# 2021-10-20 Krylov methods¶

## Last time¶

• Why iterative solvers

• Stationary iterative methods (Richardson)

## Today¶

• Limitations of stationary iterative methods

• Preconditioning as a concept

• Krylov methods (GMRES)

• Experiments with preconditioners and diagnostics in PETSc

using Plots
using LinearAlgebra
using SparseArrays

default(linewidth=4)

function advdiff_matrix(n; kappa=1, wind=[0, 0])
"Advection-diffusion with Dirichlet boundary conditions eliminated"
h = 2 / (n + 1)
rows = Vector{Int64}()
cols = Vector{Int64}()
vals = Vector{Float64}()
idx((i, j),) = (i-1)*n + j
in_domain((i, j),) = 1 <= i <= n && 1 <= j <= n
stencil_advect = [-wind, -wind, 0, wind, wind] / h
stencil_diffuse = [-1, -1, 4, -1, -1] * kappa / h^2
stencil = stencil_advect + stencil_diffuse
for i in 1:n
for j in 1:n
neighbors = [(i-1, j), (i, j-1), (i, j), (i+1, j), (i, j+1)]
end
end
sparse(rows, cols, vals)
end

function my_spy(A)
cmax = norm(vec(A), Inf)
s = max(1, ceil(120 / size(A, 1)))
spy(A, marker=(:square, s), c=:diverging_rainbow_bgymr_45_85_c67_n256, clims=(-cmax, cmax))
end

"""Minimize loss(c) via gradient descent with initial guess u0
using learning rate gamma.  Declares convergence when gradient
is less than tol or after 500 steps.
"""
u = copy(u0)
uhist = [copy(u)]
lhist = [loss(u)]
for it in 1:500
u -= omega * g
push!(uhist, copy(u))
push!(lhist, loss(u))
if norm(g) < tol
break
end
end
(u, hcat(uhist...), lhist)
end

grad_descent (generic function with 1 method)


# Visualize gradient descent¶

A = [1 1; 1 16]
loss(u) = .5 * u' * A * u
grad(u) = A * u
u, uhist, lhist = grad_descent(loss, grad, [.9, .9],
omega=.1)
plot(lhist, yscale=:log10) plot(uhist[1, :], uhist[2, :], marker=:circle)
x = LinRange(-1, 1, 30)
contour!(x, x, (x,y) -> loss([x, y])) # Richardson iteration¶

The simplest iterative method is Richardson’s method, which solves $$A u = b$$ by the iteration

$u_{k+1} = u_k + \omega (b - A u_k)$
where $$\omega > 0$$ is a damping parameter and $$u_0$$ is an initial guess (possibly the zero vector).

• Algebraically equivalent to gradient descent when $$A$$ is SPD

• Non-symmetric matrices are harder to visualize

# Richardson convergence¶

$u_{k+1} = u_k + \omega (b - A u_k)$

If $$b = A u_*$$, this iteration is equivalent to

(33)\begin{align} u_{k+1} - u_* &= (u_k - u_*) - \omega A (u_k - u_*) \\ &= (I - \omega A) (u_k - u_*) . \end{align}

It is convenient for convergence analysis to identify the “error” $$e_k = u_k - u_*$$, in which this becomes

$e_{k+1} = (I - \omega A) e_k$
or
$e_k = (I - \omega A)^k e_0$
in terms of the initial error. Evidently powers of the iteration matrix $$I - \omega A$$ tell the whole story.

Suppose that the eigendecomposition

$X \Lambda X^{-1} = I - \omega A$
exists. Then
$(I - \omega A)^k = (X \Lambda X^{-1})^k = X \Lambda^k X^{-1}$
and the convergence (or divergence) rate depends only on the largest magnitude eigenvalue. This analysis isn’t great for two reasons:

1. Not all matrices are diagonalizable.

2. The matrix $$X$$ may be very ill-conditioned.

A = [1 1e-10; 0 1]
L, X = eigen(A)
#cond(X)

Eigen{Float64, Float64, Matrix{Float64}, Vector{Float64}}
values:
2-element Vector{Float64}:
1.0
1.0
vectors:
2×2 Matrix{Float64}:
1.0  -1.0
0.0   2.22045e-6


# Aside: Schur decomposition¶

We can repair these weaknesses by using the Schur decomposition

$Q R Q^* = I - \omega A$
where $$R$$ is right-triangular and $$Q$$ is unitary (i.e., orthogonal if real-valued; $$Q^*$$ is the Hermitian conjugate of $$Q$$). The Schur decomposition always exists and $$Q$$ has a condition number of 1.

• Where are the eigenvalues in $$R$$?

