Why is the growth of exps & logs so fast & slow, respectively?

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In summary: I think you meant "big P...the largest positive real number that can be written as the product of finite, nonnegative terms".Yes, I meant "big O".
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mcastillo356
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I don't understand the theorem that proves the question of the thread's title.
Hi, PF, there goes the theorem, questions, and attempt
THEOREM 4 If ##x>0##, then ##\ln x\leq{x-1}##
PROOF Let ##g(x)=\ln x-(x-1)## for ##x>0##. Then ##g(1)=0## and

##g'(x)=\displaystyle\frac{1}{x}-1\quad\begin{cases}>0&\mbox{if}\,0<x<1\\
<0&\mbox{if}\,x>1\end{cases}## (...). Thus, ##g(x)\leq{g(1)=0}##, ## \forall {x>0}## and ##\ln x\leq{x-1}## for all such ##x##

Question: The proof, why implies the quick growth of ##e^x## and the steady growing of ##\ln x##?

Attempt: ##g## grows in ##x\in{(0,1)}## and decreases in ##x\in{(1,\infty)}## (##g'(x)>0\,\forall{(0<x<1)}## and ##g'(x)<0\,\forall{(1<x<\infty)}##

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mcastillo356 said:
Question: The proof, why implies the quick growth of ##e^x## and the steady growing of ##\ln x##?
Let us take the latter question first: "How does the proof imply the steady growth of ##\ln x##?

What do you even mean by the "steady growth" of ##\ln x##?

Certainly the proof examines the difference between the first derivative of ##\ln x## and the first derivative of ##x-1##. It concludes that the "growth rate" (first derivative) of ##\ln x## is less than the growth rate of ##x-1##.

By itself, this does not assure us that ##\ln x## even has a growth rate. But we can easily see that indeed, ##\ln x## is strictly monotone increasing.

I suppose that "strictly monotone increasing" with a growth rate that is always less than 1 might count as "steady growth" to you.

Personally, I'd use the adjective "steady growth" for something with a growth rate that converged on a positive constant. ##\ln x## does not qualify.We are in a position now to attack the other question: "How does the proof imply the quick growth of ##\ln x##?

That answer is shorter. It does not.

Roughly speaking, the proof of the first part puts a bound on the graph of ##\ln x##. It bounds it below a 45 degree line, the graph of ##f(x) = x-1##. The graph of ##e^x## will be the mirror image, obtained by swapping the x and y axes. The proof puts a lower bound on ##e^x## at the mirror image 45 degree line, the graph of ##f(x) = x + 1##.

There are plenty of "steadily growing" functions with such a lower bound.
 
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It depends on where on the x-axis whether it is increasing rapidly or slowly. I've forgotten a lot of Spanish, but I think it is asking about when x is large.
 
  • #4
You might like to check Courant’s “Differential and Integral Calculus”, Vol. I, Rev. Ed., about the order of magnitude of functions, pp. 189.
 
  • #5
Hi, PF, @jbriggs444, @scottdave, @apostolosdt.

jbriggs444 said:
Let us take the latter question first: "How does the proof imply the steady growth of ##\ln x##?

What do you even mean by the "steady growth" of ##\ln x##?

It's been an own's and free translation, @jbriggs444. I agree with your statement:

jbriggs444 said:
I suppose that "strictly monotone increasing" with a growth rate that is always less than 1 might count as "steady growth" to you.

This is, for all ##x## and ##y## such that ##x\leq y\rightarrow{f(x)\leq{f(y)}}##. One question: how could I get some info on growth rate?

Now, the question I do not solve is that ##e^x## increases more slowly than any positive power of ##x## (no matter how large the power), while ##\ln x## increases more slowly than any positive power of ##x## (no matter how small the power). The fact that ##g(x)\leq{g(1)=0}## for all ##x>0## and ##\ln\leq{x-1}## for all such ##x## is bounds, but not a proof of the singular growing properties of ##\ln x## and ##e^x##.

Now I think that it is all about introducing exponential growth and decay models, a few paragraphs forward.

Greetings!
 
  • #6
mcastillo356 said:
This is, for all ##x## and ##y## such that ##x\leq y\rightarrow{f(x)\leq{f(y)}}##. One question: how could I get some info on growth rate?
The terminology I learned (in the 70's) for that attribute is "monotone increasing".

If one strengthens the inequality to ##x \lt y \rightarrow f(x) \lt f(y)## then the term is "strictly monotone increasing".

So a constant function such as ##f(x) = 1## would be "monotone increasing" but not "strictly monotone increasing". A function such as ##f(x) = x^3## would be both monotone increasing and strictly monotone increasing despite the inflection at ##x=0##.

mcastillo356 said:
Now, the question I do not solve is that ##e^x## increases more slowly than any positive power of ##x## (no matter how large the power), while ##\ln x## increases more slowly than any positive power of ##x## (no matter how small the power).
Here, you appear not to be worried about any constant multiplier or fixed offset. So, for instance, you would say that ##x^2## increases more rapidly than ##10x + 15##.

That sounds like an application for "big O notation".
 
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FAQ: Why is the growth of exps & logs so fast & slow, respectively?

1. What are exponential functions and how do they grow?

Exponential functions are mathematical expressions of the form f(x) = a * b^x, where 'a' is a constant, 'b' is the base of the exponential (b > 1), and 'x' is the exponent. The growth of an exponential function is characterized by its rapid increase as 'x' becomes larger. This happens because the function's output is multiplied by the base for each unit increase in 'x', leading to a sharp rise in values compared to linear or polynomial functions.

2. What are logarithmic functions and how do they grow?

Logarithmic functions are the inverse of exponential functions, typically expressed as f(x) = log_b(x), where 'b' is the base of the logarithm. These functions grow very slowly compared to exponential functions. As 'x' increases, the output of a logarithmic function increases at a decreasing rate, meaning that larger increments in 'x' yield smaller increments in the function's value.

3. Why do exponential functions grow faster than logarithmic functions?

Exponential functions grow faster than logarithmic functions because of their inherent mathematical properties. In an exponential function, each increase in the input results in the output being multiplied by a constant factor (the base). In contrast, logarithmic functions increase by a diminishing amount as the input grows, reflecting a slower rate of change. This fundamental difference in how outputs respond to increases in inputs leads to the stark contrast in their growth rates.

4. Can you provide real-world examples of exponential and logarithmic growth?

Real-world examples of exponential growth include populations of bacteria, compound interest in finance, and the spread of viruses. In these cases, the quantity grows rapidly over time. On the other hand, logarithmic growth can be seen in phenomena such as the learning curve, where performance improves quickly at first but then levels off, or in measuring sound intensity (decibels), where a small increase in sound intensity results in a relatively small increase in perceived loudness.

5. How do the concepts of limits and asymptotes relate to the growth of exps and logs?

The concepts of limits and asymptotes help illustrate the behavior of exponential and logarithmic functions as they approach infinity. Exponential functions have no upper bound and continue to grow indefinitely, while logarithmic functions have a horizontal asymptote at the x-axis, meaning they approach but never reach zero as 'x' approaches zero. This distinction highlights the fundamental differences in their growth patterns: exponential functions explode in value, while logarithmic functions increase slowly and level off.

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