Failure of Property Preservation under Pointwise Convergence
Suppose a sequence of continuous functions converges, point by point, to some limit function. Must that limit be continuous? Most students expect yes. Each stage of the sequence is "tame." A limiting process feels like it should preserve tameness. The truth is starkly different. Pointwise convergence is far too weak a notion. It cannot transport continuity, integrability, or differentiability from a sequence to its limit. This matters because pointwise convergence is the most naive way to make sense of "." It is tempting to build an entire theory on top of it. This theory would cover limits, derivatives, and integrals of function sequences. Analysis needs to know exactly when such term-by-term operations are legitimate. The answer begins with understanding precisely how pointwise convergence fails. The big picture is this: pointwise convergence only controls behavior at each individual point. It works one point at a time, with no uniform control across points. A sequence can behave impeccably at every fixed . Meanwhile the "rate" of approach can degenerate as moves. It is exactly this loss of uniform control that lets discontinuities, non-integrability, and non-differentiability leak into the limit. This article delivers three sharp counterexamples. One each covers continuity, term-by-term integration, and differentiability. Each is built from elementary, fully computed functions. Each counterexample isolates exactly which hypothesis is missing. This sets up the precise remedy: Uniform Convergence.
- Pointwise convergence means for each fixed separately, with no shared rate.
- Continuous functions can converge pointwise to a discontinuous function (jump appears at a limit point).
- can fail to converge to , even when every is nicely integrable and everywhere.
- Differentiable functions can converge pointwise to a limit whose derivative is not the limit of the derivatives, or that isn't even differentiable.
- The obstruction in every case is the same: convergence speed varies wildly with , so no single "good enough" stage works everywhere at once.
- Key idea: pointwise convergence only sees each point in isolation, so bad behavior can hide in the interaction between points — which is precisely what uniform convergence is designed to control.
Where this lives today
The gap exposed here is not a historical curiosity; it governs how limits of functions are handled across modern mathematics and its applications. In Fourier analysis, partial sums of a Fourier series converge pointwise to a discontinuous function in the presence of the Gibbs phenomenon, and understanding that this convergence is not uniform (and controlling exactly how it fails) is essential to signal processing and the numerical analysis of jump discontinuities in audio and image compression. In probability and statistics, sequences of empirical distribution functions converge pointwise (in fact, by the Glivenko–Cantelli theorem, even uniformly almost surely) to a true distribution function, and the distinction between pointwise and uniform convergence of these empirical processes underlies the theory of confidence bands and Kolmogorov–Smirnov tests. In numerical analysis and PDE theory, sequences of finite-element or finite-difference approximate solutions converge pointwise to true solutions under weak hypotheses, but establishing convergence of derivatives (needed for stability and error estimates) requires exactly the stronger uniform-type control foreshadowed here. In measure theory, the failure of naive term-by-term integration under pointwise convergence is precisely what motivates the Monotone and Dominated Convergence Theorems, which supply Lebesgue-integral-based replacements that work under weaker hypotheses than uniform convergence but still avoid the pathologies of the Dirichlet-function counterexample above. In machine learning theory, uniform convergence of empirical risk to true risk (over a hypothesis class) — not mere pointwise convergence — is the technical backbone of generalization bounds (VC theory, Rademacher complexity).
:::quiz[For on , what is ?]
- 1
- 1/2
- 0 ✓
- does not exist
Ask the tutor: Can you show me why uniform convergence of does successfully force the limit to be continuous, with a full proof?
Let and let be a sequence of functions. We say converges pointwise on to a function if for every ,
that is: for every and every , there exists such that
The crucial feature of this definition is that is allowed to depend on , not just on . This is what distinguishes it from Uniform Convergence, where a single must work for all simultaneously.
Intuition
Think of as a "moving profile" over the domain, one curve for each . Pointwise convergence asks only that if you freeze your eye on a single -value and watch the sequence of numbers , it converges. It says nothing about what happens to your neighbors. A classic mental picture: a "bump" or "step" that becomes narrower and narrower, or steeper and steeper, as grows, always sliding away from any fixed point fast enough that the fixed point itself sees convergence, while the shape of the bump never disappears — it just relocates or steepens where you aren't looking. This is exactly how discontinuities, extra mass under an integral, or wild oscillations in slope can survive into the limit even though they vanish at every individual point. The first figure below shows the prototypical continuity-breaking example, on : each is continuous, but the pointwise limit has a jump at .
