Preconditioner Design via Bregman Divergences

This package implements the preconditioners in [1]. A simple use case is demonstrated below. See examples/example_pcg.py for a demo of all the preconditioners defined in this package.

[1] TODO

Build Status

Installation

pip install scaled_preconditioners

A simple example

Define some parameters

dimension = 100
psd_rank = 50

Construct S = A + B

F = csc_matrix(np.random.rand(dimension, psd_rank))
B = F @ F.T
Q = csc_matrix(np.random.rand(dimension, dimension))
S = Q @ Q.T + B

Construct the preconditioner

Here we use a randomised SVD, other options include truncated SVD, the Nyström approximation. There is support for oversampling and power iteration schemes.

rank_approx = 15
pc = compute_preconditioner(
    Q,
    B,
    algorithm="randomized",
    rank_approx=rank_approx,
    n_oversamples=4,
    n_power_iter=0,
)

Set up a right-hand side

rhs = np.random.rand(dimension)
counter = ConjugateGradientCounter()

Solve Sx=b with and without a preconditioner

_, info = linalg.cg(S, rhs, callback=counter)
print("No preconditioner:")
print(f"\t Converged: {info == 0}")
print(f"\t Iterations: {counter.n_iter}\n")

counter.reset()
_, info = linalg.cg(S, rhs, M=rsvd_pc, callback=counter)
print("Randomised SVD preconditioner:")
print(f"\t Converged: {info == 0}")
print(f"\t Iterations: {counter.n_iter}\n")