Category: C# Coding Test Tutorials

This course focuses on learning how to solve algorithm problems using C#. In particular, we will choose the topic “Calculating ATM Withdrawal Time” and explain the entire process from understanding the problem to finding the solution and writing the code in detail. Since this is a problem frequently encountered in real coding tests and interviews, this course will further enhance your algorithm problem-solving skills.

Problem Description

People line up to withdraw cash from the bank’s ATM machine. The time taken for each person to make a withdrawal may vary. The goal of this problem is to calculate the total withdrawal time when N people are waiting in line at the ATM. The following conditions are given:

• The first person begins to withdraw in order.
• Each person’s withdrawal can start only after the previous person’s withdrawal is complete.
• The withdrawal times for each person are provided as an array.

For example, suppose the withdrawal times are [3, 1, 4, 3, 2]. The third person can only withdraw after the second person’s withdrawal is finished, so the total withdrawal time is:

3 (1st) + 1 (2nd) + 4 (3rd) + 3 (4th) + 2 (5th) = 13

Input and Output Format

Input

The first line contains an integer N (1 ≤ N ≤ 1000). The second line provides each person’s withdrawal time H1, H2, …, HN (1 ≤ Hi ≤ 1000), separated by spaces.

Output

Output the total time taken for all individuals to complete their withdrawals.

Problem-Solving Approach

To solve this problem, we can approach it in several steps:

1. **Understanding the Problem**: First, we need to confirm that each person’s withdrawal time is added sequentially when making a withdrawal.
2. **Choosing the Data Structure**: Since withdrawal times are provided as an array, it is most suitable to use an array to compute the time sum.
3. **Choosing the Algorithm**: Since each withdrawal time is summed sequentially, we can calculate the total withdrawal time simply using a loop.

Code Writing

Based on the above approach, let’s write C# code. The code can be written as follows:

using System;

class Program
{
    static void Main()
    {
        // Input the number of people N
        int N = Convert.ToInt32(Console.ReadLine());
        // Create an array for withdrawal times
        int[] withdrawalTimes = Array.ConvertAll(Console.ReadLine().Split(' '), int.Parse);

        // Initialize the total withdrawal time variable
        int totalTime = 0;

        // Sum each person's withdrawal time
        foreach (var time in withdrawalTimes)
        {
            totalTime += time;
        }

        // Output the result
        Console.WriteLine(totalTime);
    }
}

Code Explanation and Analysis

The above C# code is structured as follows:

1. **Receiving Input**: Using `Console.ReadLine()` to input the number of people N in the first line and the withdrawal times in the second line.
2. **Array Conversion**: Converts the space-separated withdrawal times into an integer array using the `Array.ConvertAll` method.
3. **Total Time Calculation**: A foreach loop is used to add each person’s withdrawal time to the `totalTime` variable.
4. **Output the Result**: Finally, outputs the total withdrawal time.

Performance Analysis

The time complexity of the above algorithm is O(N), where N is the number of people making withdrawals. Since the process involves simply traversing all inputs and summing up the withdrawal times, it is very efficient. The space complexity is also O(N) as it requires an array to store the given withdrawal times.

Test Cases

Let’s create some test cases to validate the code:

• Input:

  5
  3 1 4 3 2

  Output: 13

• Input:

  4
  10 20 10 30

  Output: 70

• Input:

  3
  1 1 1

  Output: 3

Conclusion

In this course, we learned a method to enhance our C# algorithm problem-solving skills through the problem of calculating ATM withdrawal time. The process of clearly understanding the problem and efficiently implementing it in code is crucial in coding tests. We encourage you to continue solving various algorithm problems to improve your skills. Thank you!

Algorithm problems are one of the important areas of study for many people preparing for employment. In this course, we will explore how to find the Greatest Common Divisor (GCD) using C#. The GCD is the largest number that divides two integers with a remainder of 0.

Problem Description

Given two integers A and B, find the GCD of these two numbers.

Example Input:
A = 48, B = 18

Example Output:
GCD = 6

Concept of Greatest Common Divisor

The GCD refers to the largest integer that divides two or more integers without leaving a remainder. For example, the divisors of 48 and 18 are 1, 2, 3, 6, 9, and 18. The largest of these, 6, is the GCD of the two numbers.

