How do you solve Count Beautiful Splits in an Array on LeetCode?

Count Beautiful Splits in an Array (LeetCode #3388) 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: Prefix conditions are crucial for determining valid splits.

What is the time complexity of Count Beautiful Splits in an Array?

The optimal solution for Count Beautiful Splits in an Array runs in O(n) time and uses O(n) space. The algorithm processes the array in linear time and uses a fixed-size count array for tracking occurrences.

What is the brute force solution for Count Beautiful Splits in an Array?

The brute force approach for Count Beautiful Splits in an Array is Brute Force, with O(n²) time complexity and O(1) space complexity. We can try every possible way to split the array into three parts and check if the conditions for a beautiful split are met. This is straightforward but inefficient. The optimal Optimal Solution approach improves this to O(n).

What algorithm or data structure is used to solve Count Beautiful Splits in an Array?

Count Beautiful Splits in an Array uses the following concepts: Array, Dynamic Programming. The recommended patterns to study are: Hash Map, Array.

What are the interview tips for Count Beautiful Splits in an Array?

Explain your thought process clearly. Discuss time and space complexity. Consider edge cases during coding.

What are common mistakes when solving Count Beautiful Splits in an Array?

Not considering edge cases with small arrays. Overlooking the prefix condition logic.

#3388

Count Beautiful Splits in an Array

Medium

ArrayDynamic ProgrammingHash 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 prefix sum approach to count occurrences of each number, allowing us to efficiently check for valid splits without redundant checks.

⚙️

Algorithm

3 steps

1Step 1: Create a count array to store occurrences of each number up to each index.
2Step 2: Iterate through possible split points and check prefix conditions using the count array.
3Step 3: Count valid splits based on the prefix conditions.

solution.py9 lines

1def count_beautiful_splits(nums):
2    count = [0] * 51
3    for num in nums:
4        count[num] += 1
5    total = 0
6    for i in range(1, len(nums) - 1):
7        if count[nums[i]] > 0:
8            total += 1
9    return total

ℹ

Complexity note: The algorithm processes the array in linear time and uses a fixed-size count array for tracking occurrences.

1Prefix conditions are crucial for determining valid splits.
2Using counts can simplify checks for prefixes.

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