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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)
1function strangePrinter(s) {
2 const n = s.length;
3 const dp = Array.from({ length: n }, () => Array(n).fill(0));
4 for (let len = 1; len <= n; len++) {
5 for (let i = 0; i + len <= n; i++) {
6 let j = i + len - 1;
7 dp[i][j] = (i === j) ? 1 : dp[i + 1][j] + 1;
8 for (let k = i + 1; k <= j; k++) {
9 if (s[k] === s[i]) {
10 dp[i][j] = Math.min(dp[i][j], dp[i][k - 1] + (k + 1 <= j ? dp[k + 1][j] : 0));
11 }
12 }
13 }
14 }
15 return dp[0][n - 1];
16}
17
18console.log(strangePrinter('aba'));This JavaScript solution uses a similar strategy with dynamic programming, managing segments with a 2D dp array. The nested loops progressively handle string segments, recording the fewest prints needed by evaluating shared segments of 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.