How do you solve Longest Nice Subarray on LeetCode?

Longest Nice Subarray (LeetCode #2401) can be solved using Brute Force or Optimal Solution. The optimal approach is Optimal Solution with O(n) time complexity and O(1) space complexity. Key insight: The maximum length of a nice subarray cannot exceed 30 due to the bitwise nature of the problem.

What is the brute force solution for Longest Nice Subarray?

The brute force approach for Longest Nice Subarray is Brute Force, with O(n²) time complexity and O(1) space complexity. The brute-force approach involves checking all possible subarrays to see if they are nice. This means we will iterate through every possible starting and ending index of the subarray and calculate the bitwise AND for each pair of elements. The optimal Optimal Solution approach improves this to O(n).

What algorithm or data structure is used to solve Longest Nice Subarray?

Longest Nice Subarray uses the following concepts: Array, Bit Manipulation, Sliding Window. The recommended patterns to study are: Sliding Window, Bit Manipulation.

What are the interview tips for Longest Nice Subarray?

Always start with a brute-force solution to understand the problem better before optimizing. Be mindful of edge cases, such as arrays with only one element or all elements being the same. Practice explaining your thought process clearly, as communication is key in interviews.

What are common mistakes when solving Longest Nice Subarray?

Not considering the bitwise properties of numbers, leading to incorrect assumptions about subarray lengths. Failing to optimize the checks for nice subarrays, leading to excessive time complexity.

#2401

Longest Nice Subarray

Medium

ArrayBit ManipulationSliding WindowSliding WindowBit Manipulation

LeetCode ↗

Approaches

Brute ForceOptimal

Complexity Comparison

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

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Intuition

Time O(n)Space O(1)

The optimal solution uses a sliding window approach to efficiently find the longest nice subarray. By maintaining a window of elements and checking their bitwise AND, we can dynamically adjust the window size without rechecking all pairs.

⚙️

Algorithm

5 steps

1Step 1: Initialize two pointers (left and right) to represent the current window.
2Step 2: Use a variable to keep track of the bitwise AND of the current window.
3Step 3: Expand the right pointer to include new elements in the window, updating the AND.
4Step 4: If the AND becomes non-zero, move the left pointer to shrink the window until the AND is zero again.
5Step 5: Keep track of the maximum length of the window when it is nice.

solution.py14 lines

1# Full working Python code
2
3def longestNiceSubarray(nums):
4    max_length = 1
5    left = 0
6    current_and = 0
7    for right in range(len(nums)):
8        current_and &= nums[right]
9        while current_and != 0:
10            current_and &= ~nums[left]
11            left += 1
12        max_length = max(max_length, right - left + 1)
13    return max_length
14

ℹ

Complexity note: The time complexity is O(n) because we traverse the array with two pointers, and each element is processed at most twice (once when added and once when removed from the window).

1The maximum length of a nice subarray cannot exceed 30 due to the bitwise nature of the problem.
2Using bitwise operations efficiently can significantly reduce the number of checks needed.

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