Unit 1: Machines & Models

This unit covers

• algorithm analysis
• the random-access-machine model
• asymptotic approximations, Big-Oh notation

Learning outcomes

1. Understand the difference between empirical running time and algorithm analysis.
2. Understand worst/best/average case models for input data.
3. Know the RAM machine model.
4. Know the definitions of asymptotic notation (Big-Oh classes and relatives).
5. Understand the reasons to make asymptotic approximations.
6. Be able to analyze simple algorithms.

Material

• slides

• lecture notes

• Video 1-1 · transcript (2023-10-05, 14:46): §1.1 Algorithm analysis
  
• Video 1-2 · transcript (2023-10-05, 23:45): §1.2 The RAM model
  
• Video 1-3 · transcript (2023-10-05, 35:48): §1.3 Asymptotics and Big-Oh
  
  Chapters

  2:12 Why use asymptotics?
  5:55 Formal definitions
  8:33 Intuitive understanding
  10:59 Notation and implications
  14:28 Calculating complexity
  23:56 Advanced properties
  29:00 Practical algorithm analysis
  

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Further reading and sources

The RAM model is explained in more detail here:

• Sanders, Mehlhorn, Dietzfelbinger, Dementiev: Sequential and Parallel Algorithms and Data Structures (2019), Springer

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Unit 0  ⋅  Syllabus  ⋅  Unit 2