Extended mean value theorem: Difference between revisions

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(Created page with "<jsxgraph width="600" height="400" box="box"> var board = JXG.JSXGraph.initBoard('box', {boundingbox: [-5, 10, 7, -6], axis:true}); var p = []; board.suspendUpdate(); p[0] =...")
 
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The ''extended mean value theorem'' (also called ''Cauchy's mean value theorem'') is usually formulated as:
Let
:<math> f, g: [a,b] \to \mathbb{R}</math>
be continuous functions that are differentiable on the open interval <math>(a,b)</math>.
If <math>g'(x)\neq 0</math> for all <math>x\in(a,b)</math>,
then there exists a value <math>\xi \in (a,b)</math> such that
:<math>
\frac{f'(\xi)}{g'(\xi)} = \frac{f(b)-f(a)}{g(b)-g(a)}.
</math>
'''Remark:'''
It seems to be easier to state the extended mean value theorem in the following form:
Let
:<math> f, g: [a,b] \to \mathbb{R}</math>
be continuous functions that are differentiable on the open interval <math>(a,b)</math>.
Then there exists a value <math>\xi \in (a,b)</math> such that
:<math>
f'(\xi)\cdot (g(b)-g(a))  = g'(\xi) \cdot (f(b)-f(a)).
</math>
This second formulation avoids the need that
<math>g'(x)\neq 0</math> for all <math>x\in(a,b)</math> and is therefore much easier to
handle numerically.
The proof is similar, just use the function
:<math>
h(x) = f(x)\cdot(g(b)-g(a)) - (g(x)-g(a))\cdot(f(b)-f(a))
</math>
and apply Rolle's theorem.
'''Visualization:'''
The extended mean value theorem says that given the curve
:<math> C: [a,b]\to\mathbb{R}, \quad t \mapsto (f(t), g(t)) </math>
with the above prerequisites for <math>f</math> and <math>g</math>,
there exists a <math>\xi</math> such that the tangent to the curve in the point <math>C(\xi)</math> 
is parallel to the secant through <math>C(a)</math> and <math>C(b)</math>.
<jsxgraph width="600" height="400" box="box">
<jsxgraph width="600" height="400" box="box">
var board = JXG.JSXGraph.initBoard('box', {boundingbox: [-5, 10, 7, -6], axis:true});
var board = JXG.JSXGraph.initBoard('box', {boundingbox: [-5, 10, 7, -6], axis:true});
var p = [];
var p = [];


board.suspendUpdate();
p[0] = board.create('point', [0, -2], {size:2, name: 'C(a)'});
p[0] = board.create('point', [-1,-2], {size:2});
p[1] = board.create('point', [-1.5, 5], {size:2, name: ''});
p[1] = board.create('point', [6,5], {size:2});
p[2] = board.create('point', [1, 4], {size:2, name: ''});
p[2] = board.create('point', [-0.5,1], {size:2});
p[3] = board.create('point', [3, 3], {size:2, name: 'C(b)'});
p[3] = board.create('point', [3,3], {size:2});
 
var x = JXG.Math.Numerics.lagrangePolynomial(p);
// Curve
/*
var fg = JXG.Math.Numerics.Neville(p);
var graph = board.create('functiongraph', [f,-10, 10]);
var graph = board.create('curve', fg, {strokeWidth:3, strokeOpacity:0.5});
 
// Secant
line = board.create('line', [p[0], p[3]], {strokeColor:'#ff0000', dash:1});
 
var df = JXG.Math.Numerics.D(fg[0]);
var dg = JXG.Math.Numerics.D(fg[1]);


var g = function(x) {
// Usually, the extended mean value theorem is formulated as
    return JXG.Math.Numerics.D(f)(x)-(p[1].Y()-p[0].Y())/(p[1].X()-p[0].X());
// df(t) / dg(t) == (p[3].X() - p[0].X()) / (p[3].Y() - p[0].Y())
// We can avoid division by zero with the following formulation:
var quot = function(t) {
    return df(t) * (p[3].Y() - p[0].Y()) - dg(t) * (p[3].X() - p[0].X());
};
};


var r = board.create('glider', [
var r = board.create('glider', [
                    function() { return JXG.Math.Numerics.root(g,(p[0].X()+p[1].X())*0.5); },
            function() { return fg[0](JXG.Math.Numerics.root(quot, (fg[3]() + fg[2]) * 0.5)); },
                    function() { return f(JXG.Math.Numerics.root(g,(p[0].X()+p[1].X())*0.5)); },
            function() { return fg[1](JXG.Math.Numerics.root(quot, (fg[3]() + fg[2]) * 0.5)); },
                    graph], {name:' ',size:4,fixed:true});
            graph], {name: 'C(&xi;)', size: 4, fixed:true, color: 'blue'});
 
board.create('tangent', [r], {strokeColor:'#ff0000'});
board.create('tangent', [r], {strokeColor:'#ff0000'});
line = board.create('line',[p[0],p[1]],{strokeColor:'#ff0000',dash:1});
 
