How do you solve Maximum Binary String After Change on LeetCode?

Maximum Binary String After Change (LeetCode #1702) 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: You can always reduce the binary string to at most one '0'.

What is the time complexity of Maximum Binary String After Change?

The optimal solution for Maximum Binary String After Change runs in O(n) time and uses O(n) space. This complexity is linear because we only traverse the string a couple of times (once for counting '1's and once for finding the last '0').

What is the brute force solution for Maximum Binary String After Change?

The brute force approach for Maximum Binary String After Change is Brute Force, with O(n²) time complexity and O(1) space complexity. The brute force approach involves repeatedly applying the allowed operations until no more transformations can be made. This method is straightforward but inefficient, as it may require multiple passes through the string. The optimal Optimal Solution approach improves this to O(n).

What algorithm or data structure is used to solve Maximum Binary String After Change?

Maximum Binary String After Change uses the following concepts: String, Greedy. The recommended patterns to study are: Greedy, String Manipulation.

What are the interview tips for Maximum Binary String After Change?

Always look for patterns in string manipulation problems. Consider edge cases, such as strings with no '0's or only '0's. Practice explaining your thought process clearly, as communication is key in interviews.

What are common mistakes when solving Maximum Binary String After Change?

Not recognizing that the operations can lead to a maximum of one '0'. Overcomplicating the transformation process instead of focusing on counting.

#1702

Maximum Binary String After Change

Medium

StringGreedyGreedyString Manipulation

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 leverages the fact that we can only have at most one '0' in the final string. By counting the number of '1's and the position of the last '0', we can construct the maximum binary string directly.

⚙️

Algorithm

4 steps

1Step 1: Count the number of '1's in the binary string.
2Step 2: Find the position of the last '0' in the string.
3Step 3: If there are no '0's, return a string of all '1's.
4Step 4: Construct the result string with all '1's followed by a single '0' and the remaining '1's.

solution.py6 lines

1def maximumBinaryString(binary):
2    count_one = binary.count('1')
3    last_zero_index = binary.rfind('0')
4    if last_zero_index == -1:
5        return '1' * len(binary)
6    return '1' * (last_zero_index) + '0' + '1' * (count_one - (last_zero_index + 1))

ℹ

Complexity note: This complexity is linear because we only traverse the string a couple of times (once for counting '1's and once for finding the last '0').

1You can always reduce the binary string to at most one '0'.
2The maximum binary string is formed by grouping all '1's together, followed by a single '0' if present.

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