Category: C++ Coding Test Tutorials

Hello everyone! In this tutorial, we will take a close look at the basics of the C++ programming language and the algorithm problem-solving process through a problem of calculating the amount of water. Algorithm problems require not only writing simple code but also thinking about how to understand and interpret the problem. Therefore, we will cover the problem-solving approach, code writing, performance analysis, and optimization strategies in detail.

Problem Description

The overall definition of the problem is as follows: Given an array of heights, the task is to calculate how much water can accumulate in certain areas when it rains. This is widely known as the “Trapping Rain Water” problem, and there are various and interesting solutions available.

Example Problem

Let’s assume we are given a height array. For example, consider the following array:

{0, 1, 0, 2, 1, 0, 1, 3, 2, 1, 2, 1}

Given the above array, when it rains, water can accumulate as follows:

• Index 0: No water accumulated
• Index 1: No water accumulated
• Index 2: 1 unit of water
• Index 3: No water accumulated
• Index 4: 1 unit of water
• Index 5: 2 units of water
• Index 6: 1 unit of water
• Index 7: No water accumulated
• Index 8: 1 unit of water
• Index 9: 1 unit of water
• Index 10: 1 unit of water
• Index 11: No water accumulated

Thus, the total amount of accumulated water is 6 units.

Problem Approach

To solve the problem, we must first understand how water accumulates. The amount of water that can accumulate depends on the higher boundaries surrounding a specific index. For example, in order for water to accumulate at a position, there must be higher boundaries on both sides of that position, and the lower part of these two boundaries determines the height at which water can accumulate.

There are several methods to calculate this, and we will introduce two methods here:

1. Two-pointer method
2. DP (Dynamic Programming) method

Two-Pointer Method

The first approach uses the two-pointer method. This involves placing pointers on the left and right and calculating the amount of water based on the higher walls. This method can typically solve the problem by traversing the array only once, resulting in an O(n) time complexity.

Algorithm Explanation

1. Place pointers at both ends of the array and initialize variables to store the maximum heights from the left and right.
2. Repeat until the two pointers cross each other.
3. If the current height of the left pointer is lower than the height of the right pointer, move the left pointer one step to the right and calculate the amount of trapped water using the difference between the stored maximum height.
4. If not, move the right pointer one step to the left and perform a similar calculation.

Code Implementation

#include 
#include 
using namespace std;

int trap(vector& height) {
    if (height.size() == 0) return 0;

    int left = 0, right = height.size() - 1;
    int left_max = 0, right_max = 0;
    int water_trapped = 0;

    while (left < right) {
        if (height[left] < height[right]) {
            if (height[left] >= left_max) {
                left_max = height[left];
            } else {
                water_trapped += left_max - height[left];
            }
            left++;
        } else {
            if (height[right] >= right_max) {
                right_max = height[right];
            } else {
                water_trapped += right_max - height[right];
            }
            right--;
        }
    }
    return water_trapped;
}

int main() {
    vector height = {0, 1, 0, 2, 1, 0, 1, 3, 2, 1, 2, 1};
    cout << "Total water trapped: " << trap(height) << endl;
    return 0;
}

Dynamic Programming Method

There is also a method using dynamic programming. This method proceeds based on a pre-calculated array of how much water can accumulate at each index. This method also has an O(n) time complexity.

Algorithm Explanation

1. Determine the length of the integer array and create two arrays; one for the maximum height from the left and another for the maximum height from the right.
2. Fill the left maximum height array. The maximum height at each index is set by selecting the greater value between the previous index’s maximum height and the current height.
3. Similarly, fill the right maximum height array.
4. Finally, the amount of trapped water at each index is determined based on the two maximum height arrays: min(left_max[i], right_max[i]) – height[i].

