PyGMRES 0.1.0

A python implementation of the GMRES algorithm

PyGMRES

An all-python implementation of the GMRES algorithm. Numba acceleration is available (and enabled by default).

Using PyGMRES

The GMRES solves the linear b = A(x) using:

PyGMRES.gmres.GMRES(A, b, x0, e, max_iter, restart=None, debug=False)

where A is a linear operator, x0 is the initial guess for the solution x, e > |A(x) - b| is the tolerance, max_iter is the maximum number of iterations -- if GMRES does not converge on a solution, it is stopped after max_iter.

The PyGMRES solver GMRES can be run in two modes:

1. Regular mode: when restart = None the full Krylov subspace is used to solve the problem. This mode is guaranteed to find a solution (if one exists), but scales poorly.
2. Restarted mode: when restart = N then the Krylov subspace used by the solver is rebuilt every N steps. This mode is not guaranteed to find a solution for certain intial conditions, but scales well.

Discrete Laplace Operators

PyGMRES provides implementations of the 1D and 2D discrete laplace operators in the PyGMRES.linop module:

1. 1D laplacian: laplace_1d, laplace_1d_dirichlet, laplace_1d_constextrap, and laplace_1d_extrap which provide periodic, dirichlet, constant extrapolation, and linear extrapolation respectively.
2. 2D laplacian: laplace_2d, laplace_2d_dirichlet, laplace_2d_constextrap, and laplace_2d_extrap which provide periodic, dirichlet, constant extrapolation, and linear extrapolation respectively.

Numba Compiler

The PyGMRES.compiler module provides the standard Numba JIT compiler decorator. All operators and the GMRES solver are compiled when their host modules are first imported. The compiler can be enabled, or disabled using the PyGMRES.compiler.RC class. For example this will enable compilation (on by default):

from PyGMRES.compiler import RC
RC().enable_numba = True

Ance any module compiles any of its functions, RC().enable_numba cannot be changed. RC() is intended for those situations where Numba cannot be installed on a target system. If Numba is enabled, the originial (non-compiled) functions are avaialable using get_undecorated_fn(name), for example:

from PyGMRES.compiler import get_undecorated_fn
laplace_1d_uncompiled = get_undecorated_fn("laplace_1d_dirichlet")

Helper Functions

PyGMRES preovides helper functions for common tasks

Computing the Residuals

PyGMRES.linop.resid computes the residual A(x) - b, for example

from PyGMRES.linop import laplace_2d_extrap, resid
r_laplace_2d = resid(A, x, b)

Example

The following code solved the 2D Laplace equation with von Neumann (constant derivative) boundary conditions, along with the residuals of each iteration:

import numpy  as np
from PyGMRES.gmres import GMRES
from PyGMRES.linop import laplace_2d_extrap, resid

# Use odd dimension because we want to put a point-source in the center.
Nx = 81
Ny = 81

# Set up the RHS
b = np.zeros((Nx, Ny))
b[int(Nx/2), int(Ny/2)] = 1
x0 = np.zeros_like(b)

# Tolerance of the funal results
tol = 1e-10
# Note: here we chose nmax_iter to be << N[x,y] -- if not, then this is
# basically the non-restarted version of GMRES
nmax_iter = 10
restart   = 64
# Run GMRES in debug mode => intermediate solutions are returned as an array
x_laplace_2d = GMRES(
    laplace_2d_extrap, b, x0, tol, nmax_iter, restart=restart, debug=True
)

# Compute the residuals
r_laplace_2d = resid(laplace_2d_extrap, x_laplace_2d, b)