What is the brute force solution for Shortest and Lexicographically Smallest Beautiful String?

The brute force approach for Shortest and Lexicographically Smallest Beautiful String is Brute Force, with O(n²) time complexity and O(1) space complexity. The brute force approach involves checking every possible substring of the given binary string to see if it contains exactly k '1's. This is straightforward but inefficient, as it examines all combinations. The optimal Optimal Solution approach improves this to O(n).

What algorithm or data structure is used to solve Shortest and Lexicographically Smallest Beautiful String?

Shortest and Lexicographically Smallest Beautiful String uses the following concepts: String, Sliding Window. The recommended patterns to study are: Sliding Window, Two Pointers.

What are the interview tips for Shortest and Lexicographically Smallest Beautiful String?

Always clarify the problem statement and edge cases before diving into coding. Talk through your thought process and approach to the interviewer to showcase your understanding. Practice writing clean and efficient code, as readability is crucial in interviews.

What are common mistakes when solving Shortest and Lexicographically Smallest Beautiful String?

Failing to handle cases where no beautiful substring exists, leading to incorrect output. Not considering the lexicographical order properly when updating the shortest substring.

#2904

Shortest and Lexicographically Smallest Beautiful String

Medium

StringSliding WindowSliding WindowTwo Pointers

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)

The optimal solution uses a sliding window technique to efficiently find the shortest beautiful substring. By maintaining a window that expands and contracts, we can count the '1's without needing to recheck the entire substring each time.

⚙️

Algorithm

5 steps

1Step 1: Initialize two pointers (left and right) and a count for '1's.
2Step 2: Expand the right pointer to include characters until the count of '1's equals k.
3Step 3: Once k '1's are found, try to contract the left pointer to minimize the substring length while maintaining k '1's.
4Step 4: Update the shortest and lexicographically smallest substring if the current one is valid.
5Step 5: Continue this process until the right pointer reaches the end of the string.

solution.py16 lines

1def shortest_beautiful_string(s, k):
2    left, right, count = 0, 0, 0
3    shortest = ''
4    n = len(s)
5    while right < n:
6        if s[right] == '1':
7            count += 1
8        while count == k:
9            substring = s[left:right + 1]
10            if (not shortest) or (len(substring) < len(shortest)) or (len(substring) == len(shortest) and substring < shortest):
11                shortest = substring
12            if s[left] == '1':
13                count -= 1
14            left += 1
15        right += 1
16    return shortest

ℹ

Complexity note: The time complexity is O(n) because each character is processed at most twice (once by the right pointer and once by the left pointer). The space complexity is O(n) due to the substring storage.

1Sliding window technique allows for efficient substring examination without redundant checks.
2Lexicographical comparison can be efficiently managed during the substring evaluation.

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