CS_learn/tech-interview-handbook
1
0
Fork 0
mirror of https://github.com/yangshun/tech-interview-handbook.git synced 2026-05-11 18:46:39 +08:00
Code Issues Packages Projects Releases Wiki Activity

contents(algo): fix typo and suggestions (#272)

Browse Source
This commit is contained in:
jomosz
2022-05-14 11:02:26 +12:00
committed by GitHub
parent d90f358311
commit 87a923079d
7 changed files with 9 additions and 9 deletions
Download Patch File Download Diff File Expand all files Collapse all files

4
contents/algorithms/heap.md
View File

@@ -47,7 +47,7 @@ In the context of algorithm interviews, heaps and priority queues can be treated
| Operation | Big-O |
| ------------------------------------------------------ | --------- |
| Finx max/min | O(1) |
| Find max/min | O(1) |
| Insert | O(log(n)) |
| Remove | O(log(n)) |
| Heapify (create a heap out of given array of elements) | O(n) |
@@ -58,7 +58,7 @@ In the context of algorithm interviews, heaps and priority queues can be treated
If you see a top or lowest _k_ being mentioned in the question, it is usually a signal that a heap can be used to solve the problem, such as in [Top K Frequent Elements](https://leetcode.com/problems/top-k-frequent-elements/).
If you require the top _k_ elements use a Min Heap of size _k_. Iterate through each element, pushing it into the heap. Whenever the heap size exceeds _k_, remove the minimum element, that will guarantee that you have the _k_ largest elements.
If you require the top _k_ elements use a Min Heap of size _k_. Iterate through each element, pushing it into the heap (for python `heapq`, invert the value before pushing to find the max). Whenever the heap size exceeds _k_, remove the minimum element, that will guarantee that you have the _k_ largest elements.
## Recommended questions