The Constant-Multiple Rule
Picture a grassy hill, and every walking path that winds over it. Now run the whole
landscape through a machine that stretches it vertically to three times the
height — every point of ground is hoisted to triple its old altitude. What happens
to the paths? A stretch that ends three times higher must climb three times as much over the
same horizontal distance. Every path, at every point, is now exactly three times as
steep. The gentle parts are three times as gentle-ish, the brutal parts three times
as brutal — the shape of the terrain's steepness is unchanged, just multiplied
through by 3.
That is the whole of today's rule. Multiplying a function by a constant stretches its graph
vertically, and a vertical stretch scales every slope on the graph by the same
factor. In derivative language: a constant multiplier floats straight through the
derivative untouched. You never fight it, never expand it, never differentiate it —
you set it politely to one side, differentiate what's left, and multiply it back on at the
end.
The power rule
handles the bare powers x^n. But functions in the wild almost
never arrive bare — they come dressed with coefficients, like 5x^2,
-4x^3 or \tfrac{1}{2}x^4. This one
small rule is what lets the power rule loose on all of them.
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If f is differentiable and c is
any constant, then c\,f(x) is differentiable too, and
\frac{d}{dx}\bigl[\,c\cdot f(x)\,\bigr] = c\cdot f'(x).
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The constant may be any real number — positive, negative, a fraction, even an ugly one
like \sqrt{2} or \pi. It passes
through the derivative unchanged.
Scaling the curve scales the slope
Here's the hill-stretching machine in action on y = x^2. The
faint curve is the original; the bold one is y = c\,x^2. The
marked point sits at a fixed x, so the only thing
changing as you drag is the stretch factor c — and the tangent
line's slope reads c \cdot 2x, faithfully tracking it.
Watch two things as you slide. At c = 1 the bold curve lies on
top of the faint one and the slope is the plain 2x of the power
rule. Push c up and the curve rears — the rise over any little
run gets multiplied by c, while the run itself doesn't change at
all. Slope is rise over run, so slope picks up exactly one factor of
c. Not c^2, not
c + \text{something} — just c, once.
That's why doubling a function doubles its derivative, halving it halves the derivative, and
multiplying by a negative constant flips every slope's sign: the graph is reflected
as it's stretched, so uphill becomes downhill by the same amount.
Why it works: the constant escapes the limit
A picture is persuasive, but the
difference
quotient makes it airtight — and the proof is two lines long. Let's do it
concretely for g(x) = 5x^2. Build the difference quotient and
watch where the 5 sits:
\frac{g(x+h) - g(x)}{h} = \frac{5(x+h)^2 - 5x^2}{h} = \frac{5\bigl[(x+h)^2 - x^2\bigr]}{h} = 5 \cdot \frac{(x+h)^2 - x^2}{h}.
Both terms in the numerator carry the same factor of 5, so it
factors clean out of the top — and then clean out of the whole fraction. What remains inside
is exactly the difference quotient of the plain x^2. A
constant factor also passes freely through a limit (five times a quantity closing in on
L closes in on 5L), so:
g'(x) = \lim_{h \to 0} \; 5 \cdot \frac{(x+h)^2 - x^2}{h} = 5 \cdot \lim_{h \to 0} \frac{(x+h)^2 - x^2}{h} = 5 \cdot 2x = 10x.
Nothing in that argument used the 5 being a
5, or the function being x^2. Replace
them with any constant c and any differentiable
f and the same two moves — factor c
out of the quotient, carry it out of the limit — give the rule in full generality:
\frac{c\,f(x+h) - c\,f(x)}{h} = c\cdot\frac{f(x+h) - f(x)}{h} \;\xrightarrow{\,h\to 0\,}\; c\cdot f'(x).
This is worth doing from the definition once in your life — after today you'll
simply write the answer down. But knowing the constant genuinely escapes the limit, rather
than taking it on faith, is what separates using a rule from understanding one.
Read it off the graph
Here is the rule as a double portrait. The bold parabola is
f(x) = 3x^2; the faint one is the un-scaled
x^2. The bold dashed line is the derivative
f'(x) = 6x; the faint dashed line is the un-scaled
2x.
Pick any x and read vertically. At x = 1:
the plain parabola sits at height 1 with slope
2; the bold one sits at height 3 with
slope 6. At x = 2: heights
4 and 12, slopes
4 and 12. Everywhere you look, the
bold curve is exactly three times as tall and exactly three times as steep — the
derivative line 6x is just 2x put
through the same three-fold stretch. One constant, scaling both stories at once.
