C# Coding Test Course, Sliding Window

This article introduces how to solve algorithm problems using the sliding window technique. The sliding window is an effective technique for various array and string-related problems, reducing time complexity and providing efficient solutions.

Problem Description

Given an array as follows, let’s solve the problem of finding the length of the longest subarray whose sum does not exceed K.

            Input: nums = [1,2,3,4,5,6], K = 10
            Output: 4
            Explanation: [1,2,3,4] is the longest subarray whose sum does not exceed 10.
            

Problem Approach

By utilizing the sliding window technique, we traverse the array while maintaining the current sum, recording the maximum length when this sum does not exceed K. If the current sum exceeds K, we increment the starting index to reduce the sum and find a point that satisfies the condition.

Sliding Window Algorithm Implementation

            
            public class Solution {
                public int MaxLengthSubArray(int[] nums, int K) {
                    int left = 0, maxLength = 0, currentSum = 0;

                    for (int right = 0; right < nums.Length; right++) {
                        currentSum += nums[right];

                        while (currentSum > K) {
                            currentSum -= nums[left];
                            left++;
                        }

                        maxLength = Math.Max(maxLength, right - left + 1);
                    }

                    return maxLength;
                }
            }
            
            

Code Explanation

Now let’s look at each part of the code.

  • Variable Initialization: Declare left, maxLength, and currentSum variables.
  • For Loop: Use the right pointer right to traverse the array. Add the current number to currentSum.
  • While Loop: If the current sum exceeds K, increment the left pointer left, subtracting the corresponding number from currentSum, and repeat this process until the sum is less than or equal to K.
  • Update Maximum Length: Calculate the length of the current window and update maxLength.

Complexity Analysis

This algorithm traverses the array once, resulting in a time complexity of O(N). Additionally, it does not use extra space, giving it a space complexity of O(1). This demonstrates that the sliding window technique is very efficient.

Conclusion

The sliding window technique is a very useful skill in array and string problems, providing efficient solutions. This technique can be applied to various problems and will greatly aid your coding test preparation. As you practice solving problems, increase your understanding of the sliding window technique!

C# Coding Test Course, Exploring Debugging Use Cases

Hello! In this article, we will solve coding test problems using C# and explore the importance and application of debugging. We will examine how debugging skills can be helpful through specific cases while solving actual problems.

Problem Description

Problem: Longest Palindromic Substring

Create a function that returns the longest palindromic substring from a given string. The length of the string can be up to 1000.

Example input:


"babad"

Example output:


"bab" or "aba"

Problem Solving Process

Now, we will look at the approach to solve this problem step by step.

Step 1: Understanding the Problem

The key point of the problem is to check for palindromes in the given string. A palindrome is a string that reads the same forwards and backwards. For example, “racecar” is such an example. We need to generate all possible substrings from the given string and check each substring to see if it is a palindrome.

Step 2: Algorithm Design

To find the longest palindromic substring, we designed the following algorithm:

  1. Generate all substrings of the given string.
  2. Check if each substring is a palindrome.
  3. If it is a palindrome, compare the length of that substring and keep the longest one.

Step 3: C# Code Implementation

Now, let’s implement the C# code according to each step.


using System;

public class LongestPalindromicSubstring
{
    public string GetLongestPalindrome(string s)
    {
        if (string.IsNullOrEmpty(s)) return "";

        string longestPalindrome = "";

        for (int i = 0; i < s.Length; i++)
        {
            // Check for odd-length palindromes
            string oddPalindrome = ExpandFromCenter(s, i, i);
            if (oddPalindrome.Length > longestPalindrome.Length)
            {
                longestPalindrome = oddPalindrome;
            }

            // Check for even-length palindromes
            string evenPalindrome = ExpandFromCenter(s, i, i + 1);
            if (evenPalindrome.Length > longestPalindrome.Length)
            {
                longestPalindrome = evenPalindrome;
            }
        }

        return longestPalindrome;
    }

    private string ExpandFromCenter(string s, int left, int right)
    {
        while (left >= 0 && right < s.Length && s[left] == s[right])
        {
            left--;
            right++;
        }
        return s.Substring(left + 1, right - left - 1);
    }
}

Step 4: Debugging and Testing

Now it’s time to test the implemented code and debug for any errors.