Algorithm to Find GCD

There are various methods to find the GCD, but here we will explain the method using the Euclidean algorithm. This method follows these steps:

1. Given two integers A and B, repeat the following as long as B is not 0.
2. Calculate the remainder of A divided by B. (i.e., R = A % B)
3. Replace A with B, and B with R.
4. Repeat this process until B becomes 0.
5. When B becomes 0, A is the GCD.

C# Code Implementation

Now, let’s implement the above algorithm in C#. The code below is a simple program to find the GCD.

using System;

class Program
{
    static void Main(string[] args)
    {
        Console.Write("Enter A: ");
        int A = int.Parse(Console.ReadLine());

        Console.Write("Enter B: ");
        int B = int.Parse(Console.ReadLine());
        
        int gcd = GetGCD(A, B);
        Console.WriteLine($"The Greatest Common Divisor (GCD) is: {gcd}");
    }

    static int GetGCD(int a, int b)
    {
        while (b != 0)
        {
            int r = a % b;
            a = b;
            b = r;
        }
        return a;
    }
}

The above code prompts the user to input two integers A and B, and calculates the GCD by calling the GetGCD method.

Code Explanation

– using System; : Uses the System namespace to handle console input and output.
– Main method : The starting point of the program, which takes two integers from the user.
– GetGCD method : Calculates the GCD using the Euclidean algorithm. This method takes two integers as arguments and finds the GCD through a loop.

Execution Example

Let’s take a look at the execution results. When the user inputs the numbers 48 and 18:

Enter A: 48
Enter B: 18
The Greatest Common Divisor (GCD) is: 6

Testing and Exception Handling

When using the actual program, it is essential to test various inputs. Exception handling should be considered for cases where non-integer values are entered. The code below is an example with added exception handling.

using System;

class Program
{
    static void Main(string[] args)
    {
        try
        {
            Console.Write("Enter A: ");
            int A = int.Parse(Console.ReadLine());

            Console.Write("Enter B: ");
            int B = int.Parse(Console.ReadLine());
            
            int gcd = GetGCD(A, B);
            Console.WriteLine($"The Greatest Common Divisor (GCD) is: {gcd}");
        }
        catch (FormatException)
        {
            Console.WriteLine("Please enter a valid integer.");
        }
    }

    static int GetGCD(int a, int b)
    {
        while (b != 0)
        {
            int r = a % b;
            a = b;
            b = r;
        }
        return a;
    }
}

In the above code, a try-catch block is used to handle cases where the input is not an integer. If a FormatException occurs, the user is prompted to enter a valid integer.

Conclusion and Additional Learning Resources

In this course, we implemented an algorithm to find the GCD using C# and introduced a method based on the Euclidean algorithm. Algorithm problems can greatly assist in employment preparation, so it is recommended to continuously practice various problems. In the future, we will cover other algorithm problems.

For additional recommended learning resources, here are some websites:

• GeeksforGeeks – A variety of data structure and algorithm problem solutions
• LeetCode – A platform for coding tests and algorithm problem solutions
• HackerRank – A site for algorithm problems and coding tests

Feedback and Questions

If you have any feedback or questions regarding this course, please feel free to leave a comment. I will do my best to respond. Thank you!

Problem Description

This is a problem to check the existence of a specific integer x in a given integer array arr. If the integer x exists in the array, it should return true, and if it does not exist, it should return false.

Input Format

• Integer array arr (1 ≤ arr.Length ≤ 100,000)
• Integer to search for x (-1,000,000 ≤ x ≤ 1,000,000)

Output Format

• If the integer x exists in the array, it should return true, otherwise false.

Example Input and Output

Input: arr = [1, 2, 3, 4, 5], x = 3
Output: true

Input: arr = [1, 2, 3, 4, 5], x = 6
Output: false

Approach to the Problem

There are several ways to solve this problem. The simplest method is to iterate through the array sequentially to check whether the given integer exists. However, as the size of the array increases, performance may degrade, so it is necessary to use a more efficient algorithm.

Efficient Approach

If the integer array is sorted, a Binary Search algorithm can be used. Binary search operates by dividing the array in half, resulting in an average time complexity of O(log n).

Possible Methods

1. Linear Search: O(n) – Compare each element of the array one by one.
2. Binary Search: O(log n) – When the array is sorted.
3. Using HashSet: O(1) – Ensure fast search times using a hash set.

