easy

Maximum Average Subarray I

To solve this problem, we can use a sliding window approach. The idea is to maintain a window of size k and move it through the array nums, calculating the average at each position. We keep track of the maximum average seen so far and update it whenever we find a higher average.

Here's the JavaScript implementation of the algorithm:

function findMaxAverage(nums, k) {
  let sum = 0;
 
  // Calculate the sum of the first window of size k
  for (let i = 0; i < k; i++) {
    sum += nums[i];
  }
 
  let maxAverage = sum / k;
 
  // Slide the window through the array, updating the sum and average
  for (let i = k; i < nums.length; i++) {
    sum += nums[i] - nums[i - k]; // Add the next element and remove the previous first element
    maxAverage = Math.max(maxAverage, sum / k);
  }
 
  return maxAverage;
}

In this solution, we initialize the sum variable to hold the sum of the first window of size k. Then we iterate through the array starting from index k and calculate the sum by adding the next element and subtracting the element that falls outside the window. We update the maxAverage whenever we find a higher average.

The time complexity of this algorithm is O(n), where n is the length of the input array nums. We only iterate through the array once.

The space complexity is O(1) because we use a constant amount of extra space regardless of the input size.

Note that the solution assumes the input array nums has at least k elements to form a contiguous subarray of length k.