Trait AD 

pub trait AD:
    RealField
    + ComplexField
    + PartialOrd
    + PartialEq
    + Signed
    + Scalar
    + Clone
    + Copy
    + Debug
    + Display
    + Default
    + Add<F64, Output = Self>
    + AddAssign<F64>
    + Mul<F64, Output = Self>
    + MulAssign<F64>
    + Sub<F64, Output = Self>
    + SubAssign<F64>
    + Div<F64, Output = Self>
    + DivAssign<F64>
    + Rem<F64, Output = Self>
    + RemAssign<F64>
    + From<f32>
    + Into<f64>
    + SimdRealField
    + SimdComplexField
    + Serialize
    + DeserializeOwned
    + MaybeReflect
    + ScalarOperand {
    // Required methods
    fn constant(constant: f64) -> Self;
    fn to_constant(&self) -> f64;
    fn ad_num_mode() -> ADNumMode;
    fn ad_num_type() -> ADNumType;
    fn add_scalar(arg1: f64, arg2: Self) -> Self;
    fn sub_l_scalar(arg1: f64, arg2: Self) -> Self;
    fn sub_r_scalar(arg1: Self, arg2: f64) -> Self;
    fn mul_scalar(arg1: f64, arg2: Self) -> Self;
    fn div_l_scalar(arg1: f64, arg2: Self) -> Self;
    fn div_r_scalar(arg1: Self, arg2: f64) -> Self;
    fn rem_l_scalar(arg1: f64, arg2: Self) -> Self;
    fn rem_r_scalar(arg1: Self, arg2: f64) -> Self;
    fn mul_by_nalgebra_matrix<R: Clone + Dim, C: Clone + Dim, S: Clone + RawStorageMut<Self, R, C>>(
        &self,
        other: Matrix<Self, R, C, S>,
    ) -> Matrix<Self, R, C, S>;
    fn mul_by_nalgebra_matrix_ref<'a, R: Clone + Dim, C: Clone + Dim, S: Clone + RawStorageMut<Self, R, C>>(
        &'a self,
        other: &'a Matrix<Self, R, C, S>,
    ) -> Matrix<Self, R, C, S>;
    fn mul_by_ndarray_matrix_ref<D: Dimension>(
        &self,
        other: &ArrayBase<OwnedRepr<Self>, D>,
    ) -> ArrayBase<OwnedRepr<Self>, D>;

    // Provided methods
    fn to_constant_ad(&self) -> Self { ... }
    fn to_other_ad_type<T2: AD>(&self) -> T2 { ... }
}

The core trait for Automatic Differentiation (AD).

This trait defines the interface for types that can participate in automatic differentiation computations. It combines numerical traits (like RealField and ComplexField from simba) with AD-specific functionality.

Types implementing AD can represent:

• Standard floating-point values (f64, f32) for non-derivative computations.
• Forward-mode AD types (adfn<K>) for tangent propagation.
• Reverse-mode AD types (adr) for gradient backpropagation.

Required Methods

fn constant(constant: f64) -> Self

Creates a constant value of this AD type from an f64.

fn to_constant(&self) -> f64

Converts the current AD value back to its base f64 representation. This retrieves the value without its derivative information.

fn ad_num_mode() -> ADNumMode

Returns the mode of automatic differentiation used by this type.

fn ad_num_type() -> ADNumType

Returns the specific numerical type identifier for this AD type.

fn add_scalar(arg1: f64, arg2: Self) -> Self

Scalar addition between an f64 and an AD value.

fn sub_l_scalar(arg1: f64, arg2: Self) -> Self

Scalar subtraction: arg1 (f64) - arg2 (AD).

fn sub_r_scalar(arg1: Self, arg2: f64) -> Self

Scalar subtraction: arg1 (AD) - arg2 (f64).

fn mul_scalar(arg1: f64, arg2: Self) -> Self

Scalar multiplication between an f64 and an AD value.

fn div_l_scalar(arg1: f64, arg2: Self) -> Self

Scalar division: arg1 (f64) / arg2 (AD).

fn div_r_scalar(arg1: Self, arg2: f64) -> Self

Scalar division: arg1 (AD) / arg2 (f64).

fn rem_l_scalar(arg1: f64, arg2: Self) -> Self

Scalar remainder: arg1 (f64) % arg2 (AD).

fn rem_r_scalar(arg1: Self, arg2: f64) -> Self

Scalar remainder: arg1 (AD) % arg2 (f64).

