1. Asymptotic Notation Faculty Name: Ruhi Fatima
Topics Covered
Theta Notation
Oh Notation
Omega Notation
Standard Notations and Common function
CCSE-ICS353-prepared by:Shamiel .H -Natalia .A

2. Asymptotic notations
Asymptotic notation are primarily used to describe the running times of algorithms
The Running time of Algorithm is defined as : the time needed by an algorithm in order to deliver its output when presented with legal input .
In Asymptotic notation, algorithm is treated as a function.
Let us consider asymptotic notations that are well suited to characterizing running times no matter what the input.
There are 3 standard asymptotic notations
O notation: asymptotic “less than”:
f(n)=O(g(n)) implies: f(n) “≤” g(n)
 notation: asymptotic “greater than”:
f(n)=  (g(n)) implies: f(n) “≥” g(n)
 notation: asymptotic “equality”:
f(n)=  (g(n)) implies: f(n) “=” g(n)

3. Θ(Theta)-Notation(Tight Bound – Average Case)
DEFINITION: For a given function g(n), Θ (g(n)) is defined as follows
Θ (g(n)) ={ f(n) : there exist positive constants c1, c2, and n such that 0≤ c1g(n)≤ f (n) ≤ c2g(n) for all n ≥ n0}.
Consider an intuitive picture of functions f(n) and g(n), where f(n)=Θ (g(n))
For all values of n at and to the right of n0, the value of f(n) lies at or above c1g(n) and at or below c2g(n).
So, g(n) is an asymptotically tight bound for f(n).
Example: Is 10 n2+ 20n = Θ (n2 ) ?
Answer: Given f(n)=10n2+ 20n since for all n≥1
Let n0= 1,c1=10 and c2=30
Such that for all n ≥1, 10n2 ≤f(n) ≤30 n2
So, 10 n2+ 20n = Θ (n2 ) is true.

4. O(Big Oh)-notation(Upper Bound – Worst Case)
DEFINITION : For a given function g(n), we denote by O(g(n)) (pronounced “big-oh of g of n” or sometimes just “oh of g of n”) the set of functions
O (g(n)) ={ f(n) : there exist positive constant c, n such that 0≤ f (n) ≤ c g(n) for all n ≥ n0}.
For all values of n at and to the right of n0, the value of f(n) lies at or below cg(n).
So, g(n) is an asymptotically upper bound for f(n).
Example: Suppose f(n) = 4n2+5n+3. Is f(n) is O(n2 ) ?
Solution: f(n)= 4n2+5n+3
≤ 4n2+5n+3 , for all n > 0
≤ 4n2+5n2+3n2 , for all n > 1
≤12n2 for all n > 1
4n2+5n+3 ≤ 12n2
Hence f(n) = O(n2 )

5. (Omega)-notation (Lower Bound – Best Case)
DEFINITION : For a given function g(n), we denote by Ω (g(n)) (pronounced “big-omega of g of n” or sometimes just “omega of g of n”) the set of functions
Ω (g(n)) ={ f(n) : there exist positive constant c, c, and n such that 0≤ c g(n)≤ f (n) for all n ≥ n0}.
For all values of n at and to the right of n0, the value of f(n) lies at or above cg(n).
So, g(n) is an asymptotically lower bound for f(n).
Example: Let f(n)=10 n2+ 20 n since for all n≥1
f(n)=10 n2+20 n
f(n)≥10n2
10 n2+20 n ≥ 10n2
Therefore f(n) = (n2) as there exists a natural number n0 =1 and a constant
c=10>0 such that for all n ≥n0 ,f(n) ≥ cg(n)

6. Asymptotic notation in equations and inequalities
Theorem :
For any two functions f (n) and g(n), we have f (n) = Θ(g(n)) if and only if
f (n) = O(g(n)) and f (n) =Ω(g(n)).
Asymptotic notation in equations and inequalities
In general, however, when asymptotic notation appears in a formula, we interpret it as standing for some anonymous function that we do not care to name.
For example, the formula 2n2+3n+1 = 2n2 +Θ(n) means that 2n2+3n+1 = 2n2 +f(n), where f(n) is some function in the set Θ(n) . In this case, we let f (n)=3n+ 1, which indeed is in Θ(n) .
Using asymptotic notation in this manner can help eliminate inessential detail and clutter in an equation.
For example, the worst-case running time of merge sort as the recurrence T (n)= 2T(n/2) + Θ(n). Since the interset is only in the asymptotic behavior of T (n), the lower-order terms are all understood to be included in the anonymous function denoted by the term Θ(n).

