To the limit
Excerpt: Using a series of statements, students are asked to put together a chain of reasoning to find the derivative of . This leads on to looking at a special case of this derivative.
Warm-up
What is the gradient of the chord ?
Solution
This expression gives us the gradient of If is small enough, then this also gives us an estimation of the gradient of at
How could we find the exact gradient at ? See the gradient of to find out.
Gradient of
We can find the gradient of a function by taking the limit of the gradient of a chord between and as tends to .
These statements and explanations can be used to create a chain of reasoning to find the gradient function of . Sort the statements into a logical argument, either writing your own explanations of the steps, or turning over the hidden cards to use the explanations provided.
The expression is factorised as is a common factor.
As the limit will give the gradient function at .
The expression in the limit gives the gradient of a chord between and on .
This is the general formula for differentiating from first principles.
This is an expression for the gradient of
As is not affected by it can be taken outside of the limit.
What does the final statement tell you about the gradient function of ?
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What does the graph of look like?
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How will the gradient function change as changes?
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There is a special case of the gradient function of . What could it be? To find out more look at A special case.
Solution
This is the general formula for differentiating from first principles.
This is an expression for the gradient of
Remember the expression in the brackets gives the gradient of the chord between and . Taking the limit as gives us the gradient of the function at .
The expression is factorised as is a common factor.
As is not affected by it can be taken outside of the limit.
The value of is tending to , so any expressions not involving are unaffected by this limiting process.
The expression in the limit gives the gradient of a chord between and on .
Think about how the expression could be represented on the graph of . What does it show?
As the limit will give the gradient function at .
The final step implies that .
This is not a simple limit to find as both the numerator and denominator tend to zero as . However, if we look at how we find from first principles we should see some similarities. Instead of finding the gradient for a generic point , the limit on the right gives us the gradient for the specific point , i.e. .
What does your final answer tell you about the gradient function of ?
- What does the graph of look like?
The gradient function is the original function multiplied by a constant. This tells us the graph of
will look very similar to
In fact it will be a transformation of the original function; a stretch in the direction, scale factor (Or a stretch in the direction, scale factor )
What do the graphs tell you about when and when ?
- How will the gradient function change as changes?
We can see from the graphs above that as gets bigger, gets steeper more quickly and so the gradient at zero will be larger. Therefore, as gets bigger, will also get steeper more quickly. What will happen as gets smaller?
- There is a special case of the gradient function of . Can you think what it might be? To find out more look at A special case.
The gradient function itself could be steeper than the original function. This will happen if the stretch factor is bigger than one, i.e. . It could also be less steep, i.e. when This implies there will be a special case in the middle when and the gradient function and original function will be exactly the same!
A special case
We have found that the gradient function of can be written as
i.e. the gradient function is the function itself multiplied by a constant, and this constant is the gradient of
at
. There will be a special case when
as then the gradient function of
would be itself,
.
Before using the applet, look at the values given. Approximately what value do you think will take to give
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Use the slider to find the value of when .
Does the gradient function look as you expected?
Solution
We find that when . This means that the gradient function becomes , so and its gradient function are identical.
Of course this value of is an approximation, but does it look familiar?
It turns out that the exact value of for which the gradient function is the same as the original function is , in fact it is . You have probably met before as the exponential function and as the base of natural logarithms. Here we have found that if , then .
What about other values of ? To think about what is for any value, you can look at Differentiating exponentials.