The amazing adventures of Doug Hughes

Archive for March, 2017

Testing for Perfect Numbers in Java with Streams

I was challenged recently to write a Java method that could validate whether a provided number was “perfect” or not. A perfect number is one where the sum of its divisors (excluding itself) add up to the number. For example, 28 is a perfect number because it’s divisors are: 1, 2, 4, 7, and 14. If you add these up you get 28, the original number.

However, consider 16. It’s divisors are: 1, 2, 4, 4, and 8. If you add these up you get 19. Therefore, 16 is not perfect.

This calculation was to be done via a static method that accepted an argument num. The method was to return a boolean indicating whether the number provided was perfect or not.

My first attempt was naive. I stared by using a for loop to loop backwards from one less than the number being tested to 1. I tested each step to determine if the number was divisible by the step. If so, I collected both numbers into a tuple which I collected into a HashSet to remove duplicates. I then iterated over the set and summed the values in the tuple and added 1. It worked, but was considerably inefficient.

It was pointed out to me that, because I was looping backwards, I was doing two times the amount of work needed. If I looped forward to half the number being tested I could simply create a sum of the valid divisors. Understanding this, I decided to try a few other approaches…

First, I wanted to see if I could make a one-line solution to this puzzle. In particular, I wanted to experiment with streams of numbers in Java. My first attempt used a stream of integers. I reduced this to those which were valid divisors of the number being tested and summed the resulting set. If this was equal to the number, the validation passed. If not, it didn’t. This is the code:

public static boolean reduceIsPerfect(long num) {
  return LongStream.rangeClosed(2, num / 2)
    .reduce(1, (sum, test) -> num % test == 0 ? sum + test : sum) == num;

Mission accomplished! It’s one (wrapped) line of code. However, this led me to wonder if I could parallelize this. So, my next test was to make a parallel stream of numbers that I then filtered to those that cleanly divided the number being tested. I then summed these, checked for equality to the original number, and returned that.

public static boolean parallelFilteredIsPerfect(long num) {
  return LongStream.rangeClosed(1, num / 2)
    // make this a parallel stream so we can find the whole divisors more quickly (ideally)
    // filter out any non-whole divisors
    .filter(test -> num % test == 0)
    .sum() == num;

This too worked, though arguably it’s more than one line of code.

This success made me wonder how much (if at all) more efficient this method was than my first attempt. I wrote tests for the first six perfect numbers and found that the sixth one, 8,589,869,056, took quite a while to process. My first attempt with reduceIsPerfect() took about 45 secs to validate. My second attempt, parallelFilteredIsPerfect() took 23 seconds, so it was about two times faster. But, this left a bad taste in my mouth. 23 seconds is a really long time.

I tried a few other approaches, but found that, in general, most of my attempts tested in the 45 to 50 second range, which was disappointing.

So, next, I did what any good programmer would do, I turned to Google. I reasoned that the part of the process that takes the most time is determining if a number is a divisor or not. Google led me to a more efficient algorithm.

The way I was testing for perfection was by looping from 1 to half the number being tested. At each step I tested to see if the number could be divided cleanly. For example, with 28 I was doing this:

Start with a sum of 0. Loop from 1 to 14 (inclusive).

  1. 28 % 1. Yes! Add 1 to sum. sum is now 1.
  2. 28 % 2. Yes! Add 2 to sum. sum is now 3.
  3. 28 % 3. No!
  4. 28 % 4. Yes! Add 4 to sum. sum is now 7.
  5. 28 % 5. No!
  6. 28 % 6. No!
  7. 28 % 7. Yes! Add 7 to sum. sum is now 14.
  8. 28 % 8. No!
  9. 28 % 9. No!
  10. 28 % 10. No!
  11. 28 % 11. No!
  12. 28 % 12. No!
  13. 28 % 13. No!
  14. 28 % 14. Yes! Add 14 to sum. sum is now 28.

So, 28 is perfect.

But, Google pointed out to me that half of this information is already known by the time I calculate it. The divisors are 1, 2, 4, 7, 14, 28. As soon as I discover 1 I can quickly determine that it’s paired with 28. 2 is paired with 14, and 4 with 7. I don’t need to continue working at all once I’ve gone to 4! This is less than the square root of 28, 5.291502622129181.

So, if I loop from 1 to the square root of the number being tested, I can just take the number being tested as well as it’s paired divisor at the same time. Add these to the sum and I’m in business:

Start with a sum of 1. Loop from 2 to the square root of 28 (rounded down to 5) (inclusive).

  1. 28 % 2. Yes! Add 2 and 14 to sum. sum is now 17.
  2. 28 % 3. No!
  3. 28 % 4. Yes! Add 4 and 7 to sum. sum is now 28.
  4. 28 % 5. No!

And, again, I’ve confirmed that 28 is perfect.

With this in mind, I rewrote my original one line solution like this:

public static boolean reduceSqrtIsPerfect(long num) {
  return LongStream.rangeClosed(2, (long) Math.sqrt(num))
    .reduce(1, (sum, test) -> num % test == 0 ? sum + test + (num / test) : sum) == num;

And viola, my test of 8,589,869,056 went from taking 23 seconds to 3 milliseconds!! After all, rather than looping from 1 to 4294934528 and testing each step along the way, I am now looping from 2 to 92681! This is significantly less work.

And so, I’m fairly happy with the result. That, and, after all that, it’s just one line of code.

The full code and tests for this project have been posted to Github here.

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