research36 min studyLesson 836 in Mathematics

Bounded variation and distributional gradients

Measure jumps and diffuse derivatives in an almost-everywhere function class.

On this page

What you’ll understand

  • Define BV through a finite distributional gradient measure and total variation.
  • Compute signed jump derivatives and distinguish relative from zero-extended variation.
  • Explain why pointwise representatives and classical derivatives do not determine all variation.

A function can have a derivative that is a measure rather than an integrable density. Jump discontinuities are the simplest example: their changes are concentrated at points in one dimension or along surfaces in higher dimensions. The BV class retains those changes while still treating functions that agree almost everywhere as the same object. A piecewise constant example will show why a zero classical derivative almost everywhere does not imply zero distributional variation.

The derivative is defined by testing against divergence

Let Omega be open and let u be integrable there. We say u belongs to BV when its distributional gradient is a finite vector-valued Radon measure. Integration by parts defines that measure using compactly supported smooth vector fields, so no boundary trace of u is inserted at the edge of Omega. The total variation measure records the size of the vector measure without cancelling opposite directions. Its mass is the BV seminorm.

Equivalently, total variation is the supremum of the divergence pairing over compactly supported test fields with pointwise norm at most one. A smooth function has gradient measure equal to its ordinary gradient times Lebesgue measure, and its variation is the integral of gradient magnitude. General BV functions may also have singular gradient components. The definition concerns an L1 equivalence class, so changing a value on a null set does not change either its derivative measure or its variation.

∫Ωu div⁡φ dx=−∫Ωφ⋅dDu,∣Du∣(Ω)=sup⁡φ∈Cc1(Ω;Rn)∣φ∣≤1∫Ωudiv⁡φ dx.\int_\Omega u\,\operatorname{div}\varphi\,dx=-\int_\Omega\varphi\cdot dDu,\qquad |Du|(\Omega)=\sup_{\substack{\varphi\in C_c^1(\Omega;\mathbb R^n)\\|\varphi|\le1}}\int_\Omega u\operatorname{div}\varphi\,dx.
Compact support defines relative variation inside Ω; it does not automatically include jumps across its exterior boundary.↔ Scroll wide equations horizontally.

Jump, absolutely continuous, and diffuse singular parts differ

In one dimension a jump from a left value to a right value contributes their signed difference as a Dirac mass. The variation uses its absolute magnitude, so a rise and a later fall both count. A function with zero classical derivative off finitely many jumps may therefore have positive variation. A Sobolev W1,1 function instead has an absolutely continuous distributional gradient; jump masses exclude membership in that class on an interval containing the jumps.

Not every singular BV derivative is a jump. The Cantor function gives a continuous example whose derivative is singular and diffuse, illustrating why a jump-only description is incomplete. Fine BV theory uses approximate limits and one-sided traces rather than arbitrary values written on a jump surface. Those representative conventions matter when composing or tracing functions, but they do not change the underlying distributional derivative of an almost-everywhere class.

Du=∇u Ln+Dsu,Dsu=Dju+Dcu.Du=\nabla u\,\mathcal L^n+D^su,\qquad D^su=D^ju+D^cu.
The singular part can include both jumps and a diffuse Cantor part; the decomposition requires the standard BV representative theory.↔ Scroll wide equations horizontally.

Level sets and approximation explain compactness

The BV coarea identity expresses variation as the integral of the relative perimeters of superlevel sets. For almost every level, these sets have finite perimeter. This is distinct from the Lipschitz coarea formula: here the gradient is a measure and the perimeter integrand can detect jumps. Values assigned on a null set do not change superlevel sets up to null sets, consistent with the equivalence-class convention.

Lower semicontinuity says that an L1-convergent sequence cannot produce more variation in its limit than its limiting lower variation bound. On bounded Lipschitz domains, uniformly bounded L1 norms and variations give L1 subsequential compactness. Local forms use compact containment and local bounds. When extending a BV function by zero beyond its domain, boundary trace jumps may add variation, so relative and full-space compactness estimates should not be interchanged without checking the extension.