Central results
There exists a sequence of functions , each continuous on , such that converges pointwise on to a function that is not continuous at some point of .
Construction. Define, for each ,
Step 1: Each is continuous on . The function is a polynomial (a finite product of the identity function with itself times, composed with finite sums), and every polynomial is continuous on , hence on . (This uses only the algebra-of-limits characterization of continuity: sums and products of continuous functions are continuous, and constants and are continuous.)
Step 2: Identify the pointwise limit. Fix and compute .
- If : for every , so .
- If : we claim . Given with (WLOG, since larger is even easier), if this is trivial ( for all ). If , write with (possible since forces , so ). By Bernoulli's inequality, for all (proved by induction: true at ; if then , using ). Hence
since by the Archimedean property applied to choose with , giving . So by the squeeze principle, . Therefore the pointwise limit function is
This defines at every point of , so pointwise on .
Step 3: is discontinuous at . We must show it is false that . Take the sequence for ; then and for all such , so for every , giving . Since continuity at would require every sequence in to satisfy (sequential characterization of continuity), and we exhibited one that fails this, is discontinuous at .
∎There exists a sequence of functions , each continuous (hence Riemann integrable) on , such that converges pointwise on to a function , and yet
Construction. For define the "moving triangular spike"
Step 1: Each is continuous on . On each of the three closed subintervals , , (assuming so that ; for the middle piece runs to but we simply restrict to , and the argument is identical, only the third piece is vacuous), is given by a linear (hence continuous) formula. At the junction : the left formula gives , and the right formula gives ; these agree. At (for ): the middle formula gives , matching the third piece's value . By the pasting lemma for continuity (a function built from finitely many continuous pieces on closed sets, agreeing at the shared endpoints, is continuous on the union), is continuous on all of .
Step 2: The pointwise limit is identically . Fix .
- If : for every (from the first formula, ), so .
- If : by the Archimedean property, choose such that . Then for every we have , so lies in the region where by definition. Hence for all , so trivially .
In both cases , so pointwise on , and .
Step 3: Compute exactly for every . The graph of on is a triangle with base and height (computed above), and outside . (For the full triangle lies in ; for we only integrate over , which is the left half of the triangle — but let us just do the computation directly by the fundamental theorem of calculus / elementary area formula for , and note doesn't affect the limit.) For :
(This triangle-area computation can be justified rigorously by splitting the integral at and computing each linear piece via the Fundamental Theorem of Calculus: , and by direct antiderivative evaluation; summing gives .) So for every ,
Step 4: Conclude. Hence , while . These are unequal (), so
even though pointwise on all of and every is continuous (indeed Riemann integrable).
∎There exists a sequence of functions , each differentiable on with continuous derivative, such that converges pointwise on to a function that is differentiable everywhere, but
Construction. For define
Step 1: Each is differentiable with continuous derivative. is a composition/scalar multiple of the sine function, which is differentiable everywhere with (chain rule, a standard consequence of the definition of the derivative applied to ). Hence
and this is continuous since is continuous and composition/scalar multiplication of continuous functions is continuous.
Step 2: The pointwise limit is . Fix . Since for all and all (boundedness of sine, a basic property from its definition via the unit circle or its power series), we get
as , by the Archimedean property (for any choose ). By the squeeze principle, for every fixed . So pointwise on , where for all .
Step 3: is differentiable everywhere, with for all . This is immediate: the derivative of the identically-zero function is identically zero, directly from the definition
Step 4: Show at a specific point. Take . Then
So as — in particular does not converge to ; it does not converge at all (it is unbounded, hence has no finite limit — if it converged to some it would be bounded, contradicting ). Thus at ,
(indeed the left side does not even exist as a finite number), despite pointwise everywhere and every being smooth with continuous derivative.
