Mathematician's Delight (Dover Books on Mathematics)

This Month Stack Overflow 1

The logarithm

Ok let's try and fully understand what a logarithm actually is.

Imagine we have a rope and we have tied it to a horse. If the rope is directly tied to the horse, the force the horse would need to pull away (say, from a man) is directly 1.

Now imagine the rope is looped round a pole. The horse to get away will now have to pull many times harder. The amount of times will depend on the roughness of the rope and the size of the pole, but let's assume it will multiply one's strength by 10 (when the rope makes a complete turn).

Now if the rope is looped once, the horse will need to pull 10 times harder. If the human decides to make it really difficult for the horse, he may loop the rope again round a pole, increasing it's strength by an additional 10 times. A third loop will again increase the strength by a further 10 times.

We can see that for each loop, the value increases by 10. The number of turns required to get any number is called the logarithm of the number i.e. we need 3 posts to multiple your strength by 1000 times, 6 posts to multiply your strength by 1,000,000.

3 is the logarithm of 1,000, and 6 is the logarithm of 1,000,000 (base 10).

So what does O(log n) actually mean?

In our example above, our 'growth rate' is O(log n). For every additional loop, the force our rope can handle is 10 times more:

Turns | Max Force
0   |   1
1   |   10
2   |   100
3   |   1000
4   |   10000
n   |   10^n

Now the example above did use base 10, but fortunately the base of the log is insignificant when we talk about big o notation.

Now let's imagine you are trying to guess a number between 1-100.

Your Friend: Guess my number between 1-100!
Your Friend: YOU GOT IT!

Now it took you 7 guesses to get this right. But what is the relationship here? What is the most amount of items that you can guess from each additional guess?

Guesses | Items
1     |   2
2     |   4
3     |   8
4     |   16
5     |   32
6     |   64
7     |   128
10    |   1024

Using the graph, we can see that if we use a binary search to guess a number between 1-100 it will take us at most 7 attempts. If we had 128 numbers, we could also guess the number in 7 attemps but 129 numbers will takes us at most 8 attempts (in relations to logarithms, here we would need 7 guesses for a 128 value range, 10 guesses for a 1024 value range. 7 is he logarithm of 128, 10 is the logarithm of 1024 (base 2)).

Notice that I have bolded 'at most'. Big o notation always refers to the worse case. If you're lucky, you could guess the number in one attempt and so the best case is O(1), but that's another story.

We can see that for every guess our data set is shrinking. A good rule of thumb to identify if an algorithm has a logarithmtic time is to see if the data set shrinks by a certain order after each iteration

What about O(n log n)?

You will eventually come across a linerarithmic time O(n log(n) algorithm. The rule of thumb above applies again, but this time the logarithmic function has to run n times e.g. reducing the size of a list n times, which occurs in algorithms like a mergesort.

You can easily identify if the algorithmic time is n log n. Look for an outer loop which iterates through a list (O(n)). Then look to see if there is an inner loop. If the inner loop is cutting/reducing the data set on each iteration, that loop is (O(log n), and so the overall algorithm is = O(n log n).

Disclaimer: The rope-logarithm example was grabbed from the excellent Mathematician's Delight book by W.Sawyer.

> The OP is talking about teaching community college mathematics. That's not introductory university mathematics; if you are lucky it's Pythagoras' Theorem and quadratic equations.

W. W. Sawyer's books, such as Mathematician's Delight[1] and Vision in Elementary Mathematics[2], seem to be exactly for this audience.

[1] https://www.amazon.com/dp/0486462404/

[2] https://www.amazon.com/dp/048642555X/