What is the optimal time complexity of solving Power of a number?

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 Power of a number?

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 Power of a number?

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 Power of a number?

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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Python BasicsEasy

Power of a number

Learn how to solve the 'Power of a number' problem. This detailed resource details brute force and optimized approaches.

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Problem Statement

Easy

Write a function power(base, exp) that takes two non-negative integers base and exp and returns base raised to the power exp (i.e., base^exp). Note that any number raised to the power 0 is 1.

Constraints

•0 <= base <= 100
•0 <= exp <= 20

Examples

Example 1

Input

base = 2, exp = 10

Output

Explanation

2^10 = 1024.

Example 2

Input

base = 5, exp = 0

Output

Explanation

Any number raised to the power 0 is 1.

Example 3

Input

base = 3, exp = 4

Output

Explanation

3^4 = 3 × 3 × 3 × 3 = 81.

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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