Is Jump Game II easy or hard?

Jump Game II is generally classified as a medium difficulty problem. The challenge is recognizing that a greedy range expansion strategy can replace the more intuitive dynamic programming solution, reducing the complexity from O(n^2) to O(n).

Jump Game II Python/Java solution

The greedy solution is commonly implemented in Python, Java, C++, JavaScript, and C#. It iterates through the array while updating the farthest reachable index and counts jumps whenever the traversal reaches the end of the current jump range. The same logic translates cleanly across languages with a single loop.

How to solve Jump Game II in O(n)?

Use a greedy traversal that keeps track of the farthest index reachable during the current jump window. Maintain two variables: the end of the current range and the farthest reachable index. When the loop index reaches the current range boundary, increment the jump count and extend the boundary to the farthest position discovered so far.

What is the best approach for Jump Game II?

The greedy range expansion approach is the most efficient solution. It scans the array once while tracking the farthest reachable index within the current jump range. Each time the iteration reaches the end of the current range, a jump is counted and the range expands. This achieves O(n) time complexity with O(1) extra space.

Is Jump Game II asked at Google/Amazon/Meta?

Jump Game II is a common interview problem at large tech companies because it tests greedy reasoning and array traversal optimization. Variants of the problem or related Jump Game questions have appeared in interviews at Amazon, Google, Meta, and Microsoft.

What data structure is used in Jump Game II?

The problem primarily uses array traversal. The dynamic programming approach introduces a DP array to store minimum jumps to each index, while the optimal greedy solution only uses a few integer variables to track range boundaries and the farthest reachable position.

What is the time complexity of Jump Game II?

The optimal greedy algorithm runs in O(n) time because each element is processed once while updating the farthest reachable position. A dynamic programming solution also exists but requires O(n^2) time since every index may check multiple previous positions to determine the minimum jumps.

Description Solution Video 11:58

45. Jump Game II

Medium43.2% acceptance26 companies

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. In other words, if you are at nums[i], you can jump to any nums[i + j] where:

0 <= j <= nums[i] and
i + j < n

Return the minimum number of jumps to reach nums[n - 1]. The test cases are generated such that you can reach nums[n - 1].

Example 1:

Input: nums = [2,3,1,1,4]
Output: 2
Explanation: The minimum number of jumps to reach the last index is 2. Jump 1 step from index 0 to 1, then 3 steps to the last index.

Example 2: