Category: Swift Coding Test

Coding tests are one of the challenges that many developers face. In Swift coding tests, it is essential to understand and utilize various algorithms and data structures well. In this article, we will illustrate using a real algorithm problem and explore the process of solving the problem in detail.

Problem: Two Sum

Problem Solving Process

1. Understanding the Problem

This problem is about finding the sum of two numbers. The most critical point is that we need to find the indices of the two numbers. Therefore, we must think about how to find them efficiently rather than checking all possible combinations.

2. Choosing an Algorithm

There are several methods to solve this problem, but the most efficient way is to use a hashmap. By using a hashmap, we can check each number one by one and look for the value subtracted from the target value in the hashmap.

3. Algorithm Explanation

1. Initialize the hashmap.
2. Traverse the array and check each number.
3. Check if the value subtracted from the target is present in the hashmap.
4. If it exists, return the index of that number and the current index.
5. If it does not exist, add the current number to the hashmap.

4. Code Implementation

Now, let’s write the code in Swift based on the above algorithm.

    func twoSum(_ nums: [Int], _ target: Int) -> [Int] {
        var numDict = [Int: Int]() // Hashmap
        for (index, num) in nums.enumerated() {
            let complement = target - num // Subtract current number from target
            if let complementIndex = numDict[complement] { // Search in hashmap
                return [complementIndex, index] // Return indices
            }
            numDict[num] = index // Add current number to hashmap
        }
        return [] // Return empty array if no value found
    }

    // Example usage
    let nums = [2, 7, 11, 15]
    let target = 9
    let result = twoSum(nums, target)
    print(result) // Outputs: [0, 1]
    

5. Time Complexity Analysis

The time complexity of this algorithm is O(n). This is because we traverse the array once while adding values to the hashmap and checking existing values. The space complexity is O(n), which is proportional to the size of the hashmap.

Conclusion

What is important in Swift coding tests is to understand the problem and choose the appropriate algorithm to solve it efficiently. Through this problem, we learned an approach using a hashmap. Practice various algorithms and data structures to prepare well for coding tests.

Learning the programming language Swift is an exciting experience for many developers. In this course, we will address the ‘prime number’ problem, which often appears in coding tests, and explore step-by-step how to solve it using Swift. We will explain this through the problem of ‘Finding Amazing Primes.’

Problem Description

Given an integer N (1 ≤ N ≤ 10000), print all prime numbers between 1 and N.

What is a Prime Number?

A prime number is a natural number greater than 1 that has no divisors other than 1 and itself. For example, 2, 3, 5, and 7 are all prime numbers. In contrast, 4, 6, 8, and 9 are not prime numbers.

Approach to the Problem

To solve this problem, we will use the algorithm known as the Sieve of Eratosthenes, which is one of the methods for finding prime numbers. This algorithm is an efficient way to find all primes within a given range of numbers.

Sieve of Eratosthenes Algorithm

1. Create a list containing all integers from 1 to N.
2. Starting from 2, remove all multiples of the current number from the list.
3. Continue repeating the above process with the next remaining number in the list.
4. After processing all numbers up to N, the numbers remaining in the list are prime.

Implementation in Swift

Now let’s implement this algorithm in Swift. The following code shows how to print all prime numbers for a given N:

func findPrimes(upTo n: Int) -> [Int] {
    guard n >= 2 else { return [] }

    var isPrime = [Bool](repeating: true, count: n + 1)
    isPrime[0] = false
    isPrime[1] = false

    for i in 2...Int(sqrt(Double(n))) {
        if isPrime[i] {
            for j in stride(from: i * i, through: n, by: i) {
                isPrime[j] = false
            }
        }
    }

    return isPrime.enumerated().compactMap { $0.element ? $0.offset : nil }
}

// Example usage
let n = 100
let primes = findPrimes(upTo: n)
print("Prime numbers from 1 to \(n) are: \(primes)")

Code Explanation

The code above works as follows:

1. The function findPrimes takes an argument n and finds prime numbers from 1 to n.
2. An array isPrime is created, initializing all indices as prime. Indices 0 and 1 are set to false as they are not prime.
3. For all numbers from 2 to sqrt(n), if the current number is prime, its multiples are removed from the list.
4. The remaining elements in the list are printed.

Testing and Results

Now let’s execute the code and check the results. For example, when n = 100, the output should be as follows:

Prime numbers from 1 to 100 are: [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47, 53, 59, 61, 67, 71, 73, 79, 83, 89, 97]

Time Complexity

The Sieve of Eratosthenes algorithm has an average time complexity of O(n log log n). This is one of the effective methods for finding prime numbers, becoming significantly faster as n increases.

Conclusion

In this course, we addressed the algorithm for finding prime numbers using Swift. By solving these basic algorithm problems, you can enhance your coding skills and improve your ability to solve more complex problems. During the problem-solving process, try applying various algorithms and programming concepts.

If you want to learn more source code and algorithms, stay tuned for the next course!

