1608. Special Array with X Elements Greater than or Equal X - Explanation
Description
You are given an array nums of non-negative integers. nums is considered special if there exists a number x such that there are exactly x numbers in nums that are greater than or equal to x.
Notice that x does not have to be an element in nums.
Return x if the array is special, otherwise, return -1. It can be proven that if nums is special, the value for x is unique.
Example 1:
Input: nums = [3,5]
Output: 2Explanation: There are 2 values (3 and 5) that are greater than or equal to 2.
Example 2:
Input: nums = [0,0]
Output: -1Explanation: No numbers fit the criteria for x.
If x = 0, there should be 0 numbers >= x, but there are 2.
If x = 1, there should be 1 number >= x, but there are 0.
If x = 2, there should be 2 numbers >= x, but there are 0.
x cannot be greater since there are only 2 numbers in nums.
Example 3:
Input: nums = [0,4,3,0,4]
Output: 3Explanation: There are 3 values that are greater than or equal to 3.
Constraints:
1 <= nums.length <= 1000 <= nums[i] <= 1000
Topics
Prerequisites
Before attempting this problem, you should be comfortable with:
- Array Traversal - Iterating through arrays and counting elements meeting a condition
- Binary Search - Searching on a monotonic property to find an optimal value
- Sorting - Arranging elements to enable efficient counting of elements greater than or equal to a threshold
- Counting Sort - Using a frequency array when values are bounded by array length
1. Brute Force
Intuition
We need to find a value x such that exactly x elements in the array are greater than or equal to x. The simplest approach is to try every possible value of x from 1 to n (the array length) and count how many elements satisfy the condition. If we find a match, we return that value. Since x must equal the count, x cannot exceed n (we can have at most n elements).
Algorithm
- Iterate through each candidate value
ifrom1ton. - For each candidate, count how many elements in the array are greater than or equal to
i. - If the count equals
i, returnias the special value. - If no valid value is found after checking all candidates, return
-1.
Time & Space Complexity
- Time complexity:
- Space complexity:
2. Binary Search
Intuition
Instead of checking every value linearly, we can use binary search on the answer. The key observation is that as x increases, the count of elements greater than or equal to x decreases (or stays the same). This monotonic property allows us to binary search for the special value. If the count is less than mid, we need a smaller x. If the count is greater than mid, we need a larger x.
Algorithm
- Set search bounds:
l = 1andr = n(the array length). - While
l <= r:- Calculate
mid = (l + r) / 2. - Count elements greater than or equal to
mid. - If count equals
mid, returnmid. - If count is less than
mid, search the lower half by settingr = mid - 1. - Otherwise, search the upper half by setting
l = mid + 1.
- Calculate
- Return
-1if no special value exists.
Time & Space Complexity
- Time complexity:
- Space complexity:
3. Sorting
Intuition
After sorting the array, we can efficiently determine how many elements are greater than or equal to any value. For each position i in the sorted array, there are n - i elements from index i to the end. We scan through the array and check if totalRight (the count of remaining elements) could be the special value. A valid special value must fall within a valid range defined by consecutive distinct elements.
Algorithm
- Sort the array in ascending order.
- Initialize
totalRight = n(all elements to the right including current). - Traverse the sorted array:
- If
nums[i]equalstotalRight, ortotalRightfalls strictly betweenprevandnums[i], returntotalRight. - Skip duplicate elements.
- Update
prevand move to the next distinct element, updatingtotalRight.
- If
- Return
-1if no special value is found.
class Solution:
def specialArray(self, nums: List[int]) -> int:
nums.sort()
i = 0
prev = -1
total_right = len(nums)
while i < len(nums):
if nums[i] == total_right or (prev < total_right < nums[i]):
return total_right
while i + 1 < len(nums) and nums[i] == nums[i + 1]:
i += 1
prev = nums[i]
i += 1
total_right = len(nums) - i
return -1Time & Space Complexity
- Time complexity:
- Space complexity: or depending on the sorting algorithm.
4. Sorting + Two Pointers
Intuition
After sorting, we use two pointers: one for the candidate value j and another for the array index i. As we increase j, the number of elements greater than or equal to j can only decrease. We advance i to skip elements smaller than the current candidate and check if the remaining count matches j.
Algorithm
- Sort the array in ascending order.
- Initialize
i = 0(array pointer) andj = 1(candidate value). - While both pointers are within valid bounds:
- Advance
ipast all elements smaller thanj. - If the count of remaining elements
(n - i)equalsj, returnj. - Increment
jto try the next candidate.
- Advance
- Return
-1if no special value exists.
Time & Space Complexity
- Time complexity:
- Space complexity: or depending on the sorting algorithm.
5. Counting Sort
Intuition
We can use a counting array to track how many elements have each value. Since any element greater than n is effectively the same as n for our purposes (they all contribute to counts for candidates 1 through n), we cap values at n. By iterating from the largest possible candidate down to 0 and accumulating counts, we can efficiently find when the running total equals the current index.
Algorithm
- Create a count array of size
n + 1. - For each element, increment
count[min(num, n)]. - Traverse from index
ndown to0, accumulating the count intotalRight. - If at any index
i,totalRightequalsi, returnias the special value. - Return
-1if no match is found.
class Solution:
def specialArray(self, nums: List[int]) -> int:
count = [0] * (len(nums) + 1)
for num in nums:
index = min(num, len(nums))
count[index] += 1
total_right = 0
for i in range(len(nums), -1, -1):
total_right += count[i]
if i == total_right:
return total_right
return -1Time & Space Complexity
- Time complexity:
- Space complexity:
Common Pitfalls
Searching Beyond Array Length
The special value x cannot exceed n (the array length) since there are at most n elements. Searching for values greater than n is unnecessary and can lead to incorrect results or wasted computation.
Using Strict Greater Than Instead of Greater Than or Equal
The problem requires counting elements greater than or equal to x, not strictly greater than. Using the wrong comparison operator will lead to incorrect counts and potentially return -1 when a valid answer exists, or return an incorrect special value.
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