Code Implementation

#include 
#include 
using namespace std;

int trap(vector& height) {
    if (height.size() == 0) return 0;
    int n = height.size();
    vector left_max(n);
    vector right_max(n);
    int water_trapped = 0;

    // Calculate left maximum height
    left_max[0] = height[0];
    for (int i = 1; i < n; i++) {
        left_max[i] = max(left_max[i - 1], height[i]);
    }

    // Calculate right maximum height
    right_max[n - 1] = height[n - 1];
    for (int i = n - 2; i >= 0; i--) {
        right_max[i] = max(right_max[i + 1], height[i]);
    }

    // Calculate amount of trapped water
    for (int i = 0; i < n; i++) {
        water_trapped += min(left_max[i], right_max[i]) - height[i];
    }
    return water_trapped;
}

int main() {
    vector height = {0, 1, 0, 2, 1, 0, 1, 3, 2, 1, 2, 1};
    cout << "Total water trapped: " << trap(height) << endl;
    return 0;
}

Performance Analysis

Both approaches above have O(n) time complexity. However, if space complexity is important, the two-pointer method is more efficient. In contrast, the dynamic programming method requires an additional O(n) space, so care should be taken when memory usage is important.

Optimization Strategies

To maximize performance in C++ coding tests, always consider population size, array size, algorithm complexity, etc. Also, be mindful of compiler optimizations, reduce unnecessary variables, and pay attention to memory management.

Conclusion

In this tutorial, we learned about basic array handling and algorithm writing methods in C++ through the water trapping problem. By analyzing various approaches and the pros and cons of each method, we also explored how to optimize them. I hope the knowledge and experience gained in today’s coding tests will be very useful in the future.

Always be the one who practices and improves! Thank you.

Hello! In this article, we will explore the problem of finding strings using C++. Solving algorithmic problems helps improve programming skills and assists in achieving good results in coding tests. Particularly in recent years, string manipulation problems have frequently appeared in coding tests. Therefore, a thorough understanding and practice of this topic are necessary.

Problem Description

In this lecture, we will address the problem of finding a specific string (pattern) within a given text and counting how many times that string appears in the text. This problem serves as a fundamental example of string search algorithms and will solve the following problem.

Problem:

Given a string text and pattern, write a program to determine how many times pattern appears in text.

Input Format

• First line: string text (1 ≤ |text| ≤ 100,000)
• Second line: string pattern (1 ≤ |pattern| ≤ 100,000)

Output Format

• Print the count of matching patterns.

Approach

There are several algorithms that can be used to solve the problem, but we will look at a simple brute force method here. This method checks each position in the text against the pattern to determine if they match. The performance is O(n * m), where n is the length of the text and m is the length of the pattern. This method is straightforward and intuitive, but one might also consider more efficient methods like the KMP algorithm or the Rabin-Karp algorithm.

Code Implementation

Let’s implement the code. Below is a string-finding program written in C++:

#include <iostream>
#include <string>

int countOccurrences(const std::string &text, const std::string &pattern) {
    int count = 0;
    int textLength = text.length();
    int patternLength = pattern.length();

    for (int i = 0; i <= textLength - patternLength; i++) {
        // Compare substring through partial string comparison
        if (text.substr(i, patternLength) == pattern) {
            count++;
        }
    }
    return count;
}

int main() {
    std::string text, pattern;

    std::cout << "Enter text: ";
    std::getline(std::cin, text);
    std::cout << "Enter pattern: ";
    std::getline(std::cin, pattern);

    int occurrences = countOccurrences(text, pattern);
    std::cout << "The pattern appears " << occurrences << " times." << std::endl;

    return 0;
}

Code Explanation

The above C++ code solves the string-finding problem. It performs functions based on the text and pattern provided by the user.

• countOccurrences function: This function counts the occurrences of pattern in the given text. It extracts substrings from each position in the text and increases the count whenever there is a match with the given pattern.
• main function: This part handles basic input and output. It takes a string input from the user and calls the countOccurrences function to print the result.

Performance Analysis

The brute-force approach described above has a worst-case time complexity of O(n * m). As the length of the text increases, performance may degrade. However, if simplicity and intuitiveness are prioritized, this method can be useful.

For an efficient solution, consider the KMP (Knuth-Morris-Pratt) algorithm. The KMP algorithm optimizes string searches by utilizing repeated parts of the pattern. This algorithm has a time complexity of O(n + m).

KMP Algorithm Introduction

The KMP algorithm is designed to solve pattern search problems and reduces repetitive checks by utilizing previously matched parts. This algorithm consists of two main steps. The first step builds an ‘LPS (Longest Prefix Suffix)’ array for the pattern, and the second step uses this LPS array to search for the pattern in the text.

