Maximum Manhattan Distance After K Changes

Maximum Manhattan Distance After K Changes - Problem

Imagine you're controlling a robot on an infinite grid, starting at the origin (0, 0). You have a sequence of movement commands represented by a string s where each character tells the robot which direction to move:

'N': Move north (up) by 1 unit
'S': Move south (down) by 1 unit
'E': Move east (right) by 1 unit
'W': Move west (left) by 1 unit

Here's the twist: before executing the movement sequence, you can modify at most k characters in the string to any of the four directions. Your goal is to maximize the Manhattan distance from the origin that the robot reaches at any point during its journey.

The Manhattan distance between two points (x₁, y₁) and (x₂, y₂) is |x₁ - x₂| + |y₁ - y₂|.

Key insight: You want to strategically change some moves to make the robot travel as far as possible from the origin at some point during its path, not necessarily at the end.

Input & Output

example_1.py — Basic Example

$ Input: s = "NESE", k = 1

› Output: 4

💡 Note: Original path: (0,0)→(0,1)→(1,1)→(1,0)→(2,0), max distance = 2. Change S to N: (0,0)→(0,1)→(1,1)→(1,2)→(2,2), max distance = 4.

example_2.py — Multiple Changes

$ Input: s = "NSEW", k = 2

› Output: 4

💡 Note: Original path reaches max distance 1. Change N→E and S→E to get "EEEW": (0,0)→(1,0)→(2,0)→(3,0)→(2,0), max distance = 3. Better: change N→E and W→E to get "ESEE": (0,0)→(1,0)→(1,-1)→(2,-1)→(3,-1), max distance = 4.

example_3.py — No Changes Needed

$ Input: s = "NNNN", k = 0

› Output: 4

💡 Note: Already optimal path going straight North: (0,0)→(0,1)→(0,2)→(0,3)→(0,4), max distance = 4.

Constraints

1 ≤ s.length ≤ 10⁵
0 ≤ k ≤ s.length
s consists only of characters 'N', 'S', 'E', 'W'
The string represents a valid sequence of moves

Visualization

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Asked in

G Google 42 f Meta 35 a Amazon 28 ⊞ Microsoft 22

The optimal solution uses prefix sums to efficiently track the robot's position after each move, then applies a greedy strategy to identify which moves to change for maximum distance improvement. Key insight: calculate the improvement each potential change would provide and select the top k changes that maximize the Manhattan distance reached at any point during the journey.

Common Approaches

✓ Math

⏱️ Time: N/A Space: N/A

Optimized

⏱️ Time: N/A Space: N/A

Brute Force (Try All Combinations)

⏱️ Time: O(C(n,k) * 4^k * n) Space: O(n)

Generate all possible ways to change at most k characters in the string, then for each possibility, simulate the entire path and track the maximum Manhattan distance achieved at any point.

Prefix Sum + Greedy (Optimal)

⏱️ Time: O(n log n) Space: O(n)

Calculate prefix sums to determine the robot's position after each move. Then use a greedy approach to identify which moves to change to maximize the Manhattan distance at any point during the journey.

Algorithm Steps — Algorithm Steps

Code -

solution.c — C

#include <stdio.h>
#include <string.h>

int solution(int n) {
    char digitStr[20];
    sprintf(digitStr, "%d", n);
    int maxDigit = 0;
    
    for (int i = 0; i < strlen(digitStr); i++) {
        int digit = digitStr[i] - '0';
        if (digit > maxDigit) {
            maxDigit = digit;
        }
    }
    
    return maxDigit * maxDigit;
}

int main() {
    int n;
    scanf("%d", &n);
    
    int result = solution(n);
    printf("%d\n", result);
    
    return 0;
}

Time & Space Complexity

Time Complexity

⏱️

⚡ Linearithmic

Space Complexity

⚡ Linearithmic Space

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Smart Actions

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Input & Output

Constraints

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Related Problems

Common Approaches

Algorithm Steps — Algorithm Steps

Code -

Time & Space Complexity

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