Numerical Linear Algebra
where the algebra stops being the arithmetic
Concept

Anisotropy — where it appears

A coupling much stronger in one direction than another, which breaks a point smoother and dictates how a hierarchy must coarsen. It is the model problem's one knob for difficulty, and every remedy for it — line relaxation, semi-coarsening, aggregation — is a statement about which direction is strong.

Named by 12 essays across 3 fields — each of them below, with the objects they name alongside it.

10⁻⁶10⁻⁵10⁻⁴10⁻³10⁻²10⁻¹10112233445566relative drift ‖E‖ / ‖A₀‖preconditioned conjugate gradient iterationson the small eigenvalueson the large onessame drift, two placessmall end at 10⁻²38large end at 10⁻²9κ(M⁻¹A), small end36κ(M⁻¹A), large end1.3‖E‖/‖A₀‖, both0.01how much the matrix changedis not what the preconditioner cares about

Where the drift lands

The standing rule for when a preconditioner has gone stale is to rebuild it once the matrix has changed by more than some fraction of itself. Two drifts of exactly the same relative size cost 19 iterations and 5 on the same matrix, and the quantity that separates them is not in the rule at all — the perturbation is divided by the eigenvalue it lands on.

sequence · Reuse 0246810121416182001020304050member of the sequencepreconditioned conjugate gradient iterationskept from the first memberrebuilt every memberone operator, two policieskept, first member18kept, last member52rebuilt, first member18rebuilt, last member13ε at the last member0.033the problem got easierand the kept preconditioner got worse at it

The penalty for keeping it is a ratio

A kept incomplete Cholesky costs 40 iterations against a rebuilt one's 10 on 64 unknowns, and 55 against 17 on 256. Across six grids the difference between the two rises by 27 per cent and the ratio between them falls by 19. Neither quantity is free of the problem's size, and the one a policy is paid in is the one that transfers worse.

sequence · Reuse θx (frequency across x)θy0π/2π0π/2πthe coarse grid'sunder 0.2under 0.40under 0.60under 0.80under 0.95under 1.01damping per sweeptwo routessmoothing factor, scanned1closed form130×30 frequency cellsthe marker is the mode nothing removes

A direction the smoother cannot see

Give the Laplacian a strong direction and multigrid stops working — from 0.2016 a cycle to 0.9565 — with every component unchanged and the condition number identical to twelve digits. The problem did not get harder. The link between the method's two halves broke.

iterative · Anisotropy 02468101210⁻¹⁶10⁻¹³10⁻¹⁰10⁻⁷10⁻⁴10⁻¹singular value, in orderσ ⁄ σ₁eight digitsrenumberedin the separator's orderthe same cliff, from the other endσ₂ ⁄ σ₁0.062σ₄ ⁄ σ₁1.3·10⁻⁴σ₆ ⁄ σ₁1.5·10⁻⁸renumbered σ₄ ⁄ σ₁0.63rank at 10⁻⁸6the same entriesin two orders

The cliff behind the count

The fill's rank is an integer between three and six across every separator two dense half-eliminations can afford, and this field has already recorded that a handful of such integers cannot carry a law. The singular values underneath are real numbers. They say the cliff's first step is 23.0 at a separator of eleven, 19.1 at fifteen and 16.2 at twenty-three — and that a control with no differential operator behind it gives 14,672.

sparsity · Fill 10⁻⁴10⁻³10⁻²10⁻¹100.250.50.751anisotropy εsmoothing factor μpoint + fully-line + fullpoint + semi-y⅓, the one-dimensional answertwo routes, three curvesgap between the repairs2.2·10⁻¹⁶scan against closed form2.2·10⁻¹⁶the dashed curve lies on the solid one beneath itone repair, written two ways

Smoothing a whole line at once

Solve every grid line in the strong direction exactly rather than sweeping over it, and the smoothing factor goes from 0.9993 back to 0.3340 — which is the one-dimensional answer, on a problem that is not one-dimensional. The repair replaces one ε in the closed form by a one.

iterative · Anisotropy point · strong in y0.951point · strong in x0.956y-line · strong in y0.037y-line · strong in x0.967semi-y · strong in y0.107semi-y · strong in x0.9661.00 — no convergenceone problem, seen from two sidesy-line, aimed0.037y-line, turned sideways0.97the ratio2631×31 grid, twelve V-cyclesthe direction is in the code, not in the problem

