Kotlin Coding Test Course, Preparing for Resignation

Kotlin has become a very popular programming language in recent years. It is primarily used in Android development, but its utilization is also increasing in areas such as server-side development, data science, and coding tests. In this post, we will explore how to prepare for leaving a job by solving coding test problems using Kotlin.

Problem: Maximum Subarray Sum

The problem is to find the maximum sum of a contiguous subarray in a given integer array. This problem can be approached in various ways, but here we will look at how to solve it efficiently using “Kadane’s Algorithm.”

Problem Description

Given an integer array, find the maximum sum of a contiguous subarray. For example, if the array [−2,1,−3,4,−1,2,1,−5,4] is given, the contiguous subarray [4,−1,2,1] has a sum of 6, which is the maximum sum.

Input

  • Length of the array n (1 ≤ n ≤ 105)
  • Integer array nums (-104 ≤ nums[i] ≤ 104)

Output

Output the maximum sum of the subarray.

Problem Solving Process

Step 1: Understanding and Analyzing the Problem

Since this problem requires considering the sum of contiguous elements, we need to explore several contiguous subarrays that include each element. At this point, we should think about how to reduce unnecessary repetitions and efficiently find the maximum sum.

Step 2: Selecting an Algorithm

Generally, using nested loops to explore all subarrays results in a time complexity of O(n2), which is inefficient. Instead, using Kadane’s Algorithm can reduce the time complexity to O(n). The idea of this algorithm is to maintain the maximum subarray sum up to the current index while repeatedly updating the current index’s sum.

Step 3: Implementing Kadane’s Algorithm

The implementation process of Kadane’s Algorithm is as follows: 

fun maxSubArray(nums: IntArray): Int {
    var maxSoFar = nums[0]
    var maxEndingHere = nums[0]

    for (i in 1 until nums.size) {
        maxEndingHere = maxOf(nums[i], maxEndingHere + nums[i])
        maxSoFar = maxOf(maxSoFar, maxEndingHere)
    }
    return maxSoFar
}

Code Explanation

– Initially, two variables are initialized: maxSoFar is the maximum subarray sum found so far, and maxEndingHere is the maximum subarray sum ending at the current index.
– As we iterate through each element, we update maxEndingHere to be the maximum of the current element and maxEndingHere + current element. This gives us the maximum sum ending at the current position.
– Additionally, we update maxSoFar to continuously refresh the maximum value we can consider.

Step 4: Testing and Validation

We will test the recently implemented maxSubArray function with various cases to ensure it works correctly. 

fun main() {
    val nums = intArrayOf(-2, 1, -3, 4, -1, 2, 1, -5, 4)
    println("Maximum Subarray Sum: ${maxSubArray(nums)}") // 6
}

Step 5: Analyzing Time Complexity

This algorithm inspects each element of the array only once in a single loop, resulting in a time complexity of O(n). The space complexity is O(1) as it does not use any additional space.

Tips for Preparing for Resignation

Preparing for coding tests is a good way to get ready for a new job after leaving your current one. Here are some useful tips for preparing for resignation alongside coding tests:

  • Regular Practice: Periodically solve algorithm problems and get familiar with various types of problems.
  • Error Analysis: Review mistakes made while solving problems and analyze their causes.
  • Mock Interviews: Conduct mock interviews with friends or colleagues to experience real interview scenarios.
  • Utilizing Books and Online Materials: Expand your knowledge of algorithms and coding tests using good resources.

Conclusion

Professional coding test preparation takes time, but it can be accomplished through consistent learning and practice. By solving algorithm problems using Kotlin, you will further develop your coding skills and face new challenges with confidence.

I hope this article helps you in preparing for your resignation and coding tests.