The Newton–Leibniz Formula

Basis
Last updated: Tags: Calculus, Integration

Before the Newton–Leibniz formula was understood, computing a definite integral abf(x)dx\int_a^b f(x)\,dx meant forming Riemann sums, taking a limit, and somehow identifying what that limit equalled — a process that had to be carried out from scratch for every new integrand. The formula, also called the Fundamental Theorem of Calculus, cuts through all of that: it says that integration and differentiation are inverse operations, so to evaluate abf(x)dx\int_a^b f(x)\,dx you only need to find a function whose derivative is ff, then subtract its values at the endpoints. This single observation transforms pages of limit arguments into a one-line computation.

The theorem has two logically distinct halves. The first half shows that the integral with a variable upper limit is itself differentiable; the second half shows how any primitive (antiderivative) can be used to evaluate the integral. Together they form the deepest result in elementary calculus.

Part 1: the integral with a variable upper limit is a primitive

Let ff be continuous on [a,b][a, b]. Because ff is continuous it is Riemann integrable on every subinterval, so the expression

F(x)axf(t)dt,x[a,b],F(x) \coloneqq \int_a^x f(t)\,dt, \qquad x \in [a, b],

is well defined. We call FF the integral with variable upper limit, and the claim is that FF is differentiable with F(x)=f(x)F'(x) = f(x) at every point of [a,b][a, b].

Proof of Part 1

Fix x(a,b)x \in (a, b) and let h0h \neq 0 be small enough so that x+h[a,b]x + h \in [a, b]. By the additivity of the integral,

F(x+h)F(x)=ax+hf(t)dtaxf(t)dt=xx+hf(t)dt.F(x + h) - F(x) = \int_a^{x+h} f(t)\,dt - \int_a^x f(t)\,dt = \int_x^{x+h} f(t)\,dt.

Dividing by hh gives the difference quotient

F(x+h)F(x)h=1hxx+hf(t)dt.\frac{F(x+h) - F(x)}{h} = \frac{1}{h}\int_x^{x+h} f(t)\,dt.

Because ff is continuous on the interval between xx and x+hx+h, the mean value theorem for integrals guarantees the existence of a point ξh\xi_h between xx and x+hx + h such that

xx+hf(t)dt=f(ξh)h.\int_x^{x+h} f(t)\,dt = f(\xi_h)\cdot h.

Substituting back,

F(x+h)F(x)h=f(ξh).\frac{F(x+h) - F(x)}{h} = f(\xi_h).

As h0h \to 0 the point ξh\xi_h is squeezed between xx and x+hx + h, so ξhx\xi_h \to x. By continuity of ff,

f(ξh)f(x).f(\xi_h) \to f(x).

Therefore the difference quotient converges to f(x)f(x), which is exactly the definition of differentiability:

F(x)=f(x).F'(x) = f(x). \qquad \square

The same argument applies at the endpoints aa and bb using one-sided limits. Part 1 tells you that every continuous function has a primitive, and it exhibits one explicitly: the integral with variable upper limit.

Part 2: evaluating a definite integral via any primitive

Part 2 of the Newton–Leibniz formula says that if GG is any primitive of ff on [a,b][a, b] — that is, G(x)=f(x)G'(x) = f(x) for all x[a,b]x \in [a, b] — then

abf(x)dx=G(b)G(a).\int_a^b f(x)\,dx = G(b) - G(a).

Proof of Part 2

We already know from Part 1 that F(x)=axf(t)dtF(x) = \int_a^x f(t)\,dt is a primitive of ff. Since GG is also a primitive of ff, the difference GFG - F satisfies

(GF)(x)=G(x)F(x)=f(x)f(x)=0(G - F)'(x) = G'(x) - F'(x) = f(x) - f(x) = 0

for all x[a,b]x \in [a, b]. By the theorem that primitives differ by a constant (which follows from Lagrange’s mean value theorem), a function with zero derivative on an interval is constant. So there exists CRC \in \mathbb{R} with

G(x)=F(x)+Cfor all x[a,b].G(x) = F(x) + C \quad \text{for all } x \in [a, b].

