Approach #1: Left and Right Index [Accepted]

Intuition and Algorithm

An array that has degree d, must have some element x occur d times. If some subarray has the same degree, then some element x (that occured d times), still occurs d times. The shortest such subarray would be from the first occurrence of x until the last occurrence.

For each element in the given array, let's know left, the index of its first occurrence; and right, the index of its last occurrence. For example, with nums = [1,2,3,2,5] we have left[2] = 1 and right[2] = 3.

Then, for each element x that occurs the maximum number of times, right[x] - left[x] + 1 will be our candidate answer, and we'll take the minimum of those candidates.


class Solution(object):
    def findShortestSubArray(self, nums):
        left, right, count = {}, {}, {}
        for i, x in enumerate(nums):
            if x not in left: left[x] = i
            right[x] = i
            count[x] = count.get(x, 0) + 1

        ans = len(nums)
        degree = max(count.values())
        for x in count:
            if count[x] == degree:
                ans = min(ans, right[x] - left[x] + 1)

        return ans


class Solution {
    public int findShortestSubArray(int[] nums) {
        Map<Integer, Integer> left = new HashMap(),
            right = new HashMap(), count = new HashMap();

        for (int i = 0; i < nums.length; i++) {
            int x = nums[i];
            if (left.get(x) == null) left.put(x, i);
            right.put(x, i);
            count.put(x, count.getOrDefault(x, 0) + 1);

        int ans = nums.length;
        int degree = Collections.max(count.values());
        for (int x: count.keySet()) {
            if (count.get(x) == degree) {
                ans = Math.min(ans, right.get(x) - left.get(x) + 1);
        return ans;

Complexity Analysis

  • Time Complexity: , where is the length of nums. Every loop is through items with work inside the for-block.

  • Space Complexity: , the space used by left, right, and count.

Analysis written by: @awice.