  1. After running the code, input various cases to check if the results match the expectations.
  2. In particular, test complex strings that include whitespace characters, numbers, and special characters to ensure palindromic checking works well.

Example Test Cases


LongestPalindromicSubstring lps = new LongestPalindromicSubstring();
Console.WriteLine(lps.GetLongestPalindrome("babad")); // bab or aba
Console.WriteLine(lps.GetLongestPalindrome("cbbd")); // bb
Console.WriteLine(lps.GetLongestPalindrome("a")); // a
Console.WriteLine(lps.GetLongestPalindrome("ac")); // a
Console.WriteLine(lps.GetLongestPalindrome("racecar")); // racecar

Debugging Tips

When debugging, keep the following points in mind:

  • Carefully check variable values and use breakpoints to run the code if necessary.
  • Compare expected values and actual values at each step to identify where errors occur.
  • Break the code into small pieces and independently test each function to ensure they work properly.

Conclusion

In this article, we explored algorithm design and debugging processes through a palindromic problem utilizing C#. Debugging is a crucial step in solving problems, and it is a process that must be repeated to ensure that the program operates as expected. Take your time reviewing each step and do not hesitate to seek help when necessary.

Now you can use this problem to prepare for C# coding tests. Continue practicing and solving various problems!

Additional Learning Resources

Finally, you can refer to the following resources for deeper learning:

C# Coding Test Course, What Algorithm Should I Use to Solve It?

Coding tests are a mandatory procedure in modern IT companies. Many applicants are preparing to be evaluated on their understanding of algorithms and data structures, and their ability to solve problems based on this knowledge. In this article, we will take a closer look at how to solve algorithm problems using C#.

Problem: Valid Parentheses String

This is a problem of determining whether a given string is a valid parentheses string. The string is composed only of the characters ‘(‘, ‘)’, ‘{‘, ‘}’, ‘[‘ and ‘]’. A valid parentheses string must satisfy the following conditions:

  • All opened parentheses must be closed by closed parentheses.
  • A closed parenthesis must always correspond to the most recent opened parenthesis.
  • The closing of parentheses should not occur before their opening.

For example, the string “{[()]}” is a valid parentheses string, while “{[(])}” is not valid.

Problem Solving Process

1. Problem Analysis

To solve this problem, we can use a stack data structure. Since the stack operates in a Last In First Out (LIFO) manner, we can add opened parentheses to the stack and, upon encountering a closed parenthesis, remove the most recently opened parenthesis from the stack. If the stack is empty after checking all parentheses, then it is a valid parentheses string.

2. Algorithm Design

The algorithm to solve this problem is as follows:

  1. Define the pairs of parentheses: { '(': ')', '{': '}', '[': ']' }
  2. Traverse the string and add opened parentheses to the stack.
  3. Upon encountering a closed parenthesis, check if it matches with the most recent opened parenthesis from the stack.
  4. If the stack is not empty or the pairs do not match, consider it an invalid string.
  5. After exploring the entire string, if the stack is empty, then it is a valid string.

3. C# Code Implementation


using System;
using System.Collections.Generic;

public class ValidParentheses
{
    public static bool IsValid(string s)
    {
        Stack stack = new Stack();
        Dictionary parentheses = new Dictionary()
        {
            {'(', ')'},
            {'{', '}'},
            {'[', ']'}
        };

        foreach (char c in s)
        {
            // If it's an opened parenthesis, add it to the stack
            if (parentheses.ContainsKey(c))
            {
                stack.Push(c);
            }
            else
            {
                // If it's a closed parenthesis and the stack is empty, validation fails
                if (stack.Count == 0 || parentheses[stack.Pop()] != c)
                {
                    return false;
                }
            }
        }

        // If the stack is empty, it's a valid parentheses string
        return stack.Count == 0;
    }
}

class Program
{
    static void Main()
    {
        string input = "{[()]}";
        bool result = ValidParentheses.IsValid(input);
        Console.WriteLine($"\"{input}\" is it a valid parentheses string? {result}");
    }
}
    

4. Algorithm Analysis

The above code uses a stack to traverse the string once, resulting in a time complexity of O(n), where n is the length of the string. The space complexity is also O(n) because up to n opened parentheses can be stored on the stack. This algorithm can operate efficiently even as the input string length increases.

