C# Coding Test Course, Finding the Product of a Range

Problem Description

There is a given integer array nums and a query array queries. Each query consists of two integers (l, r), requesting the value of nums[l] * nums[l + 1] * ... * nums[r]. The size of the nums array can be up to 10^5, and the length of queries can be up to 10^4. Since the result of the multiplication can be very large, the output must be the remainder when divided by 10^9 + 7.

Input Format

The first line contains the size of the array n and the number of queries q. The second line contains an array nums of n integers, followed by q lines where each line contains the query values l and r.

Output Format

Output the results of the calculations for each query, one result per line.

Example

Input
5 3
2 3 4 5 6
0 2
1 3
2 4
Output
24
60
120

Solution Explanation

To solve this problem, we need a method to quickly compute the product over a range. Simply iterating through the range for each query to calculate the product would lead to a time complexity of O(q * n), which could result in up to 10^9 operations in the worst case. This is impractical, so we use a Prefix Product strategy to reduce time complexity.

Prefix Product Method

First, we store the cumulative product of the elements in the nums array to quickly calculate the results of the queries.

C#
int mod = 1000000007;
int[] prefixProduct = new int[n + 1];
prefixProduct[0] = 1;

for (int i = 1; i <= n; i++) {
    prefixProduct[i] = (int)((1L * prefixProduct[i - 1] * nums[i - 1]) % mod);
}

Here, prefixProduct[i] stores the value of nums[0] * nums[1] * ... * nums[i - 1]. With this structure, we can easily and quickly compute the product over a range.

Calculating the Range Product

Now, for each query (l, r), the range product nums[l] * nums[l + 1] * ... * nums[r] can be calculated as follows:

C#
int result = (int)((1L * prefixProduct[r + 1] * modInverse(prefixProduct[l]) % mod) % mod);

Here, the modInverse function calculates the modular inverse. The inverse can be implemented as follows:

C#
int modInverse(int a) {
    return power(a, mod - 2);
}

int power(int x, int y) {
    int res = 1;
    while (y > 0) {
        if ((y & 1) == 1) {
            res = (int)((1L * res * x) % mod);
        }
        y >>= 1;
        x = (int)((1L * x * x) % mod);
    }
    return res;
}

Final Code

Based on the above explanation, the complete code would look like this:

C#
using System;

class Program {
    static int mod = 1000000007;

    static void Main() {
        string[] firstLine = Console.ReadLine().Split();
        int n = int.Parse(firstLine[0]);
        int q = int.Parse(firstLine[1]);
        int[] nums = Array.ConvertAll(Console.ReadLine().Split(), int.Parse);
        int[] prefixProduct = new int[n + 1];
        prefixProduct[0] = 1;

        for (int i = 1; i <= n; i++) {
            prefixProduct[i] = (int)((1L * prefixProduct[i - 1] * nums[i - 1]) % mod);
        }

        for (int i = 0; i < q; i++) {
            string[] query = Console.ReadLine().Split();
            int l = int.Parse(query[0]);
            int r = int.Parse(query[1]);
            int result = (int)((1L * prefixProduct[r + 1] * modInverse(prefixProduct[l]) % mod) % mod);
            Console.WriteLine(result);
        }
    }

    static int modInverse(int a) {
        return power(a, mod - 2);
    }

    static int power(int x, int y) {
        int res = 1;
        while (y > 0) {
            if ((y & 1) == 1) {
                res = (int)((1L * res * x) % mod);
            }
            y >>= 1;
            x = (int)((1L * x * x) % mod);
        }
        return res;
    }
}

Conclusion

In this lecture, we learned how to effectively calculate the product over an array range by using the Prefix Product method and the concept of modular inverses. This method can be applied to handle more complex query processes as well. By utilizing the features of the C# language, efficient code can be written, further enhancing algorithm problem-solving skills.

Additionally, the Prefix Product technique used in this problem can be useful in various forms of query problems, so it is good to try applying it to different problems. Thank you!

