main page  —  COMP 526 Applied Algorithmics

Unit 1: Machines & Models

This is an archived version of this module from Spring 2022.
Click here for the current iteration.

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.


Further reading and sources

The RAM model is explained in more detail here:

Unit 0  ⋅  Syllabus  ⋅  Unit 2