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 "fn→ff_n \to f." 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 xx. Meanwhile the "rate" of approach can degenerate as xx 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.

Key takeaways
  • Pointwise convergence fn→ff_n \to f means fn(x)→f(x)f_n(x) \to f(x) for each fixed xx separately, with no shared rate.
  • Continuous functions can converge pointwise to a discontinuous function (jump appears at a limit point).
  • ∫fn\int f_n can fail to converge to ∫f\int f, even when every fnf_n is nicely integrable and fn→ff_n \to f 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 xx, 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 fn(x)=xnf_n(x) = x^n on [0,1][0,1], what is lim⁡n→∞fn(1/2)\lim_{n\to\infty} f_n(1/2)?]

  • 1
  • 1/2
  • 0 ✓
  • does not exist

Ask the tutor: Can you show me why uniform convergence of fnf_n does successfully force the limit to be continuous, with a full proof?

DefinitionPointwise convergence of a sequence of functions

Let E⊆RE \subseteq \mathbb{R} and let fn:E→Rf_n : E \to \mathbb{R} be a sequence of functions. We say (fn)(f_n) converges pointwise on EE to a function f:E→Rf : E \to \mathbb{R} if for every x∈Ex \in E,

lim⁡n→∞fn(x)=f(x),\lim_{n \to \infty} f_n(x) = f(x),

that is: for every x∈Ex \in E and every ε>0\varepsilon > 0, there exists N=N(x,ε)∈NN = N(x, \varepsilon) \in \mathbb{N} such that

n≥N  ⟹  ∣fn(x)−f(x)∣<ε.n \ge N \implies |f_n(x) - f(x)| < \varepsilon.

The crucial feature of this definition is that NN is allowed to depend on xx, not just on ε\varepsilon. This is what distinguishes it from Uniform Convergence, where a single NN must work for all x∈Ex \in E simultaneously.

Intuition

Think of fnf_n as a "moving profile" over the domain, one curve for each nn. Pointwise convergence asks only that if you freeze your eye on a single xx-value and watch the sequence of numbers fn(x)f_n(x), 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 nn 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, fn(x)=xnf_n(x) = x^n on [0,1][0,1]: each fnf_n is continuous, but the pointwise limit has a jump at x=1x=1.

Figure
Pointwise limit of fn(x)=xnf_n(x)=x^n develops a jump at x=1x=1
x y 0 1 1 n = 2 n = 8 n = 30 limit f(x): 0 on [0,1), f(1)=1

Central results

TheoremFailure of continuity under pointwise limits

There exists a sequence of functions fn:[0,1]→Rf_n : [0,1] \to \mathbb{R}, each continuous on [0,1][0,1], such that (fn)(f_n) converges pointwise on [0,1][0,1] to a function ff that is not continuous at some point of [0,1][0,1].

Proof

Construction. Define, for each n∈Nn \in \mathbb{N},

fn(x)=xn,x∈[0,1].f_n(x) = x^n, \qquad x \in [0,1].

Step 1: Each fnf_n is continuous on [0,1][0,1]. The function x↦xnx \mapsto x^n is a polynomial (a finite product of the identity function with itself nn times, composed with finite sums), and every polynomial is continuous on R\mathbb{R}, hence on [0,1][0,1]. (This uses only the algebra-of-limits characterization of continuity: sums and products of continuous functions are continuous, and constants and x↦xx \mapsto x are continuous.)

Step 2: Identify the pointwise limit. Fix x∈[0,1]x \in [0,1] and compute lim⁡n→∞xn\lim_{n\to\infty} x^n.