C# Coding Test Course, Determine Bipartite Graph

1. Introduction

Graph theory is an important area in computer science and algorithms, utilized to solve various real-world problems.
In this article, we will introduce the concept of ‘bipartite graphs’ and the algorithmic problem of determining them,
detailing how to solve it using C#. A bipartite graph is one in which the vertices of the graph are divided into two sets,
ensuring that no two vertices within the same set are connected. Such graphs play a significant role in various applications.
For example, they are frequently used to represent connections between two different types of objects or in bipartite matching problems.

2. Problem Description

In this problem, you are required to implement an algorithm to determine whether a given graph is a bipartite graph.
For instance, the number of vertices V and the number of edges E are provided, along with
V vertices and E edges connecting them. If the given graph is a bipartite graph, you should output “YES”; otherwise, output “NO”.

Input Format

        The first line contains the number of vertices V and the number of edges E. 
        The next E lines contain two integers representing the endpoints of each edge.
    

Output Format

        If the given graph is a bipartite graph, output "YES"; otherwise, output "NO".
    

3. Algorithmic Approach

To solve this problem, we will use either BFS (Breadth-First Search) or DFS (Depth-First Search).
The method we will employ for determining whether a graph is bipartite involves coloring each vertex with two different colors.
Specifically, one set will be marked with color 1 and the other with color 2. If two adjacent vertices are colored the same, it indicates that the graph is not bipartite.

Algorithm Steps

  1. Create a data structure to represent the graph in an adjacency list format.
  2. Visit the vertices using BFS or DFS and determine the colors of the vertices.
  3. Check if adjacent vertices have the same color to determine bipartiteness.
  4. After visiting all vertices, output the final result.

4. C# Code Implementation

Now, based on the described algorithm, we will implement the code in C#.
Below is the C# code for determining whether a graph is bipartite.

using System;
using System.Collections.Generic;

class Program
{
    static List[] graph;
    static int[] color;

    static void Main(string[] args)
    {
        string[] input = Console.ReadLine().Split();
        int V = int.Parse(input[0]);
        int E = int.Parse(input[1]);

        graph = new List[V + 1];
        for (int i = 0; i <= V; i++)
            graph[i] = new List();

        for (int i = 0; i < E; i++)
        {
            input = Console.ReadLine().Split();
            int u = int.Parse(input[0]);
            int v = int.Parse(input[1]);
            graph[u].Add(v);
            graph[v].Add(u);
        }

        color = new int[V + 1];

        bool isBipartite = true;
        for (int i = 1; i <= V; i++)
        {
            if (color[i] == 0)
                isBipartite &= BFS(i);
        }

        Console.WriteLine(isBipartite ? "YES" : "NO");
    }

    static bool BFS(int start)
    {
        Queue queue = new Queue();
        queue.Enqueue(start);
        color[start] = 1;  // Color the starting vertex

        while (queue.Count > 0)
        {
            int node = queue.Dequeue();
            foreach (int neighbor in graph[node])
            {
                if (color[neighbor] == 0)  // If not colored yet
                {
                    color[neighbor] = 3 - color[node];  // Color with different color
                    queue.Enqueue(neighbor);
                }
                else if (color[neighbor] == color[node])  // If the adjacent vertices have the same color
                {
                    return false;
                }
            }
        }
        return true;
    }
}
    

5. Code Explanation

I will explain the key components and logic used in the C# code above.

Data Structures

List[] graph: Used to represent the graph in an adjacency list format.
It stores the list of connected vertices using the vertex numbers as keys.

int[] color: Stores the colors of each vertex. 0 indicates uncolored, while 1 and 2 represent the two different colors.

Main Method

– Receives the number of vertices and edges as input, based on which the graph is constructed.

– Iterates through all vertices, calling BFS to check if the graph is bipartite.
After checking all given vertices, the result is printed.

