Given an array of non-negative integers, you are initially positioned at the first index of the array.
Each element in the array represents your maximum jump length at that position.
Your goal is to reach the last index in the minimum number of jumps.
Example:
Input: [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.
Convert the previous solution to an iterative one. Your algorithm should run in O(n^2) time and use O(n) extra space.
The core challenge of this problem is to find the minimum number of jumps needed to reach the last index of the array. Each element in the array represents the maximum number of steps you can jump forward from that position. The goal is to determine the minimum jumps required to reach the end.
This problem is significant in scenarios where you need to find the shortest path or minimum steps in a constrained environment. Common applications include game development, pathfinding algorithms, and network routing.
Potential pitfalls include misunderstanding the jump lengths and not considering all possible paths, which can lead to incorrect solutions.
To solve this problem, we can use a greedy approach combined with dynamic programming. The naive solution would involve exploring all possible paths, which is not optimal. Instead, we can use an iterative approach to keep track of the minimum jumps required to reach each index.
We will maintain an array jumps where jumps[i] represents the minimum number of jumps needed to reach index i. We initialize jumps[0] to 0 (since we start at the first index) and all other elements to infinity. We then iterate through the array and update the jumps array based on the maximum jump lengths.
jumps of the same length as the input array, with jumps[0] set to 0 and all other elements set to infinity.i, update the jumps array for all reachable indices from i.jumps[-1], which represents the minimum number of jumps needed to reach the last index.def min_jumps(arr):
n = len(arr)
if n == 0 or arr[0] == 0:
return float('inf') # If the first element is 0, we cannot move anywhere
jumps = [float('inf')] * n
jumps[0] = 0
# Iterate through the array
for i in range(1, n):
for j in range(i):
# Check if i is reachable from j
if i <= j + arr[j] and jumps[j] != float('inf'):
jumps[i] = min(jumps[i], jumps[j] + 1)
break
return jumps[-1]
# Example usage
arr = [2, 3, 1, 1, 4]
print(min_jumps(arr)) # Output: 2
The time complexity of this approach is O(n^2) because of the nested loops. The space complexity is O(n) due to the additional jumps array.
While this solution is not the most optimal, it meets the problem's requirements and provides a clear iterative approach to solving the problem.
Potential edge cases include:
Testing for these edge cases ensures the robustness of the solution.
To test the solution comprehensively, we can use a variety of test cases:
def test_min_jumps():
assert min_jumps([2, 3, 1, 1, 4]) == 2
assert min_jumps([1, 1, 1, 1, 1]) == 4
assert min_jumps([0]) == 0
assert min_jumps([0, 1, 2, 3, 4]) == float('inf')
assert min_jumps([1, 2, 3]) == 2
print("All test cases pass")
test_min_jumps()
When approaching such problems, it's essential to break down the problem into smaller parts and think about the constraints and requirements. Developing a clear understanding of the problem and iterating through possible solutions can help in finding the optimal approach.
Practicing similar problems and studying different algorithms can significantly improve problem-solving skills. Platforms like LeetCode, HackerRank, and CodeSignal offer a variety of problems to practice and enhance your skills.
In this blog post, we discussed the problem of finding the minimum number of jumps to reach the last index of an array. We explored an iterative approach with a time complexity of O(n^2) and space complexity of O(n). By understanding the problem, breaking it down, and testing comprehensively, we can develop robust solutions to such challenges.
Understanding and solving such problems is crucial for improving algorithmic thinking and problem-solving skills. Continuous practice and exploration of different approaches can lead to better and more efficient solutions.