diff --git a/src/data/roadmaps/datastructures-and-algorithms/content/two-heaps@MGb8ufztTK75aXAyEAuaf.md b/src/data/roadmaps/datastructures-and-algorithms/content/two-heaps@MGb8ufztTK75aXAyEAuaf.md index 616226267..61df1c65a 100644 --- a/src/data/roadmaps/datastructures-and-algorithms/content/two-heaps@MGb8ufztTK75aXAyEAuaf.md +++ b/src/data/roadmaps/datastructures-and-algorithms/content/two-heaps@MGb8ufztTK75aXAyEAuaf.md @@ -1,7 +1,8 @@ # Two Heaps + The two heaps method uses a max-heap to store the lower half of the numbers and a min-heap to store the upper half. This setup allows you to quickly access the largest value of the lower half and the smallest value of the upper half in constant time. Insertions and deletions take logarithmic time, and the heaps are balanced so that the median can be found in O(1) time. This approach is especially useful for dynamically maintaining the median of a long or streaming data sequence, where repeatedly sorting the data would be inefficient (O(n log n) per sort). Learn more from the following resources: -- [@article@Two Heaps — A Coding Pattern for Median-finding (Emre Bolat)](https://emre.me/coding-patterns/two-heaps/) -- [@video@Coding Pattern - Two Heaps (Imran Sarwar)](https://www.youtube.com/watch?v=9P7W5aEaatQ) +- [@article@Two Heaps — A Coding Pattern for Median-finding (Emre Bolat)](https://emre.me/coding-patterns/two-heaps/) +- [@video@Coding Pattern - Two Heaps](https://www.youtube.com/watch?v=9P7W5aEaatQ)