Understanding time complexity is very important in algorithm problem solving. Time complexity is a key factor in evaluating the efficiency of an algorithm, indicating how quickly the algorithm runs based on the given input size. In this course, we will analyze time complexity while solving algorithm problems using Swift and learn how to write efficient code through this process.

Problem: Finding Common Elements in Two Arrays

You are given two integer arrays, A and B. Write a function that returns an array containing all the elements that are common to both arrays.

Problem Description

• Input: Two integer arrays A and B (0 ≤ A.length, B.length ≤ 1000)
• Output: An array containing the common elements from both arrays (duplicates excluded)

Example

    Input: A = [1, 2, 3, 4, 5], B = [3, 4, 5, 6, 7]
    Output: [3, 4, 5]
    

Solution Process

1. Understanding the Problem

This is a problem of finding the common elements from the given two arrays, excluding duplicates.
Since the size of each array can be up to 1000, we may need to compare a maximum of 2,000 elements.
Therefore, it can be solved with a simple nested loop that has O(n^2) complexity, but let’s explore a more efficient solution.

2. Exploring Efficient Methods

To find the common elements, let’s consider comparing the two arrays, listing their elements and thinking of ways to avoid duplicates.
Utilizing a HashSet can solve this with O(n) time complexity.
First, we’ll store the elements of array A in a HashSet, then traverse through array B to check for elements that exist in the HashSet.

3. Implementing Swift Code

    func findCommonElements(A: [Int], B: [Int]) -> [Int] {
        var setA = Set(A) // Store elements of array A in a HashSet
        var result: [Int] = [] // Array to store the results
        
        for element in B { // Traverse each element of array B
            if setA.contains(element) { // Check if it exists in the HashSet
                result.append(element) // If it exists, add to result array
                setA.remove(element) // Remove from HashSet to avoid duplicates
            }
        }
        return result
    }
    
    // Example execution
    let A = [1, 2, 3, 4, 5]
    let B = [3, 4, 5, 6, 7]
    let commonElements = findCommonElements(A: A, B: B)
    print(commonElements) // Output: [3, 4, 5]
    

4. Analyzing Time Complexity

In the above code, adding elements to the HashSet takes O(1) time, leading to O(n) time based on the size of array A.
Subsequently, traversing through array B to check each element in the HashSet also takes O(1) time.
Therefore, the overall time complexity is O(n + m), where n is the size of array A and m is the size of array B.
This is much more efficient compared to the original O(n^2) approach.

Conclusion

Analyzing time complexity during the process of solving algorithm problems is essential.
Always keep in mind that choosing an algorithm with optimal time complexity can greatly enhance the performance of your code.
Through solving this problem using Swift, I hope you have practiced writing efficient algorithms by leveraging time complexity.
Continue to build your skills by solving various algorithm problems!

References

• Apple Swift Documentation
• Big O Notation – Wikipedia
• GeeksforGeeks – Algorithm Tutorials

Introduction

In the world of software development, coding tests are an important element. Especially for developers using the Swift language, it is essential to understand the characteristics of that language and algorithmic problems. In this article, we will solve a Swift coding test problem and learn about time complexity notation.

Problem Definition

The following is an algorithm problem that can be solved in Swift:

Problem: Check if the sum of all pairs in a given integer array reaches a specific target value.

Given an integer array numbers and an integer target, write a function to determine if there exists a pair of indices such that the sum of the values at those indices equals target. The indices must be different, and it is assumed that one solution always exists.

Input Example

    numbers = [10, 15, 3, 7]
    target = 17
    

Output Example

    true (10 + 7 = 17)
    

Problem Approach

To solve this problem, we can consider various approaches. The simplest method is to use two nested for loops. However, this method has a time complexity of O(n^2), making it inefficient.

Therefore, a more efficient solution can use a hash map. By using a hash map, both data retrieval and insertion average O(1) time complexity, allowing us to solve the problem much more efficiently.

Implementation of the Solution

Swift Code

    func hasPairWithSum(numbers: [Int], target: Int) -> Bool {
        var numSet = Set()
        
        for number in numbers {
            let complement = target - number
            if numSet.contains(complement) {
                return true
            }
            numSet.insert(number)
        }
        return false
    }
    
    // Example usage
    let numbers = [10, 15, 3, 7]
    let target = 17
    let result = hasPairWithSum(numbers: numbers, target: target)
    print(result) // true
    

Time Complexity Analysis

The above Swift code traverses the array with a single loop, inserting and searching for values in a hash set. As a result, the time complexity is O(n), where n represents the size of the array. The space complexity is also O(n), as the hash set may need to store up to n elements.

Conclusion

In this tutorial, we explored the process of solving a given algorithm problem in a Swift coding test. We learned how to reduce time complexity by using a hash map, which allows for writing more efficient code. Algorithmic thinking will be increasingly necessary in future coding tests as well. I encourage you to develop your ability to think through and optimize various problems.

References

Here are useful resources on time complexity and algorithms dealing with data:

• Time Complexity – Wikipedia
• GeeksforGeeks Fundamentals of Algorithms
• HackerRank – Coding Test Practice

Comments

Please leave your thoughts or questions below. I would like to help you with your coding test preparation!