  • If x=1x = 1: fn(1)=1n=1f_n(1) = 1^n = 1 for every nn, so fn(1)→1f_n(1) \to 1.
  • If 0≤x<10 \le x < 1: we claim xn→0x^n \to 0. Given ε>0\varepsilon > 0 with ε<1\varepsilon < 1 (WLOG, since larger ε\varepsilon is even easier), if x=0x = 0 this is trivial (fn(0)=0f_n(0)=0 for all n≥1n\ge 1). If 0<x<10 < x < 1, write x=11+hx = \frac{1}{1+h} with h>0h > 0 (possible since 0<x<10<x<1 forces 1/x>11/x > 1, so h:=1/x−1>0h := 1/x - 1 > 0). By Bernoulli's inequality, (1+h)n≥1+nh(1+h)^n \ge 1 + nh for all n≥1n \ge 1 (proved by induction: true at n=1n=1; if (1+h)n≥1+nh(1+h)^n \ge 1+nh then (1+h)n+1=(1+h)n(1+h)≥(1+nh)(1+h)=1+(n+1)h+nh2≥1+(n+1)h(1+h)^{n+1} = (1+h)^n(1+h) \ge (1+nh)(1+h) = 1+(n+1)h+nh^2 \ge 1+(n+1)h, using h>0h>0). Hence
0<xn=1(1+h)n≤11+nh→0as n→∞,0 < x^n = \frac{1}{(1+h)^n} \le \frac{1}{1+nh} \to 0 \quad \text{as } n \to \infty,

since 1/(1+nh)→01/(1+nh) \to 0 by the Archimedean property applied to choose NN with N>1εhN > \frac{1}{\varepsilon h}, giving 11+Nh<ε\frac{1}{1+Nh} < \varepsilon. So by the squeeze principle, xn→0x^n \to 0. Therefore the pointwise limit function is

f(x)=lim⁡n→∞xn={0,0≤x<1,1,x=1.f(x) = \lim_{n\to\infty} x^n = \begin{cases} 0, & 0 \le x < 1,\\ 1, & x = 1. \end{cases}

This defines ff at every point of [0,1][0,1], so (fn)→f(f_n) \to f pointwise on [0,1][0,1].

Step 3: ff is discontinuous at x=1x = 1. We must show it is false that lim⁡x→1−f(x)=f(1)\lim_{x \to 1^-} f(x) = f(1). Take the sequence xk=1−1kx_k = 1 - \frac{1}{k} for k≥2k \ge 2; then xk→1x_k \to 1 and xk∈[0,1)x_k \in [0,1) for all such kk, so f(xk)=0f(x_k) = 0 for every kk, giving f(xk)→0≠1=f(1)f(x_k) \to 0 \ne 1 = f(1). Since continuity at 11 would require every sequence xk→1x_k \to 1 in [0,1][0,1] to satisfy f(xk)→f(1)f(x_k) \to f(1) (sequential characterization of continuity), and we exhibited one that fails this, ff is discontinuous at x=1x=1. ■\blacksquare

∎
TheoremFailure of term-by-term integration under pointwise limits

There exists a sequence of functions gn:[0,1]→Rg_n : [0,1] \to \mathbb{R}, each continuous (hence Riemann integrable) on [0,1][0,1], such that (gn)(g_n) converges pointwise on [0,1][0,1] to a function g≡0g \equiv 0, and yet

lim⁡n→∞∫01gn(x) dx≠∫01g(x) dx=0.\lim_{n\to\infty} \int_0^1 g_n(x)\,dx \ne \int_0^1 g(x)\,dx = 0.
Proof

Construction. For n≥1n \ge 1 define the "moving triangular spike"

gn(x)={n2x,0≤x≤1n,n2(2n−x),1n≤x≤2n,0,2n≤x≤1.g_n(x) = \begin{cases} n^2 x, & 0 \le x \le \frac{1}{n}, \\[2pt] n^2\left(\frac{2}{n} - x\right), & \frac{1}{n} \le x \le \frac{2}{n}, \\[2pt] 0, & \frac{2}{n} \le x \le 1. \end{cases}

Step 1: Each gng_n is continuous on [0,1][0,1]. On each of the three closed subintervals [0,1n][0,\tfrac1n], [1n,2n][\tfrac1n,\tfrac2n], [2n,1][\tfrac2n,1] (assuming n≥2n \ge 2 so that 2n≤1\tfrac2n \le 1; for n=1n=1 the middle piece runs to x=2x=2 but we simply restrict to [0,1]⊂[0,2][0,1]\subset[0,2], and the argument is identical, only the third piece is vacuous), gng_n is given by a linear (hence continuous) formula. At the junction x=1/nx = 1/n: the left formula gives n2⋅1n=nn^2 \cdot \frac1n = n, and the right formula gives n2(2n−1n)=n2⋅1n=nn^2\left(\frac2n - \frac1n\right) = n^2 \cdot \frac1n = n; these agree. At x=2/nx = 2/n (for n≥2n\ge 2): the middle formula gives n2(2n−2n)=0n^2\left(\frac2n-\frac2n\right)=0, matching the third piece's value 00. 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), gng_n is continuous on all of [0,1][0,1].

