Chapter 8 Logarithmic growth
Section 8.1 The natural logarithm
Pattern 7.4.2 tells us how to calculate a quantityβs accumulation for any rate model that is a power of except for the special case of It turns out there is no simple formula for the accumulation for this special rate function, so we will have to resort to using integral notation to represent it. For convenience, we will also give this special accumulation function a name.
In Figure 8.1.2, the height of the logarithm functionβs graph on the right at the input corresponds to the magnitude of the shaded blue area under the graph of the rate function on the left.
Notice that the graph of the logarithm function in Figure 8.1.2.(b) shows negative accumulation values for This is because of Property 2 in Convention 6.3.12 β if we allow time to βrun backwardsβ, then we consider the quantity to be dissipating instead of accumulating, so that
Example 8.1.4. Using the natural logarithm to represent integral values.
Suppose we would like to calculate
Similarly to Example 7.4.6, we split into two integrals, and then use Pattern 6.3.14 to re-base our integrals to match our formulas.
Remark 8.1.5.
It might seem like βcheatingβ to write as the value of
since Definition 8.1.1 says that to calculate we would need to calculate that very integral (and similarly for ). But this is an acceptable way to represent the value of this integral, as there are several ways to numerically approximate values of the natural logarithm β using Riemann sums, for example. Your calculator even has a button.
Section 8.2 Properties of the natural logarithm
First, our convention about integrating over a time domain of length leads immediately to a special value of the natural logarithm.
Pattern 8.2.1. Logarithm at .
Justification.
Next, we note the domain of the natural logarithm.
Pattern 8.2.2. Domain of the natural logarithm.
Justification.
Since the graph of our rate function used to define the natural logarithm is continuous on that rate function is integrable on any (finite) subdomain of However, given our βinitialβ point at in the integration defining the logarithm, any second domain endpoint that lies in will cause the singularity of at to be included, resulting in an integral that cannot be computed.
From the graph of the logarithm function (see Figure 8.1.2.(b) or Figure 8.1.3.(b)), we can see obvious long-term and singular behaviour.
Pattern 8.2.3. Long-term and singular behaviour of the natural logarithm.
-
Long-term behaviour.As
also -
Singular behaviour.As
There are also a few algebraic patterns to the logarithm function that are not immediately obvious from its graph or from its definition as an accumulation function. The first involves the logarithm of a product of two numbers.
Pattern 8.2.4. Logarithm of a product.
Justification.
By definition,
So we already have the first part of our desired formula, What about the second integral
How does it compare to
(We have changed the integration variable to help keep things separate during our comparison of these two integrals.) It appears that the domain in integral (βΆ) has been stretched by a factor of compared to the domain in the integral defining Remember that an integral is a calculation of (oriented) area under a graph, added up βslice by sliceβ (see Figure 6.3.3). For each βsliceβ at position in the integral for we can create a corresponding βstretched sliceβ at position where if then also
Because we are stretching horizontally by a factor of the slice at position on the second graph will also be wider by a factor of
But we are only transforming our domain values, we are not actually stretching our rate graph. So the slice at has both a different width and a different height compared to the original slice at position since we have moved to a different place on the rate graph:
However, when calculate the βareaβ of this βstretched sliceβ, we find itβs the same area as the slice at position for the integral:
So as we add up βslicesβ of area to compute the integral for or we add up βslicesβ of area to compute integral (βΆ), we will be adding up the same values for those slices of area at corresponding positions, and so we will come out to the same total area:
and thus
Remark 8.2.6.
We can be a little more precise in our justification above by considering actual Riemann sums of rectangular areas when we compare integral (βΆ) with the integral for What our βcorresponding slicesβ argument essentially shows is that for each Riemann sum approximating the integral for we can create a Riemann sum approximating integral (βΆ) that computes to the same value. So the pattern of upper and lower sums squeezing down to the value for the integral for must be the same as the pattern of upper and lower sums squeezing down to the value of integral (βΆ).
So the logarithm turns a product into a sum. What does it do to a reciprocal?
Pattern 8.2.7. Logarithm of a reciprocal.
Justification.
This time we will deal with an actual rectangular area in a typical Riemann sum, instead of just working with βslicesβ. For the integral
consider a typical rectangle at sample point in an upper sum approximation. Because of the shape of the graph of the rate function, this sample point will appear at the left endpoint of the base of the rectangular area. Mark the right endpoint as so that
By taking reciprocals, we can create a βmirror-imageβ rectangular base between and To create a rectangle that represents a typical rectangle in an upper sum approximation of the integral
weβll take the sample point to be at
To relate the areas of these two rectangles, first calculate
So the area of the rectangle in Figure 8.2.9 is
which is exactly equal to the area of the rectangle in Figure 8.2.8.
By matching up areas of rectangles in this way, we see that the two upper sum approximations would compute to the same total approximate area. That is, every upper sum approximation for the integral defining corresponds to an upper sum approximation for integral (βΆβΆ) with the same value. This would also work the other way β every upper sum approximation for integral (βΆβΆ) could be βflippedβ over by taking reciprocals of the rectangle base boundary points to create an upper sum approximation for the integral defining And by changing sample points, we would see the same correspondence for lower sum approximations of these two integrals.
Therefore, the patterns of upper and lower sum approximations for each integral would βsqueezeβ down to the same values. That is,
The first integral is the value of while the second integral is the value of using Property 2 of Convention 6.3.12. Flipping the negative sign to the other side of the equality of integrals above, we obtain
as desired.
We could rewrite Pattern 8.2.7 as
Using Pattern 8.2.4, we also have
In both instances we see an exponent-becomes-coefficient pattern. Though we are not yet prepared to justify it, in fact this pattern holds for all exponents.