JavaScript Coding Test Course, What Algorithm Should I Use?

Coding tests are an important gateway for developers. Especially for developers using JavaScript, it is crucial to have a good understanding of the characteristics of this language and algorithms. In this course, we will select one algorithm problem using JavaScript and explain the process of solving it step by step.

Problem Description

Problem: Two Sum

This problem involves finding two numbers in a given integer array such that their sum equals a specific target value, and returning the indices of those two numbers.

Example:
    Input: nums = [2, 7, 11, 15], target = 9
    Output: [0, 1]
    Explanation: nums[0] + nums[1] = 2 + 7 = 9, thus it returns [0, 1].

Problem Analysis

This problem can generally be solved in O(n) time complexity using a hash map. By iterating through all elements of the array, we check the difference between each element and the target value, and store the corresponding indices.

Approach

  1. Create a hash map (object) to store each element of the array.
  2. While iterating through the array, calculate the difference between each element’s value and the target value.
  3. Check if a value corresponding to this difference exists in the hash map.
  4. If it exists, return the corresponding index and the current index.

JavaScript Code

function twoSum(nums, target) {
        const map = new Map();
        
        for (let i = 0; i < nums.length; i++) {
            const complement = target - nums[i];
            if (map.has(complement)) {
                return [map.get(complement), i];
            }
            map.set(nums[i], i);
        }
        
        throw new Error("No two sum solution");
    }

Code Explanation

The code above works in the following way:

  • It creates a hash map `map`. This is where each number and its index are stored.
  • It checks each element of the array through a loop, calculating the difference with the target value.
  • If this difference exists as a key in the hash map, it returns the index of that key and the current index.
  • If no result is found after checking all elements, it throws an error.

Time Complexity Analysis

The above algorithm has a time complexity of O(n) since it only iterates through all elements of the array once. The insertion and lookup operations for the hash map have an average time complexity of O(1), making it an efficient solution overall.

Space Complexity Analysis

The space complexity is O(n), as it may require this amount of space to store all elements of the array in the hash map.

Conclusion

Such problems are frequently presented in coding tests. When approaching each problem, it is important to consider efficient algorithms and data structures. By utilizing hash maps as shown above, you can achieve good performance in solving problems.

Tips for Future Algorithm Problem Solving

1. Learn about various data structures and algorithms.
2. Practice recognizing specific patterns when solving problems.
3. Learn from others' approaches through code reviews.
4. Solve problems on online platforms and receive feedback.
5. Regularly practice coding to maintain and improve your skills.