2966. Divide Array Into Arrays With Max Difference - Explanation
Prerequisites
Before attempting this problem, you should be comfortable with:
- Sorting Algorithms - Understanding how sorting brings similar elements together for grouping problems
- Greedy Algorithms - Making locally optimal choices (grouping consecutive sorted elements) to achieve a global optimum
- Counting Sort - An efficient O(n + k) sorting technique when the range of values is bounded
1. Sorting
Intuition
To minimize the difference within each group of three, we should place elements that are close in value together. Sorting the array achieves this naturally. After sorting, we greedily form groups of three consecutive elements. For each group, we only need to check if the difference between the largest and smallest element (first and third in the triplet) is within k. If any group fails this check, no valid division exists.
Algorithm
- Sort the array in ascending order.
- Iterate through the array in steps of
3. - For each group of three consecutive elements, check if
nums[i+2] - nums[i] > k. - If the condition is violated, return an empty array.
- Otherwise, add the triplet to the result.
- Return the list of triplets.
Time & Space Complexity
- Time complexity:
- Space complexity: for the output array.
2. Counting Sort
Intuition
When the range of values is bounded, counting sort can be faster than comparison-based sorting. We count occurrences of each number, then iterate through possible values in order, building groups of three. As we form each group, we verify that the difference between the smallest and largest element in the group does not exceed k.
Algorithm
- Find the maximum value in the array and create a
countarray. - Count the frequency of each number.
- Iterate through numbers from
0to the maximum. - For each number with remaining count, add it to the current group.
- When the group reaches size
3, check ifgroup[2] - group[0] > k. If so, return an empty array. - Otherwise, add the group to the result and start a new group.
- Return all groups.
class Solution:
def divideArray(self, nums: List[int], k: int) -> List[List[int]]:
max_num = max(nums)
count = [0] * (max_num + 1)
for num in nums:
count[num] += 1
res = []
group = []
for num in range(max_num + 1):
while count[num] > 0:
group.append(num)
count[num] -= 1
if len(group) == 3:
if group[2] - group[0] > k:
return []
res.append(group)
group = []
return resTime & Space Complexity
- Time complexity:
- Space complexity:
Where is the size of the array and is the maximum element in .
Common Pitfalls
Checking Wrong Elements for Difference
The maximum difference in a sorted triplet is between the first and third elements (smallest and largest), not adjacent elements. Checking nums[i+1] - nums[i] misses the actual constraint.
# Wrong: Checking adjacent differences
for i in range(0, len(nums), 3):
if nums[i+1] - nums[i] > k or nums[i+2] - nums[i+1] > k:
return []
# Correct: Check first and third elements
for i in range(0, len(nums), 3):
if nums[i+2] - nums[i] > k:
return []Forgetting to Sort First
The greedy approach only works on sorted arrays. Without sorting, consecutive elements may not be close in value, making it impossible to form valid groups even when solutions exist.
# Wrong: Missing sort
def divideArray(nums, k):
res = []
for i in range(0, len(nums), 3):
if nums[i+2] - nums[i] > k: # Not meaningful without sorting!
return []
res.append(nums[i:i+3])
return res
# Correct: Sort first
def divideArray(nums, k):
nums.sort() # Critical step!
res = []
for i in range(0, len(nums), 3):
if nums[i+2] - nums[i] > k:
return []
res.append(nums[i:i+3])
return resIncorrect Handling of Return Value
When no valid division exists, return an empty 2D array, not None or an array with empty subarrays. The return type matters for correctness.
// Wrong: Returning null
if (nums[i + 2] - nums[i] > k) {
return null; // Incorrect type!
}
// Wrong: Returning array with empty element
return new int[][]{ {} };
// Correct: Return empty 2D array
if (nums[i + 2] - nums[i] > k) {
return new int[][]{};
}