How do you approach Jump Game II?

Greedily track the farthest index reachable within the current jump.

What are common mistakes on Jump Game II?

Assuming the locally best choice is globally best with no exchange argument. If you cannot state why it holds, it is probably a DP problem. Sorting by the wrong key - by start time where the proof needs end time is the interval-scheduling classic. Missing the case where two greedy choices tie and the tiebreak decides correctness.

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Jump Game II

MediumAlgorithms

Description

You are given a 0-indexed array `nums` where `nums[i]` is the maximum jump length from index `i`. You start at index 0 and want to reach the last index. The test data guarantees you can. Return the **minimum number of jumps** to reach the last index.

Example 1

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

Output: 2

Explanation: Jump 1 step to index 1, then 3 steps to the end.

Constraints

- 1 <= nums.length <= 10^4 - 0 <= nums[i] <= 1000 - It is always possible to reach nums[n-1]

Jump Game II: approach, complexity and pitfalls

A medium greedy problem, graded against 4 test cases (2 of them hidden).

The pattern: Greedy

Take the locally best option each step and prove it stays globally optimal.

Reach for it when you see: Interval scheduling, jump/reachability questions, or an optimisation with an obvious local choice.

More Greedyproblems →

If you are stuck

Show the 2-step hint ladder

Greedily track the farthest index reachable within the current jump.
When you reach the end of the current jump's range, take another jump.

The optimal approach

Show the approach: The approach

Greedy BFS over ranges. Sweep indices tracking `farthest` reachable. Each time you hit `curEnd` (the boundary of the current jump), increment the jump count and extend the boundary to `farthest`. This yields the minimum jumps in linear time.

Time: O(n). Space: O(1).

The full reference solution in every supported language stays in the editor above - reveal it there once you have had a real attempt.

What the tests check

Read off this problem's own test suite, so these are the cases a submission actually has to survive.

Single element. A one-element case is included. Two-pointer and window solutions frequently return the wrong thing when the two indices start out equal.
Duplicate values. Repeated values appear in the input, so any logic that assumes distinct elements - or that skips over equal neighbours - is exercised.
Zero. Zero appears in the input, which is the value most likely to break a truthiness check used in place of an explicit comparison.

Common Greedy mistakes

These apply to the pattern as a whole, not just this problem.

Assuming the locally best choice is globally best with no exchange argument. If you cannot state why it holds, it is probably a DP problem.
Sorting by the wrong key - by start time where the proof needs end time is the interval-scheduling classic.
Missing the case where two greedy choices tie and the tiebreak decides correctness.