- Called: Algorithm complexity analysis, Asymptotic behavior, Worst case analysis
- Profilers can tell you how long an algorithm takes
- 2n ∈ Θ(n), which is pronounced as “two n is theta of n”
- Θ(1) is constant time
- Any program without loops
- It will take the same amount of time no matter how big n is

- Θ(n) is linear
- Pronounced theta of n

- Θ(n
^{2}) is quadratic- Two nested loops
- O(n
^{2}) is pronounced “big oh of n squared”.

- Θ(log(n)) is logarithmic
- Notice that in the i-th iteration, our array has n / 2i elements. This is because in every iteration we’re cutting our array into half, meaning we’re dividing its number of elements by two. This translates to multiplying the denominator with a 2. If we do that i times, we get n / 2i.

- Θ(n!) is factorial
- Traveling salesman
- 5! is 120

- When looking at a Ω (omega), the bar is at the bottom, so it is an (asymptotic) lower bound.
- Big Omega means your algorithm will execute in no fewer steps than in the given expression(n
^{2})

- Big Omega means your algorithm will execute in no fewer steps than in the given expression(n
- When looking at a Θ (theta), the bar is obviously in the middle. So it is an (asymptotic) tight bound.
- When both conditions are true for the same expression (O and Ω are the same), you can use the big theta notation. This is the most precise notation

- When handwriting O, you usually finish at the top, and draw a squiggle. Therefore O(n) is the upper bound of the function.
- Big O means your algorithm will execute in no more steps than in given expression(n
^{2})

- Big O means your algorithm will execute in no more steps than in given expression(n
- Small o or omega is for when bounds are not tight
- Computers can touch everything in memory in about a second (rough standard)

log defaults to log_{10} but we will default to 2

10^{2}=100 <-> log_{10}100=2

lg is log_{2}