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Exchange sort is similar to selection sort, just swaps "overly eagerly" instead of finding the minimum and doing only one swap. And the swaps affect how often the if ... is true. For example for 8000 random numbers, in exchange sort it's true ~270 times more often:

        trues in      trues in
   n  exchangeSort  selectionSort  ratio
----------------------------------------
1000      244828        5368        45.6
2000     1002555       12341        81.2
4000     4017151       27691       145.1
8000    16359262       60394       270.9

Why is it so much more often? Does someone have a good explanation? I did expect some noticeable difference, but not this much.

What's the expected number of trues for each?. For exchange sort it looks quadratic, for selection sort maybe linearithmic?

Code (Try it online!):

def exchangeSort(nums):
    trues = 0
    for i in range(len(nums)-1):
        for j in range(i + 1, len(nums)):
            if nums[j] < nums[i]:
                trues += 1
                nums[j], nums[i] = nums[i], nums[j]
    return trues

def selectionSort(nums):
    trues = 0
    for i in range(len(nums)-1):
        idx_min = i
        for j in range(i + 1, len(nums)):
            if nums[j] < nums[idx_min]:
                trues += 1
                idx_min = j
        nums[idx_min], nums[i] = nums[i], nums[idx_min]
    return trues

from random import random
print('        trues in      trues in')
print('   n  exchangeSort  selectionSort  ratio')
print('----------------------------------------')
for n in 1000, 2000, 4000, 8000:
    nums = [random() for _ in range(n)]
    trues1 = exchangeSort(nums.copy())
    trues2 = selectionSort(nums.copy())
    print(f'{n}  {trues1:10}  {trues2:10}  {trues1/trues2:10.1f}')

This was inspired by another question, from which I also took the code.

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2 Answers 2

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Interesting question! Indeed, the expected numbers are quadratic and linearithmic respectively.

Assumptions

For ease of analysis, assume the numbers in the given array are distinct. This assumption does not change the asymptotic nature of problems at hand.

WLOG, assume the given array is selected uniformly from all permutations of $0, 1,\cdots, n-1$.

Expected Number by Exchange Sort

Claim 1. Given array $nums$, the number of inversions found by exchange-sort is the number of inversions in $nums$.
Proof. It is easy to check that each swap reduces one instance of inversion. $\quad\checkmark$

Hence, the expected number of inversions found by exchanges-sort is the expected inversions in a permutation, which is $(n(n-1)/2)/2=n(n-1)/4$.

Expected Number by Selection Sort

Claim 2. The expected number of inversions found by selection-sort in the inner loop is $\frac12 + \frac13+\cdots+\frac1{n-i}$.
Proof. At the start of inner loop, the "comparison structure" of $nums[i:n]$ is uniformly random. The probability of $nums[j] < nums[idx\_min]$ is the probability of the random number at index $j$ being less than all $j-i$ random numbers right before it, which is $\frac1{j-i+1}$ since the randomness here comes from sampling from distinct numbers without replacement. $\quad\quad\checkmark$

Hence, the expected number of inversions found by selection-sort is $$\begin{aligned} &\quad\sum_{i=0}^{n-2}\sum_{j=i+1}^{n-1}\frac1{j-i+1}\\ &=\sum_{k=2}^{n}\sum_{\ell=2}^{k}\frac1\ell\\ &=\sum_{k=2}^{n}(-1+\ln k+\gamma +{\frac {1}{2k}}-\varepsilon_k)\\ &=\ln (n!) + (n-1)(\gamma-1) +\sum_{k=2}^{n}{\frac {1}{2k}}-\sum_{k=2}^{n}\varepsilon_k\\ &=\ln (n!) + (n-1)(\gamma-1) +\frac12(-1 + \ln n+\gamma + \frac1{2n}-\varepsilon_{n})-\sum_{k=2}^{n}\varepsilon_k\\ &=\ln\left({\sqrt {2\pi n}}\left({\frac {n}{e}}\right)^{n}\right)+ n(\gamma-1) + o(n)\\ &=n\log n + n(\gamma-2) + o(n)\\ &\sim n\log n \end{aligned}$$

where the second equality and the fourth equality use the growth rate of partial sums of harmonic series and the second equality from the last uses Stirling's approximation.

"Why is it so much more often?"

The reason is embodied in the proof of two claims above. Also see asker's wonderful and enlightening explanation.

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Here's another perspective. Less mathematical, but I think also insightful. Using a fabulous sort visualizer (taken from this), here's random data and how the data looks midway through sorting with the two algorithms.

Visualization of the sort progress

It visualizes sorting many arrays in parallel, each array is a column, index 0 is on top, and dark colors are "smaller" than bright colors. Try it yourself, select the sorting algorithms on the right, then click "Start".

For selection sort, we see what John L. said: The still-to-be-sorted part nums[i:n] looks completely random. And it is: It is at the very beginning, and what's done inside the outer loop keeps this property. If nums[i] is the smallest in nums[i:n], then we don't really swap and nums[i+1:n] remains as it is, i.e., remains completely random. If on the other hand nums[i] isn't the smallest, then its value plays no role whatsoever in determining idx_min. That index is entirely determined by wherever the smallest element is. And since nums[i] plays no role for this, its relationship with all other values in nums[i+1:n] (other than the smallest) can be anything. Hence swapping it it to idx_min makes nums[i+1:n] completely random as well.

Look at the exchange sort visualization instead. There we see that the still-to-be-sorted bottom half already looks somewhat (reverse-)sorted. A lot of bright colors, i.e., large values, near index i. And getting darker going down. Why is that? Consider what the inner loop does. It goes through the remaining nums[i:n], puts the smallest value at nums[i], and whenever it finds an even smaller value, it swaps nums[i] there. That finds a descending subsequence in nums[i:n] and "rotates" it to the right. Here's an example where the descending subsequence is 30, 20, 10 (and what the other values between/after might be):

Example of exchange sort's inner loop action

Now imagine that for example the block of numbers containing values from 31 to infinity actually contains the number 31. And that the other two later blocks similarly contain the number 21 and 11, respectively. Before the swaps, we see the order 30-31-20-21-10-11. So going up-down-up-down-up. But after the swaps, we see the order 10-31-30-21-20-11. So going up-down-down-down-down-down. The part after the 10 is all going down. We've taken the subsequence 30-20 and shifted it to the right so that it "falls more in line" (order-wise) with the rest, so that the remainder is now more sorted (descendingly) than before. And these longer descending subsequences are then found and handled in later rounds (for larger i), causing more work there.

So to summarize: Selection sort keeps the remainder unsorted, which is good. While exchange sort shoots itself in the foot by partially sorting the remainder in a way that's detrimental for it (causes more inversions / more work).

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  • $\begingroup$ @JohnBollinger "lots of descending subsequences" - Their number doesn't matter for this. Their length does. In random data, the descending subsequences are pretty short. Exchange sort's partial sorting of the remainder creates much longer descending subsequences, which then hurts it. $\endgroup$ May 7 at 20:17
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    $\begingroup$ @JohnBollinger Fixed the answer now and removed my first comment. $\endgroup$ May 7 at 21:31

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