∎Worked examples
Example 1 (an asymmetric continuity-failure with a two-sided glued limit). Let be defined by
Each is continuous on : the denominator for all , so is a quotient of continuous functions with nonvanishing denominator, hence continuous. Compute the pointwise limit. Fix .
- If : for every , so .
- If : then is a fixed positive number, call it . Then
as , since given , by the Archimedean property pick ; then for , , so . So the pointwise limit is
on . This function is discontinuous at : taking (all in for large), . Note the asymmetry here compared to : the spike sits at an interior point rather than an endpoint, and the domain is not symmetric about it either, showing the phenomenon is not an artifact of endpoints. Numerically, at : , while and in the limit — consistent with convergence to but illustrating how slowly it happens near : even at , a point only away from the spike still retains half its peak value.
Example 2 (a fully computed integration-failure with asymmetric, taller/thinner spikes and an explicit finite- table). Let be
(This is a downward parabolic bump on , vanishing at both ends of that subinterval, scaled up.) Continuity: On , is a polynomial in , hence continuous; at , the formula gives , matching the constant on ; both pieces are continuous on closed subintervals agreeing at the shared point , so by the pasting lemma is continuous on . Pointwise limit: For : for all . For : choose with (Archimedean property); then for all , , so and . Hence for every , i.e. pointwise. Exact integral for each : Using the Fundamental Theorem of Calculus with antiderivative ,
So , which diverges to as — an even more dramatic failure than Theorem 2's example, where the integral merely failed to converge to the right constant. Here it doesn't converge at all, while . A concrete numerical check at : exactly (matching ), with peak value at the vertex given by . So a bump of height over an interval of width still yields area exactly this time, and these areas grow with rather than staying fixed at as in the triangular example — showing the failure mode can be tuned to be arbitrarily severe.
One might hope that some weaker replacement for continuity of the 's — say, mere boundedness — combined with pointwise convergence would at least guarantee integrability of the limit and correct term-by-term integration. This is false, and exhibiting why sharpens exactly what uniform convergence buys us. Let be an enumeration of (countable, since is countable). Define
Each is bounded (values only or ) and Riemann integrable: it differs from the identically-zero function at only finitely many points ( of them), and a function that agrees with an integrable function except at finitely many points is integrable with the same integral (a standard fact: finite sets have Riemann outer content zero, so they don't affect upper/lower sums in the limit). Hence
Now fix . If is irrational, for all (since is never any ), so . If for some specific , then for all , (since is already listed by stage ), so for all , giving . So the pointwise limit is
the Dirichlet function restricted to . This function is famously not Riemann integrable: its upper Riemann sum over any partition is always (every subinterval contains a rational, by density of ) while its lower Riemann sum is always (every subinterval contains an irrational, by density of the irrationals), so upper and lower integrals disagree () and does not exist in the Riemann sense at all. So here, weakening "continuous" to merely "bounded and Riemann integrable" for the 's, together with pointwise convergence, is not even enough to guarantee the limit is integrable — the conclusion of Theorem 2 (a numerical discrepancy) is replaced by something worse (no Riemann integral exists). This shows the hypothesis "each continuous" (or at least: uniformly controlled regularity) genuinely does work, and cannot be discarded for free; some extra structure — ultimately, uniform convergence, or in more advanced settings uniform boundedness plus a measure-theoretic framework — is required to get any positive integration theorem at all.
Scope, edge cases & common traps
| Situation | What can go wrong | What is still guaranteed |
|---|---|---|
| pointwise, each continuous | can be discontinuous (Thm. 1) | Nothing about continuity of in general |
| pointwise, each Riemann integrable | , or may not exist (Thm. 2, Counterexample) | Nothing, without extra hypotheses (e.g. uniform boundedness + uniform convergence, or dominated convergence in Lebesgue theory) |
| pointwise, each differentiable | need not converge to , even where is differentiable (Thm. 3) | Nothing about derivatives; not even that is differentiable in general |
| uniformly, each continuous | — | is continuous (this is the theorem uniform convergence is built to deliver) |
The mathematician behind it
Origins · 1 minAugustin-Louis Cauchy and how this was discovered
Cauchy is the figure most associated with exposing — if only partially, and initially by mistake — exactly the fragility this article documents. In his 1821 Cours d'Analyse, Cauchy stated a theorem asserting that a pointwise-convergent series of continuous functions has a continuous sum; the claim, as literally stated, is false, and the very example that refutes it (-type series, made explicit later by Abel) is essentially the counterexample used in Theorem 1 above. The episode became one of the most instructive cautionary tales in the history of rigorous analysis, and it directly motivated the eventual isolation of uniform convergence as the correct sufficient hypothesis.
