Defuse the Bomb Solution
In this tutorial, we will solve the Defuse the Bomb problem using a circular array and sliding window approach. We will provide the implementation of the solution in C++, Java, and Python.
Problem Description​
You have a bomb to defuse, and your informer will provide you with a circular array code
of length n
and a key k
. To decrypt the code, you must replace every number as follows:
- If
k > 0
, replace thei-th
number with the sum of the nextk
numbers. - If
k < 0
, replace thei-th
number with the sum of the previousk
numbers. - If
k == 0
, replace thei-th
number with 0.
As code
is circular, the next element of code[n-1]
is code[0]
, and the previous element of code[0]
is code[n-1]
.
Examples​
Example 1:
Input: code = [5,7,1,4], k = 3
Output: [12,10,16,13]
Explanation: Each number is replaced by the sum of the next 3 numbers. The decrypted code is [7+1+4, 1+4+5, 4+5+7, 5+7+1]. Notice that the numbers wrap around.
Example 2:
Input: code = [1,2,3,4], k = 0
Output: [0,0,0,0]
Explanation: When k is zero, the numbers are replaced by 0.
Example 3:
Input: code = [2,4,9,3], k = -2
Output: [12,5,6,13]
Explanation: The decrypted code is [3+9, 2+3, 4+2, 9+4]. Notice that the numbers wrap around again. If k is negative, the sum is of the previous numbers.
Constraints​
n == code.length
1 <= n <= 100
1 <= code[i] <= 100
-(n - 1) <= k <= n - 1
Solution for Defuse the Bomb Problem​
Intuition and Approach​
The problem can be solved using a sliding window approach to handle the circular nature of the array. Depending on the value of k
, we sum the appropriate elements and use modular arithmetic to handle the circular behavior.
- Brute Force
- Optimized
Approach 1: Brute Force (Naive)​
The brute force approach involves iterating through each element and calculating the sum of the next or previous k
elements based on the value of k
.
Code in Different Languages​
- C++
- Java
- Python
class Solution {
public:
vector<int> decrypt(vector<int>& code, int k) {
int n = code.size();
vector<int> result(n, 0);
if (k == 0) return result;
for (int i = 0; i < n; ++i) {
int sum = 0;
for (int j = 1; j <= abs(k); ++j) {
if (k > 0) {
sum += code[(i + j) % n];
} else {
sum += code[(i - j + n) % n];
}
}
result[i] = sum;
}
return result;
}
};
class Solution {
public int[] decrypt(int[] code, int k) {
int n = code.length;
int[] result = new int[n];
if (k == 0) return result;
for (int i = 0; i < n; ++i) {
int sum = 0;
for (int j = 1; j <= Math.abs(k); ++j) {
if (k > 0) {
sum += code[(i + j) % n];
} else {
sum += code[(i - j + n) % n];
}
}
result[i] = sum;
}
return result;
}
}
class Solution:
def decrypt(self, code: List[int], k: int) -> List[int]:
n = len(code)
result = [0] * n
if k == 0:
return result
for i in range(n):
sum_val = 0
for j in range(1, abs(k) + 1):
if k > 0:
sum_val += code[(i + j) % n]
else:
sum_val += code[(i - j + n) % n]
result[i] = sum_val
return result
Complexity Analysis​
- Time Complexity: due to nested loops.
- Space Complexity: for the result array.
- Where
n
is the length of the code array. - The time complexity is because we iterate through each element and calculate the sum of
k
elements. - The space complexity is because we store the result in a new array.
Approach 2: Sliding Window (Optimized)​
The sliding window approach uses a more efficient way to calculate the sum by maintaining a running sum and updating it as the window slides over the array.
Code in Different Languages​
- C++
- Java
- Python
class Solution {
public:
vector<int> decrypt(vector<int>& code, int k) {
int n = code.size();
vector<int> result(n, 0);
if (k == 0) return result;
int start = k > 0 ? 1 : k;
int end = k > 0 ? k : -1;
int sum = 0;
for (int i = start; i != end + 1; ++i) {
sum += code[(i + n) % n];
}
for (int i = 0; i < n; ++i) {
result[i] = sum;
sum -= code[(start + i + n) % n];
sum += code[(end + 1 + i + n) % n];
}
return result;
}
};
class Solution {
public int[] decrypt(int[] code, int k) {
int n = code.length;
int[] result = new int[n];
if (k == 0) return result;
int start = k > 0 ? 1 : k;
int end = k > 0 ? k : -1;
int sum = 0;
for (int i = start; i != end + 1; ++i) {
sum += code[(i + n) % n];
}
for (int i = 0; i < n; ++i) {
result[i] = sum;
sum -= code[(start + i + n) % n];
sum += code[(end + 1 + i + n) % n];
}
return result;
}
}
class Solution:
def decrypt(self, code: List[int], k: int) -> List[int]:
n = len(code)
result = [0] * n
if k == 0:
return result
start, end = (1, k) if k > 0 else (k, -1)
sum_val = sum(code[i % n] for i in range(start, end + 1))
for i in range(n):
result[i] = sum_val
sum_val -= code[(start + i) % n]
sum_val += code[(end + 1 + i) % n]
return result
Complexity Analysis​
- Time Complexity: due to the sliding window.
- Space Complexity: for the result array.
- Where
n
is the length of the code array. - The time complexity is $O(n
)$ because we iterate through each element once with a running sum.
- The space complexity is because we store the result in a new array.