How do you solve Subarrays with K Different Integers on LeetCode?

Subarrays with K Different Integers (LeetCode #992) can be solved using Brute Force or Optimal Solution. The optimal approach is Optimal Solution with O(n) time complexity and O(n) space complexity. Key insight: Using a sliding window allows us to efficiently count subarrays without generating all possible combinations.

What is the brute force solution for Subarrays with K Different Integers?

The brute force approach for Subarrays with K Different Integers is Brute Force, with O(n²) time complexity and O(1) space complexity. We can generate all possible subarrays and check how many unique integers each subarray contains. If the count of unique integers matches k, we increment our result. This method is straightforward but inefficient for large arrays. The optimal Optimal Solution approach improves this to O(n).

What algorithm or data structure is used to solve Subarrays with K Different Integers?

Subarrays with K Different Integers uses the following concepts: Array, Hash Table, Sliding Window, Counting. The recommended patterns to study are: Hash Map, Array.

What are the interview tips for Subarrays with K Different Integers?

Always consider edge cases and clarify constraints before diving into coding. Explain your thought process clearly while coding; it shows your understanding and helps interviewers follow your logic. Practice writing both brute force and optimal solutions, as it demonstrates your ability to improve and optimize.

What are common mistakes when solving Subarrays with K Different Integers?

Failing to handle edge cases, such as when k is greater than the number of unique integers in the array. Not properly managing the frequency map, leading to incorrect counts.

#992

Subarrays with K Different Integers

Hard

ArrayHash TableSliding WindowCountingHash MapArray

LeetCode ↗

Approaches

Brute ForceOptimal

Complexity Comparison

	Brute Force	Optimal Solution★
Time	O(n²)	O(n)
Space	O(1)	O(n)

💡

Intuition

Time O(n)Space O(n)

We can use a sliding window approach to efficiently count subarrays with exactly k distinct integers. By maintaining two pointers and a frequency map, we can dynamically adjust the window size to ensure we have the exact number of distinct integers.

⚙️

Algorithm

3 steps

1Step 1: Define a helper function to count subarrays with at most k distinct integers.
2Step 2: Use the helper function to count subarrays with at most k distinct integers and at most (k-1) distinct integers.
3Step 3: The result is the difference between the two counts, which gives us the count of subarrays with exactly k distinct integers.

solution.py18 lines

1def subarraysWithKDistinct(nums, k):
2    def atMostK(k):
3        count = 0
4        left = 0
5        freq = {}
6        for right in range(len(nums)):
7            if nums[right] in freq:
8                freq[nums[right]] += 1
9            else:
10                freq[nums[right]] = 1
11            while len(freq) > k:
12                freq[nums[left]] -= 1
13                if freq[nums[left]] == 0:
14                    del freq[nums[left]]
15                left += 1
16            count += right - left + 1
17        return count
18    return atMostK(k) - atMostK(k - 1)

ℹ

Complexity note: The time complexity is O(n) because we traverse the array with two pointers, and the space complexity is O(n) due to the frequency map that stores counts of elements.

1Using a sliding window allows us to efficiently count subarrays without generating all possible combinations.
2The difference between counts of at most k and at most (k-1) gives us the exact count of subarrays with exactly k distinct integers.

Solutions and explanations are original Tejav content. Problem titles © LeetCode — use the LeetCode button above for the full problem statement.