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This approach involves using dynamic programming with memoization to find the minimum number of turns needed to print the string. The idea is to define a recursive function that prints a substring from index i to index j, using memoization to avoid unnecessary repeated calculations.
Time Complexity: O(n^3), Space Complexity: O(n^2)
1class Solution:
2 def strangePrinter(self, s: str) -> int:
3 n = len(s)
4 dp = [[0] * n for _ in range(n)]
5 for length in range(1, n + 1):
6 for i in range(n - length + 1):
7 j = i + length - 1
8 dp[i][j] = dp[i + 1][j] + 1
9 for k in range(i + 1, j + 1):
10 if s[k] == s[i]:
11 dp[i][j] = min(dp[i][j], dp[i][k - 1] + (dp[k + 1][j] if k + 1 <= j else 0))
12 return dp[0][n - 1]
13
14sol = Solution()
15print(sol.strangePrinter("aba"))The Python solution also uses dynamic programming to fill a 2D dp array. The array helps manage overlapping problems, focusing on minimizing printing operations for increasing segment lengths, checked using nested loops across segments characterized by identical characters.
This approach uses a greedy strategy to minimize the number of character segments that need to be printed. Instead of forming a full dp matrix, it strategizes the printing operations based on visible segments of similar characters, exploiting shared coverage.
Time Complexity: O(n^3), Space Complexity: O(n^2). This primarily stems from recursive splitting subproblems by string breakdown.
1class Solution:
2 def strangePrinter(self, s: str) ->
This optimized greedy Python solution leverages a streamlined version of the string by compressing repeated characters. It utilizes memoization within a divided recursive structure. Segments are collapsed as the helper ranges over valid substrings, aiming for spaced repetition minimization.