What is the optimal time complexity of solving Pow(x, n)?

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 Pow(x, n)?

The space complexity is O(n) 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 Pow(x, n)?

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 Pow(x, n)?

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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Pow(x, n)

Learn how to solve the 'Pow(x, n)' problem. This detailed resource details brute force and optimized approaches.

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Problem Statement

Easy

Implement pow(x, n), which calculates x raised to the power n (i.e., x^n).

Implement a function myPow(x: float, n: int) -> float.

Constraints

•-100.0 < x < 100.0
•-2^31 <= n <= 2^31 - 1
•n is an integer
•Either x is not zero or n > 0
•-10^4 <= x^n <= 10^4

Examples

Example 1

Input

2.00000, 10

Output

1024.00000

Explanation

2^10 = 1024.

Example 2

Input

2.10000, 3

Output

9.26100

Explanation

2.1^3 = 9.261.

Example 3

Input

2.00000, -2

Output

0.25000

Explanation

2^(-2) = 1/(2^2) = 1/4 = 0.25.

Need a Hint?

Analyze the input constraints. Try sorting first (O(n log n)) or using a hash map/set to track seen elements in O(n) time.

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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