So I have 2 sorted arrays which will have the same length and what I need to do is sort the union array (the union of the two sorted arrays) and then find two median values, I need to do this in O(log n). I was able to come up with finding the median in log(n) but that is without sorting the arrays, I could use one of the sorting algorithms but then the time complexity will be more than log(n).

My way for finding the median in log(n):

  1. divide the array in half
  2. Go to the result index (it will be the number we get after dividing the array in half)
  3. Same thing as above but add 1 to the index number.

Again this will be log(n) if the union of the 2 arrays were sorted but in this is not the case here. I think binary search could be used but I am not 100% sure exactly how to use it in this problem.

  • $\begingroup$ If the arrays aren't sorted, then you can't find a median in less than $\Omega(n)$. Did you mean by any chance that the two arrays are already sorted? To be a bit more precise, is it correct that each of the two arrays are sorted (individually), and when you said "sorting" you meant sorting the union of them? $\endgroup$
    – nir shahar
    Apr 5, 2023 at 19:59
  • $\begingroup$ @nirshahar Yes that's what I meant, sorry for not being that clear. The two given arrays are already sorted, I need to sort the union of them and then find 2 median values from that union array. $\endgroup$
    – 111
    Apr 5, 2023 at 20:25
  • $\begingroup$ Maybe try to edit your question so it will be more clear for other people as well :) $\endgroup$
    – nir shahar
    Apr 5, 2023 at 20:51
  • 1
    $\begingroup$ I think the question is still not specified. When you speak of "sorting the union array", does your algorithm need to produce the new array, or do you only need to return the 2 middle values of this theoretical array? $\endgroup$
    – Matthew C
    Apr 6, 2023 at 0:56

2 Answers 2


The general idea of the algorithm is pretty straightforward, it basically boils down to the dichotomic search idea by cutting your problem in half at each step. But the parity of the length of the arrays introduces some noisy intricacies.


  • $X$ and $Y$ two sorted arrays of the same size (their starting index is 0).

  • $n = |X| = |Y|$ the arrays' size

  • $m = \bigg\lfloor \dfrac{n}{2} \bigg\rfloor$ the middle index

  • $x = X[m]$ and $y = Y[m]$ the middle values

  • Note that, since the two arrays are the same length, there is a unique pair of median values.

The basic idea

At each step of the algorithm, we are discarding the same number of values to the left and right of the merged sorted arrays : the medians are preserved. For example :


The mathematical proof would be cumbersome to write.

The algorithm

Let's assume that $n$ is odd. If :

  • $x = y$ then they must be our two medians. If we sorted the merged array, those values would necessarily appear next to each other in the middle of it.

  • $x < y$ the problem can be reduced. It is the same as to find the two medians of the arrays $X[m:n]$ and $Y[0:m + 1]$ (I use the Python's slice notation here, endpoint is excluded).

  • $x > y$ is the symmetrical case, we should consider the outer part $X[0:m+1]$ and $Y[m:n]$.

Note that at each step, $X$ and $Y$ are always of the same length. As you can see, we can recursively apply this process until the two arrays are of total size of 4 or 2, they are the base cases.

If $n$ is pair, we have to be careful about the next endpoints we consider as the two medians, as they aren't a true middle in the two arrays. Let be $m^- = m - 1$, $~x^- = X[ m^- ]$ and $~y^- = Y[ m^- ]$. If :

  • $x = y$ then the two medians must be the two medians of $[x^-, x],~[y^-, y]$ : it is one of our base case.

  • $x < y$ then we should consider $X[m - 1:n]$ and $Y[0:m + 1]$

  • $x < y$ we should consider $X[0: m + 1]$ and $Y[m - 1: n]$

As you can see, we carry the two middle elements to the next iteration !

