Kotlin Kadane’s algorithm is a well-known algorithm used for **finding the maximum subarray sum in a given array**. It is an efficient algorithm that works in **O(n) time complexity**. In this blog, we will discuss Kadane’s algorithm and how to implement it using the Kotlin programming language.

**Kotlin** **Kadane’s Algorithm**

Kadane’s algorithm is a **dynamic programming algorithm** that works by iterating over the array and keeping track of the maximum subarray sum seen so far. The algorithm maintains two variables, **max_so_far** and **max_ending_here**, **where max_so_far is the maximum subarray sum seen so far, and max_ending_here is the maximum subarray sum that ends at the current index**.

**The algorithm starts by setting both max_so_far and max_ending_here to the first element of the array**. It then **iterates over the remaining elements** of the array,** updating max_ending_here by adding the current element to it**. **If max_ending_here becomes negative, it is reset to zero, as any subarray with a negative sum cannot be the maximum subarray**. **If max_ending_here is greater than max_so_far, max_so_far is updated with the value of max_ending_here**. **At the end of the iteration**, **max_so_far** will contain the **maximum subarray sum**.

**Kotlin Implementation**

Now let’s see how we can implement Kadane’s algorithm using Kotlin:

```
fun maxSubArraySum(arr: IntArray): Int {
var max_so_far = arr[0]
var max_ending_here = arr[0]
for (i in 1 until arr.size) {
max_ending_here = max_ending_here + arr[i]
if (max_ending_here < arr[i])
max_ending_here = arr[i]
if (max_so_far < max_ending_here)
max_so_far = max_ending_here
}
return max_so_far
}
```

In this implementation, we first initialize max_so_far and max_ending_here to the first element of the array. We then loop over the remaining elements of the array and update max_ending_here by adding the current element to it. If max_ending_here becomes negative, it is reset to zero. If max_ending_here is greater than max_so_far, max_so_far is updated with the value of max_ending_here. Finally, the function returns max_so_far.

Let’s test our implementation with an example:

```
fun main() {
val arr = intArrayOf(-2, -3, 4, -1, -2, 1, 5, -3)
val maxSum = maxSubArraySum(arr)
println(\"Maximum subarray sum is: $maxSum\")
}
```

**Output:**

Maximum subarray sum is: 7

In this example, we have an array of integers, and we want to find the maximum subarray sum. Our implementation correctly returns 7, which is the maximum subarray sum.

**Conclusion**

Kadane’s algorithm is a simple yet powerful algorithm for finding the maximum subarray sum in an array. In this blog, we have seen how to implement Kadane’s algorithm using Kotlin. This implementation works in **O(n) time complexity**, making it an **efficient algorithm for solving the maximum subarray problem**.