Step 2: The pointwise limit is identically 00. Fix x∈[0,1]x \in [0,1].

  • If x=0x = 0: gn(0)=0g_n(0) = 0 for every nn (from the first formula, n2⋅0=0n^2\cdot 0 = 0), so gn(0)→0g_n(0) \to 0.
  • If x∈(0,1]x \in (0,1]: by the Archimedean property, choose NN such that 2N<x\frac{2}{N} < x. Then for every n≥Nn \ge N we have 2n≤2N<x\frac{2}{n} \le \frac{2}{N} < x, so xx lies in the region [2n,1]\left[\frac2n, 1\right] where gn(x)=0g_n(x) = 0 by definition. Hence gn(x)=0g_n(x) = 0 for all n≥Nn \ge N, so trivially gn(x)→0g_n(x) \to 0.

In both cases gn(x)→0g_n(x) \to 0, so (gn)→g≡0(g_n) \to g \equiv 0 pointwise on [0,1][0,1], and ∫01g(x) dx=∫010 dx=0\int_0^1 g(x)\,dx = \int_0^1 0\,dx = 0.

Step 3: Compute ∫01gn(x) dx\int_0^1 g_n(x)\,dx exactly for every nn. The graph of gng_n on [0,2/n][0, 2/n] is a triangle with base 2/n2/n and height gn(1/n)=ng_n(1/n) = n (computed above), and gn=0g_n = 0 outside [0,2/n]∩[0,1][0,2/n] \cap [0,1]. (For n≥2n \ge 2 the full triangle lies in [0,1][0,1]; for n=1n=1 we only integrate over [0,1][0,1], 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 n≥2n\ge 2, and note n=1n=1 doesn't affect the limit.) For n≥2n \ge 2:

∫01gn(x) dx=∫02/ngn(x) dx=12⋅(base)⋅(height)=12⋅2n⋅n=1.\int_0^1 g_n(x)\,dx = \int_0^{2/n} g_n(x)\,dx = \frac{1}{2}\cdot(\text{base})\cdot(\text{height}) = \frac12 \cdot \frac{2}{n} \cdot n = 1.

(This triangle-area computation can be justified rigorously by splitting the integral at 1/n1/n and computing each linear piece via the Fundamental Theorem of Calculus: ∫01/nn2x dx=n2⋅(1/n)22=12\int_0^{1/n} n^2x\,dx = n^2\cdot\frac{(1/n)^2}{2} = \frac12, and ∫1/n2/nn2(2n−x)dx=n2[2nx−x22]1/n2/n=12\int_{1/n}^{2/n} n^2\left(\frac2n - x\right)dx = n^2\left[\frac{2}{n}x - \frac{x^2}{2}\right]_{1/n}^{2/n} = \frac12 by direct antiderivative evaluation; summing gives 11.) So for every n≥2n \ge 2,

∫01gn(x) dx=1.\int_0^1 g_n(x)\,dx = 1.

Step 4: Conclude. Hence lim⁡n→∞∫01gn(x) dx=lim⁡n→∞1=1\lim_{n\to\infty} \int_0^1 g_n(x)\,dx = \lim_{n\to\infty} 1 = 1, while ∫01g(x) dx=0\int_0^1 g(x)\,dx = 0. These are unequal (1≠01 \ne 0), so

lim⁡n→∞∫01gn≠∫01lim⁡n→∞gn,\lim_{n\to\infty}\int_0^1 g_n \ne \int_0^1 \lim_{n\to\infty} g_n,

even though gn→gg_n \to g pointwise on all of [0,1][0,1] and every gng_n is continuous (indeed Riemann integrable). ■\blacksquare