∣Du∣(Ω)=∫RP({u>t};Ω) dt,uj→u in L1⟹∣Du∣(Ω)≤lim inf⁡j∣Duj∣(Ω).|Du|(\Omega)=\int_{\mathbb R}P(\{u>t\};\Omega)\,dt,\qquad u_j\to u\text{ in }L^1\Longrightarrow |Du|(\Omega)\le\liminf_j|Du_j|(\Omega).
Perimeters and variations here are relative to the same open domain Ω.↔ Scroll wide equations horizontally.

Signed jumps with zero classical derivative almost everywhere

On Ω=(−2,3)\Omega=(-2,3), let u equal zero for x below zero, two for 0<x<10<x<1, and minus one for 1<x<31<x<3. Compute Du, its total variation, and its L1 norm. Values at zero and one may be chosen arbitrarily.

  1. The jump at zero is a rise of two, giving 2δ02\delta_0. The jump at one is from two to minus one, giving −3δ1-3\delta_1. No domain-endpoint mass appears because the test functions are compactly supported inside Omega.

  2. For a scalar test function, integrating u times its derivative gives −2φ(0)+3φ(1)-2\varphi(0)+3\varphi(1), exactly minus the pairing with the stated derivative measure. Thus the distributional signs agree with direct integration by parts.

  3. The variation mass is 2+3=52+3=5. The L1 norm is two on the unit-length middle interval plus one on the length-two final interval, giving four. The classical derivative is zero almost everywhere, yet the singular jump measure is nonzero.

Du=2δ0−3δ1,∣Du∣((−2,3))=5,∥u∥L1=4.Du=2\delta_0-3\delta_1,\qquad |Du|((-2,3))=5,\qquad \|u\|_{L^1}=4.
Signed derivative masses can cancel in a total signed sum, but their total variation adds their magnitudes.↔ Scroll wide equations horizontally.
The result

Du=2δ₀−3δ₁, relative variation five, and L1 norm four. The function is BV but not W1,1 on this domain.

Explore the model

Interactive lab

Interactive lab · heat

Calculate smooth periodic variation during heat decay

Pause and Reset, then set spatial mode to 1 and diffusivity to 0.5. The displayed solution is e^(−0.5t)sin x. Predict periodic total variation four initially and 4e^(−0.5) after two Steps, when t=1; compute it by integrating the absolute spatial derivative. The scene displays the analytic sine mode and an L2 readout, not the variation itself or a jump approximation. For each fresh comparison Reset and set both controls, because parameter changes retain elapsed time.

Calculate smooth periodic variation during heat decayThe 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. Current readings: Current amplitude: 1; Spatial L² norm: 1.772; Decay rate · κn²: 3.6 /s. All parameters are adjustable with the labeled controls below.Periodic heat equation mode 3 at time 0: the amplitude is 1.0π/2π3π/22π−1−0.500.51x · periodic intervalTemperature perturbation uExact mode · n = 3
Current temperature modeInitial sineCurrent amplitude
Current amplitude
1
Spatial L² norm
1.772
Decay rate · κn²
3.6 /s
t = 0 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 forTotal variation measures derivative magnitude; even a smooth example requires an absolute-value integral rather than a signed net change.

Check your understanding

Which quantity is unchanged when a BV function is altered at a single point?

Try it yourself

Extend the worked function by zero outside (−2,3). Find its full-space derivative measure and total variation, and explain the difference from relative variation.

Take a moment to make a prediction or work it out on paper. Open a hint when you need one.

Give me a hint

Check the two domain endpoint traces. Only an endpoint with a nonzero interior trace creates an additional jump to zero.

Compare with the solution

The trace near minus two is zero, so that endpoint adds nothing. At three the function changes from minus one to zero, adding δ3\delta_3. Thus the full-space derivative is 2δ0−3δ1+δ32\delta_0-3\delta_1+\delta_3 and its total variation is six. The extra unit records the zero-extension boundary jump, which the original compactly supported interior tests did not see.

Take it with you

  • BV gradients are measures and can contain jumps or diffuse singular parts.
  • Almost-everywhere representative changes leave distributional variation unchanged.
  • Relative variation omits exterior trace jumps that can appear under zero extension.

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