Function eigen_rrule 

pub fn eigen_rrule<T, C>(
    ctx: &mut C,
    tensor: &Tensor<T>,
    cotangent: &EigenCotangent<T, T::Real>,
) -> AdResult<Tensor<T>>
where
    T: KernelLinalgScalar + Conjugate,
    T::Real: KernelLinalgScalar<Real = T::Real> + Float,
    C: TensorLinalgContextFor<T>,
    C::Backend: 'static,

Reverse-mode AD rule for eigendecomposition (VJP / pullback).

Examples

use tenferro_linalg::{eigen_rrule, EigenCotangent};
use tenferro_prims::CpuContext;
use tenferro_tensor::{Tensor, MemoryOrder};
use tenferro_device::LogicalMemorySpace;

let col = MemoryOrder::ColumnMajor;
let mem = LogicalMemorySpace::MainMemory;
let mut ctx = CpuContext::new(1);
let a = Tensor::<f64>::zeros(&[3, 3], mem, col).unwrap();
let cotangent = EigenCotangent {
    values: Some(Tensor::ones(&[3], mem, col).unwrap()),
    vectors: None,
};
let grad_a = eigen_rrule(&mut ctx, &a, &cotangent).unwrap();