Teamwork with the power rule
In practice you'll use this rule welded to the power rule as a single reflex. To
differentiate c\,x^n: keep the c,
power-rule the x^n, and multiply the two numbers out front
together:
\frac{d}{dx}\bigl[\,c\,x^n\,\bigr] = c\cdot n\,x^{\,n-1}.
Example 1. \dfrac{d}{dx}\bigl[7x^4\bigr]. Hold
the 7; the power rule turns x^4 into
4x^3; multiply back:
\frac{d}{dx}\bigl[7x^4\bigr] = 7 \cdot 4x^3 = 28x^3.
Example 2 — a negative constant.
\dfrac{d}{dx}\bigl[-4x^3\bigr]. The minus sign is part of the
constant c = -4, and it rides along like any other constant:
\frac{d}{dx}\bigl[-4x^3\bigr] = (-4) \cdot 3x^2 = -12x^2.
Geometrically that minus sign makes sense: -4x^3 is
4x^3 flipped upside down, so wherever the original climbed, the
flipped copy descends at four times x^3's rate — every slope
negated and quadrupled.
Example 3 — a fractional constant.
\dfrac{d}{dx}\bigl[\tfrac{1}{2}x^4\bigr] = \tfrac{1}{2}\cdot 4x^3 = 2x^3.
Fractions squash instead of stretch, but the arithmetic is identical: the two front numbers
just multiply. A few more, at a glance:
| Function |
Hold the constant, power-rule the rest |
Derivative |
| 5x^2 | 5 \cdot 2x | 10x |
| 7x^4 | 7 \cdot 4x^3 | 28x^3 |
| -4x^3 | (-4) \cdot 3x^2 | -12x^2 |
| \tfrac{1}{2}x^4 | \tfrac{1}{2} \cdot 4x^3 | 2x^3 |
| \pi x^2 | \pi \cdot 2x | 2\pi x |
| -x^5 | (-1) \cdot 5x^4 | -5x^4 |
Notice the last two rows. A constant needn't be a tidy integer —
\pi is just a number, so it floats through like any other. And a
lone minus sign is the constant c = -1 in disguise:
differentiating -f(x) always just negates
f'(x).
Two traps, both of them classics:
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The rule is for constants that multiply — c\,f(x),
on the outside. It says nothing whatsoever about f(c\,x),
where the constant is inside the function. Those are different transformations:
3x^2 stretches the parabola vertically, but
(3x)^2 = 9x^2 squashes it horizontally — and a
horizontal squash changes slopes in its own, different way (here by a factor of
9, not 3). Inside-the-function
constants are the business of the
chain rule,
which comes later. Until then: check where the constant sits before you let it
float through.
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Don't differentiate the constant too. A tempting wrong move: "the
derivative of a
constant is zero, so
\frac{d}{dx}[5x^2] = 0 \cdot 2x = 0." No! That zero rule is for
a constant standing alone, a flat graph with no x in
sight. A constant multiplying a function isn't standing alone — it's scaling a
live, changing quantity, and it survives intact:
\frac{d}{dx}[5x^2] = 5 \cdot 2x = 10x. One constant vanishes
when differentiated; the other is preserved. Which happens depends entirely on whether the
constant is added on or multiplied on.
Suppose you and a French friend both track the same road trip: distance travelled as a
function of time. You record miles; she records kilometres. Her function is just yours
multiplied by the constant 1.609 — kilometres are a constant
multiple of miles. Now you each differentiate to get speed. Do you need two different
theories of differentiation, one per unit system? The constant-multiple rule says no: her
derivative is exactly 1.609 times yours, everywhere, forever. Your
speedometers agree about every overtake, every slowdown, every moment of peak speed — they
just report it in different units.
This is quietly profound. Every unit conversion — miles to kilometres, pounds to kilograms,
dollars to euros at a fixed rate — is multiplication by a constant, and the rule guarantees
that calculus commutes with changing units: convert then differentiate, or
differentiate then convert, you land in the same place. That's why a physicist can write
F = ma without a footnote about which country you're in.
And there's a deeper pattern waiting here. Pair today's rule with its sibling, the
sum rule,
and you get the two defining properties of what mathematicians call a linear
operator: the derivative respects addition and respects constant scaling.
Linearity turns out to be perhaps the most consequential property in all of mathematics —
it's the reason matrices, Fourier series and quantum mechanics all run on the same
machinery. This little rule is your first taste of it.