Majority Element Ii
To solve this coding challenge, we need to identify all elements in an integer array that appear more than ⌊n/3⌋ times. The problem demands an O(n) time complexity and O(1) space complexity, making the solution non-trivial. We're utilizing a modified version of the Boyer-Moore Voting Algorithm to achieve this.
times. This involves a few key steps:
Explanation
Here, we try to find elements in the array which appear more thann/3
- Candidate Selection: We first try to identify up to two potential candidates that might be the majority elements. This is because there can be at most 2 elements in an array that appear more than ⌊n/3⌋ times. We use a counter approach for this.
- Validation Phase: After identifying the candidates, we need to verify them by counting their actual occurrences in the array.
- Initialization :
- Create two candidate variables and their associated counts, initializing counts to 0.
- First Pass (Candidate Identification) :
- Iterate through the array.
- Adjust counts based on the current element and update candidates if necessary.
- Second Pass (Validation) :
- Count the occurrences of the identified candidates.
-
Check if they meet the required
n/3
-
Initialize variables
candidate1
candidate2
-
Initialize
count1
count2
candidate1
candidate2
-
Iterate through each number
num
nums
-
If
num
candidate1
count1
-
Else if
num
candidate2
count2
-
Else if
count1
candidate1
num
count1
-
Else if
count2
candidate2
num
count2
-
Otherwise, decrement both
count1
count2
- Reset counts to 0 for the second pass:
-
count1
candidate1
-
count2
candidate2
-
Iterate through the array again to count the actual occurrences of
candidate1
candidate2
-
Check if
candidate1
candidate2
n/3
Step-by-Step Explanation
Detailed Steps in Pseudocode:
Initialization Phase:
# Initialize counters and candidates
count1 = 0
count2 = 0
candidate1 = None
candidate2 = None
First Pass: Candidate Identification
# Iterate through the array to identify potential candidates
for num in nums:
if num == candidate1:
count1 += 1 # Increment count for candidate1
elif num == candidate2:
count2 += 1 # Increment count for candidate2
elif count1 == 0:
candidate1 = num # Set new candidate1
count1 = 1 # Initialize count1
elif count2 == 0:
candidate2 = num # Set new candidate2
count2 = 1 # Initialize count2
else:
count1 -= 1 # Decrement count1
count2 -= 1 # Decrement count2
Second Pass: Validation Phase
# Reset counts for validation
count1 = 0
count2 = 0
# Count occurrences of candidate1 and candidate2 in the array
for num in nums:
if num == candidate1:
count1 += 1 # Increment count for candidate1
elif num == candidate2:
count2 += 1 # Increment count for candidate2
# Prepare the result list
result = []
# Verify if candidates appear more than n/3 times
if count1 > len(nums) / 3:
result.append(candidate1) # Add candidate1 to result
if count2 > len(nums) / 3:
result.append(candidate2) # Add candidate2 to result
# Result contains the majority elements
return result
This approach ensures a time complexity of O(n) and space complexity of O(1). The detailed steps break down how we first select potential candidates and then validate them, ensuring thorough understanding of each phase in this algorithm.