BFS Method

– The BFS method performs breadth-first search using a queue.
It colors the starting vertex and colors adjacent vertices with a different color if they are not colored yet.

– If an already colored vertex is found to be colored the same as the current vertex, it returns false, indicating that the graph is not bipartite.

6. Complexity Analysis

The time complexity of this algorithm is O(V + E).
Here, V is the number of vertices and E is the number of edges, as the algorithm explores all vertices and edges of the graph.
Thus, it can operate efficiently given the input size.

7. Conclusion

This article explained the definition of bipartite graphs and how to solve the problem of determining them using C#.
I hope it helped in understanding the basics of graph theory and the BFS/DFS algorithms, enabling practical applications.
Moreover, I encourage you to enhance your understanding of graph algorithms by solving various problems.

8. Additional References

C# Coding Test Course, Finding Minimum Value 1

In coding tests, algorithm problems provide a very important opportunity to understand and apply various data structures and algorithms. In this course, we will address the problem of finding the minimum value and explain the process of solving it using the C# language in detail.

Problem Description

Given the following array, write a program to find the minimum value of the array. This includes implementing the algorithm for finding the minimum value and analyzing the time complexity and space complexity of that algorithm.

Problem

Find and return the minimum value from the given integer array.

Input:
[3, 5, 1, 8, 2, 0, -3, 7]

Output:
-3

Problem Analysis

This problem is very intuitive and simple, but there are several ways to approach the process of finding the minimum value within the array. Basically, assuming the length of the array is n, it takes O(n) time to scan the array once to find the minimum value.

Solution

The basic algorithm to solve this problem is as follows.

  1. Initialize the first element of the array as the initial minimum value.
  2. Compare each element of the array one by one, updating the minimum value if the current value is smaller.
  3. Return the minimum value after checking all elements.

C# Code Implementation

Now let’s look at the C# code that implements the above algorithm.

using System;

class Program
{
    static void Main()
    {
        int[] numbers = { 3, 5, 1, 8, 2, 0, -3, 7 };
        int minValue = FindMinimum(numbers);
        Console.WriteLine("Minimum value: " + minValue);
    }

    static int FindMinimum(int[] array)
    {
        // Set the first element as the initial minimum value
        int min = array[0];

        // Scan through the array once to find the minimum value
        for (int i = 1; i < array.Length; i++)
        {
            if (array[i] < min)
            {
                min = array[i];
            }
        }
        return min;
    }
}

Code Explanation

The code above is a program written in C# to find the minimum value in an array. Below is an explanation of each part of the code:

  • using System;
    The namespace required to use basic functionalities in C#.
  • class Program
    Defines the main class and provides the entry point for the program.
  • static void Main()
    The main entry point for the program, where the method to find the minimum value is called.
  • int[] numbers = { 3, 5, 1, 8, 2, 0, -3, 7 };
    Initializes the integer array for which the minimum value is to be found.
  • int minValue = FindMinimum(numbers);
    Calls the FindMinimum method to find the minimum value in the array.
  • Console.WriteLine("Minimum value: " + minValue);
    Prints the found minimum value to the console.
  • static int FindMinimum(int[] array)
    A method to find the minimum value that takes an array as a parameter.
  • int min = array[0];
    Sets the first element of the array as the initial minimum value.
  • for (int i = 1; i < array.Length; i++)
    Iterates through the remaining elements of the array and compares them with the current minimum value.
  • if (array[i] < min)
    Updates the minimum value if the current element is smaller than the minimum value.

Time Complexity and Space Complexity Analysis

The time complexity of the algorithm is O(n). This is because the array is scanned once, taking time proportional to the number of elements in the array. The space complexity is O(1), as no additional data structures are used, and only a constant number of variables are utilized.

Examples of Algorithm Usage

The algorithm for finding the minimum value can be applied in various fields. For instance:

  • It is used in statistics to find the minimum value of a data set.
  • It is useful when needing to find the minimum value that satisfies specific conditions in a list.
  • In game development, it may be necessary to set limits on the position or attributes of specific objects.