fn mul_by_nalgebra_matrix<R: Clone + Dim, C: Clone + Dim, S: Clone + RawStorageMut<Self, R, C>>( &self, other: Matrix<Self, R, C, S>, ) -> Matrix<Self, R, C, S>

Multiplies this scalar by an nalgebra matrix of AD values.

fn mul_by_nalgebra_matrix_ref<'a, R: Clone + Dim, C: Clone + Dim, S: Clone + RawStorageMut<Self, R, C>>( &'a self, other: &'a Matrix<Self, R, C, S>, ) -> Matrix<Self, R, C, S>

Multiplies this scalar by a reference to an nalgebra matrix of AD values.

fn mul_by_ndarray_matrix_ref<D: Dimension>( &self, other: &ArrayBase<OwnedRepr<Self>, D>, ) -> ArrayBase<OwnedRepr<Self>, D>

Multiplies this scalar by an ndarray matrix of AD values.

Provided Methods

fn to_constant_ad(&self) -> Self

Returns the constant version of the current value as a new AD object.

fn to_other_ad_type<T2: AD>(&self) -> T2

Converts this AD value to another AD type. This is useful for reparameterizing functions during the differentiation process.

Examples found in repository

examples/example1.rs (line 28)

    pub fn to_other_ad_type<T2: AD>(&self) -> Test<T2> {
        Test { coeff: self.coeff.to_other_ad_type::<T2>() }
    }

Dyn Compatibility

This trait is not dyn compatible.

In older versions of Rust, dyn compatibility was called "object safety", so this trait is not object safe.

Implementations on Foreign Types

impl AD for f32

fn constant(v: f64) -> Self

fn to_constant(&self) -> f64

fn ad_num_mode() -> ADNumMode

fn ad_num_type() -> ADNumType

fn add_scalar(arg1: f64, arg2: Self) -> Self

fn sub_l_scalar(arg1: f64, arg2: Self) -> Self

fn sub_r_scalar(arg1: Self, arg2: f64) -> Self

fn mul_scalar(arg1: f64, arg2: Self) -> Self

fn div_l_scalar(arg1: f64, arg2: Self) -> Self

fn div_r_scalar(arg1: Self, arg2: f64) -> Self

fn rem_l_scalar(arg1: f64, arg2: Self) -> Self

fn rem_r_scalar(arg1: Self, arg2: f64) -> Self

fn mul_by_nalgebra_matrix<R: Clone + Dim, C: Clone + Dim, S: Clone + RawStorageMut<Self, R, C>>( &self, other: Matrix<Self, R, C, S>, ) -> Matrix<Self, R, C, S>

fn mul_by_nalgebra_matrix_ref<'a, R: Clone + Dim, C: Clone + Dim, S: Clone + RawStorageMut<Self, R, C>>( &'a self, other: &'a Matrix<Self, R, C, S>, ) -> Matrix<Self, R, C, S>

fn mul_by_ndarray_matrix_ref<D: Dimension>( &self, other: &ArrayBase<OwnedRepr<Self>, D>, ) -> ArrayBase<OwnedRepr<Self>, D>

impl AD for f64

fn constant(v: f64) -> Self

fn to_constant(&self) -> f64

fn ad_num_mode() -> ADNumMode

fn ad_num_type() -> ADNumType

fn add_scalar(arg1: f64, arg2: Self) -> Self

fn sub_l_scalar(arg1: f64, arg2: Self) -> Self

fn sub_r_scalar(arg1: Self, arg2: f64) -> Self

fn mul_scalar(arg1: f64, arg2: Self) -> Self

fn div_l_scalar(arg1: f64, arg2: Self) -> Self

fn div_r_scalar(arg1: Self, arg2: f64) -> Self

fn rem_l_scalar(arg1: f64, arg2: Self) -> Self

fn rem_r_scalar(arg1: Self, arg2: f64) -> Self

fn mul_by_nalgebra_matrix<R: Clone + Dim, C: Clone + Dim, S: Clone + RawStorageMut<Self, R, C>>( &self, other: Matrix<Self, R, C, S>, ) -> Matrix<Self, R, C, S>

fn mul_by_nalgebra_matrix_ref<'a, R: Clone + Dim, C: Clone + Dim, S: Clone + RawStorageMut<Self, R, C>>( &'a self, other: &'a Matrix<Self, R, C, S>, ) -> Matrix<Self, R, C, S>

fn mul_by_ndarray_matrix_ref<D: Dimension>( &self, other: &ArrayBase<OwnedRepr<Self>, D>, ) -> ArrayBase<OwnedRepr<Self>, D>

Implementors

impl AD for adr

impl<const N: usize> AD for adfn<N>