mirtorch.alg.CG
- class mirtorch.alg.CG(A, max_iter=20, tol=0.01, P=None, alert=False, eval_func=None)
Solve the equation \(Ax = b\) with the conjugate gradient method, where A is a PSD matrix. The backpropagation still calls the CG to calculate the Jacobian to save the memory.
- A
LinearMap of a PSD matrix
- tol
float, exiting tolerance
- max_iter
int, max number of iterations
- alert
bool, print the norm of residuals at the end
- eval_func
user-defined function to calculate the loss at each iteration.
- P
LinearMap of a Preconditioner
- run()
run the CG algorithm
- __init__(A, max_iter=20, tol=0.01, P=None, alert=False, eval_func=None)
Methods
__init__
(A[, max_iter, tol, P, alert, eval_func])run
(x0, b)Run the CG iterations.