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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.
1def diStringMatch(s: str):
2 low, high = 0, len(s)
3 result = []
4 for char in s:
5 if char == 'I':
6 result.append(low)
7 low += 1
8 else:
9 result.append(high)
10 high -= 1
11 result.append(low)
12 return resultThe Python solution uses a list to store the result permutation, iterating over 's' and adjusting 'low' and 'high' to fill the permutation according to 'I'/'D'.
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 Python approach illustrates greed through simultaneous list construction. It configures 'low' and 'high' updates directly, expediently filling the result.