02-asymp.ppt01_Intro.ppt algorithm for preperation stu used
1. July 13, 2025 Comp 122, Spring 2004
Asymptotic Notation,
Review of Functions &
Summations
2. Comp 122
ymp - 2
Asymptotic Complexity
Running time of an algorithm as a function of
input size n for large n.
Expressed using only the highest-order term in
the expression for the exact running time.
Instead of exact running time, say (n2
).
Describes behavior of function in the limit.
Written using Asymptotic Notation.
3. Comp 122
ymp - 3
Asymptotic Notation
, O, , o,
Defined for functions over the natural numbers.
Ex: f(n) = (n2
).
Describes how f(n) grows in comparison to n2
.
Define a set of functions; in practice used to compare
two function sizes.
The notations describe different rate-of-growth
relations between the defining function and the
defined set of functions.
4. Comp 122
ymp - 4
-notation
(g(n)) = {f(n) :
positive constants c1, c2, and n0,
such that n n0,
we have 0 c1g(n) f(n)
c2g(n)
}
For function g(n), we define (g(n)),
big-Theta of n, as the set:
g(n) is an asymptotically tight bound for f(n).
Intuitively: Set of all functions that
have the same rate of growth as g(n).
5. Comp 122
ymp - 5
-notation
(g(n)) = {f(n) :
positive constants c1, c2, and n0,
such that n n0,
we have 0 c1g(n) f(n)
c2g(n)
}
For function g(n), we define (g(n)),
big-Theta of n, as the set:
Technically, f(n) (g(n)).
Older usage, f(n) = (g(n)).
I’ll accept either…
f(n) and g(n) are nonnegative, for large n.
6. Comp 122
ymp - 6
Example
10n2
- 3n = (n2
)
What constants for n0, c1, and c2 will work?
Make c1 a little smaller than the leading
coefficient, and c2 a little bigger.
To compare orders of growth, look at the
leading term.
Exercise: Prove that n2
/2-3n= (n2
)
(g(n)) = {f(n) : positive constants c1, c2, and n0,
such that n n0, 0 c1g(n) f(n) c2g(n)}
7. Comp 122
ymp - 7
Example
Is 3n3
(n4
) ??
How about 22n
(2n
)??
(g(n)) = {f(n) : positive constants c1, c2, and n0,
such that n n0, 0 c1g(n) f(n) c2g(n)}
8. Comp 122
ymp - 8
O-notation
O(g(n)) = {f(n) :
positive constants c and n0,
such that n n0,
we have 0 f(n) cg(n) }
For function g(n), we define O(g(n)),
big-O of n, as the set:
g(n) is an asymptotic upper bound for f(n).
Intuitively: Set of all functions
whose rate of growth is the same as
or lower than that of g(n).
f(n) = (g(n)) f(n) = O(g(n)).
(g(n)) O(g(n)).
9. Comp 122
ymp - 9
Examples
Any linear function an + b is in O(n2
). How?
Show that 3n3
=O(n4
) for appropriate c and n0.
O(g(n)) = {f(n) : positive constants c and n0,
such that n n0, we have 0 f(n) cg(n) }
10. Comp 122
ymp - 10
-notation
g(n) is an asymptotic lower bound for f(n).
Intuitively: Set of all functions
whose rate of growth is the same
as or higher than that of g(n).
f(n) = (g(n)) f(n) = (g(n)).
(g(n)) (g(n)).
(g(n)) = {f(n) :
positive constants c and n0,
such that n n0,
we have 0 cg(n) f(n)}
For function g(n), we define (g(n)),
big-Omega of n, as the set:
11. Comp 122
ymp - 11
Example
n = (lg n). Choose c and n0.
(g(n)) = {f(n) : positive constants c and n0, such
that n n0, we have 0 cg(n) f(n)}
13. Comp 122
ymp - 13
Relations Between , , O
I.e., (g(n)) = O(g(n)) (g(n))
In practice, asymptotically tight bounds are
obtained from asymptotic upper and lower bounds.
Theorem : For any two functions g(n) and f(n),
f(n) = (g(n)) iff
f(n) = O(g(n)) and f(n) = (g(n)).
14. Comp 122
ymp - 14
Running Times
“Running time is O(f(n))” Worst case is O(f(n))
O(f(n)) bound on the worst-case running time
O(f(n)) bound on the running time of every input.
(f(n)) bound on the worst-case running time
(f(n)) bound on the running time of every input.
“Running time is (f(n))” Best case is (f(n))
Can still say “Worst-case running time is (f(n))”
Means worst-case running time is given by some
unspecified function g(n) (f(n)).
