Leetcode #644: Maximum Average Subarray II
In this guide, we solve Leetcode #644 Maximum Average Subarray II in Python and focus on the core idea that makes the solution efficient.
You will see the intuition, the step-by-step method, and a clean Python implementation you can use in interviews.
Problem Statement
You are given an integer array nums consisting of n elements, and an integer k. Find a contiguous subarray whose length is greater than or equal to k that has the maximum average value and return this value.
Quick Facts
- Difficulty: Hard
- Premium: Yes
- Tags: Array, Binary Search, Prefix Sum
Intuition
The problem structure suggests a monotonic decision, which makes binary search a natural fit.
By halving the search space each step, we reach the answer efficiently.
Approach
Search either directly on a sorted array or on the answer space using a check function.
Each check is fast, and the logarithmic search keeps the overall runtime low.
Steps:
- Define the search bounds.
- Check the mid point condition.
- Narrow the bounds until convergence.
Example
Input: nums = [1,12,-5,-6,50,3], k = 4
Output: 12.75000
Explanation:
- When the length is 4, averages are [0.5, 12.75, 10.5] and the maximum average is 12.75
- When the length is 5, averages are [10.4, 10.8] and the maximum average is 10.8
- When the length is 6, averages are [9.16667] and the maximum average is 9.16667
The maximum average is when we choose a subarray of length 4 (i.e., the sub array [12, -5, -6, 50]) which has the max average 12.75, so we return 12.75
Note that we do not consider the subarrays of length < 4.
Python Solution
class Solution:
def findMaxAverage(self, nums: List[int], k: int) -> float:
def check(v: float) -> bool:
s = sum(nums[:k]) - k * v
if s >= 0:
return True
t = mi = 0
for i in range(k, len(nums)):
s += nums[i] - v
t += nums[i - k] - v
mi = min(mi, t)
if s >= mi:
return True
return False
eps = 1e-5
l, r = min(nums), max(nums)
while r - l >= eps:
mid = (l + r) / 2
if check(mid):
l = mid
else:
r = mid
return l
Complexity
The time complexity is , where and are the length of the array and the difference between the maximum and minimum values in the array, respectively. The space complexity is .
Edge Cases and Pitfalls
Watch for boundary values, empty inputs, and duplicate values where applicable. If the problem involves ordering or constraints, confirm the invariant is preserved at every step.
Summary
This Python solution focuses on the essential structure of the problem and keeps the implementation interview-friendly while meeting the constraints.