UnitMul<<X as Tensor>::Unit>>::Output as UnitSum>::Output: Is<<F as Tensor>::Unit>, H: Hessian, J: Jacobian + Erase<Erased = E>, for<'a> &'a J: Div<H, Output = X>, X: Erase<Erased = E> + Solution, E: Tensor, <X as Tensor>::Unit: UnitDiv<<X as Tensor>::Unit, Output = Dimensionless>, for<'a> &'a X: Mul<Quantity<Dimensionless>, Output = X> + Mul<Scalar, Output = X>, for<'a> &'a Matrix: Mul<&'a X, Output = Vector>,

fn minimize( &self, function: impl FnMut(&X) -> Result<F, String>, jacobian: impl FnMut(&X) -> Result<J, String>, hessian: impl FnMut(&X) -> Result<H, String>, initial_guess: X, equality_constraint: EqualityConstraint, sparse: Option<SparseSolver>, ) -> Result<X, OptimizationError>

impl<F, U, V, Ru, Rv, Kuu, Kvu, Kuv, Kvv> SecondOrderOptimizationBlock<F, U, V, Ru, Rv, Kuu, Kvu, Kuv, Kvv> for NewtonRaphson

where F: Erase<Erased = Scalar> + Tensor, <Ru as Tensor>::Unit: UnitMul<<U as Tensor>::Unit>, <<Ru as Tensor>::Unit as UnitMul<<U as Tensor>::Unit>>::Output: UnitSum, <<<Ru as Tensor>::Unit as UnitMul<<U as Tensor>::Unit>>::Output as UnitSum>::Output: Is<<F as Tensor>::Unit>, <Rv as Tensor>::Unit: UnitMul<<V as Tensor>::Unit>, <<Rv as Tensor>::Unit as UnitMul<<V as Tensor>::Unit>>::Output: UnitSum, <<<Rv as Tensor>::Unit as UnitMul<<V as Tensor>::Unit>>::Output as UnitSum>::Output: Is<<F as Tensor>::Unit>, U: Solution, V: Solution, Ru: Jacobian, Rv: Jacobian, Kuu: HessianBlock, Kvu: HessianBlock, Kuv: HessianBlock, Kvv: HessianBlock, for<'a> &'a CscMatrix: Mul<&'a U, Output = Vector> + Mul<&'a V, Output = Vector>,

fn minimize_block( &self, function: impl FnMut(&U, &V) -> Result<F, String>, residual_global: impl FnMut(&U, &V) -> Result<Ru, String>, residual_local: impl FnMut(&U, &V) -> Result<Rv, String>, tangents: impl FnMut(&U, &V) -> Result<(Kuu, Kvu, Kuv, Kvv), String>, initial_guess: (U, V), constraint_global: (CscMatrix, Vector), constraint_local: (CscMatrix, Vector), sparse: Option<SparseSolver>, strategy: SolveStrategy, ) -> Result<(U, V), OptimizationError>

impl<F, J, H, X, E> SecondOrderOptimizationIncremental<F, J, H, X> for NewtonRaphson

where F: Erase<Erased = Scalar> + Tensor, <J as Tensor>::Unit: UnitMul<<X as Tensor>::Unit>, <<J as Tensor>::Unit as UnitMul<<X as Tensor>::Unit>>::Output: UnitSum, <<<J as Tensor>::Unit as UnitMul<<X as Tensor>::Unit>>::Output as UnitSum>::Output: Is<<F as Tensor>::Unit>, H: Hessian, J: Jacobian + Erase<Erased = E>, for<'a> &'a J: Div<H, Output = X>, X: Erase<Erased = E> + Solution, E: Tensor, <X as Tensor>::Unit: UnitDiv<<X as Tensor>::Unit, Output = Dimensionless>, for<'a> &'a X: Mul<Quantity<Dimensionless>, Output = X> + Mul<Scalar, Output = X>, for<'a> &'a Matrix: Mul<&'a X, Output = Vector>,

fn minimize_incremental( &self, function: impl FnMut(&X) -> Result<F, String>, jacobian: impl FnMut(&X) -> Result<J, String>, hessian: impl FnMut(&X) -> Result<H, String>, update: impl FnMut(&X, &Vector, Scalar, bool) -> Result<(), String>, initial_guess: X, equality_constraint: EqualityConstraint, sparse: Option<SparseSolver>, ) -> Result<X, OptimizationError>