For any function that is continuous on [a, b] and differentiable on (a, b) there exists some c in the interval (a, b) such that the secant joining the endpoints of the interval [a, b] is parallel to the tangent at c.
In mathematics, the mean value theorem states, roughly: that given a planar arc between two endpoints, there is at least one point at which the tangent to the arc is parallel to the secant through its endpoints.
The theorem is used to prove global statements about a function on an interval starting from local hypotheses about derivatives at points of the interval.
More precisely, if a function f is continuous on the closed interval [a, b], where a < b, and differentiable on the open interval (a, b), then there exists a point c in (a, b) such that

f'(c) = \frac{f(b)  f(a)}{ba} \, .^{[1]}
A special case of this theorem was first described by Parameshvara (1370–1460) from the Kerala school of astronomy and mathematics in his commentaries on Govindasvāmi and Bhaskara II.^{[2]} The mean value theorem in its modern form was later stated by Augustin Louis Cauchy (1789–1857). It is one of the most important results in differential calculus, as well as one of the most important theorems in mathematical analysis, and is useful in proving the fundamental theorem of calculus. The mean value theorem follows from the more specific statement of Rolle's theorem, and can be used to prove the more general statement of Taylor's theorem (with Lagrange form of the remainder term).
Contents

Formal statement 1

Proof 2

A simple application 3

Cauchy's mean value theorem 4

Proof of Cauchy's mean value theorem 4.1

Generalization for determinants 5

Mean value theorem in several variables 6

Mean value theorem for vectorvalued functions 7

Mean value theorems for integration 8

First mean value theorem for integration 8.1

Proof of the first mean value theorem for integration 8.2

Second mean value theorem for integration 8.3

A probabilistic analogue of the mean value theorem 9

Generalization in complex analysis 10

See also 11

Notes 12

External links 13
Formal statement
Let f : [a, b] → R be a continuous function on the closed interval [a, b], and differentiable on the open interval (a, b), where a < b. Then there exists some c in (a, b) such that


f ' (c) = \frac{f(b)  f(a)}{b  a}.
The mean value theorem is a generalization of Rolle's theorem, which assumes f(a) = f(b), so that the righthand side above is zero.
The mean value theorem is still valid in a slightly more general setting. One only needs to assume that f : [a, b] → R is continuous on [a, b], and that for every x in (a, b) the limit

\lim_{h\to 0}\frac{f(x+h)f(x)}{h}
exists as a finite number or equals +∞ or −∞. If finite, that limit equals f′(x). An example where this version of the theorem applies is given by the realvalued cube root function mapping x to x^{1/3}, whose derivative tends to infinity at the origin.
Note that the theorem, as stated, is false if a differentiable function is complexvalued instead of realvalued. For example, define f(x) = e^{ix} for all real x. Then

f(2π) − f(0) = 0 = 0(2π − 0)
while f′(x) ≠ 0 for any real x.
Proof
The expression (f(b) − f(a)) / (b − a) gives the slope of the line joining the points (a, f(a)) and (b, f(b)), which is a chord of the graph of f, while f '(x) gives the slope of the tangent to the curve at the point (x, f(x)). Thus the Mean value theorem says that given any chord of a smooth curve, we can find a point lying between the endpoints of the chord such that the tangent at that point is parallel to the chord. The following proof illustrates this idea.
Define g(x) = f(x) − rx, where r is a constant. Since f is continuous on [a, b] and differentiable on (a, b), the same is true for g. We now want to choose r so that g satisfies the conditions of Rolle's theorem. Namely

\begin{align}g(a)=g(b)&\iff f(a)ra=f(b)rb\\ &\iff r(ba)=f(b)f(a) \\&\iff r=\frac{f(b)f(a)}{ba}\cdot\end{align}
By Rolle's theorem, since g is differentiable and g(a) = g(b), there is some c in (a, b) for which g′(c) = 0, and it follows from the equality g(x) = f(x) − rx that,

f '(c)=g '(c)+r=0+r=\frac{f(b)f(a)}{ba}
as required.
A simple application
Assume that f is a continuous, realvalued function, defined on an arbitrary interval I of the real line. If the derivative of f at every interior point of the interval I exists and is zero, then f is constant.
Proof: Assume the derivative of f at every interior point of the interval I exists and is zero. Let (a, b) be an arbitrary open interval in I. By the mean value theorem, there exists a point c in (a,b) such that

