Lecture 1
Today’s Topics: Introduction and asymptotic analysis
- Introduction/revision on algorithm analysis
- Worst case running time.
- Introduction to asymptotic analysis $O$,$\Theta$ and $\Omega$.
Links to Slides and other material
- Slides
on introduction and logistics.
- Slides
on an asymptotic analysis.
Reading Guide
What should I know by the end of this lecture?
- How is the course structured? How do the assignments, help sessions
and labs work?
- What does it mean to analyse an algorithm?
- What is worst case performance?
- What is the definition of $O(f(n))$, $\theta(f(n)$, and
$\Omega(f(n))$?
- What are some of the basic properties of $O()$,$\theta()$, and
$\Omega()$.
Last updated on Aug 19, 2020