What is the optimal time complexity of solving Jump Game II?

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 Jump Game II?

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 Jump Game II?

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 Jump Game II?

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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Jump Game II

Learn how to solve the 'Jump Game II' problem. This detailed resource details brute force and optimized approaches.

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Problem Statement

Easy

You are given a 0-indexed array of integers nums of length n. You are initially positioned at nums[0]. Each element nums[i] represents the maximum length of a forward jump from index i. Return the minimum number of jumps to reach nums[n - 1]. The test cases are generated such that you can reach the last index.

Write a function jump(nums: List[int]) -> int.

Constraints

•1 <= len(nums) <= 10^4
•0 <= nums[i] <= 1000

Examples

Example 1

Input

nums = [2,3,1,1,4]

Output

Explanation

Jump 1 step from index 0 to 1, then 3 steps to the last index.

Example 2

Input

nums = [2,3,0,1,4]

Output

Explanation

Jump 1 step from index 0 to 1, then 3 steps to the last index.

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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