Coarsening in one direction only

Leave the smoother alone and halve only the strong direction, and the smoothing factor is 0.3340 — identical to line relaxation's, at every anisotropy and every weight, to twelve digits. The convergence factors are then a factor of three apart, and at 45° both repairs fail outright.

iterative · Anisotropy the grid the operator came froman edge is a coupling the matrix calls strongkeptinterpolatedwhat the entries decidedcoupling ratio, x against y0.001strong couplings across x0strong couplings along y110rows kept or dropped whole1111×11 grid, θ = 0.25the coarse grid, from the matrix alone

The coarse grid the matrix chooses

Given a tridiagonal matrix and no information about a grid, the coarsening keeps every other point and derives the weights ½, 1, ½ — the operators the geometric method was handed. Given the anisotropic operator, it discovers semi-coarsening, in the right direction, without a coordinate.

iterative · Algebraic multigrid 081624324000.250.50.7511.25angle of the strong direction (degrees)ratio, and convergence factorenergy ratio of oneenergy ratioclassicalminimiserwith the constraintwhere the formula is optimalenergy ratio at 0°1energy ratio at 45°1.2classical rate at 45°0.35the minimiser's rate there0.28a ratio of exactly one while the assumption holdsand a diagnostic when it stops

The formula that was already optimal

Ask for the interpolation that minimises the energy of its own columns and the answer is the classical AMG formula — to zero at every row of the one-dimensional Laplacian, and to four digits in two dimensions. On the operator rotated to 45° the two part company, and the gap between them is a diagnostic that needs no reference solution.

iterative · Algebraic multigrid 75 aggregates over 225 unknownsthe matrix chose thismean extent along y3mean extent along x1points adopted by pass two15no coordinate enters the methodand the shape follows the coupling

Aggregating what the matrix calls strong

The depth phase measured every method it had on the 45°-rotated anisotropic operator — 0.784, 0.883, 0.844 — and diagnosed the failure as being in the discretisation rather than in the hierarchy. Smoothed aggregation is the standard answer to anisotropy. It returns 0.789.

iterative · Anisotropy 05101520253035404510⁻¹⁷10⁻¹⁴10⁻¹¹10⁻⁸10⁻⁵10⁻²angle between the flow and the grid (degrees)worst nodal errortunedcentralupwindexact, and then nottuned, on the axis2.4·10⁻¹⁷tuned, five degrees off0.0079tuned at 45°0.066upwind at 45°0.0019fifteen orders of magnitude for five degreesand the worst of the three by forty-five

Exact along one axis

The tuned diffusion makes the answer exact at every node, and in two dimensions it holds at exactly one flow angle. Five degrees off the grid the relative error goes from 1.2·10⁻¹⁴ to 6.9, and by twenty degrees the scheme is worse than the upwinding it was built to improve on.

iterative · Convection -213284300.250.50.751rotation of the anisotropy (degrees)factor / couplingusableconvergence factoraxis couplingdiagonal couplingthe standard answer, and the anglefactor at 0°0.19factor at 45°0.8axis ÷ diagonal coupling at 45°2the hierarchy reads the matrixand the matrix lost the direction

How much direction there was to lose

At 45° the nine-point stencil hands smoothed aggregation the same wrong hierarchy at every anisotropy — six strong neighbours per interior point, 121 aggregates, the identical partition from ε = 10⁻⁴ to 0.099. The convergence factor that one hierarchy produces runs from 0.802 to 0.581 over the same range.

iterative · Anisotropy 10⁻¹100.20.40.60.8strength threshold θresidual reduction per cycleθ = εsemi-coarseningkept whole rowsfull coarseningone parameter, two methodsbest factor above ε0.053best factor below ε0.1the ratio across the switch231×31 anisotropic operatora switch, not a dial

The switch does not know which side is better

The strength threshold moves the coarsening from full to semi at θ = ε exactly, at every anisotropy. Which of the two converges faster is a separate question with a separate answer, and it changes sign between ε = 0.33 and ε = 0.34 — where nothing whatever happens to the switch.

iterative · Algebraic multigrid

Named alongside it

The objects these essays reach for when they reach for this one.

Strength of connectionAlgebraic multigridSemi-coarseningCondition numberGalerkin coarse operatorOperator complexityRotated anisotropySmoothing factorWeighted jacobiCholesky factorisationCoarse fine splittingConjugate gradients

All concepts