Conclusion

We implemented a basic minimum value finding algorithm using C#. Most coding tests evaluate problem-solving abilities through such fundamental problems, and this problem can be expanded in various ways. Through this course, I hope you establish the concept of finding the minimum value and develop the ability to apply it in diverse ways.

We will cover various algorithm problems in future courses, so please stay tuned.

C# Coding Test Course, Bellman-Ford

As you often solve algorithm problems, you will encounter various shortest path problems. Among them, the “Bellman-Ford algorithm” is a useful algorithm for finding the shortest path in a graph that includes edges with negative weights. In this lecture, we will gain a deep understanding of the Bellman-Ford algorithm and solve a problem using it.

Introduction to the Bellman-Ford Algorithm

The Bellman-Ford algorithm is an algorithm for finding the shortest path from the starting point to all vertices in a given graph. This algorithm has the following characteristics:

  • It can find the shortest path even in graphs with edges that have negative weights.
  • If a negative cycle exists, the shortest path cannot be defined.
  • The time complexity is O(VE), where V is the number of vertices and E is the number of edges.

How the Bellman-Ford Algorithm Works

This algorithm works as follows:

  1. Initialize the distance values of all vertices to infinity. However, set the distance value of the starting vertex to 0.
  2. For all edges, update the distance value if it can be moved through that edge.
  3. Repeat this process V – 1 times because the longest path can consist of V – 1 edges.
  4. Finally, check once more for all edges to see if a negative cycle exists.

Problem Description

Problem: Finding the Shortest Path

Given the number of vertices N and edges M in a graph, find the shortest path from the starting vertex S to all other vertices. There may be edges with negative weights, and if a negative cycle exists, print “Negative Cycle”.

Input
3 3 1
1 2 2
1 3 3
2 3 -5

Output
1 0 2

Explanation of the Input Example

The above input example represents the following graph:

Number of vertices: 3

Number of edges: 3

Starting vertex: 1

  • 1 ↔ 2 : Weight 2
  • 1 ↔ 3 : Weight 3
  • 2 ↔ 3 : Weight -5

Code Implementation

using System;
using System.Collections.Generic;

class Program
{
    static void Main()
    {
        // Input
        string[] input = Console.ReadLine().Split();
        int N = int.Parse(input[0]); // Number of vertices
        int M = int.Parse(input[1]); // Number of edges
        int S = int.Parse(input[2]); // Starting vertex

        // Initialize graph
        List> edges = new List>();
        for (int i = 0; i < M; i++)
        {
            input = Console.ReadLine().Split();
            int u = int.Parse(input[0]); // Starting vertex
            int v = int.Parse(input[1]); // Destination vertex
            int w = int.Parse(input[2]); // Weight
            edges.Add(Tuple.Create(u, v, w));
        }

        // Initialize distance array
        int[] distance = new int[N + 1];
        Array.Fill(distance, int.MaxValue);
        distance[S] = 0;

        // Bellman-Ford algorithm
        for (int i = 1; i <= N - 1; i++)
        {
            foreach (var edge in edges)
            {
                int u = edge.Item1;
                int v = edge.Item2;
                int w = edge.Item3;
                if (distance[u] != int.MaxValue && distance[u] + w < distance[v])
                {
                    distance[v] = distance[u] + w;
                }
            }
        }

        // Check for negative cycle
        bool hasNegativeCycle = false;
        foreach (var edge in edges)
        {
            int u = edge.Item1;
            int v = edge.Item2;
            int w = edge.Item3;
            if (distance[u] != int.MaxValue && distance[u] + w < distance[v])
            {
                hasNegativeCycle = true;
                break;
            }
        }

        // Output result
        if (hasNegativeCycle)
        {
            Console.WriteLine("Negative Cycle");
        }
        else
        {
            for (int i = 1; i <= N; i++)
            {
                Console.WriteLine(distance[i] == int.MaxValue ? "INF" : distance[i].ToString());
            }
        }
    }
}

Code Explanation

The above code implements the Bellman-Ford algorithm. Let me explain the main parts of the code.