Conclusion

In this article, we explored how to solve the valid parentheses string problem using C#. When solving algorithm problems, it is important to analyze the problem well and choose the appropriate data structures. Utilizing simple data structures like stacks can help efficiently solve complex problems. I hope this helps in developing algorithmic thinking through a variety of problems.

C# Coding Test Course, Calculating the Amount of Water

Problem Description

There is a tank that can hold water in a specific area. Each wall of the tank has a different height. When it rains, water will accumulate, and the problem is to determine how much water can be held between each wall.

Input

The first line contains an integer N (1 ≤ N ≤ 100,000). N represents the number of walls.
The second line contains N integers h_i (1 ≤ h_i ≤ 1,000,000), which represent the height of each wall.

Output

Print the amount of water that can be held on one line.

Example Problem

Example Input:
5
0 1 0 2 1 0 1 3 2 1 2 1
Example Output:
6

Problem Solving Process

To solve this problem, we will use two arrays. One will store the maximum height of walls to the left of the current position, and the other will store the maximum height of walls to the right.

1. Problem Analysis

The amount of water that can be held at each position is the difference between the minimum of the maximum heights of the walls on the left and right, and the current wall height.
For example, the amount of water at position i can be calculated as follows:
water[i] = max(0, min(left_max[i], right_max[i]) - height[i])
where left_max represents the maximum height of walls to the left of i, and right_max represents the maximum height of walls to the right.

2. Algorithm Design

We can design the algorithm in the following steps:

  1. Receive the input.
  2. Create an array left_max to store the maximum heights of the left walls.
  3. Create an array right_max to store the maximum heights of the right walls.
  4. Calculate the amount of water that can be held at each position.
  5. Finally, output the total amount of water.

3. Code Implementation

Below is the code that implements the above algorithm in C#.


using System;

class Program
{
    static void Main(string[] args)
    {
        int N = int.Parse(Console.ReadLine());
        int[] height = Array.ConvertAll(Console.ReadLine().Split(), int.Parse);

        // Left maximum height array
        int[] left_max = new int[N];
        left_max[0] = height[0];

        for (int i = 1; i < N; i++)
        {
            left_max[i] = Math.Max(left_max[i - 1], height[i]);
        }

        // Right maximum height array
        int[] right_max = new int[N];
        right_max[N - 1] = height[N - 1];

        for (int i = N - 2; i >= 0; i--)
        {
            right_max[i] = Math.Max(right_max[i + 1], height[i]);
        }

        // Calculate the amount of water
        int water = 0;
        for (int i = 0; i < N; i++)
        {
            water += Math.Max(0, Math.Min(left_max[i], right_max[i]) - height[i]);
        }

        Console.WriteLine(water);
    }
}
    

4. Code Explanation

The algorithm used in the above C# code is as follows:

  • int N = int.Parse(Console.ReadLine());
    – Takes the input for N and stores the number of walls.
  • int[] height = Array.ConvertAll(Console.ReadLine().Split(), int.Parse);
    – Takes the height input and converts it into an array.
  • left_max array stores the maximum height from the left. To do this, after setting the height of the first wall, a loop is run to compare the height of each wall and store the maximum value.
  • right_max array does the same to store the maximum height from the right.
  • Finally, the amount of water that can be held at each wall position is calculated to find the total amount of water.

5. Complexity Analysis

The time complexity of this algorithm is O(N). It performs operations proportional to the number of walls N, so it operates efficiently within the provided input range. The space complexity is also O(N) as it needs space to store the two arrays.

