A common exercise in number theory is to find the last digits of a large power, like 22009, without using a computer. 22009 is a 605-digit number, so evaluating it by hand is out of the question. So how do you find its last digits — efficiently?
Modular arithmetic, and in particular, modular exponentiation, comes to the rescue. It provides an efficient way to find the last m digits of a power, by hand, with perhaps only a little help from a pocket calculator. All you need to do is compute the power incrementally, modulo 10m.
In this article, I will discuss three methods — all based on modular exponentiation and the laws of exponents — for finding the ending digits of a positive power of two. The techniques I use are easily adapted to powers of any number.
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The positive powers of two — 2, 4, 8, 16, 32, 64, 128, 256, … — follow an obvious repeating pattern in their ending digit: 2, 4, 8, 6, 2, 4, 8, 6, … . This cycle of four digits continues forever. There are also cycles beyond the last digit — in the last m digits in fact — in the powers of two from 2m on. For example, the last two digits repeat in a cycle of length 20 starting with 04, and the last three digits repeat in a cycle of length 100 starting with 008.
In this article, I will show you why these cycles exist, how long they are, how they are expressed mathematically, and how to visualize them.
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In my article “Nines in Binary”, I proved the following: positive integers of the form 10n-1, that is, integers made up of n digits of 9s, have binary representations with exactly n digits of trailing 1s. Pat Ballew made a clever observation, adapting my result to prove an equivalent statement for base 5 (quinary): positive integers of the form 10n-1 have quinary representations that have exactly n digits of trailing 4s. For example, 9999 in decimal is 304444 in quinary.
In “Nines in Binary”, I derived an expression for 10n – 1 that shows its structure as a binary number:
10n – 1 = (5n – 1) 2n + (2n – 1)
Pat derived a similar expression for 10n – 1 that shows its structure as a quinary number:
10n – 1 = (2n – 1) 5n + (5n – 1)
In essence, he swapped the 2s and 5s, making it the “dual” of my formula, if you will.
I’ll show the details of the derivation and prove why the formula works.
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PARI/GP is an open source computer algebra system I use frequently in my study of binary numbers. It doesn’t manipulate binary numbers directly — input, and most output, is in decimal — so I use it mainly to do the next best thing: calculate with powers of two. Calculations with powers of two are, indirectly, calculations with binary numbers.
PARI/GP is a sophisticated tool, with several components — yet it’s easy to install and use. I use its command shell in particular, the PARI/GP calculator, or gp for short. I will show you how to use simple gp commands to explore binary numbers.
PARI/GP Calculator (Sample of Calculations Used to Explore Binary Numbers)
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Pradeep Mutalik of The New York Times recently blogged about a puzzle that is an instance of the Josephus Problem. The problem, restated simply, is this: there are n people standing in a circle, of which you are one. Someone outside the circle goes around clockwise and repeatedly eliminates every other person in the circle, until one person — the winner — remains. Where should you stand so you become the winner?
Here’s an example with 13 participants:
Alternating Elimination with 13 people, order of elimination shown in red (winner is person 11)
As Pradeep and his readers point out, there’s no need to work through the elimination process — a simple formula will give the answer. This formula, you won’t be surprised to hear, has connections to the powers of two and binary numbers. I will discuss my favorite solution, one based on the powers of two.
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I discovered a cool property of positive integers of the form 10n-1, that is, integers made up of n digits of 9s: they have binary representations that have exactly n digits of trailing 1s. For example, 9,999,999 in decimal is 100110001001011001111111 in binary.
The property is interesting in and of itself, but what is more interesting is the process I went through to discover it. It’s a small-scale example of experimental mathematics: I observed something interesting, experimented to collect more data, developed a hypothesis, and constructed a proof.
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If you’re a sports fan, you think of basketball when you see this:
Basketball or Binary?
If you’re like me, you also think of powers of two, binary trees, logarithms, laws of exponents, geometric sequences, geometric series, and Bernoulli trials — in short, the elements of binary numbers, binary code, and binary logic.
I hope you’re thinking: “wow — all that math stuff I hated actually has a practical use!”
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Recently I showed that programming languages vary in how much precision they allow in printed floating-point fractions. Not only do they vary, but most don’t meet my standard — printing, to full precision, decimal values that have exact floating-point representations. Here I’ll present a similar study for floating-point integers, which had similar results.
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Interestingly, programming languages vary in how much precision they allow in printed floating-point fractions. You would think they’d all be the same, allowing you to print as many decimal places as you ask for. After all, a floating-point fraction is a dyadic fraction; it has as many decimal places as it has bits in its fractional representation.
Consider the dyadic fraction 5,404,319,552,844,595/253. Its decimal expansion is 0.59999999999999997779553950749686919152736663818359375, and its binary expansion is 0.10011001100110011001100110011001100110011001100110011. Both are 53 digits long. The ideal programming language lets you print all 53 decimal places, because all are meaningful. Unfortunately, many languages won’t let you do that; they typically cap the number of decimal places at between 15 and 17, which for our example might be 0.59999999999999998.
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A power of two, when expressed as a binary number, is easy to spot: it has one, and only one, 1 bit. For example, 1000, 10, and 0.001 are powers of two. Inside a computer, however, numbers are more generally represented in binary code, not as “pure” binary numbers. As a result, you may not be able to look at the binary representation of a number and tell at a glance whether it’s a power of two or not; it depends on how it’s encoded.
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The following infinite set of numbers is known as the powers of two:
.
Why are they called powers of two? What is the pattern you see? How is the set described mathematically? What are the set’s components? We will answer those questions in this article.
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What is a power of two exactly? Is 20 a power of two? Is 2-1 a power of two? How about 
or 
? It depends on how you define it; there are several definitions from which you could choose. Let’s see if we can sort them out and propose a standard definition, or at least a standard definition for our use.
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(Published October 14th, 2009) 
