161. OCR A Level (H446) SLR26 – 2.3 Comparison of the complexity of algorithms
About this video
OCR Specification Reference
A Level 2.3.1d
This video takes a look at how we are able to compare the complexity of one algorithm against another using a method known as Big O notation.
Key questions:
- What is the difference between time and space complexity?
- What do we mean when we talk about an algorithm's performance in terms of best-, average- and worst-case?
00:00 Comparing the complexity of algorithms
00:07 Intro
00:12 A note about this video
00:24 Prior knowledge
01:05 Arrays and lists
02:14 Best, average and worst-case scenarios
03:22 Arrays and lists continued
05:30 Stacks and queues
06:16 Hash tables
06:46 Overflow tables
07:25 Linked lists
08:19 Binary trees
08:57 Bubble sort and insertion sort
09:19 Comparing searching algorithms
09:48 Comparing sorting algorithms
10:45 A note from the exam board
11:43 Key questions
12:00 Big O notation cheat sheet
12:40 Going beyond the specification
12:53 Big O vs Big Omega vs Big Theta
13:53 Outro
Last updated: 28.07.26