0 = f'(c) = \frac{f(b)f(a)}{ba}.
This implies that f(a) = f(b). Thus, f is constant on the interior of I and thus is constant on I by continuity. (See below for a multivariable version of this result.)
Remarks:
Cauchy's mean value theorem
Cauchy's mean value theorem, also known as the extended mean value theorem, is a generalization of the mean value theorem. It states: If functions f and g are both continuous on the closed interval [a,b], and differentiable on the open interval (a, b), then there exists some c ∈ (a,b), such that
Geometrical meaning of Cauchy's theorem

(f(b)f(a))g\,'(c)=(g(b)g(a))f\,'(c).\,
Of course, if g(a) ≠ g(b) and if g′(c) ≠ 0, this is equivalent to:

\frac{f'(c)}{g'(c)}=\frac{f(b)f(a)}{g(b)g(a)}\cdot
Geometrically, this means that there is some tangent to the graph of the curve

\begin{array}{ccc}[a,b]&\longrightarrow&\mathbb{R}^2\\t&\mapsto&\bigl(f(t),g(t)\bigr),\end{array}
which is parallel to the line defined by the points (f(a),g(a)) and (f(b),g(b)). However Cauchy's theorem does not claim the existence of such a tangent in all cases where (f(a),g(a)) and (f(b),g(b)) are distinct points, since it might be satisfied only for some value c with f′(c) = g′(c) = 0, in other words a value for which the mentioned curve is stationary; in such points no tangent to the curve is likely to be defined at all. An example of this situation is the curve given by

t\mapsto(t^3,1t^2),
which on the interval [−1,1] goes from the point (−1,0) to (1,0), yet never has a horizontal tangent; however it has a stationary point (in fact a cusp) at t = 0.
Cauchy's mean value theorem can be used to prove l'Hôpital's rule. The mean value theorem is the special case of Cauchy's mean value theorem when g(t) = t.
Proof of Cauchy's mean value theorem
The proof of Cauchy's mean value theorem is based on the same idea as the proof of the mean value theorem.

Suppose that g(a) ≠g(b). Define h(x) = f(x) − rg(x), where r is fixed in such a way that h(a) = h(b), namely

\begin{align}h(a)=h(b)&\iff f(a)r\,g(a)=f(b)r\,g(b)\\ &\iff r\,(g(b)g(a))=f(b)f(a)\\ &\iff r=\frac{f(b)f(a)}{g(b)g(a)}.\end{align}

Since f and g are continuous on [a, b] and differentiable on (a, b), the same is true for h. All in all, h satisfies the conditions of Rolle's theorem: consequently, there is some c in (a, b) for which h′(c) = 0.

From the equality h(x) = f(x) − rg(x) it follows that,

0=h'(c)=f'(c)r\, g'(c) \Rightarrow(g(b)g(a))\,f'(c)=(g(b)g(a))\,r\,g'(c)=(f(b)f(a))\,g'(c)

as required.

If instead g(a) = g(b), then, applying Rolle's theorem to g, it follows that there exists c in (a, b) for which g′(c) = 0. Using this choice of c, Cauchy's mean value theorem (trivially) holds.
Generalization for determinants
Assume that f, g, and h are differentiable functions on (a,b) that are continuous on [a,b]. Define

D(x)=\left\begin{array}{ccc}f(x) & g(x)& h(x)\\ f(a) & g(a) & h(a)\\ f(b) & g(b)& h(b)\end{array}\right
There exists c\in(a,b) such that D'(c)=0.
Notice that

