What is the time complexity of Maximum Element After Decreasing and Rearranging?

The optimal solution for Maximum Element After Decreasing and Rearranging runs in O(n log n) time and uses O(1) space. The time complexity is O(n log n) due to the sorting step, while the adjustment step runs in O(n), making this approach efficient for large arrays.

What is the brute force solution for Maximum Element After Decreasing and Rearranging?

The brute force approach for Maximum Element After Decreasing and Rearranging is Brute Force, with O(n²) time complexity and O(1) space complexity. The brute force approach involves checking all possible configurations of the array to find the maximum element after satisfying the conditions. This is inefficient but helps to understand the problem. The optimal Optimal Solution approach improves this to O(n log n).

What algorithm or data structure is used to solve Maximum Element After Decreasing and Rearranging?

Maximum Element After Decreasing and Rearranging uses the following concepts: Array, Greedy, Sorting. The recommended patterns to study are: Greedy, Sorting.

What are the interview tips for Maximum Element After Decreasing and Rearranging?

Always start with a brute-force solution to understand the problem better. Practice recognizing patterns in problems, such as sorting and adjusting values. Communicate your thought process clearly during the interview.

What are common mistakes when solving Maximum Element After Decreasing and Rearranging?

Not understanding how to manipulate the sorted array effectively. Overlooking the importance of the first element being 1.

#1846

Maximum Element After Decreasing and Rearranging

Medium

ArrayGreedySortingGreedySorting

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Approaches

Brute ForceOptimal

Complexity Comparison

	Brute Force	Optimal Solution★
Time	O(n²)	O(n log n)
Space	O(1)	O(1)

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Intuition

Time O(n log n)Space O(1)

The optimal approach involves sorting the array and then adjusting the elements to satisfy the conditions. This is efficient and ensures we maximize the largest element.

⚙️

Algorithm

3 steps

1Step 1: Sort the array.
2Step 2: Initialize the first element to 1.
3Step 3: Iterate through the sorted array, adjusting each element to ensure it is at most 1 greater than the previous element.

solution.py8 lines

1# Full working Python code
2def maxElementAfterDecreasing(arr):
3    arr.sort()
4    arr[0] = 1
5    for i in range(1, len(arr)):
6        arr[i] = min(arr[i], arr[i - 1] + 1)
7    return arr[-1]
8

ℹ

Complexity note: The time complexity is O(n log n) due to the sorting step, while the adjustment step runs in O(n), making this approach efficient for large arrays.

1Sorting the array helps in easily adjusting the elements to meet the conditions.
2The maximum possible value is determined by how we can manipulate the sorted array while maintaining the constraints.

Solutions and explanations are original Tejav content. Problem titles © LeetCode — use the LeetCode button above for the full problem statement.