Python3 || O(n) and O(1) TIME AND SPACE || May 28 2024 Daily
Intuition\nThe problem requires finding the maximum length of a substring in string s that can be transformed into the corresponding substring in string t with 
# Intuition\nThe problem requires finding the maximum length of a substring in string s that can be transformed into the corresponding substring in string t with a cost less than or equal to maxCost. We can approach this problem using a sliding window technique.\n\n# Approach\nSliding Window: Initialize two pointers i and j at the start of s. Use these pointers to define a window that represents the current substring being considered.\nCost Calculation: Iterate through the characters of s and t within the window. Calculate the cost of transforming s[i] to t[i] using the absolute difference of their ASCII values.\nCost Constraint Check: If the cost exceeds maxCost, move the window by incrementing j and adjusting the cost accordingly until the cost constraint is satisfied again.\nTrack Maximum Length: Keep track of the maximum length of the substring that satisfies the cost constraint throughout the iterations.\nReturn Result: After iterating through the entire string s, return the maximum length of the substring that satisfies the cost constraint.\n\n# Complexity\n- Time complexity:\n The algorithm traverses the entire strings s and t once, so the time complexity is $$O(n)$$ where $$n$$ is the length of the strings.\n\n- Space complexity:\nThe algorithm uses only a constant amount of extra space, so the space complexity is $$O(1)$$\n\n# Code\n```\nclass Solution:\n  def equalSubstring(self, s: str, t: str, maxCost: int) -> int:\n    j = 0\n    for i in range(len(s)):\n      maxCost -= abs(ord(s[i]) - ord(t[i]))\n      if maxCost < 0:\n        maxCost += abs(ord(s[j]) - ord(t[j]))\n        j += 1\n\n    return len(s) - j\n```
['Python3']