D'(x)=\left\begin{array}{ccc}f'(x) & g'(x)& h'(x)\\ f(a) & g(a) & h(a)\\ f(b) & g(b)& h(b)\end{array}\right
and if we place h(x)=1, we get Cauchy's mean value theorem. If we place h(x)=1 and g(x)=x we get Lagrange's mean value theorem.
The proof of the generalization is quite simple: each of D(a) and D(b) are determinants with two identical rows, hence D(a)=D(b)=0. The Rolle's theorem implies that there exists c\in (a,b) such that D'(c)=0.
Mean value theorem in several variables
The mean value theorem in one variable generalizes to several variables by applying the theorem in one variable via parametrization. Let G be an open connected subset of R^{n}, and let f : G → R be a differentiable function. Fix points x, y ∈ G such that the interval x y lies in G, and define g(t) = f((1 − t)x + ty). Since g is a differentiable function in one variable, the mean value theorem gives:

g(1)  g(0) = g'(c) \!
for some c between 0 and 1. But since g(1) = f(y) and g(0) = f(x), computing g′(c) explicitly we have:

f(y)  f(x) = \nabla f ((1 c)x + cy) \cdot (y  x)
where ∇ denotes a gradient and · a dot product. Note that this is an exact analog of the theorem in one variable (in the case n = 1 this is the theorem in one variable). By the Schwarz inequality, the equation gives the estimate:

f(y)  f(x) \le \nabla f ((1 c)x + cy) \, y  x.
In particular, when the partial derivatives of f are bounded, f is Lipschitz continuous (and therefore uniformly continuous). Note that f is not assumed to be continuously differentiable nor continuous on the closure of G. However, in the above, we used the chain rule so the existence of ∇f would not be sufficient.
As an application of the above, we prove that f is constant if G is open and connected and every partial derivative of f is 0. Pick some point x_{0} ∈ G, and let g(x) = f(x) − f(x_{0}). We want to show g(x) = 0 for every x ∈ G. For that, let E = {x ∈ G : g(x) = 0} . Then E is closed and nonempty. It is open too: for every x ∈ E,

g(y) = g(y)  g(x) \le (0) y  x = 0
for every y in some neighborhood of x. (Here, it is crucial that x and y are sufficiently close to each other.) Since G is connected, we conclude E = G.
Remark that all arguments in the above are made in a coordinatefree manner; hence, they actually generalize to the case when G is a subset of a Banach space.
Mean value theorem for vectorvalued functions
There is no exact analog of the mean value theorem for vectorvalued functions. Jean Dieudonné in his classic treatise Foundations of Modern Analysis discards the mean value theorem and replaces it by mean inequality as the proof is not constructive and by no way one can find the mean value. In applications one only needs mean inequality. Serge Lang in Analysis I uses the mean value theorem, in integral form, as an instant reflex but this use requires the continuity of the derivative. If one uses the HenstockKurzweil integral one can have the mean value theorem in integral form without the additional assumption that derivative should be continuous as every derivative is HenstockKurzweil integrable. The problem is roughly speaking the following: If f : U → R^{m} is a differentiable function (where U ⊂ R^{n} is open) and if x + th, x, h ∈ R^{n}, t ∈ [0, 1] is the line segment in question (lying inside U), then one can apply the above parametrization procedure to each of the component functions f_{i} (i = 1, ..., m) of f (in the above notation set y = x + h). In doing so one finds points x + t_{i}h on the line segment satisfying

f_i(x+h)  f_i(x) = \nabla f_i (x + t_ih) \cdot h.\,
But generally there will not be a single point x + t*h on the line segment satisfying

f_i(x+h)  f_i(x) = \nabla f_i (x + t^* h) \cdot h.\,
for all i simultaneously. (As a counterexample one could take f : [0, 2π] → R^{2} defined via the component functions f_{1}(x) = cos(x), f_{2}(x) = sin(x). Then f(2π) − f(0) = 0 ∈ R^{2}, but \,f_1'(x)=\sin (x) and \,f_2'(x)=\cos (x) are never simultaneously zero as x ranges over [0, 2π].)
However a certain type of generalization of the mean value theorem to vectorvalued functions is obtained as follows: Let f be a continuously differentiable realvalued function defined on an open interval I, and let x as well as x + h be points of I. The mean value theorem in one variable tells us that there exists some t* between 0 and 1 such that

f(x+h)f(x) = f'(x+t^*h)\cdot h. \,
On the other hand we have, by the fundamental theorem of calculus followed by a change of variables,

f(x+h)f(x) = \int_x^{x+h} f'(u)du = \left(\int_0^1 f'(x+th)\,dt\right)\cdot h.
Thus, the value f′(x + t*h) at the particular point t* has been replaced by the mean value