7. Cont…
The number of anonymous functions in an expression is understood to be equal to the number of times the asymptotic notation appears.
If the Asymptotic notation appears on the left-hand side of an equation, as in 2n2 + Θ(n) = Θ(n2) , then
The above equations are interpreted using the following rule: “No matter how the anonymous functions are chosen on the left of the equal sign, there is a way to choose the anonymous functions on the right of the equal sign to make the equation valid”.
Thus, our example means that for any function f(n) ϵ Θ(n), there is some function g(n) ϵ Θ(n2), such that 2n2 + f (n)= g (n) for all n. In other words, the right-hand side of an equation provides a coarser (rough) level of detail than the left-hand side.
We can chain together a number of such relationships, as in
2n2 + 3n + 1 = 2n2 + Θ (n) = Θ (n2)

8. o(little oh)-notation
We use o-notation to denote an upper bound that is not asymptotically tight. We formally define o(g(n)) (“little-oh of g of n”) as the set
o(g(n)) = { f(n) : for any positive constant c>0, there exists
a constant n0 >0 such that 0≤ f(n) < c g(n) for all n ≥ n0 }.
Example: 2n = o(n2), but 2n2 ≠ o(n2).
f(n) becomes insignificant relative to g(n) as n approaches infinity:
lim [f(n) / g(n)] = 0
n

9. w(little omega)-notation
For a given function g(n), the set little-omega:
w(g(n)) = {f(n):  c > 0,  n0 > 0 such that  n  n0, we have 0  cg(n) < f(n)}.
Example, n2/2 = w(n), but n2/2 ≠w(n2)
f(n) becomes arbitrarily large relative to g(n) as n approaches infinity:
lim [f(n) / g(n)] = .
n
g(n) is a lower bound for f(n) that is not asymptotically tight.

10. Relational Properties
Transitivity
f(n) = (g(n)) & g(n) = (h(n))  f(n) = (h(n))
f(n) = O(g(n)) & g(n) = O(h(n))  f(n) = O(h(n))
f(n) = (g(n)) & g(n) = (h(n))  f(n) = (h(n))
f(n) = o (g(n)) & g(n) = o (h(n))  f(n) = o (h(n))
f(n) = w(g(n)) & g(n) = w(h(n))  f(n) = w(h(n))
Reflexivity
f(n) = (f(n))
f(n) = O(f(n))
f(n) = (f(n))
Symmetry
f(n) = (g(n)) iff g(n) = (f(n))
Transpose symmetry (Complementarity)
f(n) = O(g(n)) iff g(n) = (f(n))
f(n) = o(g(n)) iff g(n) = w((f(n))

11. Comparison of Functions
f  g  a  b
f (n) = O(g(n))  a  b , f (n) = o(g(n))  a < b
f (n) = (g(n))  a  b, f (n) = w (g(n))  a > b
f (n) = (g(n))  a = b
Limits
lim [f(n) / g(n)] = 0 Þ f(n) Î o(g(n))
n
lim [f(n) / g(n)] <  Þ f(n) Î O(g(n))
0 < lim [f(n) / g(n)] <  Þ f(n) Î Q(g(n))
0 < lim [f(n) / g(n)] Þ f(n) Î W(g(n))
lim [f(n) / g(n)] =  Þ f(n) Î w(g(n))
lim [f(n) / g(n)] undefined Þ can’t say

12. Excercise
Let f (n) and g (n) be asymptotically nonnegative functions. Using the basic definition of ‚Θ -notation, prove that max ( f (n), g(n)) = Θ (f (n) + g (n)).
Show that for any real constants a and b, where
b >0,
(n+a)b = Θ(nb).

13. Standard notations and common functions
Monotonicity : A function f (n) is monotonically increasing if m ≤ n implies f (m) ≤ f (n).
Similarly, it is monotonically decreasing if m ≤ n implies f(m) ≥ f (n).
A function f (n) is strictly increasing if m < n implies f (m) < f (n) and strictly decreasing if m < n implies f (m) > f (n).
Floors and ceilings: For any real number x, we denote the greatest integer less than or equal to x by (read “the floor of x”) and the least integer greater than or equal to x by (read “the ceiling of x”).
For all real x,

14. Cont…
For any integer n,

15. Exponentials & Logarithms
Exponentials: For all real a > 0, m, and n, we have the following identities:
Logarithms:
x = logba is the exponent for a = bx.
Natural log: ln a = logea
Binary log: lg a = log2a
Exponential log :
lg2a = (lg a)2
Composition log :
lg lg a = lg (lg a)
CCSE-ICS353-prepared by:Shamiel .H -Natalia .A

16. Functional iteration
We use the notation f (i) (n) to denote the function f(n) iteratively applied i times to an initial value of n. Formally, let f (n) be a function over the reals. For nonnegative integers i , we recursively define:
For example, if f (n) = 2n, then f (i) (n) = 2i n.
CCSE-ICS353-prepared by:Shamiel .H -Natalia .A