Leetcode 169 Majority Element  Summary and Q&A
TL;DR
This video presents three effective methods to find the majority element in an array.
Key Insights
 â The majority element problem is foundational in algorithms, requiring an understanding of element frequency.
 đģ Using an unordered map allows for efficient counting and a straightforward implementation for identifying the majority element.
 â Sorting provides an alternative method, taking advantage of element clustering but at a higher time complexity.
 đž Moore's algorithm is optimal for this problem, balancing time and space complexities efficiently.
 đŽ The video stresses the importance of carefully interpreting problem statements, particularly conditions like the guaranteed existence of a majority element.
 đ¤ Each solution presented offers valuable lessons about algorithmic thinking and problemsolving strategies in coding interviews.
 đĻģ Complexity analysis aids in selecting the most appropriate method based on potential input size and constraints.
Transcript
hey there welcome back to lead coding today we are here with a very famous interview question called maturity element we are going to solve the problem using three approaches starting from the basic solution to the most optimized one so please watch the complete video the problem description is we are given n elements and we have to return the elem... Read More
Questions & Answers
Q: What is the definition of a majority element as described in the video?
A majority element is defined as an element that appears more than n/2 times in a given array of n elements. The problem assures that such an element always exists and that there can only be one majority element in any case.
Q: Can you explain the first approach used to find the majority element?
The first method involves maintaining a count of each element using an unordered map. We increment the count as we traverse the array. When an element's count exceeds n/2, we immediately return that element as the majority. This approach has a time complexity of O(n) and a space complexity of O(n).
Q: How does sorting the array lead to identifying the majority element?
By sorting the array, all majority elements become consecutive, and at least one instance will be positioned at the center of a sliding window of size n/2 + 1. Returning the element at index n/2 of the sorted array guarantees fetching the majority element, with a time complexity of O(log n) due to sorting.
Q: What is Moore's algorithm and how does it work?
Moore's algorithm is a more efficient technique that uses a candidatebased approach and voting counts. It iterates through the array, adjusting votes based on whether the current number matches the candidate. If votes reach zero, a new candidate is established. This ensures that the majority candidate survives through all cancellations, resulting in O(n) time and O(1) space complexity.
Q: Why is it unnecessary to verify the candidate if a majority element is guaranteed to exist?
When the problem explicitly states that a majority element always exists, there's no need for verification. If this condition were not present, one would need to count occurrences of the candidate to confirm it is indeed a majority element.
Q: What complexities are associated with the basic counting approach?
The basic counting approach has a time complexity of O(n), as each element is traversed once. The space complexity can reach O(n) in the worst case where all elements are distinct, since they would require individual entries in the unordered map utilized for counting.
Q: How does canceling votes work in Moore's algorithm?
In Moore's algorithm, when a nonmajority element appears, it effectively cancels out a vote for the current candidate. This backandforth vote cancellation ensures that the majority element, which occurs more than n/2 times, will remain as the candidate since nonmajority elements cannot outvote it sufficiently.
Q: What is the significance of the proof for Moore's algorithm mentioned in the video?
The proof provides a rigorous explanation for why Moore's algorithm works, showing how the vote cancelation method ensures the majority element remains as the final candidate. Understanding the proof can offer deeper insights into the algorithm's reliability but is not essential for implementing the solution.
Summary & Key Takeaways

The content explains the majority element problem, where the goal is to identify an element occurring more than n/2 times in a given array.

Three solutions are provided, advancing from a basic counting approach using an unordered map to a more optimized method known as Moore's algorithm.

The video also discusses the time and space complexities of each solution, noting that Moore's algorithm is the most efficient.