Why does pointwise convergence allow such wild failures if each genuinely converges at every point?
The subtlety is quantifier order. Pointwise convergence says: for each , and each , there's some depending on both. Nothing forces that to be reusable across different 's. In the example, to get you effectively need larger than roughly , which blows up as . So near , convergence is arbitrarily slow, even though at every single fixed it eventually happens. The discontinuity of the limit at is the trace left by this non-uniformity — the "badness" never disappears, it just gets pushed to points closer and closer to as grows.
Is it true that pointwise convergence never preserves continuity, integrability, or differentiability?
No — pointwise convergence can preserve these properties in specific cases; the theorems here only show it need not. For instance, if on , then pointwise, and is continuous, integrable on any bounded interval, and differentiable, with too — no failure here. The point of the counterexamples is that pointwise convergence by itself, with no further hypotheses, gives no guarantee either way; you must check case by case, or upgrade to a stronger convergence mode that gives blanket guarantees.
What exactly does uniform convergence add that fixes these examples?
Uniform convergence requires one , depending only on , that works for every simultaneously: . This lets you bound the "worst-case error at any point" and push it through continuity's own -style estimates, through Riemann sum estimates (bounding ), and (with an extra hypothesis on convergence of derivatives) through difference quotients for differentiability. All three counterexamples above fail exactly this uniform bound: e.g. for , for every (approached as ), so the convergence is emphatically not uniform, consistent with the discontinuity that results.
Why does the integration counterexample use a spike that gets taller as it gets narrower?
This is the crucial mechanism: to keep at every fixed (pointwise convergence to zero), the base of the spike must shrink to width , since eventually any fixed must fall outside it. But to keep the integral (area) from vanishing, the height must grow fast enough to compensate for the shrinking width — height width constant, e.g. up to the constant factor from the triangle. This exact trade-off, "height diverges as width vanishes, holding area fixed (or growing)," is the mechanism behind Dirac-delta-like approximate identities in distribution theory, and is precisely why term-by-term integration requires more than pointwise convergence.
Does the same kind of example work with series of functions, not just sequences?
Yes, and historically that is exactly how the issue was first discovered — Cauchy's original erroneous claim was about infinite series of continuous functions. Any sequence counterexample converts to a series counterexample by taking and for , so that the partial sums recover the original sequence exactly. So the example becomes a series whose partial sums are , and Abel's 1826 counterexample to Cauchy is of essentially this form.
If pointwise convergence is so unreliable, why is it still taught and used at all?
Because it is the most basic, minimal notion of "the functions get close," and many important convergences genuinely are only pointwise (Fourier series at jump points, empirical distribution functions before invoking Glivenko–Cantelli, generic limits of measurable functions in Lebesgue theory). Pointwise convergence is also the natural first step before checking whether something stronger holds. Moreover, weaker but well-chosen replacement theorems — dominated convergence, monotone convergence, Egorov's theorem (pointwise convergence plus a small-exception uniform convergence) — recover usable conclusions without requiring full uniform convergence, so pointwise convergence remains the entry point to a rich hierarchy of convergence theorems, not a dead end.
How would I quickly check, on an exam, whether interchanging limit and integral/derivative is valid?
First check whether uniform convergence is stated or provable (e.g., via the Weierstrass M-test for series, or by directly bounding ). If yes, continuity and integral-interchange are justified immediately; for derivative-interchange, you additionally need to converge uniformly (not just ) plus pointwise convergence of at a single point. If uniform convergence fails or isn't given, do not interchange without another justification (dominated/monotone convergence for integrals, or a direct hands-on estimate); default to suspicion, as these three theorems show naive interchange can fail in elementary, easily-checked ways.