\int_0^1 f'(x+th)\,dt.
This last version can be generalized to vector valued functions:
Let U ⊂ R^{n} be open, f : U → R^{m} continuously differentiable, and x ∈ U, h ∈ R^{n} vectors such that the whole line segment x + th, 0 ≤ t ≤ 1 remains in U. Then we have:

\text{(*)} \qquad f(x+h)f(x) = \left(\int_0^1 Df(x+th)\,dt\right)\cdot h,
where the integral of a matrix is to be understood componentwise. (Df denotes the Jacobian matrix of f.)
From this one can further deduce that if Df(x + th) is bounded for t between 0 and 1 by some constant M, then

\text{(**)} \qquad \f(x+h)f(x)\ \leq M\h\.
Proof of (*). Write f_{i} (i = 1, ..., m) for the real valued components of f. Define the functions g_{i}: [0, 1] → R by g_{i}(t) := f_{i}(x + th).
Then we have

f_i(x+h)f_i(x)\, =\, g_i(1)g_i(0) =\int_0^1 g_i'(t)dt = \int_0^1 \left(\sum_{j=1}^n \frac{\partial f_i}{\partial x_j} (x+th)h_j\right)\,dt =\sum_{j=1}^n \left(\int_0^1 \frac{\partial f_i}{\partial x_j}(x+th)\,dt\right)h_j.
The claim follows since Df is the matrix consisting of the components \frac{\partial f_i}{\partial x_j}, q.e.d.
Proof of (**). From (*) it follows that

\f(x+h)f(x)\=\left\\int_0^1 (Df(x+th)\cdot h)\,dt\right\ \leq \int_0^1 \Df(x+th)\ \cdot \h\\, dt \leq M\ h\.
Here we have used the following
Lemma. Let v : [a, b] → R^{m} be a continuous function defined on the interval [a, b] ⊂ R. Then we have
\text{(***)}\qquad \left\\int_a^b v(t)\,dt\right\\leq \int_a^b \v(t)\\,dt.
Proof of (***). Let u in R^{m} denote the value of the integral

u:=\int_a^b v(t)\,dt.
Now

\u\^2 = \langle u,u \rangle = \left\langle \int_a^b v(t) dt,u \right\rangle = \int_a^b \langle v(t),u \rangle \,dt \leq \int_a^b \ v(t) \\cdot \u \\,dt = \u\ \int_a^b \v(t)\\,dt,
thus \ u\ \leq \int_a^b \v(t)\\,dt as desired. (Note the use of the Cauchy–Schwarz inequality.) This shows (***) and thereby finishes the proof of (**).
Mean value theorems for integration
First mean value theorem for integration
The first mean value theorem for integration states

If G : [a, b] → R is a continuous function and \varphi is an integrable function that does not change sign on the interval (a, b), then there exists a number x in [a, b] such that


\int_a^b G(t)\varphi (t) \, dt=G(x) \int_a^b \varphi (t) \, dt.
In particular, if φ(t) = 1 for all t in [a, b], then there exists x in (a, b) such that

\int_a^b G(t) \, dt=\ G(x)(b  a).\,
When presented in the equivalent form

\frac{1}{ba} \int_a^b G(t) \, dt=\ G(x),
the theorem's conclusion says that the mean value of G(t) on [a, b] (which is defined by the left side) is achieved as the point value G(x) for some x in (a, b).
Proof of the first mean value theorem for integration
Without loss of generality assume the onesigned function \varphi(t)\ge 0 for all t (the negative case just changes direction of some inequalities). It follows from the extreme value theorem that the continuous function G has a finite infimum m and a finite supremum M on the interval [a, b]. From the monotonicity of the integral and the fact that m ≤ G(t) ≤ M, it follows from the nonnegativity of \varphi(t) that

m I= \int_a^b m\varphi(t)\,dt \le \int^b_aG(t)\varphi(t) \, dt \le \int_a^b M\varphi(t)\,dt = M I,
where