  • List<Tuple<int, int, int>> edges: A list to store edge information.
  • int[] distance: Stores the shortest distance to each vertex. The initial value is set to infinity, and the starting vertex's distance is initialized to 0.
  • In the core loop of the Bellman-Ford algorithm, distances are updated by checking each edge.
  • At the end, edges are checked once more to verify if a negative cycle exists.

Conclusion

In this lecture, we examined the concept of the Bellman-Ford algorithm and the process of solving a problem using it. By deeply understanding the principles of the algorithm and implementing it in code, you can enhance your ability to apply it in coding tests.

The Bellman-Ford algorithm is also used in various practical shortest path problems, so be sure to understand it thoroughly and practice to prepare for coding tests.

C# Coding Test Course, Greedy Algorithm

Hello! In this lecture, we will cover greedy algorithms using C#. The greedy algorithm is a method of making the optimal choice at each moment, which is very useful for solving given problems. In this article, we will explain the concept of greedy algorithms and the process of solving a specific problem using them in detail.

Basic Concept of Greedy Algorithms

A greedy algorithm is a method of solving a problem by making the “best choice available at the moment”. This approach has the following characteristics:

  • Finding a local optimum leads to a global optimum.
  • It provides efficiency and simplicity for certain problems.
  • It does not require considering all possible cases.

However, greedy algorithms do not guarantee the optimal solution in every situation. Therefore, it is important to choose problems suitable for the algorithm. Generally, greedy algorithms are well applied in the following cases:

  • Minimum spanning tree problem
  • Shortest path problem
  • Fractional knapsack problem
  • Profit distribution and resource allocation problems

Problem Description

Problem: Coin Change Problem

The problem is to make a specific amount with the minimum number of coins. Given the types of coins and the target amount, the task is to find a way to create that amount using the least number of coins.

Input

  • Types of coins: {1 won, 5 won, 10 won, 50 won, 100 won, 500 won}
  • Target amount N (1 ≤ N ≤ 10000)

Output

  • Minimum number of coins needed to make the target amount N

Solution Process

To solve this problem, we will use the greedy algorithm approach. The steps are as follows:

  1. Start with the largest coin and choose coins to make the target amount.
  2. Use as many of the current coin as possible, then solve the remaining amount with the next largest coin.
  3. Repeat the above steps until the target amount is 0.

C# Code Implementation


using System;

class Program
{
    static void Main(string[] args)
    {
        int[] coins = { 500, 100, 50, 10, 5, 1 };
        int N = 1260;  // Target amount
        int coinCount = 0;

        for (int i = 0; i < coins.Length; i++)
        {
            if (N == 0)
                break;
            coinCount += N / coins[i];  // Add number of coins to use
            N %= coins[i];  // Update remaining amount
        }
        
        Console.WriteLine("Minimum number of coins: " + coinCount);
    }
}

The above code selects coins to make the target amount using the given types of coins and calculates the minimum number of coins. The target amount in this example is 1260 won, which is calculated as follows:

  • Use 2 of 500 won to make 1000 won
  • Use 2 of 100 won to make 1200 won
  • Use 1 of 50 won to make 1250 won
  • Use 1 of 10 won to make 1260 won

Ultimately, the minimum number of coins is 6.

Conclusion

In this lecture, we explored the concept of greedy algorithms using C# and the process of solving the coin change problem. Greedy algorithms can be powerful tools for problem-solving and can be applied to various problems. It is important to note that greedy choices may not always be the optimal choice. Therefore, it is essential to apply methods suitable for the nature of the problem.

We plan to share more algorithm problem-solving processes in the future, so please stay tuned!