Building the LPS Array

The LPS array stores the lengths of the longest prefixes and suffixes among each prefix of the pattern. For example, for the pattern “ABAB,” the LPS array becomes [0, 0, 1, 2]. This has the following implications:

• No prefix and suffix for A at index 0, so it’s 0
• No prefix and suffix for B at index 1, so it’s 0
• AB is both prefix and suffix at index 2, so it’s 1
• ABA is both prefix and suffix at index 3, so it’s 2

KMP Algorithm Implementation

Below is the C++ code that implements the KMP algorithm:

#include <iostream>
#include <string>
#include <vector>

std::vector computeLPS(const std::string &pattern) {
    int m = pattern.length();
    std::vector lps(m);
    int len = 0; 
    lps[0] = 0; 
    int i = 1;

    while (i < m) {
        if (pattern[i] == pattern[len]) {
            len++;
            lps[i] = len;
            i++;
        } else {
            if (len != 0) {
                len = lps[len - 1];
            } else {
                lps[i] = 0;
                i++;
            }
        }
    }
    return lps;
}

int KMP(const std::string &text, const std::string &pattern) {
    std::vector lps = computeLPS(pattern);
    int n = text.length();
    int m = pattern.length();
    int i = 0; 
    int j = 0; 
    int count = 0;

    while (i < n) {
        if (pattern[j] == text[i]) {
            i++;
            j++;
        }
        if (j == m) {
            count++;
            j = lps[j - 1];
        } else if (i < n && pattern[j] != text[i]) {
            if (j != 0)
                j = lps[j - 1];
            else
                i++;
        }
    }
    return count;
}

int main() {
    std::string text, pattern;

    std::cout << "Enter text: ";
    std::getline(std::cin, text);
    std::cout << "Enter pattern: ";
    std::getline(std::cin, pattern);

    int occurrences = KMP(text, pattern);
    std::cout << "The pattern appears " << occurrences << " times." << std::endl;

    return 0;
}

Conclusion

In this article, we learned how to solve the string-finding problem using C++. After introducing the brute-force method, we explored how the efficient KMP algorithm operates. Problems related to strings can further develop based on basic algorithm knowledge. Therefore, it is essential to continue practicing such problems to enhance programming skills.

Try solving various string problems and develop your own algorithms! You’ll gain confidence in coding tests. Thank you!

This article will explain in detail how to count the number of leaf nodes using the C++ programming language. It is structured to be easily understandable from start to finish, including the process of solving the algorithm problem and the code.

Problem Description

A leaf node refers to a node that has no child nodes. Write a program to count the number of leaf nodes in a given binary tree. The input for this problem will be the root node of the binary tree.

Input format:

• The root node of the binary tree (the root node may be nullptr.)

Output format:

• The number of leaf nodes

Algorithm Approach

To solve this problem, we will traverse the binary tree recursively. Each time we visit a node, we check if it is a leaf node. A leaf node has no child nodes, so we simply need to check if both the left and right child nodes are nullptr.

Specific Algorithm:

1. If the root node is nullptr, return 0.
2. If the node is a leaf node, return 1.
3. Recursively traverse the left and right subtrees, summing and returning the results.

C++ Code Implementation

Now let’s take a look at the code that implements the above algorithm in C++.

#include <iostream>

struct TreeNode {
    int val;
    TreeNode *left;
    TreeNode *right;
    
    TreeNode(int x) : val(x), left(nullptr), right(nullptr) {}
};

int countLeafNodes(TreeNode* root) {
    // 1. If the root is nullptr
    if (root == nullptr) {
        return 0;
    }
    
    // 2. If it is a leaf node
    if (root->left == nullptr && root->right == nullptr) {
        return 1;
    }
    
    // 3. Explore the left and right subtrees
    return countLeafNodes(root->left) + countLeafNodes(root->right);
}

int main() {
    // Create a binary tree for testing
    TreeNode* root = new TreeNode(1);
    root->left = new TreeNode(2);
    root->right = new TreeNode(3);
    root->left->left = new TreeNode(4);
    root->left->right = new TreeNode(5);
    
    // Print the number of leaf nodes
    int leafCount = countLeafNodes(root);
    std::cout << "Number of leaf nodes: " << leafCount << std::endl;
    
    // Free allocated memory
    delete root->left->left;
    delete root->left->right;
    delete root->left;
    delete root->right;
    delete root;

    return 0;
}

Code Explanation

The code above implements the countLeafNodes function, which takes the root node of a binary tree and returns the number of leaf nodes.

