What is the time complexity of Minimum Difference Between Largest and Smallest Value in Three Moves?

The optimal solution for Minimum Difference Between Largest and Smallest Value in Three Moves runs in O(n log n) time and uses O(1) space. The sorting step dominates the time complexity, making it O(n log n). The space complexity is O(1) as we are not using any additional data structures.

What is the brute force solution for Minimum Difference Between Largest and Smallest Value in Three Moves?

The brute force approach for Minimum Difference Between Largest and Smallest Value in Three Moves is Brute Force, with O(n²) time complexity and O(1) space complexity. The brute force approach involves trying all possible combinations of changing up to three elements in the array to minimize the difference between the maximum and minimum values. This is straightforward but inefficient. The optimal Optimal Solution approach improves this to O(n log n).

What algorithm or data structure is used to solve Minimum Difference Between Largest and Smallest Value in Three Moves?

Minimum Difference Between Largest and Smallest Value in Three Moves uses the following concepts: Array, Greedy, Sorting. The recommended patterns to study are: Sorting, Greedy Algorithms.

What are the interview tips for Minimum Difference Between Largest and Smallest Value in Three Moves?

Always think about edge cases and how they might affect your solution. Discuss your thought process clearly, especially when transitioning from brute force to optimal solutions. Practice explaining your code as you write it, as this helps in interviews.

What are common mistakes when solving Minimum Difference Between Largest and Smallest Value in Three Moves?

Failing to sort the array before trying to find the minimum difference. Not considering edge cases where the array length is less than or equal to 4.

#1509

Minimum Difference Between Largest and Smallest Value in Three Moves

Medium

ArrayGreedySortingSortingGreedy Algorithms

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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 leverages sorting and focuses on the largest and smallest values in the array. By removing the three largest or three smallest values, we can quickly determine the minimum difference.

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Algorithm

3 steps

1Step 1: Sort the array.
2Step 2: Calculate the minimum difference by considering the scenarios of removing up to three largest or smallest values.
3Step 3: Return the minimum of these differences.

solution.py8 lines

1# Full working Python code
2
3def minDifference(nums):
4    nums.sort()
5    n = len(nums)
6    if n <= 4:
7        return 0
8    return min(nums[n-1] - nums[3], nums[n-2] - nums[2], nums[n-3] - nums[1], nums[n-4] - nums[0])

ℹ

Complexity note: The sorting step dominates the time complexity, making it O(n log n). The space complexity is O(1) as we are not using any additional data structures.

1The minimum difference can be achieved by focusing on the extremes of the sorted array.
2Sorting the array allows us to efficiently find the smallest and largest values to manipulate.

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