∎
TheoremFailure of differentiation under pointwise limits

There exists a sequence of functions hn:R→Rh_n : \mathbb{R} \to \mathbb{R}, each differentiable on R\mathbb{R} with continuous derivative, such that (hn)(h_n) converges pointwise on R\mathbb{R} to a function hh that is differentiable everywhere, but

lim⁡n→∞hn′(x)≠h′(x)for some x∈R.\lim_{n\to\infty} h_n'(x) \ne h'(x) \quad \text{for some } x \in \mathbb{R}.
Proof

Construction. For n≥1n \ge 1 define

hn(x)=sin⁡(n2x)n,x∈R.h_n(x) = \frac{\sin(n^2 x)}{n}, \qquad x \in \mathbb{R}.

Step 1: Each hnh_n is differentiable with continuous derivative. hnh_n is a composition/scalar multiple of the sine function, which is differentiable everywhere with ddxsin⁡(u)=cos⁡(u)⋅u′\frac{d}{dx}\sin(u) = \cos(u)\cdot u' (chain rule, a standard consequence of the definition of the derivative applied to sin⁡\sin). Hence

hn′(x)=1n⋅n2cos⁡(n2x)=ncos⁡(n2x),h_n'(x) = \frac{1}{n}\cdot n^2\cos(n^2x) = n\cos(n^2 x),

and this is continuous since cos⁡\cos is continuous and composition/scalar multiplication of continuous functions is continuous.

Step 2: The pointwise limit is h≡0h \equiv 0. Fix x∈Rx \in \mathbb{R}. Since ∣sin⁡(n2x)∣≤1|\sin(n^2 x)| \le 1 for all nn and all xx (boundedness of sine, a basic property from its definition via the unit circle or its power series), we get

∣hn(x)−0∣=∣sin⁡(n2x)n∣≤1n→0|h_n(x) - 0| = \left|\frac{\sin(n^2x)}{n}\right| \le \frac{1}{n} \to 0

as n→∞n \to \infty, by the Archimedean property (for any ε>0\varepsilon>0 choose N>1/εN > 1/\varepsilon). By the squeeze principle, hn(x)→0h_n(x) \to 0 for every fixed xx. So (hn)→h(h_n) \to h pointwise on R\mathbb{R}, where h(x)=0h(x) = 0 for all xx.

Step 3: hh is differentiable everywhere, with h′(x)=0h'(x) = 0 for all xx. This is immediate: the derivative of the identically-zero function is identically zero, directly from the definition

h′(x)=lim⁡t→0h(x+t)−h(x)t=lim⁡t→00−0t=0.h'(x) = \lim_{t\to 0} \frac{h(x+t)-h(x)}{t} = \lim_{t\to 0}\frac{0-0}{t} = 0.

Step 4: Show hn′(x)↛h′(x)h_n'(x) \not\to h'(x) at a specific point. Take x=0x = 0. Then

hn′(0)=ncos⁡(n2⋅0)=ncos⁡(0)=n⋅1=n.h_n'(0) = n\cos(n^2 \cdot 0) = n \cos(0) = n \cdot 1 = n.

So hn′(0)=n→∞h_n'(0) = n \to \infty as n→∞n \to \infty — in particular (hn′(0))(h_n'(0)) does not converge to h′(0)=0h'(0) = 0; it does not converge at all (it is unbounded, hence has no finite limit — if it converged to some LL it would be bounded, contradicting hn′(0)=n→∞h_n'(0) = n \to \infty). Thus at x=0x=0,

lim⁡n→∞hn′(0)≠h′(0)=0\lim_{n\to\infty} h_n'(0) \ne h'(0) = 0

(indeed the left side does not even exist as a finite number), despite hn→hh_n \to h pointwise everywhere and every hnh_n being smooth with continuous derivative. ■\blacksquare

∎

Worked examples

Example 1 (an asymmetric continuity-failure with a two-sided glued limit). Let kn:[−1,2]→Rk_n : [-1,2] \to \mathbb{R} be defined by

kn(x)=11+n(x−1)2.k_n(x) = \frac{1}{1 + n(x-1)^2}.

