leetcode solution of 2 sum in Java

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https://leetcode.com/problems/two-s...

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Welcome to this in-depth look at the LeetCode 2 Sum problem, a classic coding challenge that tests your understanding of algorithms and data structures. We'll explore the problem statement, dive into different solutions, and analyze their time and space complexities.

The 2 Sum problem asks you to find two numbers in an array that add up to a specific target number. You're given an array of integers and a target value, and your goal is to return the indices of the two numbers that sum to the target.

Here's a naive solution using nested loops. It iterates through the array, checking every possible pair of numbers to see if they sum to the target. This approach has a time complexity of O(N^2), which can be inefficient for large arrays.

The naive solution is simple to understand, but it's not the most efficient. Let's explore a more optimized approach.

A more efficient solution utilizes a hash table to store the elements of the array and their indices. This allows us to check for the complement of each number in constant time.

This solution iterates through the array once, storing each number and its index in the hash table. For each number, we calculate its complement (the number that would add up to the target). If the complement exists in the hash table, we've found our solution and return the indices. Otherwise, we add the current number and its index to the hash table.

This hash table approach significantly improves the time complexity to O(N), making it much faster for larger arrays.

"One-pass Hash Table: we iterate and inserting elements into the table, we also look back to check if current element's complement already exists in the table. If it exists, we have found a solution and return immediately." - GitHub, 2024

The 2 Sum problem is a great example of how choosing the right data structure can dramatically improve the efficiency of your solution. By using a hash table, we were able to reduce the time complexity from O(N^2) to O(N), making it a much more efficient solution for larger arrays.

Remember, understanding the problem and choosing the right data structure are crucial for solving coding challenges effectively.

The 2 Sum problem asks you to find two numbers in an array that add up to a specific target number. You're given an array of integers and a target value, and your goal is to return the indices of the two numbers that sum to the target.

Here's a naive solution using nested loops. It iterates through the array, checking every possible pair of numbers to see if they sum to the target. This approach has a time complexity of O(N^2), which can be inefficient for large arrays.

The naive solution is simple to understand, but it's not the most efficient. Let's explore a more optimized approach.

A more efficient solution utilizes a hash table to store the elements of the array and their indices. This allows us to check for the complement of each number in constant time.

This solution iterates through the array once, storing each number and its index in the hash table. For each number, we calculate its complement (the number that would add up to the target). If the complement exists in the hash table, we've found our solution and return the indices. Otherwise, we add the current number and its index to the hash table.

This hash table approach significantly improves the time complexity to O(N), making it much faster for larger arrays.

"One-pass Hash Table: we iterate and inserting elements into the table, we also look back to check if current element's complement already exists in the table. If it exists, we have found a solution and return immediately." - GitHub, 2024

The 2 Sum problem is a great example of how choosing the right data structure can dramatically improve the efficiency of your solution. By using a hash table, we were able to reduce the time complexity from O(N^2) to O(N), making it a much more efficient solution for larger arrays.

Remember, understanding the problem and choosing the right data structure are crucial for solving coding challenges effectively.