Evaluate at both endpoints. At x=ax = a:

G(a)=F(a)+C=aaf(t)dt+C=0+C=C.G(a) = F(a) + C = \int_a^a f(t)\,dt + C = 0 + C = C.

At x=bx = b:

G(b)=F(b)+C=abf(t)dt+C.G(b) = F(b) + C = \int_a^b f(t)\,dt + C.

Subtracting the first equation from the second:

G(b)G(a)=abf(t)dt.G(b) - G(a) = \int_a^b f(t)\,dt. \qquad \square

The bracket notation

It is standard to write

[G(x)]abG(b)G(a).\bigl[G(x)\bigr]_a^b \coloneqq G(b) - G(a).

This notation is compact and reduces the risk of sign errors. With it, the Newton–Leibniz formula reads

abf(x)dx=[G(x)]ab,\int_a^b f(x)\,dx = \bigl[G(x)\bigr]_a^b,

where GG is any primitive of ff. You may add an arbitrary constant to GG without affecting the result, since the constant cancels in G(b)G(a)G(b) - G(a); this is why the choice of primitive does not matter.

Worked examples

Example 1: 01x2dx\int_0^1 x^2\,dx

A primitive of f(x)=x2f(x) = x^2 is G(x)=x33G(x) = \dfrac{x^3}{3}. Applying Newton–Leibniz:

01x2dx=[x33]01=133033=13.\int_0^1 x^2\,dx = \left[\frac{x^3}{3}\right]_0^1 = \frac{1^3}{3} - \frac{0^3}{3} = \frac{1}{3}.

Example 2: 0πsinxdx\int_0^\pi \sin x\,dx

A primitive of sinx\sin x is cosx-\cos x. Therefore

0πsinxdx=[cosx]0π=(cosπ)(cos0)=1+1=2.\int_0^\pi \sin x\,dx = \bigl[-\cos x\bigr]_0^\pi = (-\cos\pi) - (-\cos 0) = 1 + 1 = 2.

Geometrically, the result 22 is the area of one arch of the sine curve above the xx-axis, which is a satisfying sanity check.

Example 3: 1e1xdx\int_1^e \frac{1}{x}\,dx

A primitive of 1x\dfrac{1}{x} on (0,)(0, \infty) is lnx\ln x. Therefore

1e1xdx=[lnx]1e=lneln1=10=1.\int_1^e \frac{1}{x}\,dx = \bigl[\ln x\bigr]_1^e = \ln e - \ln 1 = 1 - 0 = 1.

This confirms the geometric meaning of ee: it is precisely the number for which the area under y=1/xy = 1/x from 11 to ee equals 11.

Summary

  • Part 1 (FTC1): If ff is continuous on [a,b][a, b], then F(x)axf(t)dtF(x) \coloneqq \int_a^x f(t)\,dt is differentiable and F(x)=f(x)F'(x) = f(x). In particular, every continuous function has a primitive.
  • Part 2 (FTC2): If GG is any primitive of ff on [a,b][a, b], then abf(x)dx=G(b)G(a)=[G(x)]ab\int_a^b f(x)\,dx = G(b) - G(a) = \bigl[G(x)\bigr]_a^b.
  • The proof of Part 1 uses the mean value theorem for integrals to identify the limit of the difference quotient with f(x)f(x).
  • The proof of Part 2 uses the fact that two primitives of the same function differ by a constant (from the primitives checkpoint), then evaluates that constant by plugging in x=ax = a.
  • The bracket notation [G(x)]ab=G(b)G(a)\bigl[G(x)\bigr]_a^b = G(b) - G(a) is a compact shorthand for the evaluation step.
  • The formula completely decouples the two problems that initially seemed inseparable: computing areas (definite integrals) and finding antiderivatives (primitives). To integrate, you only need to differentiate backwards.