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Description: Use two pointers, or indices - one starting at 0 and the other at 'n'. For each 'I' in the string, assign the lowest available number and increment the 'low' index. For each 'D', assign the highest available number and decrement the 'high' index. This approach efficiently fills the permutation array by maximizing the immediate decision at each step.
Time Complexity: O(n), where n is the length of the string.
Space Complexity: O(1), excluding the output array.
1#include <vector>
2#include <string>
3using namespace std;
4
5vector<int> diStringMatch(string s) {
6 int low = 0, high = s.size();
7 vector<int> result;
8 for (char c : s) {
9 result.push_back(c == 'I' ? low++ : high--);
10 }
11 result.push_back(low);
12 return result;
13}This C++ solution is similar to the C solution but utilizes a 'vector' for dynamic array management. It assigns values based on 'I' and 'D' and finally appends the last remaining number, which will be 'low'.
Description: The Greedy Character Matching approach involves tracking two dynamic bounds, 'low' and 'high', to determine at every step what the current optimal choice for our permutation should be. By iterating over the string 's', for every 'I', append the current lowest available integer and increment it. For 'D', add the current highest integer and decrement it, ensuring the correct permutation ordering through direct engagement with the constraints.
Time Complexity: O(n), where n is the length of the string.
Space Complexity: O(1), discounting the result storage.
The JavaScript solution integrates a perceptively proactive low-high boundary meld, using 'push' to deliver outcomes determined during iterations matched by character requirements.