Evidently we must find $$\omega$$ to minimize the maximum eigenvalue of $$I - \omega A$$. We can do this if $$A$$ is well conditioned, but not in general.

# Ill-conditioning¶

## Question: What is the condition number of the Laplacian on 100 evenly spaced points?¶

• How does it scale under grid refinement?

A = advdiff_matrix(20)
cond(Matrix(A))

178.06427461085948

omega = .008 / 4
ev = eigvals(Matrix(I - omega * A))
scatter(real.(ev), imag.(ev)) # Monic polynomials small on the spectrum¶

Equivalently to finding $$\omega$$ such that $$\lVert I - \omega A \rVert$$ is minimized, we may seek a monic polynomial $$p(z) = 1 - \omega z$$ that minimizes

$\max_{\lambda \in \sigma(A)} \lvert p(\lambda) \rvert .$

This concept can be extended to higher degree polynomials, which is essentially what Krylov methods do (discovering the polynomial adaptively, weighted by the right hand side).

ev = eigvals(Matrix(A))
scatter(real.(ev), zero.(ev))
plot!(x -> 1 - omega * x) # Preconditioning¶

Preconditioning is the act of creating an “affordable” operation “$$P^{-1}$$” such that $$P^{-1} A$$ (or $$A P^{-1}$$) is is well-conditoned or otherwise has a “nice” spectrum. We then solve the system

$P^{-1} A x = P^{-1} b \quad \text{or}\quad A P^{-1} \underbrace{(P x)}_y = b$

in which case the convergence rate depends on the spectrum of the iteration matrix

$I - \omega P^{-1} A .$

• The preconditioner must be applied on each iteration.

• It is not merely about finding a good initial guess.

There are two complementary techniques necessary for efficient iterative methods:

• “accelerators” or Krylov methods, which use orthogonality to adaptively converge faster than Richardson

• preconditioners that improve the spectrum of the preconditioned operator

Although there is ongoing research in Krylov methods and they are immensely useful, I would say preconditioning is 90% of the game for practical applications, particularly as a research area.

# Krylov subspaces¶

All matrix iterations work with approximations in a Krylov subspace, which has the form

$K_n = \big[ b \big| Ab \big| A^2 b \big| \dotsm \big| A^{n-1} b \big] .$

This matrix is horribly ill-conditioned and cannot stably be computed as written. Instead, we seek an orthogonal basis $$Q_n$$ that spans the same space as $$K_n$$. We could write this as a factorization

$K_n = Q_n R_n$

where the first column $$q_0 = b / \lVert b \rVert$$. The $$R_n$$ is unnecessary and hopelessly ill-conditioned, so a slightly different procedure is used.

# Arnoldi iteration¶

The Arnoldi iteration applies orthogonal similarity transformations to reduce $$A$$ to Hessenberg form, starting from a vector $$q_0 = b$$,

$A = Q H Q^* .$

Let’s multiply on the right by $$Q$$ and examine the first $$n$$ columns,

$A Q_n = Q_{n+1} H_n$

where $$H_n$$ is an $$(n+1) \times n$$ Hessenberg matrix.

# Aside: Conditioning of Arnoldi process¶

The Arnoldi process is well-conditioned because $$Q$$ is orthogonal and

\begin{align}\begin{aligned} \lVert H_n \rVert \le \lVert Q_{n+1}^* \rVert \lVert A \rVert \lVert Q_n \rVert \le \lVert A \rVert .\\For a lower bound, we have\\ \sigma_{\min}(A)^2 \le x^* A^* A x \end{aligned}\end{align}

for all $$x$$ of norm 1. It must also be true for any $$x = Q_n y$$ where $$\lVert y\rVert = 1$$, thus

$\sigma_{\min}(A)^2 \le y^* Q_n^* A^* A Q_n y = y^* H_n^* Q_{n+1}^* Q_{n+1} H_n y = y^* H_n^* H_n y .$

# GMRES¶

$A Q_n = Q_{n+1} H_n$

GMRES (Generalized Minimum Residual) minimizes

$\lVert A x - b \rVert$
over the subspace $$Q_n$$. I.e., $$x = Q_n y$$ for some $$y$$. By the Arnoldi recurrence, this is equivalent to
$\lVert Q_{n+1} H_n y - b \lVert$
which can be solved by minimizing
$\lVert H_n y - Q_{n+1}^* b \rVert .$
Since $$q_0 = b/\lVert b \lVert$$, the least squares problem is to minimize
$\Big\lVert H_n y - \lVert b \rVert e_0 \Big\rVert .$
The solution of this least squares problem is achieved by incrementally updating a $$QR$$ factorization of $$H_n$$.