Code Implementation

Let’s implement the code using the binary search method described above. Below is the code written in C#.

using System;

class Program
{
    static void Main()
    {
        int[] arr = { 1, 2, 3, 4, 5 };
        int x = 3;
        bool result = Contains(arr, x);
        Console.WriteLine(result);  // true
        
        x = 6;
        result = Contains(arr, x);
        Console.WriteLine(result);  // false
    }

    static bool Contains(int[] arr, int x)
    {
        int left = 0;
        int right = arr.Length - 1;

        while (left <= right)
        {
            int mid = left + (right - left) / 2;

            // Return true if x equals the mid value
            if (arr[mid] == x)
                return true;
            // Search the left half if x is smaller than the mid value
            else if (arr[mid] > x)
                right = mid - 1;
            // Search the right half if x is larger than the mid value
            else
                left = mid + 1;
        }

        return false; // x does not exist in the array
    }
}

Complexity Analysis

Time Complexity

In the case of binary search, it maintains O(log n) efficiency even in the worst case. This is because the search range is halved with each iteration.

Space Complexity

This algorithm has a space complexity of O(1). It is very memory efficient because it does not use any additional data structures.

Conclusion

In this lecture, we covered the problem of finding a desired integer. Depending on the size or characteristics of the array, various algorithms can be used, and it is important to understand the pros and cons of each algorithm. In addition to binary search, utilizing data structures like hash sets can help check for the existence of elements even more quickly. I hope to gain more experience by solving various problems in the future.

1. Introduction

In the field of computer science and programming, algorithms are essential tools for solving problems. In particular, coding tests are one of the very important aspects for developers, requiring an understanding of algorithms and data structures. In this course, we will cover the concept of Minimum Spanning Tree (MST) and algorithm problems that utilize it.

2. What is a Minimum Spanning Tree?

A minimum spanning tree is a tree with the minimum sum of edge weights that includes all vertices and does not contain cycles among the subgraphs of a connected graph. It is important to understand the vertices, edges, and weights of the graph. Minimum spanning trees are mainly used in clustering, network design, and solving various problems with minimal distances maintained.

2.1 Properties of MST

• All vertices must be included.
• No cycles exist.
• The sum of the weights is minimal.

3. Algorithms to find a Minimum Spanning Tree

The methods to find a minimum spanning tree are the Kruskal’s algorithm and Prim’s algorithm. Both algorithms employ different approaches but ultimately produce the same result.

3.1 Kruskal’s Algorithm

Kruskal’s algorithm builds the spanning tree by selecting edges starting from the smallest weight. This algorithm sorts the given edges in ascending order of weight and then selects them one by one. Care must be taken to avoid cycles.

3.2 Prim’s Algorithm

Prim’s algorithm starts from a single vertex and selects the edge with the least weight that is connected to that vertex. This method is more efficient when the number of vertices is large.

4. Problem Description

Now, let’s look at the process of solving a real problem. The following is a problem to find a minimum spanning tree.

Problem: Finding the Minimum Spanning Tree

Input:

• Number of vertices V (1 ≤ V ≤ 1000)
• Number of edges E (1 ≤ E ≤ 10000)
• Each edge is given in the form u, v, w, where u and v are the vertex numbers, and w is the weight of the edge connecting the two vertices.

Output: Outputs the sum of the weights of the edges forming the minimum spanning tree.

5. Problem Solving

To solve the problem, I will use Kruskal’s algorithm. This algorithm proceeds in the following steps.

5.1 Edge Sorting

Sort all the edges received as input in ascending order of their weights. The sorted edges will act as the criteria for selecting edges that form the minimum spanning tree.

5.2 Cycle Check

Select the sorted edges one by one and check if the two vertices belong to different sets. For this, the Union-Find data structure is used.

5.3 Adding Edges and Summing Weights

If no cycle occurs, add the edge to the list of edges of the minimum spanning tree and sum its weight. This process repeats until all edges are processed.

5.4 C# Implementation

Now, let’s implement the above process in C# code.

using System;
using System.Collections.Generic;
using System.Linq;

class Edge
{
    public int U { get; set; }
    public int V { get; set; }
    public int Weight { get; set; }
}

class UnionFind
{
    private int[] parent;

    public UnionFind(int size)
    {
        parent = new int[size];
        for (int i = 0; i < size; i++)
            parent[i] = i;
    }

    public int Find(int u)
    {
        if (parent[u] != u)
            parent[u] = Find(parent[u]);
        return parent[u];
    }

    public void Union(int u, int v)
    {
        int rootU = Find(u);
        int rootV = Find(v);
        if (rootU != rootV)
            parent[rootU] = rootV;
    }
}

