Preconditioner strategies

1. Relaxation

Split into lower, diagonal, upper parts: $A = L + D + U$ .

1.1. Jacobi

Cheapest preconditioner: $P^{-1}=D^{-1}$ .

# sequential
pc-type=jacobi
# parallel
pc-type=block_jacobi

bash

1.2. Successive over-relaxation (SOR)

$\left(L + \frac 1 \omega D\right) x_{n+1} = \left[\left(\frac 1\omega-1\right)D - U\right] x_n + \omega b \\ P^{-1} = \text{$k$ iterations starting with $x_0=0$}$

Implemented as a sweep.
$\omega = 1$ corresponds to Gauss-Seidel.
Very effective at removing high-frequency components of residual.

# sequential
pc-type=sor

bash

2. Factorization

Two phases

symbolic factorization: find where fill occurs, only uses sparsity pattern.
numeric factorization: compute factors.

2.1. LU decomposition

preconditioner.
Expensive, for $m\times m$ sparse matrix with bandwidth $b$ , traditionally requires $\mathcal{O}(mb^2)$ time and $\mathcal{O}(mb)$ space.
- Bandwidth scales as $m^{\frac{d-1}{d}}$ in d-dimensions.
- Optimal in 2D: $\mathcal{O}(m \cdot \log m)$ space, $\mathcal{O}(m^{3/2})$ time.
- Optimal in 3D: $\mathcal{O}(m^{4/3})$ space, $\mathcal{O}(m^2)$ time.
Symbolic factorization is problematic in parallel.

2.2. Incomplete LU

Allow a limited number of levels of fill: ILU( $k$ ).
Only allow fill for entries that exceed threshold: ILUT.
Usually poor scaling in parallel.
No guarantees.

3. 1-level Domain decomposition

Domain size $$L$$, subdomain size $$H$$, element size $$h$$

Overlapping/Schwarz
- Solve Dirichlet problems on overlapping subdomains.
- No overlap: $\textit{its} \in \mathcal{O}\big( \frac{L}{\sqrt{Hh}} \big)$ .
- Overlap $\delta: \textit{its} \in \big( \frac L {\sqrt{H\delta}} \big)$ .
Neumann-Neumann
- Solve Neumann problems on non-overlapping subdomains.
- $\textit{its} \in \mathcal{O}\big( \frac{L}{H}(1+\log\frac H h) \big)$ .
- Tricky null space issues (floating subdomains).
- Need subdomain matrices, not globally assembled matrix.

Multilevel variants knock off the leading

$\frac L H$ .
Both overlapping and nonoverlapping with this bound.

BDDC and FETI-DP
- Neumann problems on subdomains with coarse grid correction.
- $\textit{its} \in \mathcal{O}\big(1 + \log\frac H h \big)$ .

4. Multigrid

4.1. Introduction

Hierarchy: Interpolation and restriction operators $\Pi^\uparrow : X_{\text{coarse}} \to X_{\text{fine}} \qquad \Pi^\downarrow : X_{\text{fine}} \to X_{\text{coarse}}$

Geometric: define problem on multiple levels, use grid to compute hierarchy.
Algebraic: define problem only on finest level, use matrix structure to build hierarchy.

Galerkin approximation

Assemble this matrix: $A_{\text{coarse}} = \Pi^\downarrow A_{\text{fine}} \Pi^\uparrow$

Application of multigrid preconditioner ( $V$ -cycle)

Apply pre-smoother on fine level (any preconditioner).
Restrict residual to coarse level with $\Pi^\downarrow$ .
Solve on coarse level $A_{\text{coarse}} x = r$ .
Interpolate result back to fine level with \Pi^\uparrow.
Apply post-smoother on fine level (any preconditioner).

4.2. Multigrid convergence properties

Textbook: $P^{-1}A$ is spectrally equivalent to identity
- Constant number of iterations to converge up to discretization error.
Most theory applies to SPD systems
- variable coefficients (e.g. discontinuous): low energy interpolants.
- mesh- and/or physics-induced anisotropy: semi-coarsening/line smoothers.
- complex geometry: difficult to have meaningful coarse levels.
Deeper algorithmic difficulties
- nonsymmetric (e.g. advection, shallow water, Euler).
- indefinite (e.g. incompressible flow, Helmholtz).
Performance considerations
- Aggressive coarsening is critical in parallel.
- Most theory uses SOR smoothers, ILU often more robust.
- Coarsest level usually solved semi-redundantly with direct solver.
Multilevel Schwarz is essentially the same with different language
- assume strong smoothers, emphasize aggressive coarsening.