The idea: the average slope is reached somewhere

Join the two ends of a curve, A = (a, f(a)) and B = (b, f(b)), with a straight line, the secant. Its slope is the average rate of change m = (f(b) − f(a)) / (b − a). The Mean Value Theorem says that if f is continuous on [a, b] and differentiable on (a, b), then at some point c strictly between a and b the tangent has exactly that slope:

f′(c) = (f(b) − f(a)) / (b − a)

In pictures: somewhere the tangent is parallel to the secant.

The curve y = square root of x from A at x = 0 to B at x = 4. A red secant joins A and B. A green tangent at c = 1 is parallel to the secant.
The secant from A to B and the tangent at c have the same slope m.

Finding c: slide the secant

Copy the secant and slide the copy sideways without turning it. The last place it still touches the curve is a point where the curve does not cross it, so it is a tangent there, with slope m. That point is c. For √x on [0, 4], m = (2 − 0) / 4 = 1/2 and f′(x) = 1 / (2√x) = 1/2 at c = 1. (Demo: √x on [0, 4])

Why must this happen? Look at the gap g(x) = f(x) − (secant at x). It is 0 at a and at b. A continuous g on [a, b] has a highest and a lowest point, and if one of them is inside (a, b), the slope of g is 0 there, which means f′(c) = m. The sliding line is just the level of that highest or lowest point.

The theorem promises at least one c, not exactly one. x³ − 3x on [−2, 2] has m = 1 and two points with 3c² − 3 = 1, at c = ±2/√3 ≈ ±1.1547: slide up to one, down to the other. (Demo: x³ − 3x)

f on [a, b]mc with f′(c) = m
√x on [0, 4]1/21
x³ − 3x on [−2, 2]1−1.1547 and 1.1547
sin x on [0, π]0π/2
eˣ on [0, 1]e − 1 ≈ 1.718ln(e − 1) ≈ 0.5413

Rolle's theorem: the flat case

When f(a) = f(b) the secant is horizontal, m = 0, and the theorem says there is a horizontal tangent somewhere in between: f′(c) = 0. This special case is Rolle's theorem, and the general theorem is proved from it by subtracting the secant (the gap g above has g(a) = g(b) = 0). For sin x on [0, π] the flat tangent is at the top, c = π/2. (Demo: Rolle)

The speed story

A car drives 120 km in 2 hours. Its average speed is 60 km/h, the slope of the secant on the position graph. The car's speed at each moment is the slope of the tangent, the number on the speedometer. The theorem says: at some instant the speedometer reads exactly 60. In the demo the car starts and ends at rest and peaks at 90 km/h, so it passes 60 twice, at t ≈ 0.42 h speeding up and t ≈ 1.58 h slowing down. (Demo: car trip)

Left: the car's position s(t) over 2 hours with a red secant of slope 60 and green tangents of slope 60 at 0.42 h and 1.58 h. Right: the speed curve rises from 0 to 90 and back to 0, crossing the dashed 60 km/h line at 0.42 h and 1.58 h.
Average 60 km/h: the speed curve must cross 60, here at 0.42 h and 1.58 h.

This is how average-speed cameras work: if your time between two cameras gives an average above the limit, then at some moment you were going above it.

When the hypotheses fail

Both conditions matter. If f has a corner (not differentiable) or a jump (not continuous), the sliding line can last touch at that bad point, where there is no tangent at all.

  • |x| on [−1, 2]: m = (2 − 1) / 3 = 1/3, but the slope of |x| is −1 or +1 everywhere, never 1/3. The line last touches at the corner (0, 0). (Demo: fails for |x|)
  • A step, 0 for x < 1 and 1 for x ≥ 1, on [0, 2]: m = 1/2, but the slope is 0 wherever it exists. The line touches only the top of the jump, and slides through the hole at (1, 0). (Demo: fails for a jump)
Left: |x| on minus 1 to 2 with a red secant of slope one third; a dashed parallel line touches only the corner at the origin. Right: a step function with a hole at (1, 0) and a filled point at (1, 1); the dashed parallel line touches only the top of the jump.
A corner or a jump: the parallel line touches only at the bad point, so no c has f′(c) = m.

Why it matters

  • If f′(x) = 0 everywhere on an interval, f is constant: any two points have m = f′(c) = 0. So two antiderivatives differ by a constant, which the Fundamental Theorem of Calculus relies on.
  • If f′ > 0 on an interval, f is increasing; this is the reason the sign of f′ tells where a graph rises (Derivative Graphs).
  • Error bounds: |f(b) − f(a)| ≤ (max |f′|) · |b − a|. It bounds the error of Riemann sums, Euler's method and the remainder of Taylor series.
  • Local linearity says f(x + h) ≈ f(x) + f′(x)·h; the theorem makes it exact: f(x + h) = f(x) + f′(c)·h for some c between x and x + h.

See also the derivative as a limit (where the tangent slope comes from) and Limits (continuity).

The mean value theorem turns a chord's slope Δy/Δx into f′(c), which is how the arc length integral is derived on Arc Length.

What the page leaves out

The c values come from solving f′(c) = m by hand, not from a numeric search. The page does not prove that a continuous function on [a, b] has a maximum (the extreme value theorem), and it does not cover Cauchy's mean value theorem (used for L'Hôpital's rule), the mean value theorem for integrals, or functions of several variables.

On the mind map

Open the Calculus mind mapCalculusLimits and derivatives: slope at a point, as a limitLimits and derivativesReading derivatives: what f′ and f″ say about fReading derivativesIntegrals: add up thin slicesIntegralsSeries: a function as an infinite sumSeriesNumerical methods: approximate when exact is hardNumerical methodsDifferential equations: know the slope, find the curveDifferential equationsChaos: simple rules, unpredictable pathsChaosVector calculus: fields that spread and spinVector calculusf, f′, f″ graphsf, f′, f″ graphssign of f′: up or down; f″: concavityCritical pointsCritical pointsf′ = 0 or undefined: max, min or neitherMean value theoremsome tangent is parallel to the secantLevel curvesLevel curvesclose contour lines mean steep groundLagrange multipliersLagrange multipliersoptimum where ∇f = λ∇g, curves tangentOptimization problemsOptimization problemsmax where f′ = 0, check the endsRelated ratesRelated ratesdifferentiate the link with timePartial derivativesPartial derivativesslice the surface, take one slopeOpen the Calculus mind mapCalculuswhat f′ and f″ say about fReading derivativesf, f′, f″ graphsf, f′, f″ graphssign of f′: up or down; f″: concavityCritical pointsCritical pointsf′ = 0 or undefined: max, min or neitherMean value theoremsome tangent is parallel to the secantLevel curvesLevel curvesclose contour lines mean steep groundLagrange multipliersLagrange multipliersoptimum where ∇f = λ∇g, curves tangentOptimization problemsOptimization problemsmax where f′ = 0, check the endsRelated ratesRelated ratesdifferentiate the link with timePartial derivativesPartial derivativesslice the surface, take one slope
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