Each knk_n is continuous on [−1,2][-1,2]: the denominator 1+n(x−1)2≥1>01+n(x-1)^2 \ge 1 > 0 for all xx, so knk_n is a quotient of continuous functions with nonvanishing denominator, hence continuous. Compute the pointwise limit. Fix x∈[−1,2]x \in [-1,2].

  • If x=1x = 1: kn(1)=11+0=1k_n(1) = \frac{1}{1+0} = 1 for every nn, so kn(1)→1k_n(1) \to 1.
  • If x≠1x \ne 1: then (x−1)2>0(x-1)^2 > 0 is a fixed positive number, call it c>0c > 0. Then
kn(x)=11+nc→0k_n(x) = \frac{1}{1+nc} \to 0

as n→∞n \to \infty, since given ε>0\varepsilon>0, by the Archimedean property pick N>1cεN > \frac{1}{c\varepsilon}; then for n≥Nn \ge N, 1+nc>nc>1ε1+nc > nc > \frac1\varepsilon, so kn(x)<εk_n(x) < \varepsilon. So the pointwise limit is

k(x)={1,x=10,x≠1,k(x) = \begin{cases} 1, & x=1\\ 0, & x \ne 1,\end{cases}

on [−1,2][-1,2]. This function is discontinuous at x=1x=1: taking xj=1+1j→1x_j = 1 + \frac1j \to 1 (all in (1,2]⊂[−1,2](1,2]\subset[-1,2] for jj large), k(xj)=0→0≠1=k(1)k(x_j) = 0 \to 0 \ne 1 = k(1). Note the asymmetry here compared to xnx^n: the spike sits at an interior point x=1x=1 rather than an endpoint, and the domain [−1,2][-1,2] is not symmetric about it either, showing the phenomenon is not an artifact of endpoints. Numerically, at n=100n = 100: k100(1.1)=11+100(0.01)=12=0.5k_{100}(1.1) = \dfrac{1}{1+100(0.01)} = \dfrac{1}{2} = 0.5, while k100(1)=1k_{100}(1) = 1 and k(1.1)=0k(1.1) = 0 in the limit — consistent with convergence to 00 but illustrating how slowly it happens near x=1x=1: even at n=100n=100, a point only 0.10.1 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-nn table). Let pn:[0,1]→Rp_n : [0,1] \to \mathbb{R} be

pn(x)=n3x(1−nx)  for 0≤x≤1n,pn(x)=0  for 1n≤x≤1.p_n(x) = n^3 x (1-nx)\ \text{ for } 0 \le x \le \tfrac1n, \qquad p_n(x) = 0 \ \text{ for } \tfrac1n \le x \le 1.

(This is a downward parabolic bump on [0,1/n][0,1/n], vanishing at both ends of that subinterval, scaled up.) Continuity: On [0,1/n][0,1/n], pnp_n is a polynomial in xx, hence continuous; at x=1/nx=1/n, the formula gives n3⋅1n⋅(1−1)=0n^3\cdot\frac1n\cdot(1-1) = 0, matching the constant 00 on [1/n,1][1/n,1]; both pieces are continuous on closed subintervals agreeing at the shared point 1/n1/n, so by the pasting lemma pnp_n is continuous on [0,1][0,1]. Pointwise limit: For x=0x=0: pn(0)=0p_n(0)=0 for all nn. For x∈(0,1]x \in (0,1]: choose NN with 1/N<x1/N < x (Archimedean property); then for all n≥Nn \ge N, 1/n≤1/N<x1/n \le 1/N < x, so x∈[1/n,1]x \in [1/n,1] and pn(x)=0p_n(x)=0. Hence pn(x)→0p_n(x) \to 0 for every x∈[0,1]x\in[0,1], i.e. pn→p≡0p_n \to p \equiv 0 pointwise. Exact integral for each nn: Using the Fundamental Theorem of Calculus with antiderivative n3(x22−nx33)n^3\left(\frac{x^2}{2} - \frac{n x^3}{3}\right),

∫01/nn3x(1−nx) dx=n3[x22−nx33]01/n=n3(12n2−n3n3)=n3(12n2−13n2)=n3⋅16n2=n6.\int_0^{1/n} n^3 x(1-nx)\,dx = n^3\left[\frac{x^2}{2} - \frac{nx^3}{3}\right]_0^{1/n} = n^3\left(\frac{1}{2n^2} - \frac{n}{3n^3}\right) = n^3\left(\frac{1}{2n^2}-\frac{1}{3n^2}\right) = n^3\cdot\frac{1}{6n^2} = \frac{n}{6}.