I:=\int^b_a\varphi(t) \, dt
denotes the integral of \varphi(t). Hence, if I = 0, then the claimed equality holds for every x in [a, b]. Therefore, we may assume I > 0 in the following. Dividing through by I we have that

m \le \frac1I\int^b_aG(t)\varphi(t) \, dt\le M.
The extreme value theorem tells us more than just that the infimum and supremum of G on [a, b] are finite; it tells us that both are actually attained. Thus we can apply the intermediate value theorem, and conclude that the continuous function G attains every value of the interval [m, M], in particular there exists x in [a, b] such that

G(x) = \frac1I\int^b_aG(t)\varphi(t) \, dt.
This completes the proof.
Second mean value theorem for integration
There are various slightly different theorems called the second mean value theorem for integration. A commonly found version is as follows:

If G : [a, b] → R is a positive monotonically decreasing function and φ : [a, b] → R is an integrable function, then there exists a number x in (a, b] such that


\int_a^b G(t)\varphi(t)\,dt = G(a+0) \int_a^x \varphi(t)\,dt.
Here G(a + 0) stands for {\underset{a_+}{\lim}G}, the existence of which follows from the conditions. Note that it is essential that the interval (a, b] contains b. A variant not having this requirement is:

If G : [a, b] → R is a monotonic (not necessarily decreasing and positive) function and φ : [a, b] → R is an integrable function, then there exists a number x in (a, b) such that


\int_a^b G(t)\varphi(t)\,dt = G(a+0) \int_a^x \varphi(t)\,dt + G(b0) \int_x^b \varphi(t)\,dt.
This variant was proved by Hiroshi Okamura in 1947.^{[3]}
A probabilistic analogue of the mean value theorem
Let X and Y be nonnegative random variables such that E[X] < E[Y] < ∞ and X\leq_{st} Y (i.e. X is smaller than Y in the usual stochastic order). Then there exists an absolutely continuous nonnegative random variable Z having probability density function


f_Z(x)={\Pr(Y>x)\Pr(X>x)\over {\rm E}[Y]{\rm E}[X]}\,, \qquad x\geq 0.
Let g be a measurable and differentiable function such that E[g(X)], E[g(Y)] < ∞, and let its derivative g′ be measurable and Riemannintegrable on the interval [x, y] for all y ≥ x ≥ 0. Then, E[g′(Z)] is finite and^{[4]}


{\rm E}[g(Y)]{\rm E}[g(X)]={\rm E}[g'(Z)]\,[{\rm E}(Y){\rm E}(X)].
Generalization in complex analysis
As noted above, the theorem does not hold for differentiable complexvalued functions. Instead, a generalization of the theorem is stated such:^{[5]}
Let f : Ω → C be a holomorphic function on the open convex set Ω, and let a and b be distinct points in Ω. Then there exist points u, v on L_{ab} (the line segment from a to b) such that


\mathrm{Re}(f'(u)) = \mathrm{Re}\left( \frac{f(b)f(a)}{ba} \right),

\mathrm{Im}(f'(v)) = \mathrm{Im}\left( \frac{f(b)f(a)}{ba} \right).
Where Re() is the Real part and Im() is the Imaginary part of a complexvalued function.
See also
Notes

^ Weisstein, Eric. "MeanValue Theorem".

^ J. J. O'Connor and E. F. Robertson (2000). Paramesvara, MacTutor History of Mathematics archive.

^ "On the second mean value theorem of integral". Mathematics, edited by theMath. Soc., Vol. 1 (1947).

^ A. Di Crescenzo (1999). A probabilistic analogue of the mean value theorem and its applications to reliability theory. J. Appl. Prob. 36, 706719.

^ "Complex MeanValue Theorem".
External links

Hazewinkel, Michiel, ed. (2001), "Cauchy theorem",

PlanetMath: MeanValue Theorem

Weisstein, Eric W., "Mean value theorem", MathWorld.

Weisstein, Eric W., "Cauchy's MeanValue Theorem", MathWorld.

"Mean Value Theorem: Intuition behind the Mean Value Theorem" at the Khan Academy
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