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Mathematical Expression Editor
We use a method called “linear approximation” to estimate the value of a
(complicated) function at a given point.
Given a function, a linear approximation is a fancy phrase for something you already
know:
The line tangent to the graph of a function at a point is
very close to the graph of the function near that point.
This tangent line is the graph of a linear function, called the linear approximation.
Let \(f\) be a function that is differentiable on some interval I that contains the point \(a\).
The graph of a function \(f\) and the line tangent to the curve
\(y=f(x)\) at the point where \(x=a\) are given in the figure below. Find the equation of the tangent
line.
First, find the expression
for \(m\), the slope of the tangent line to the curve \(y=f(x)\) at the point \((a,f(a))\). Select the correct choice.
\(m= f(a)\)\(m=f'(a)\)\(m= \frac {f(a)-f(0)}{a-0}\)\(m= \frac {f(a+h)-f(a)}{h}\) We don’t have enough information to determine the slope.
Since we
know that the point \((a,f(a))\) lies on the tangent line, we can write an equation of the tangent
line.
\[ y= f'(a)(x-a) +f(a). \]
Now, we define a function, \(L\), by \(L(x)= f'(a)(x-a) +f(a)\). This function is linear and its graph is the line
tangent to the curve \(y=f(x)\) at the point where \(x=a\). This function deserves a special name.
If \(f\)
is a function differentiable at \(x=a\), then a linear approximation to the function \(f\) at \(x=a\) is
given by
\[ L(x) = f'(a)(x-a) +f(a). \]
Let \(f\) be a function defined by
\[ f(x) =\sqrt [3]{x}. \]
Approximate \(\sqrt [3]{50}\), using \(L\), a linear approximation to the
function \(f\) at \(a=64\).
Now we evaluate \(L(50) \approx 3.71\)
and compare it to \(\sqrt [3]{50}\approx 3.68\). From this we see that the linear approximation, while
perhaps inexact, is computationally easier than computing the cube root.
What would happen if we chose \(a=27\) instead?
Then we would use \(L_{27}\), the linear approximation to the function \(f\) at \(a=27\). In that case, \(L_{27}=f(27)+f'(27)(x-27)=3+\frac {1}{27}(x-27)\). The
graph of \(L_{27}\), together with the graphs of \(f\) and \(L=L_{64}\) is given in the figure below.
From the picture we
can see that
\(L_{27}(50)>L_{64}(50)>f(50)\).
So, our choice, \(a=64\), was better!
With modern calculators and computing software, it
may not appear necessary to use linear approximations. In fact they are quite useful.
In cases requiring an explicit numerical approximation, they allow us to get a quick
rough estimate which can be used as a “reality check” on a more complex calculation.
In some complex calculations involving functions, the linear approximation
makes an otherwise intractable calculation possible, without serious loss of
accuracy.
Use a linear approximation of \(f(x) =\sin (x)\) at \(a=0\) to approximate \(\sin (0.3)\).
so our linear
approximation is \begin{align*} L(x) &= 0+\answer [given]{\cos (0)}\cdot (x-0)\\ &= x. \end{align*}
Hence, a linear
approximation for \(\sin (x)\) at \(a=0\) is \(L(x) = x\), and so \(L(0.3) = 0.3\). Comparing this to \(\sin (.3) \approx 0.295\), we see that the approximation
is quite good. For this reason, it is common to approximate \(\sin (x)\) with its linear
approximation \(L(x) = x\) when \(x\) is near zero.
1 Differentials
The graph of a function \(f\) and the graph of \(L\), the linear approximation of \(f\) at \(a\), are
shown in the figure below. Also, two quantities, \(dx\) and \(df\), and a point \(P\) are marked
in the figure. Look carefully at the figure when answering the questions
below.
Select all the
correct expressions for the quantity \(dx\).
So, we can write \(df=f'(a)dx\) and call it a differential of \(f\) at \(a\). Notice that we can define a
differential at any point \(x\) of the domain of \(f\), provided that \(f'(x)\) exists. We will do that in
our next definition.
Let \(f\) be a differentiable function, let \(x\) be a point in the domain of \(f\),
and let \(dx\) be some quantity, called a differential of \(x\). We define \(df\), a differential of \(f\), at a
point \(x\) by
\[ df=f'(x)\cdot dx \]
Geometrically, differentials can be interpreted via the diagram below.
Note, it is now the case (by definition!) that
\[ f'(x)=\frac {df}{dx}. \]
We should not be surprised, since the
slope of the tangent line in the figure is \(f'(x)\), and this slope is also given by
\(\frac {df }{dx}\).
