Function solve_frule 

pub fn solve_frule<T: KernelLinalgScalar, C>(
    ctx: &mut C,
    a: &Tensor<T>,
    b: &Tensor<T>,
    tangent_a: &Tensor<T>,
    tangent_b: &Tensor<T>,
) -> AdResult<(Tensor<T>, Tensor<T>)>
where
    C: TensorLinalgContextFor<T> + TensorScalarContextFor<Standard<T>>,
    C::Backend: 'static,
    <C as TensorScalarContextFor<Standard<T>>>::ScalarBackend: 'static + TensorAnalyticPrims<Standard<T>, Context = C>,

Forward-mode AD rule for linear solve (JVP / pushforward).

Examples

use tenferro_linalg::solve_frule;
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>::eye(3, mem, col).unwrap();
let b = Tensor::<f64>::ones(&[3], mem, col).unwrap();
let da = Tensor::<f64>::ones(&[3, 3], mem, col).unwrap();
let db = Tensor::<f64>::ones(&[3], mem, col).unwrap();
let (x, dx) = solve_frule(&mut ctx, &a, &b, &da, &db).unwrap();