Naive first implementation

If we implement the above algorithm in Python :

def is_odd(n: int) -> bool:
    return n & 1

def base_case_medians_2(a: int, b: int) -> tuple[int, int]:
    """ Returns the two medians (ordered) of the 2 given values. """
    return (a, b) if a < b else (b, a)

def base_case_medians_4(a: int, b: int, c: int, d: int) -> tuple[int, int]:
    """ Returns the two medians (ordered) of the 4 given values. """
    return ((c, d) if b >= d else (b, c) if b <= c else (c, b)) if a <= c else ((a, b) if b <= d else (a, d) if a <= d else (d, a))

def medians_recursive_naive(X: list, Y: list) -> tuple[int, int]:
    """ Returns the two medians of the merged (assumed sorted and of the same size) given arrays.
        Runs in quasilinear time as unecessary copies of the arrays are made. """
    n = len(X)

    # Recursion's base cases
    if n == 1:
        return base_case_medians_2(X[0], Y[0])
    elif n == 2:
        return base_case_medians_4(X[0], X[1], Y[0], Y[1])

    m = n // 2
    x, y = X[m], Y[m]

    # Branch on the parity of the arrays' size
    if is_odd(n):
        if x == y:
            return (x, y)
        elif x < y:
            return medians_recursive_naive(X[m:], Y[:m + 1])
            return medians_recursive_naive(X[:m + 1], Y[m:])
        if x == y:
            return base_case_medians_4(X[m - 1], x, Y[m - 1], y)
        elif x < y:
            return medians_recursive_naive(X[m - 1:], Y[: m + 1])
            return medians_recursive_naive(X[: m + 1], Y[m - 1:])

As the code shown above, an iteration takes $O(n)$ as copies of the arrays are made when forwarded to the next recursion call, but copies are unecessary if we instead keep track of the start and end indices of the two subarrays we consider.

Achieving logarithmic complexity

So, if we use instead indices, our function signature would look like :

def medians_recursive(x_start, x_end, y_start, y_end):

But, this recursive algorithm could be simplified into an iterative algorithm :

def medians_iterative(X: list, Y: list) -> tuple[int, int]:
    """ Returns the two medians of the merged (assumed sorted and of the same size) given arrays.
        Runs in logarithmic time. """
    x_start, x_end = 0, len(X)  # note that the `end` is excluded
    y_start, y_end = 0, len(Y)

    # Algorithm's main loop, cut in half (almost) the arrays at each iteration
    while x_end - x_start > 2:    
        x_mid = (x_start + x_end) // 2
        y_mid = (y_start + y_end) // 2
        x, y = X[x_mid], Y[y_mid]

        odd = is_odd(x_end - x_start)
        if x == y:
            return (x, x) if odd else base_case_medians_4(X[x_mid - 1], x, Y[y_mid - 1], y)
        elif x < y:
            x_start = x_mid + odd - 1
            y_end = y_mid + 1
            x_end = x_mid + 1
            y_start = y_mid + odd - 1

    # Base cases
    if x_end - x_start == 1:
        return base_case_medians_2(X[x_start], Y[y_start])
        return base_case_medians_4(X[x_start], X[x_start + 1], Y[y_start], Y[y_start + 1])

I'm assuming the two arrays are of size $n$ each. Now to unite them into a single sorted array one can use the merge procedure from Merge Sort (see here). This runs in $\Theta(n)$ which is asymptotically tight, that is you can not do better as you have to observe all the elements from both arrays. Thus $O(\log n)$ is not possible.

Probably what you are asking is that, you need to obatain the median (or medians) of the union array in $O(\log n)$, which actually does not require you to actually merge two arrays. The idea is similar to the Binary Search technique, you need to throw away half of the array in each iteration. Suppose the two arrays are called $A$ and $B$. You compare $A[mid]$ with $B[mid]$, if they are the same, you return any one of them as your median (or both as two medians). If not, lets assume without loss of any generality, that $A[mid] < B[mid]$, then your median can not be in $A[1:mid]$. You discard the left half of $A$, that is, $A\leftarrow A[mid+1:n]$ and repeat this process. Eventually one of the array will exhaust, then simply return the median of the other one.

Detalied descriptions along with coded solutions are avilable here and here.


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