*/
board.unsuspendUpdate();
</jsxgraph>
</jsxgraph>


===The underlying JavaScript code===
===The underlying JavaScript code===
<source lang="javascript">
<source lang="javascript">
var board = JXG.JSXGraph.initBoard('box', {boundingbox: [-5, 10, 7, -6], axis:true});
var p = [];
p[0] = board.create('point', [0, -2], {size:2, name: 'C(a)'});
p[1] = board.create('point', [-1.5, 5], {size:2, name: ''});
p[2] = board.create('point', [1, 4], {size:2, name: ''});
p[3] = board.create('point', [3, 3], {size:2, name: 'C(b)'});
// Curve
var fg = JXG.Math.Numerics.Neville(p);
var graph = board.create('curve', fg, {strokeWidth:3, strokeOpacity:0.5});
// Secant
line = board.create('line', [p[0], p[3]], {strokeColor:'#ff0000', dash:1});
var df = JXG.Math.Numerics.D(fg[0]);
var dg = JXG.Math.Numerics.D(fg[1]);
// Usually, the extended mean value theorem is formulated as
// df(t) / dg(t) == (p[3].X() - p[0].X()) / (p[3].Y() - p[0].Y())
// We can avoid division by zero with the following formulation:
var quot = function(t) {
    return df(t) * (p[3].Y() - p[0].Y()) - dg(t) * (p[3].X() - p[0].X());
};
var r = board.create('glider', [
            function() { return fg[0](JXG.Math.Numerics.root(quot, (fg[3]() + fg[2]) * 0.5)); },
            function() { return fg[1](JXG.Math.Numerics.root(quot, (fg[3]() + fg[2]) * 0.5)); },
            graph], {name: 'C(&xi;)', size: 4, fixed:true, color: 'blue'});
board.create('tangent', [r], {strokeColor:'#ff0000'});
</source>
</source>


[[Category:Examples]]
[[Category:Examples]]
[[Category:Calculus]]
[[Category:Calculus]]

Latest revision as of 11:38, 4 February 2019

The extended mean value theorem (also called Cauchy's mean value theorem) is usually formulated as:

Let

[math]\displaystyle{ f, g: [a,b] \to \mathbb{R} }[/math]

be continuous functions that are differentiable on the open interval [math]\displaystyle{ (a,b) }[/math]. If [math]\displaystyle{ g'(x)\neq 0 }[/math] for all [math]\displaystyle{ x\in(a,b) }[/math], then there exists a value [math]\displaystyle{ \xi \in (a,b) }[/math] such that

[math]\displaystyle{ \frac{f'(\xi)}{g'(\xi)} = \frac{f(b)-f(a)}{g(b)-g(a)}. }[/math]

Remark: It seems to be easier to state the extended mean value theorem in the following form:

Let

[math]\displaystyle{ f, g: [a,b] \to \mathbb{R} }[/math]

be continuous functions that are differentiable on the open interval [math]\displaystyle{ (a,b) }[/math]. Then there exists a value [math]\displaystyle{ \xi \in (a,b) }[/math] such that

[math]\displaystyle{ f'(\xi)\cdot (g(b)-g(a)) = g'(\xi) \cdot (f(b)-f(a)). }[/math]

This second formulation avoids the need that [math]\displaystyle{ g'(x)\neq 0 }[/math] for all [math]\displaystyle{ x\in(a,b) }[/math] and is therefore much easier to handle numerically.

The proof is similar, just use the function

[math]\displaystyle{ h(x) = f(x)\cdot(g(b)-g(a)) - (g(x)-g(a))\cdot(f(b)-f(a)) }[/math]

and apply Rolle's theorem.

Visualization: The extended mean value theorem says that given the curve

[math]\displaystyle{ C: [a,b]\to\mathbb{R}, \quad t \mapsto (f(t), g(t)) }[/math]

with the above prerequisites for [math]\displaystyle{ f }[/math] and [math]\displaystyle{ g }[/math], there exists a [math]\displaystyle{ \xi }[/math] such that the tangent to the curve in the point [math]\displaystyle{ C(\xi) }[/math] is parallel to the secant through [math]\displaystyle{ C(a) }[/math] and [math]\displaystyle{ C(b) }[/math].


The underlying JavaScript code

var board = JXG.JSXGraph.initBoard('box', {boundingbox: [-5, 10, 7, -6], axis:true});
var p = [];

p[0] = board.create('point', [0, -2], {size:2, name: 'C(a)'});
p[1] = board.create('point', [-1.5, 5], {size:2, name: ''});
p[2] = board.create('point', [1, 4], {size:2, name: ''});
p[3] = board.create('point', [3, 3], {size:2, name: 'C(b)'});

// Curve
var fg = JXG.Math.Numerics.Neville(p);
var graph = board.create('curve', fg, {strokeWidth:3, strokeOpacity:0.5});

// Secant 
line = board.create('line', [p[0], p[3]], {strokeColor:'#ff0000', dash:1});

var df = JXG.Math.Numerics.D(fg[0]);
var dg = JXG.Math.Numerics.D(fg[1]);

// Usually, the extended mean value theorem is formulated as
// df(t) / dg(t) == (p[3].X() - p[0].X()) / (p[3].Y() - p[0].Y())
// We can avoid division by zero with the following formulation:
var quot = function(t) {
    return df(t) * (p[3].Y() - p[0].Y()) - dg(t) * (p[3].X() - p[0].X());
};

var r = board.create('glider', [
            function() { return fg[0](JXG.Math.Numerics.root(quot, (fg[3]() + fg[2]) * 0.5)); },
            function() { return fg[1](JXG.Math.Numerics.root(quot, (fg[3]() + fg[2]) * 0.5)); },
            graph], {name: 'C(&xi;)', size: 4, fixed:true, color: 'blue'});

board.create('tangent', [r], {strokeColor:'#ff0000'});