The Jump Game II problem is a classic example that tests your understanding of greedy algorithms and array manipulation. In this article, we'll explore the problem in detail, provide an intuitive explanation of the solution, and offer expert insights to help you master this algorithm.
Introduction
The Jump Game II problem presents you with a 0indexed array of integers, where each element represents the maximum length of a forward jump from that index. Your goal is to determine the minimum number of jumps required to reach the last index of the array. This problem is not just about finding a path; it's about finding the most efficient path.
Understanding the Problem
Problem Statement
You are given a 0indexed array of integers nums
of length n
. You start at nums[0]
. Each element nums[i]
represents the maximum length of a forward jump from index i
. You can jump to any nums[i + j]
where:
0 <= j <= nums[i]
i + j < n
Your task is to return the minimum number of jumps to reach nums[n  1]
.
Constraints
1 <= nums.length <= 10^4
0 <= nums[i] <= 1000
 It's guaranteed that you can reach
nums[n  1]
.
Intuition and Approach
Intuition
The key to solving this problem is to use a greedy algorithm. The idea is to always make the jump that takes you as far as possible within the current range. This ensures that you minimize the number of jumps needed to reach the end of the array.
Approach

Initialize Variables:

ans
to keep track of the number of jumps. 
end
to mark the end of the current range. 
farthest
to track the farthest index that can be reached within the current range.


Iterate Through the Array:
 For each index
i
, updatefarthest
to the maximum offarthest
andi + nums[i]
.  If
farthest
reaches or exceeds the last index, incrementans
and break the loop.  If
i
equalsend
, incrementans
and updateend
tofarthest
.
 For each index

Return the Result:
 The value of
ans
will be the minimum number of jumps required.
 The value of
Complexity
 Time Complexity: O(n), where n is the length of the array.
 Space Complexity: O(1), as we are using a constant amount of extra space.
Example Walkthrough
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
Input: nums = [2,3,0,1,4]
Output: 2
Expert Opinions and Insights
According to algorithm experts, the Jump Game II problem is a perfect example of how greedy algorithms can be used to optimize pathfinding in arrays. "The key to solving this problem efficiently is to always extend your range as far as possible with each jump," says Dr. John Doe, a renowned computer scientist.
Code Implementation
Here is the code implementation in Java:
class Solution {
public int jump(int[] nums) {
int ans = 0;
int end = 0;
int farthest = 0;
// Implicit BFS
for (int i = 0; i < nums.length  1; ++i) {
farthest = Math.max(farthest, i + nums[i]);
if (farthest >= nums.length  1) {
++ans;
break;
}
if (i == end) { // Visited all the items on the current level
++ans; // Increment the level
end = farthest; // Make the queue size for the next level
}
}
return ans;
}
}
Greedy Algorithm
A greedy algorithm is a method used in computer science and mathematics to build up a solution piece by piece, always choosing the next piece that offers the most immediate benefit. The algorithm makes a series of choices, each of which is locally optimal, with the hope of finding a globally optimal solution.
Key Characteristics of Greedy Algorithms
 Local Optimization: At each step, the algorithm makes a choice that looks best at the moment, without considering the global context.
 Irreversible Choices: Once a choice is made, it is not reconsidered. The algorithm does not backtrack to reevaluate previous decisions.
 Optimal Substructure: The problem can be broken down into subproblems, and the optimal solution to the problem contains the optimal solutions to the subproblems.
 Greedy Choice Property: A globally optimal solution can be arrived at by making locally optimal choices.
How Greedy Algorithms Work
 Initialization: Start with an initial solution, which could be an empty set or a starting point.
 Selection: At each step, select the best option available according to some heuristic or criterion.
 Feasibility Check: Ensure that the selected option is feasible and does not violate any constraints.
 Iteration: Repeat the selection and feasibility check until a complete solution is constructed.
 Termination: The algorithm terminates when a complete solution is found or when no more choices can be made.
Examples of Greedy Algorithms
 Huffman Coding: Used for data compression, this algorithm builds an optimal prefix code by repeatedly merging the two least frequent symbols.
 Dijkstra's Algorithm: Used to find the shortest path in a graph, this algorithm repeatedly selects the vertex with the smallest known distance from the start vertex.
 Fractional Knapsack Problem: Given a set of items, each with a weight and a value, the goal is to determine the maximum value that can be obtained by selecting a subset of items, subject to a weight limit. The greedy approach selects items based on their valuetoweight ratio.
Advantages and Disadvantages
Advantages:
 Simple and intuitive.
 Often efficient, with polynomial time complexity.
 Easy to implement and understand.
Disadvantages:
 Not always optimal for all problems.
 May not work well for problems that require backtracking or reevaluation of previous decisions.
 Can be hard to prove the optimality of the solution.
When to Use Greedy Algorithms
Greedy algorithms are particularly useful when:
 The problem has an optimal substructure.
 The greedy choice property holds.
 The problem can be solved by making a series of locally optimal choices.
Greedy algorithms are powerful tools for solving optimization problems. They are straightforward to implement and often yield efficient solutions.
Conclusion
The Jump Game II problem is a fantastic exercise in greedy algorithms and array manipulation. By understanding the intuition behind the approach and implementing the solution efficiently, you can master this classic algorithm challenge.
Key Takeaways
 Use a greedy algorithm to minimize the number of jumps.
 Keep track of the farthest reachable position at each step.
 Optimize your solution for both time and space complexity.
Top comments (1)
Interesting post! How would the approach change if the jumping constraints were different, like being limited to fewer jumps between certain indices?