I wonder how many of my blog posts are based on stories from This American Life. Probably not enough. Every time I start to listen to a show, I’m certain that I’m not going to be interested and within a minute I’m trapped and can’t stop listening. It’s that good. A recent show was about people who people who pursued crazy ideas. The first story was about a mathematician named Frank Nelson Cole. Marin Mersenne had claimed in the 17th century that 267-1 was a prime number. He was prominent enough that the claim was felt to be accepted wisdom and there was certainly no way to test the claim in the days before computers. That’s a big honking number: 147,573,952,589,676,412,927.
In 1903, Frank Nelson Cole walked into a meeting of mathematicians to present his talk. The title was boring, something like “On the factoring of large numbers”. Without speaking a word, he walked up to the chalkboard and started to write a large number, followed by another large number and then started to multiply them together. It took a while, but by the time he started to get towards the solution, the crowd of mathematicians understood that he was proving that he had found two numbers whose product was the famous “prime” number 267-1. As he came close to finishing, the anticipation peaked and cheering began. He finished the calculation and sat down, never speaking a word.
Stories like that give me the chills. Can you imagine how excited he must have been when he found those 2 roots? I get that sensation every once in a while when I make a programming breakthrough, solving a problem which I had been banging my head on for a while. It’s nowhere as profound as what he did, but I think I can understand the exhilaration he must have felt.
I decided to see what those 2 roots are, using my new favorite language, Clojure. I’m by no means an expert in anything, let alone programming and especially functional programming, but here’s how I went about it. The REPL is such a fun way to explore things like this:
I need a range of numbers which I’ll then test one-by-one to see if they divide into 267-1 evenly.
user=> (def n 18) #'user/n user=> (range 2 n) (2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17)
I need a way to take a square root. (There’s no need to check numbers higher than the square root of 267-1)
user=> (Math/sqrt n) 4.242640687119285
So the numbers I need to test are:
user=> (range 2 (inc (int (Math/sqrt n)))) (2 3 4)
I need to find the remainder of a division (Of note,
modalso does the same thing, but
remis a lot faster)
user=> (rem 9 2) 1
Can I find all the lower roots of a number? The
#(...)syntax is syntactic sugar for an anonymous function. The
%is a placeholder for the value which is being iterated over. So the following function takes each value in the list returned by
rangeand supplies it to
rem. If that
remfunction is equal to zero, then the
#(...)anonymous function returns true and
filterkeeps the value.
user=> (def n 100) #'user/n user=> (filter #(= (rem n %) 0) (range 2 (inc (int (Math/sqrt n))))) (2 4 5 10)
Now let’s map over each of those values, finding the corresponding higher root for each value.
vectorcreates a vector (think list) with the first value being the supplied value and the second value being the other root
(/ n %):
user=> (map #(vector % (/ n %)) (filter #(= (rem n %) 0) (range 2 (inc (int (Math/sqrt n)))))) ([2 50] [4 25] [5 20] [10 10])
Based on that experimentation, here’s my first stab at the function:
user=> (defn roots [n] (let [lower-factors (range 2 (inc (int (Math/sqrt n)))) is-factor? (fn [a] (= (rem n a) 0))] (map #(vector % (/ n %)) (filter is-factor? lower-factors)))) #'user/roots user=> (roots 294) ([2 147] [3 98] [6 49] [7 42] [14 21])
Now let’s get our big number (267-1)
user=> (dec (Math/pow 2 67)) 1.4757395258967641E20
Hmmm… that looks like it might not be a precise value. Oh well, let’s try it:
user=> (roots (dec (Math/pow 2 67))) ([2 7.378697629483821E19] [3 4.9191317529892135E19] [4 3.6893488147419103E19] [5 2.9514790517935284E19] [6 2.4595658764946067E19] [7 2.108199322709663E19] [8 1.8446744073709552E19] [9 1.6397105843297378E19] [10 1.4757395258967642E19] [11 1.3415813871788765E19] [12 1.2297829382473034E19] [13 1.1351842506898186E19] [14 1.0540996613548315E19] [15 9.838263505978427E18] [16 9.223372036854776E18] [17 8.6808207405692006E18] [18 8.1985529216486892E18] [19 7.7670501362987581E18] [20 7.3786976294838211E18..... C-c C-c (ABORT, ABORT!!!)
OK, that didn’t work. It clearly found way too many roots, because 267-1 was an approximation. We have to use BigIntegers which have appropriate precision even with large numbers.
user=> (def two67minus1 (dec (.pow (BigInteger. "2") 67))) #'user/two67minus1 user=> two67minus1 147573952589676412927
Cool, that looks more precise than our previous value. To see the difference, check their type.
user=> (type (dec (Math/pow 2 67))) java.lang.Double user=> (type (dec (.pow (BigInteger. "2") 67))) java.math.BigInteger
OK, Time for the big test:
user=> (roots two67minus1) ([193707721 761838257287])
Cool!!! It works! Those are the roots of 267-1. How long did that take to compute? (We need
doallto make the
timecommand wait for all of the values to be calculated, otherwise it will return after the first value is calculated. This has something to do with the laziness of clojure sequences)
user=> (time (doall (roots two67minus1))) "Elapsed time: 2.2697318183297E7 msecs" ([193707721 761838257287])
6 hours 18 minutes. I’m sure there’s a quicker way to do this. I do a lot of redundant testing. For example, once we know that 2 is not a factor, we shouldn’t check any more even factors. I’d be interested in any advice to make it run faster.
I can’t even fathom how you’d go about doing this without a computer. Can you imagine how frustrating it must’ve been any time you had a simple error? Just amazing…