So ∫01pn(x) dx=n6\int_0^1 p_n(x)\,dx = \dfrac{n}{6}, which diverges to +∞+\infty as n→∞n\to\infty — 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 ∫01p=0\int_0^1 p = 0. A concrete numerical check at n=6n=6: ∫01p6 dx=1\int_0^1 p_6\,dx = 1 exactly (matching n/6=1n/6=1), with peak value p6(x∗)p_6(x^*) at the vertex x∗=12n=112x^* = \frac{1}{2n} = \frac{1}{12} given by p6(1/12)=63⋅112(1−6⋅112)=216⋅112⋅12=9p_6(1/12) = 6^3 \cdot \frac{1}{12}\left(1 - 6\cdot\frac{1}{12}\right) = 216\cdot\frac{1}{12}\cdot\frac12 = 9. So a bump of height 99 over an interval of width 1/61/6 still yields area exactly 11 this time, and these areas grow with nn rather than staying fixed at 11 as in the triangular example — showing the failure mode can be tuned to be arbitrarily severe.

CounterexampleWhy "continuity of each fnf_n" alone cannot be dropped or weakened without another compensating hypothesis

One might hope that some weaker replacement for continuity of the fnf_n'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 {q1,q2,q3,… }\{q_1, q_2, q_3, \dots\} be an enumeration of Q∩[0,1]\mathbb{Q} \cap [0,1] (countable, since Q\mathbb{Q} is countable). Define

rn(x)={1,x∈{q1,…,qn},0,otherwise, for x∈[0,1].r_n(x) = \begin{cases} 1, & x \in \{q_1,\dots,q_n\}, \\ 0, & \text{otherwise, for } x \in [0,1].\end{cases}

Each rnr_n is bounded (values only 00 or 11) and Riemann integrable: it differs from the identically-zero function at only finitely many points (nn 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

∫01rn(x) dx=0for every n.\int_0^1 r_n(x)\,dx = 0 \quad \text{for every } n.

Now fix x∈[0,1]x \in [0,1]. If xx is irrational, rn(x)=0r_n(x) = 0 for all nn (since xx is never any qkq_k), so rn(x)→0r_n(x)\to 0. If x=qkx = q_k for some specific kk, then for all n≥kn \ge k, x∈{q1,…,qn}x \in \{q_1,\dots,q_n\} (since qkq_k is already listed by stage kk), so rn(x)=1r_n(x) = 1 for all n≥kn\ge k, giving rn(x)→1r_n(x) \to 1. So the pointwise limit is

r(x)={1,x∈Q∩[0,1]0,x∉Q, x∈[0,1],r(x) = \begin{cases} 1, & x \in \mathbb{Q}\cap[0,1]\\ 0, & x \notin \mathbb{Q}, \ x\in[0,1],\end{cases}

the Dirichlet function restricted to [0,1][0,1]. This function is famously not Riemann integrable: its upper Riemann sum over any partition is always 11 (every subinterval contains a rational, by density of Q\mathbb{Q}) while its lower Riemann sum is always 00 (every subinterval contains an irrational, by density of the irrationals), so upper and lower integrals disagree (1≠01 \ne 0) and ∫01r\int_0^1 r does not exist in the Riemann sense at all. So here, weakening "continuous" to merely "bounded and Riemann integrable" for the fnf_n'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 fnf_n 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

SituationWhat can go wrongWhat is still guaranteed
fn→ff_n \to f pointwise, each fnf_n continuousff can be discontinuous (Thm. 1)Nothing about continuity of ff in general
fn→ff_n \to f pointwise, each fnf_n Riemann integrable∫fn↛∫f\int f_n \not\to \int f, or ∫f\int f may not exist (Thm. 2, Counterexample)Nothing, without extra hypotheses (e.g. uniform boundedness + uniform convergence, or dominated convergence in Lebesgue theory)
fn→ff_n \to f pointwise, each fnf_n differentiablefn′f_n' need not converge to f′f', even where ff is differentiable (Thm. 3)Nothing about derivatives; not even that ff is differentiable in general
fn→ff_n \to f uniformly, each fnf_n continuous—ff 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
AC
Augustin-Louis Cauchy
1789–1857 · Early 19th-century French analysis, École Polytechnique

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 (xnx^n-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.