Conclusion

In this article, we solved the problem of calculating the amount of water using C#. Through the process of designing and implementing the algorithm step by step, we were able to confirm problem types that are frequently encountered in coding tests.
I hope this lecture helps you in your preparation for coding tests.

C# Coding Test Course, Counting Steps

1. Problem Description

The staircase number is a problem of counting numbers that meet specific conditions.
Given a number N, we will address the problem of finding N-digit staircase numbers.
A staircase number is a number where each digit must be either one more or one less than the next digit. For example, 1234 and 3210 are staircase numbers.
However, 1123 and 2222 are not staircase numbers.
This problem will help you understand the basic ideas and implementation methods of dynamic programming.

2. Input & Output Format

Input

N (1 ≤ N ≤ 100), the given natural number N represents the number of digits in the staircase number.

Output

Print the number of N-digit staircase numbers modulo 1,000,000,000.

3. Approach to the Problem

To count staircase numbers, we need to choose a dynamic programming method.
We can approach the problem in the following way.
To find N-digit staircase numbers, we calculate values using (N-1)-digit staircase numbers.
We can find the following rules:

  • If each digit is 0, the next digit can only be 1.
  • If each digit is 9, the next digit can only be 8.
  • If each digit is between 1 and 8, there are two options for the next digit: one less or one more.

Based on this, we can construct a DP table and keep track of staircase numbers for each digit,
ultimately allowing us to find the N-digit staircase numbers.
The approach involves initializing the DP array and gradually filling it out.

4. C# Code Implementation

Now we will implement the code to find staircase numbers in C#.
Below is the C# code to solve the problem.


using System;

class Program
{
    static void Main()
    {
        int N = int.Parse(Console.ReadLine());
        long[,] dp = new long[N + 1, 10];

        // Initialize 1-digit staircase numbers (1~9)
        for (int i = 1; i <= 9; i++)
        {
            dp[1, i] = 1;
        }

        // Use DP to find N-digit staircase numbers
        for (int i = 2; i <= N; i++)
        {
            for (int j = 0; j <= 9; j++)
            {
                if (j == 0)
                {
                    dp[i, j] = dp[i - 1, j + 1] % 1000000000; // 0 -> 1
                }
                else if (j == 9)
                {
                    dp[i, j] = dp[i - 1, j - 1] % 1000000000; // 9 -> 8
                }
                else
                {
                    dp[i, j] = (dp[i - 1, j - 1] + dp[i - 1, j + 1]) % 1000000000; // j-1, j+1
                }
            }
        }

        // Calculate the total number of N-digit staircase numbers
        long result = 0;
        for (int j = 0; j <= 9; j++)
        {
            result = (result + dp[N, j]) % 1000000000;
        }

        // Print the result
        Console.WriteLine(result);
    }
}
            

5. Code Explanation

I will explain the code step by step.
1. First, read the value of N from the user’s input.
2. Create a two-dimensional array dp, where dp[i, j] stores the number of i-digit staircase numbers ending with j.
3. Since 1-digit numbers can only use digits from 1 to 9, we initialize this.
4. Next, we calculate the number of digits from 2 up to N.
5. Each digit’s calculation is done according to the rules explained above.
6. Finally, sum all N-digit staircase numbers and print the result.

6. Complexity Analysis

The time complexity of this algorithm is O(N).
When N is 100, we check the possibilities for each digit through a double loop,
which results in O(N) * O(10) = O(N).
The space complexity is O(N * 10), indicating relatively low memory usage.

7. Example

Input Example


3
            

Output Example


32
            

In the above example, there are 32 three-digit staircase numbers.
This helps to understand the problem.

8. Conclusion

In this tutorial, we learned the basic concepts of dynamic programming
through the problem of finding staircase numbers.
The staircase number problem can serve as a good practice problem to improve algorithm skills.
I hope you gain more experience by solving various problems.
Continue to challenge yourself with algorithm problem-solving!