What is the optimal time complexity of solving Sum of N natural numbers?

The optimized solution achieves a time complexity of O(n) by using sorting, mathematical shortcuts, or hash table lookups.

What is the auxiliary space complexity for Sum of N natural numbers?

The space complexity is O(1) since we only use a minimal/constant amount of extra memory for tracking variables (or auxiliary space if dynamic structures are allocated).

What are the typical edge cases we must handle in Sum of N natural numbers?

Key edge cases include empty collections, negative or large bounds causing integer overflow, arrays with only one element, or inputs where target criteria are not met.

How can we optimize the brute force approach for Sum of N natural numbers?

Brute force methods often inspect all possible combinations. We optimize this by sorting the input list to enable binary search, or tracking processed items in a set to avoid redundant nested loop lookups.

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Sum of N natural numbers

Learn how to solve the 'Sum of N natural numbers' problem. This detailed resource details brute force and optimized approaches.

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Problem Statement

Easy

Write a function sum_n_natural(n) that takes a non-negative integer n and returns the sum of the first n natural numbers using the formula n * (n + 1) / 2. If n is 0, return 0.

Constraints

•0 <= n <= 10^6

Examples

Example 1

Input

n = 6

Output

Explanation

Using the formula: 6 * 7 / 2 = 21.

Example 2

Input

n = 0

Output

Explanation

There are no natural numbers to sum, so the result is 0.

Example 3

Input

n = 100

Output

Explanation

100 * 101 / 2 = 5050.

Need a Hint?

Use simple arithmetic operators (like modulo `%`, division `//`), conditional checks, or loops to inspect number properties.

Edge Cases to Watch

Empty list or null input variables
Single item lists/arrays
Extremely large input bounds causing integer or stack overflow

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