class Program
{
    static void Main()
    {
        int V = int.Parse(Console.ReadLine());
        int E = int.Parse(Console.ReadLine());
        List edges = new List();

        for (int i = 0; i < E; i++)
        {
            string[] input = Console.ReadLine().Split();
            edges.Add(new Edge { U = int.Parse(input[0]), V = int.Parse(input[1]), Weight = int.Parse(input[2]) });
        }

        edges = edges.OrderBy(e => e.Weight).ToList();
        UnionFind uf = new UnionFind(V + 1);
        int totalWeight = 0;

        foreach (var edge in edges)
        {
            if (uf.Find(edge.U) != uf.Find(edge.V))
            {
                uf.Union(edge.U, edge.V);
                totalWeight += edge.Weight;
            }
        }

        Console.WriteLine(totalWeight);
    }
}

6. Time Complexity

The overall time complexity of Kruskal’s algorithm is as follows.

• Edge sorting: O(E log E)
• Union-Find: O(E α(V)) (α is the inverse function of the Ackermann function)

Therefore, the overall time complexity is O(E log E). This provides relatively efficient performance even when the number of edges is large.

7. Conclusion

In this course, we learned about minimum spanning trees. We learned how to find a minimum spanning tree using Kruskal’s algorithm and practiced the process through C# code. Minimum spanning trees can be applied to various graph problems and play an important role in developing algorithmic thinking. In the next course, we will explore a wider variety of algorithms and data structures.

Hello, everyone preparing for the coding test! In this course, we will solve an algorithm problem titled “Finding the Minimum Number of Coins,” and I will explain the detailed solution process. This problem will greatly help you understand algorithms and develop logical thinking.

Problem Definition

To purchase items at a store, a specific amount of money is required. However, we may have limitations on the types and quantities of coins we possess. The goal of this problem is to find a way to minimize the number of coins used to create a specific amount using the available coin denominations.

Problem Description

Here is an example of the problem:

• Available coin denominations: 1 won, 5 won, 10 won, 50 won, 100 won
• Target amount: 125 won

In this case, we need to find a way to make 125 won with the minimum number of coins.

Problem Approach

This problem is a typical greedy algorithm problem. A greedy algorithm solves a problem by making the best choice at each step.

Principle of Greedy Algorithm

1. Use the largest coin available as much as possible.
2. Repeat the same process for the remaining amount.

Problem Solving Steps

The specific steps for solving the problem are as follows:

1. Initialize the types of coins and the amount.
2. Proceed by sorting and using larger coins first.
3. Count the number of each coin while reducing the amount.
4. Repeat this until the remaining amount is 0.

C# Code Implementation

Now, let’s implement the C# code based on the above approach.

using System;

class Program
{
    static void Main(string[] args)
    {
        // Types of coins
        int[] coins = new int[] { 100, 50, 10, 5, 1 };
        // Target amount
        int targetAmount = 125;
        
        // Number of coins
        int totalCoins = 0;

        // Making amount with coins
        for (int i = 0; i < coins.Length; i++)
        {
            // Maximum number of coins that can be used
            while (targetAmount >= coins[i])
            {
                targetAmount -= coins[i];
                totalCoins++;
            }
        }

        // Output result
        Console.WriteLine("Minimum number of coins: " + totalCoins);
    }
}

Code Explanation

• First, the required types of coins are initialized in an array.
• The amount to be used is stored in a variable, and the totalCoins variable is declared to count the number of coins.
• For each coin, if the amount is greater than the coin’s value, the coin is used, and the coin count is increased.
• Finally, the minimum number of coins is printed to the console.

Code Execution Result

Running the above code will produce the following result:

Minimum number of coins: 3

We can see that at least 3 coins must be used to create 125 won with the given combinations of coins. For example, using one 100 won coin, one 5 won coin, and one 1 won coin would yield a valid combination.

Summary of the Problem-Solving Process

• Understand the requirements of the problem accurately.
• Select an appropriate algorithm (greedy).
• Sequentially solve the problem while ensuring reliable results through the iterative process.
• Once solved, review the efficiency and check for areas of improvement.

Conclusion

In this course, we learned the concept and application of greedy algorithms through the “Finding the Minimum Number of Coins” problem. I hope solving this problem has helped you improve your algorithmic thinking. Continue to face various problems to gain experience and grow as a better algorithm developer!

Thank you for participating in the C# coding test course. If you have any questions or would like to know more, please leave a comment!