Author: root

Recently, many companies are conducting coding tests during the hiring process. In this article, I will cover a problem that can help you prepare for coding tests using the Swift programming language, and I will explain in detail how to solve that problem efficiently.

Problem: Sum of Two Numbers in an Array

Given an integer array nums and an integer target, you need to solve the problem of finding the two indices in the array such that their sum is equal to target. It is assumed that each input has exactly one solution, and you may not use the same element twice.

Input Format

• nums: [2, 7, 11, 15]
• target: 9

Output Format

[0, 1] (nums[0] + nums[1] = 2 + 7 = 9)

Problem Analysis

This problem is quite famous and can be solved in various ways. The most basic approach is to use a double loop, which has a time complexity of O(n^2). However, let’s explore a more efficient way.

Efficient Approach: Using Hash Map

You can use a method of iterating through the given array and storing each element in a hash map. By using a hash map, you can reduce the search time to O(1), thereby decreasing the overall time complexity to O(n).

Steps to Solve the Problem

1. Create an empty hash map.
2. Iterate through the array and calculate the difference between the current number current and target - current.
3. If target - current exists in the hash map, return the corresponding index and the current index.
4. Add the current number and its index to the hash map.

Swift Code

let nums = [2, 7, 11, 15]
let target = 9

func twoSum(nums: [Int], target: Int) -> [Int]? {
    var numDict = [Int: Int]()
    
    for (index, num) in nums.enumerated() {
        let complement = target - num
        
        if let complementIndex = numDict[complement] {
            // Return the two indices
            return [complementIndex, index]
        }
        
        // Add the current number and index to the hash map
        numDict[num] = index
    }
    
    // Return nil if no solution exists
    return nil
}

if let result = twoSum(nums: nums, target: target) {
    print(result) // [0, 1]
}
    

Result Verification

When you run the above code, it will print the result [0, 1]. This confirms that the sum of nums[0] and nums[1] is equal to target.

Optimization Considerations

This algorithm demonstrates an optimized approach to the given problem. Using the hash map allows us to solve the problem with an average time complexity of O(n). However, in the worst case, performance can degrade due to hash collisions, so it’s essential to use an appropriate hash function.

Conclusion

In this article, we explored how to solve coding test problems using Swift. The hash map approach can be applied in various situations and can significantly help in writing efficient code. I encourage you to continue solving various algorithm problems to improve your skills.

There will be more courses covering various algorithm problems and their solutions, so please stay tuned!

Problem Description

The meeting room management system is a process that allocates meeting rooms according to the schedule of meetings to use multiple meeting rooms efficiently. Given the start and end times of a meeting, propose a method to allocate meeting rooms as efficiently as possible.

When a specific meeting starts, it is necessary to find a meeting room that is not occupied by another meeting. The meeting room allocation problem includes the following inputs:

• The number of meetings n
• An array meetings containing the start and end times of the meetings, where each meeting consists of a start time and an end time.

Input

n = 3
meetings = [(0, 30), (5, 10), (15, 20)]

Output

Number of meeting rooms: 2

Problem Solving Approach

To solve this problem, I will follow these steps:

1. Sort the meetings based on their end times with respect to their start and end times.
2. Iterate through each meeting, comparing the start time of the current meeting with the end time of the previous meeting.
3. If a meeting room is needed, increase the number of meeting rooms, and release that room when the meeting ends.

Algorithm Implementation

I will implement an algorithm to solve the meeting room allocation problem using Swift. Below is the actual implementation code:

func minMeetingRooms(_ meetings: [[Int]]) -> Int {
    guard meetings.count > 0 else {
        return 0
    }
    
    var startTimes = meetings.map { $0[0] }
    var endTimes = meetings.map { $0[1] }
    
    startTimes.sort()
    endTimes.sort()
    
    var roomCount = 0
    var endPointer = 0
    
    for startTime in startTimes {
        if startTime < endTimes[endPointer] {
            roomCount += 1
        } else {
            endPointer += 1
        }
    }
    
    return roomCount
}

Code Explanation

The above code separates the start and end times of each meeting from the input array and sorts them. Then it uses two pointers to compare the start and end times of the meetings to calculate the number of meeting rooms needed.

1. The guard statement returns 0 if there are no meetings.
2. Extract and sort the start and end times of the meetings.
3. The first pointer checks the start time while iterating through each meeting, and the second pointer tracks the end times.
4. If the start time is earlier than the end time, a new meeting room is needed, so roomCount is increased.
5. After all meetings are processed, the number of required meeting rooms is returned.

Complexity Analysis

This algorithm has the following complexities:

• Time Complexity: O(n log n) – it takes O(n log n) time to sort the start and end times.
• Space Complexity: O(n) – it uses additional space to store the meeting start and end times.

Conclusion

The meeting room allocation problem is an important issue of managing overlapping meetings. The given algorithm can enhance the efficiency of meeting room usage and is a common problem in coding tests. Understanding how to solve the problem with Swift and practicing actual code implementations can be beneficial.

Additional Practice Problems

• Determine how to handle cases where there are meetings with the same end time.
• Randomly generate meeting times to test meeting room allocation based on the given number of meetings.

I hope this post helps you. I encourage you to improve your algorithm problem-solving skills through this problem!