History
- 1821 — Cauchy's Cours d'Analyse states (erroneously, as literally worded) that pointwise limits of continuous function series are continuous.
- 1826 — Niels Henrik Abel publishes an explicit counterexample to Cauchy's claim, essentially the xnx^n-type phenomenon central to Theorem 1 of this article.
- 1841–1848 — Karl Weierstrass, in unpublished lecture notes and later published work, develops the precise ε\varepsilon–NN formulation of uniform convergence, isolating exactly the missing hypothesis.
- 1853 — Philipp Ludwig von Seidel independently identifies the gap in Cauchy's reasoning and begins to articulate the distinction between convergence "at a point" versus convergence "arbitrarily fast, uniformly."
- 1870s — Uniform convergence becomes standard machinery in rigorous real analysis courses, cementing the modern framework in which continuity, term-by-term integration, and term-by-term differentiation theorems are correctly and precisely stated.
Why does pointwise convergence allow such wild failures if each fn(x)f_n(x) genuinely converges at every point?

The subtlety is quantifier order. Pointwise convergence says: for each xx, and each ε\varepsilon, there's some NN depending on both. Nothing forces that NN to be reusable across different xx's. In the xnx^n example, to get xn<εx^n < \varepsilon you effectively need nn larger than roughly ln⁡(1/ε)ln⁡(1/x)\frac{\ln(1/\varepsilon)}{\ln(1/x)}, which blows up as x→1−x \to 1^-. So near x=1x=1, convergence is arbitrarily slow, even though at every single fixed x<1x<1 it eventually happens. The discontinuity of the limit at x=1x=1 is the trace left by this non-uniformity — the "badness" never disappears, it just gets pushed to points closer and closer to 11 as nn 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 fn(x)=x/nf_n(x) = x/n on R\mathbb{R}, then fn→0f_n \to 0 pointwise, and 00 is continuous, integrable on any bounded interval, and differentiable, with fn′=1/n→0=(limit)′f_n' = 1/n \to 0 = (\text{limit})' 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 NN, depending only on ε\varepsilon, that works for every xx simultaneously: sup⁡x∣fn(x)−f(x)∣→0\sup_x |f_n(x)-f(x)| \to 0. This lets you bound the "worst-case error at any point" and push it through continuity's own ε/3\varepsilon/3-style estimates, through Riemann sum estimates (bounding ∣∫fn−∫f∣≤(b−a)sup⁡∣fn−f∣|\int f_n - \int f| \le (b-a)\sup|f_n-f|), 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 xnx^n, sup⁡x∈[0,1]∣xn−f(x)∣=1\sup_{x\in[0,1]} |x^n - f(x)|=1 for every nn (approached as x→1−x\to1^-), 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 gn(x)→0g_n(x) \to 0 at every fixed xx (pointwise convergence to zero), the base of the spike must shrink to width O(1/n)O(1/n), since eventually any fixed xx 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 ×\times width constant, e.g. n×2n=2n \times \frac{2}{n} = 2 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 ∑un(x)\sum u_n(x) of continuous functions. Any sequence counterexample converts to a series counterexample by taking u1=f1u_1 = f_1 and un=fn−fn−1u_n = f_n - f_{n-1} for n≥2n \ge 2, so that the partial sums SN=∑n=1Nun=fNS_N = \sum_{n=1}^N u_n = f_N recover the original sequence exactly. So the xnx^n example becomes a series whose partial sums are xNx^N, 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 sup⁡x∣fn(x)−f(x)∣\sup_x|f_n(x)-f(x)|). If yes, continuity and integral-interchange are justified immediately; for derivative-interchange, you additionally need fn′f_n' to converge uniformly (not just fnf_n) plus pointwise convergence of fnf_n 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.

Before this

Where this leads