Essentially, differentials allow us to solve the problems presented in the previous
examples from a slightly different point of view. Recall, when \(h\) is near but not
equal zero,
\[ f'(x) \approx \frac {f(x+h)-f(x)}{h}. \]
Hence,
\[ f'(x)h \approx f(x+h)-f(x). \]
We can replace a quantity \(h\) with a quantity \(dx\) to write
\begin{align*} f'(x)\cdot dx &\approx f(x+dx)-f(x)\\ df &\approx f(x+dx)-f(x). \end{align*}
Adding \(f(x)\) to both sides we see
\[ f(x) + df\approx f(x+dx) \]
or, equivalently
\[ f(x+dx)\approx f(x) + df . \]
There are contexts where
the language of differentials is common. Here is the basic strategy:
We will repeat our previous examples using differentials.
Use differentials to approximate \(\sqrt [3]{50}\).
Set \(f(x) = \sqrt [3]{x}\). We want to know \(\sqrt [3]{50}\). Since \(4^3 = 64\), we set \(x=64\). Setting \(dx=50-64=-14\),
we have \begin{align*} \sqrt [3]{50} = f(x + dx) &\approx f(x) + df\\ &\approx \sqrt [3]{64} + df. \end{align*}
Here we see a plot of \(y=\sqrt [3]{x}\) with the differentials above marked:
Set \(y = \sin (x)\). We want to know \(\sin (0.3)\). Since \(\sin (0) = 0\), we will set \(x = 0\) and \(dx=0.3\).
Write with me \begin{align*} \sin (0.3) = \sin (x + dx) &\approx \sin (x) + dy\\ &\approx 0 + dy. \end{align*}
Here we see a plot of \(y=\sin (x)\) with the differentials above marked:
Now we must compute \(dy\):
\begin{align*} dy &= \left (\frac {d}{dx} \sin (x)\right ) \cdot dx\\ &=\answer [given]{\cos (0)} \cdot dx\\ &= 1 \cdot (\answer [given]{0.3})\\ &= 0.3 \end{align*}
The upshot is that linear approximations and differentials are simply two slightly
different ways of doing the exact same thing.
2 Error approximation
Differentials also help us estimate error in real life settings.
The cross-section of a \(250\) ml glass can be modeled by the function \(r(x) = \frac {x^4}{3}\):
At \(16.8\) cm from the base of the glass, there is a mark indicating when the glass is filled
to \(250\) ml. If the glass is filled within \(\pm 2\) millimeters of the mark, what are the bounds on
the volume? As a gesture of friendship, we will tell you that the volume in milliliters,
as a function of the height of water in centimeters, \(y\), is given by
\[ V(y) = \frac {2\pi y^{3/2}}{\sqrt {3}}. \]
Note: If you persist
in your quest to learn calculus, you will be able to derive the formula above like it’s
no-big-deal.
We want to know what a small change in the height, \(y\), does to the
volume \(V\). These small changes can be modeled by the differentials \(dV\) and \(dy\). Since
\[ dV = V'(y) dy \]
and \(V'(y) = \answer [given]{\pi \sqrt {3 y}}\)
we use the fact that \(dy = \pm 0.2\) with \(y=\answer [given]{16.8}\) to see
Hence the volume will vary by roughly \(\pm \answer [given,tolerance=.01]{4.46062}\) milliliters.
3 New and old friends
You might be wondering, given a plot \(y=f(x)\),
What’s the difference between \(\Delta x\) and \(dx\)? What about \(\Delta y\) and \(dy\)?
Regardless, it is now a pressing question. Here’s the deal:
\[ \frac {\Delta y}{\Delta x} \]
is the average rate of
change of \(y=f(x)\) with respect to \(x\). On the other hand:
\[ \frac {dy}{dx} \]
is the instantaneous
rate of change of \(y=f(x)\) with respect to \(x\). Essentially, \(\Delta x\) and \(dx\) are the same type of
thing, they are (usually small) changes in \(x\). However, \(\Delta y\) and \(dy\) are very different
things.
\(\Delta y=f(x+\Delta x)-f(x)\); it is the change in \(y=f(x)\) associated to \(\Delta x\).
\(dy=L(x+dx)-L(x)\), it is the change in \(y=L(x)\) associated to \(\Delta x=dx\).
\[ dy =f'(x)dx \]
Note: \( L(x+dx)= f(x)+f'(x)dx\).
So, the change \begin{align*} dy &= L(x+dx)-L(x)\\ &= f(x)+f'(x)dx-L(x)\\ &= f(x)+f'(x)dx-f(x)\\ &=f'(x)dx \end{align*}
Suppose \(f(x) = x^2\). If we are at the point \(x=1\) and \(\Delta x =dx = 0.1\), what is \(\Delta y\)? What is \(dy\)?
\( \Delta y=f(1+\Delta x)-f(1)=f(1.1)-f(1)\)
\(dy=f'(1)\cdot dx=f'(1)\cdot 0.1\)
\[ \Delta y = \answer {0.21}\qquad dy = \answer {0.2} \]
Differentials can
be confusing at first. However, when you master them, you will have a powerful tool
at your disposal.