# pyamg.relaxation#

Relaxation methods.

The multigrid cycle is formed by two complementary procedures: relaxation and coarse-grid correction. The role of relaxation is to rapidly damp oscillatory (high-frequency) errors out of the approximate solution. When the error is smooth, it can then be accurately represented on the coarser grid, where a solution, or approximate solution, can be computed.

Iterative methods for linear systems that have an error smoothing property are valid relaxation methods. Since the purpose of a relaxation method is to smooth oscillatory errors, its effectiveness on non-oscillatory errors is not important. This point explains why simple iterative methods like Gauss-Seidel iteration are effective relaxation methods while being very slow to converge to the solution of Ax=b.

- PyAMG implements relaxation methods of the following varieties:
Jacobi iteration

Gauss-Seidel iteration

Successive Over-Relaxation

Polynomial smoothing (e.g. Chebyshev)

Jacobi and Gauss-Seidel on the normal equations (A.H A and A A.H)

Krylov methods: gmres, cg, cgnr, cgne

No pre- or postsmoother

Refer to the docstrings of the individual methods for additional information.