Struct Declaration:

The TreeNode struct represents each node in the binary tree. Each node has an integer value (val) and pointers to the left child (left) and right child (right).

Counting Leaf Nodes:

The countLeafNodes function first checks if the root is nullptr. If nullptr, it returns 0, and if it’s a leaf node, it returns 1. Otherwise, it recursively calls the left and right subtrees to count and sum the number of leaf nodes.

Main Function:

The main function creates a binary tree for testing purposes and prints the number of leaf nodes corresponding to it. Finally, it is important to free the allocated memory.

Testing and Results

Executing this code will yield the following result:

Number of leaf nodes: 3

In the above binary tree, the leaf nodes are nodes 4, 5, and 3, resulting in a total of 3 leaf nodes.

Conclusion

In this article, we explained how to count the number of leaf nodes in a binary tree using C++. Through examples of understanding and implementing algorithms, we can learn the basic recursive calls and struct usage in C++. To solve algorithm problems, it is important to break down the problem and approach it step by step. We hope you continue to practice various problems to build your algorithm skills.

References

• C++ Reference
• LeetCode
• HackerRank C++ Tutorials

The coding test is an important process that evaluates not only understanding of programming languages but also problem-solving abilities. C++ is an efficient and powerful language, and many companies use C++-based coding tests to assess candidates. In this article, I will introduce an algorithm problem from a C++ coding test and explain the process of solving it in detail. I will also provide an in-depth discussion on the importance of debugging.

Problem: Sum of Two Numbers in an Array

Problem Description: Given an integer array nums and an integer target, write a function that returns the indices of the two numbers in the nums array that add up to target. It is assumed that each input has exactly one solution and you may not use the same element twice.

Input

• nums: integer array
• target: integer

Output

Returns an array of corresponding indices. For example, given nums = [2, 7, 11, 15] and target = 9, it should return [0, 1].

Problem-Solving Process

1. Problem Analysis

Before solving the problem, it is important to thoroughly understand it. The key is to find the indices of the specific numbers. While scanning the given array, you need to check if using each number can lead to the target number. For example, if the sum of two numbers equals the target, you need to find the indices of those two numbers.

2. Approach

This problem can be approached in several ways. The most basic method is to use two nested loops. However, this increases the time complexity to O(n^2), making it inefficient. Therefore, a HashMap can be used to improve the access speed. By using a HashMap, you can store previously checked numbers and find the number needed to reach the target with the current number in one go.

3. Code Implementation

#include <iostream>
#include <vector>
#include <unordered_map>

std::vector<int> twoSum(std::vector<int> &nums, int target) {
    std::unordered_map<int, int> map; // HashMap to store numbers and indices
    for (int i = 0; i < nums.size(); ++i) {
        int complement = target - nums[i]; // Number needed when using the current number
        if (map.find(complement) != map.end()) { // Check if the number exists in the HashMap
            return {map[complement], i}; // Return indices
        }
        map[nums[i]] = i; // Store the current number in the HashMap
    }
    return {}; // Return if no result
}

int main() {
    std::vector<int> nums = {2, 7, 11, 15};
    int target = 9;
    std::vector<int> result = twoSum(nums, target);
    std::cout << "Result: " << result[0] << ", " << result[1] << std::endl;
    return 0;
}

4. Code Explanation

The code has the following structure:

• Uses unordered_map to store each number and its index.
• Iterates through the array with a for loop. In each iteration, it compares the current number with complement.
• If map contains complement, the result is returned immediately.
• If not, the current number and index are stored in the map.

