### Tutorial 2

```Computer Sciences
1
GROWTH OF FUNCTIONS
TUTORIAL 2
Computer Sciences
2
Objective
* Describe growth of functions.
* How we indicate running times of algorithms
O ≈ ≤
Ω ≈ ≥
Θ ≈ =
By using the definition of O-notation show that :
n3 ≠ O(n2)
Solution :
0 ≤ h(n) ≤ cg(n)
0 ≤ n3 ≤ cn2
0/n2 ≤ n3/n2 ≤ cn2/n2
0≤n≤c
divides by n2
because n → ∞, no c exists where ∀ n ≥ n0 true
By using the definition of Ω-notation show that :
n ≠ Ω (n2)
Solution :
0 ≤ cg(n) ≤ h(n)
0 ≤ cn2 ≤ n
0/n2 ≤ cn2/n2 ≤ n/n2
0 ≤ c ≤ 1/n
Divide by n2
Θ-notation
By using the definition of Θ-notation
c1n2 ≤ n2/2-2n ≤ c2n2
with c1=1/2, c2=1/2 & n0=5
Θ-notation
Solution :
½ (5)2 ≤ (5)2/2-2(5) ≤ ½ (5)2
25/2 ≤ 25/2- 10 ≤ 25/2
25/2 ≤ 25/2- 20/2 ≤ 25/2
25/2 ≤ 5/2 ≤ 25/2
Doesn't hold because 25/2 > 5/2
Conclusion
* O-notation :
_ O(g(n)) = { f (n) : there exist positive constants c and n0
such that 0 ≤ f (n) ≤ cg(n) for all n ≥ n0} .
* Ω-notation
_(g(n)) = { f (n) : there exist positive constants c and n0 such
that 0 ≤ cg(n) ≤ f (n) for all n ≥ n0} .
* Θ-notation
_(g(n)) = { f (n) : there exist positive constants c1, c2, and n0
such that 0 ≤ c1g(n) ≤ f (n) ≤ c2g(n) for all n ≥ n0} .
```