Multigrid and smoothing properties
Combine high-frequency damping with coarse correction rather than mistaking either operation for a complete solver.
On this page
The question for today
What you’ll understand
- Derive a weighted-Jacobi Fourier smoothing factor for the one-dimensional Poisson stencil.
- State the Galerkin coarse-correction projector in the SPD energy norm.
- Distinguish smoothing, approximation and mesh-independent full-cycle convergence.
A relaxation method can quickly remove oscillatory error and still converge slowly overall. Multigrid uses that imbalance: smooth on the fine grid, represent the remaining smooth error on a coarser grid and correct it there. The word smoothing describes one part of this mechanism. A complete convergence claim must also justify transfer operators, coarse approximation and the interaction of all cycle components.
Analyze error, not only the size of the residual
For the uniform one-dimensional Poisson matrix, a weighted-Jacobi update applies the inverse diagonal to the residual with a weight omega. Its error iteration is identity minus omega D inverse A. On a periodic mean-zero grid, or in local Fourier analysis away from boundaries, the eigenvalue on a frequency theta is one minus omega times(1−cos theta). The constant periodic mode must be removed because Poisson has a nullspace there. A separate SPD boundary-value system can then be used for the coarse-projector discussion.
With omega=2/3, frequencies from pi/2 to pi have amplification between−1/3 and 1/3, so one sweep damps that high-frequency band by at least a factor three in magnitude. Low frequencies have multipliers approaching one as the mesh is refined. Ordinary unweighted Jacobi at omega=1 gives multiplier−1 at theta=pi and does not damp that highest periodic mode at all. Merely naming Jacobi without its damping weight can therefore obscure the smoothing property the multigrid argument needs.
Coarse correction removes a representable error component
For an SPD fine-grid matrix A and a full-column-rank prolongation P, define the Galerkin coarse matrix as P transpose A P. Solve its residual equation exactly and prolong the correction. The resulting fine-grid error operator is identity minus P times the coarse inverse times P transpose A. This is the A-orthogonal projector onto the complement of the coarse space, so it removes errors in the range of P and cannot increase the A-energy norm. It does not remove arbitrary fine-grid error in one step.
The coarse space must approximate what the smoother leaves behind. For a regular elliptic problem with appropriate nested grids and transfers, smooth fine-grid errors can often be represented cheaply on the next level. Strong anisotropy or discontinuous coefficients may violate this simple picture and require line smoothers, altered interpolation or another coarse space. An exact Galerkin solve supplies a clean projector identity, but an approximate recursive coarse solve adds another error operator that belongs in the full analysis.
A full cycle needs complementary estimates
The two-grid error propagator combines presmoothing, coarse correction and postsmoothing in their actual order. A bound strictly below one in the energy norm, uniform over mesh sizes, yields mesh-independent contraction. Obtaining that bound requires smoothing and approximation estimates that complement each other; neither the high-frequency factor nor the coarse projector’s nonexpansiveness alone gives it. The same care is needed when recursive coarse solves are used to form V or W cycles.
Work estimates also have assumptions. For geometrically shrinking grids and a bounded amount of work per degree of freedom per level, one cycle can cost order the number of fine-grid unknowns. A uniform contraction then leads to an efficient solve to a stated tolerance, but a smoother that stalls or a poor coarse space can erase that advantage. Report the norm, problem class, transfers and stopping rule alongside timing claims. The worked calculation verifies smoothing of selected modes; it intentionally does not convert three favorable multipliers into a theorem for an arbitrary multigrid hierarchy.
Work it through
Three Poisson frequencies under damped Jacobi
For omega=2/3, compute the error multiplier at theta=pi/6,2pi/3,pi, and the remaining amplitude after three sweeps from a unit-amplitude mode. A periodic grid with twelve points represents all three.
At theta=pi/6, the multiplier is . Three sweeps leave amplitude approximately 0.755271.
At theta=2pi/3, cos theta=−1/2, so the multiplier is 1−(2/3)(3/2)=0. This mode is removed in one ideal arithmetic sweep.
At theta=pi, the multiplier is 1−(2/3)·2=−1/3. Three sweeps leave signed amplitude−1/27 and magnitude 1/27≈0.037037, much smaller than the low-frequency remainder.
Damped Jacobi strongly suppresses the tested high frequencies while leaving most of the low-frequency error, motivating coarse correction.
The living notebook
Explore the model
Interactive lab · heat
A continuum analogy for frequency-selective smoothing
Pause and Reset, then set spatial mode n = 1 and diffusivity kappa = 0.50. After one Step to t=0.5, predict amplitude e⁻⁰⋅²⁵≈0.7788. Reset and set both n = 4 and kappa = 0.50, then Step once: predict e⁻⁴≈0.0183. This exact heat-flow model illustrates preferential damping of fine scales; it is not a Jacobi sweep, a residual equation or a complete multigrid cycle.
- Current amplitude
- 1
- Spatial L² norm
- 1.772
- Decay rate · κn²
- 3.6 /s
The exact periodic heat-equation mode is u(x,t) = exp(−κn²t) sin(nx), on [0,2π]. Playback advances physical time. The initial sine is shown separately, and the unnormalized L² norm is √π exp(−κn²t). This analytic instrument does not run a finite-difference solver or test discretization stability. Parameter changes pause playback and recompute the model at the current elapsed time. Reset restores both parameters and returns time to zero. Playback starts paused. Adjust either parameter, then choose Play or Step.
Look forFast removal of high frequencies leaves a coarse-scale error component that a multigrid method must represent and correct.
Pause & reason
Check your understanding
Your turn
Try it yourself
Compare omega=1 and omega=2/3 at theta=pi. For an SPD coarse correction, what happens to e=Pz, and why does this not settle the rate on every error vector?
Take a moment to make a prediction or work it out on paper. Open a hint when you need one.
Give me a hint
Evaluate the Jacobi multiplier at cos pi=−1, then multiply the coarse-correction operator by P.
Compare with the solution
At omega=1 the multiplier is−1, so the highest periodic error alternates without damping. At omega=2/3 it is−1/3, so the magnitude is reduced by three per sweep. For e=Pz, the Galerkin correction gives . Errors outside the coarse range are not automatically removed, and components poorly damped by the smoother must be approximated effectively by that range. Thus these two individual observations describe complementary mechanisms, while a full uniform convergence rate still requires an estimate for their composed error operator in the stated energy norm.
Keep the idea
Take it with you
- Smoothing is frequency-selective and depends on the relaxation weight.
- Galerkin correction is an energy-orthogonal projector under SPD and exact-solve assumptions.
- Mesh-independent full-cycle contraction requires compatible smoothing, approximation and transfer properties.
Read further
Original sources and deeper explanations to follow at your own pace.
Optional progress, saved in this browser when available. Revisit any lesson at any time.
Lesson not marked complete.