5. Debugging Process

After writing the code, it is essential to find errors through the debugging process. The reasons why debugging is important are:

• Finding Errors: Debugging can help easily identify logical and syntax errors.
• Code Improvement: By fixing identified errors, you can improve the quality and performance of the code.
• Self-Check: In the review process, you can discover other enhancements or optimization opportunities.

Techniques for debugging include:

• Using Print Statements: Adding print statements to specific parts of the code to check how values change.
• Using Debugging Tools: Utilizing IDE debugging tools to run the program step by step.
• Unit Testing: Testing functions against multiple input cases to verify all possibilities.

6. Conclusion

In this blog, we examined the problem-solving process in detail through an example of a C++ coding test. Along with the problem-solving process, we discussed the significance of debugging, providing direction in the coding and modification process. Coding tests go beyond merely writing code; they involve understanding the problem and finding solutions, requiring such approaches. Remember not to forget the process of finding errors and improving performance through debugging.

In the future, I will cover additional concepts and algorithm problems related to C++ to help enhance your coding skills. Thank you!

Author: [Your Name]

Publication Date: [Publication Date]

1. Introduction to the Algorithm Problem

In this post, we will examine an algorithm problem that can be solved using C++. Through this problem, we will explore the basic syntax of C++ and the approach to algorithms, as well as experience the process of solving problems using debugging techniques.

Problem: Two Sum

The problem is to return the indices of two numbers from a given integer array that add up to a target integer. If such a pair does not exist, -1 should be returned.

Example Input:

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

Example Output:

            [0, 1]
            

Since the sum of the two numbers 2 and 7 is 9, we return indices 0 and 1.

2. Problem Approach

This problem is somewhat intuitive. You can check all pairs using nested loops, but this method has a time complexity of O(n^2). A better approach is to use a hashmap. By iterating through each number and storing it in the hashmap while checking the difference between the target and the current number, this method can be solved with a time complexity of O(n).

3. C++ Code Implementation

Now, let’s implement this problem in C++.

            #include 
            #include 
            #include 
            using namespace std;

            vector twoSum(vector& nums, int target) {
                unordered_map map; // Hashmap to store numbers and their indices
                vector result;

                for (int i = 0; i < nums.size(); i++) {
                    int complement = target - nums[i]; // The value of target minus the current number
                    if (map.find(complement) != map.end()) { // Search
                        result.push_back(map[complement]);
                        result.push_back(i);
                        return result; // Return the result
                    }
                    map[nums[i]] = i; // Store the current number and its index in the hashmap
                }

                return {-1}; // In case there are no pairs
            }

            int main() {
                vector nums = {2, 7, 11, 15};
                int target = 9;
                vector result = twoSum(nums, target);

                cout << "[" << result[0] << ", " << result[1] << "]" << endl;
                return 0;
            }
            

4. Code Explanation

The code above first takes the integer array nums and the target value target as input. It creates a hashmap map to store each number and its index using unordered_map. Then, while iterating through the array:

• It calculates the complement for the current number: complement = target - nums[i].
• It checks whether the complement exists in the hashmap. If it does, it returns the indices of that number and the current index.
• If the complement does not exist, it stores the current number and index in the hashmap.

Finally, if no pairs are found after checking all numbers, it returns -1.

5. Debugging Use Case

Now let's learn about the importance and methods of debugging. While writing code, several problems may arise, such as the complement not being stored properly in the hashmap or returning incorrect indices.

In such cases, you can print intermediate results using iostream. You can modify the code to print intermediate values as follows:

            // Add intermediate result printing
            for (int i = 0; i < nums.size(); i++) {
                int complement = target - nums[i];
                cout << "Current number: " << nums[i] << ", Complement: " << complement << endl;
                if (map.find(complement) != map.end()) {
                    result.push_back(map[complement]);
                    result.push_back(i);
                    return result;
                }
                map[nums[i]] = i;
            }
            

By adding print statements like this, you can understand which values are being processed at each iteration, which can be a great help in solving the problem.

6. Conclusion

In this post, we explored the process of solving algorithm problems using C++ and the importance of debugging. Programming involves various problems, each requiring different approaches; however, effective debugging skills can help solve difficult problems. I hope you continue to practice and improve your algorithm problem-solving abilities in the future.

Thank you!