15. Comp 122
ymp - 15
Example
Insertion sort takes (n2
) in the worst case, so
sorting (as a problem) is O(n2
). Why?
Any sort algorithm must look at each item, so
sorting is (n).
In fact, using (e.g.) merge sort, sorting is (n lg n)
in the worst case.
Later, we will prove that we cannot hope that any
comparison sort to do better in the worst case.
16. Comp 122
ymp - 16
Asymptotic Notation in Equations
Can use asymptotic notation in equations to
replace expressions containing lower-order terms.
For example,
4n3
+ 3n2
+ 2n + 1 = 4n3
+ 3n2
+ (n)
= 4n3
+ (n2
) = (n3
). How to interpret?
In equations, (f(n)) always stands for an
anonymous function g(n) (f(n))
In the example above, (n2
) stands for
3n2
+ 2n + 1.
17. Comp 122
ymp - 17
o-notation
f(n) becomes insignificant relative to g(n) as n
approaches infinity:
lim [f(n) / g(n)] = 0
n
g(n) is an upper bound for f(n) that is not
asymptotically tight.
Observe the difference in this definition from previous
ones. Why?
o(g(n)) = {f(n): c > 0, n0 > 0 such that
n n0, we have 0 f(n) < cg(n)}.
For a given function g(n), the set little-o:
18. Comp 122
ymp - 18
(g(n)) = {f(n): c > 0, n0 > 0 such that
n n0, we have 0 cg(n) < f(n)}.
-notation
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.
For a given function g(n), the set little-omega:
19. Comp 122
ymp - 19
Comparison of Functions
f g a b
f (n) = O(g(n)) a b
f (n) = (g(n)) a b
f (n) = (g(n)) a = b
f (n) = o(g(n)) a < b
f (n) = (g(n)) a > b
23. July 13, 2025 Comp 122, Spring 2004
Common Functions
24. Comp 122
ymp - 24
Monotonicity
f(n) is
monotonically increasing if m n f(m) f(n).
monotonically decreasing if m n f(m) f(n).
strictly increasing if m < n f(m) < f(n).
strictly decreasing if m > n f(m) > f(n).
25. Comp 122
ymp - 25
Exponentials
Useful Identities:
Exponentials and polynomials
n
m
n
m
mn
n
m
a
a
a
a
a
a
a
)
(
1
1
)
(
0
lim
n
b
n
b
n
a
o
n
a
n
26. Comp 122
ymp - 26
Logarithms
x = logba is the
exponent for a = bx
.
Natural log: ln a = logea
Binary log: lg a = log2a
lg2
a = (lg a)2
lg lg a = lg (lg a)
a
c
a
b
b
b
c
c
b
b
n
b
c
c
c
a
b
b
b
c
a
b
a
a
a
b
a
a
a
n
a
b
a
ab
b
a
log
log
log
log
1
log
log
)
/
1
(
log
log
log
log
log
log
log
log
)
(
log
27. Comp 122
ymp - 27
Logarithms and exponentials – Bases
If the base of a logarithm is changed from one
constant to another, the value is altered by a
constant factor.
Ex: log10 n * log210 = log2 n.
Base of logarithm is not an issue in asymptotic
notation.
Exponentials with different bases differ by a
exponential factor (not a constant factor).
Ex: 2n
= (2/3)n
*3n
.
28. Comp 122
ymp - 28
Polylogarithms
For a 0, b > 0, lim n ( lga
n / nb
) = 0,
so lga
n = o(nb
), and nb
= (lga
n )
Prove using L’Hopital’s rule repeatedly
lg(n!) = (n lg n)
Prove using Stirling’s approximation (in the text) for lg(n!).
29. Comp 122
ymp - 29
Exercise
Express functions in A in asymptotic notation using functions in B.
A B
5n2
+ 100n 3n2
+ 2
A (n2
), n2
(B) A (B)
log3(n2
) log2(n3
)
logba = logca / logcb; A = 2lgn / lg3, B = 3lgn, A/B =2/(3lg3)
nlg4
3lg n
alog b
=blog a
; B =3lg n
=nlg 3
; A/B =nlg(4/3)
as n
lg2
n n1/2
lim ( lga
n / nb
) = 0 (here a = 2 and b = 1/2) A (B)
n
A (B)
A (B)
A (B)
A (B)
30. July 13, 2025 Comp 122, Spring 2004
Summations – Review
31. Comp 122
ymp - 31
Review on Summations
Why do we need summation formulas?
For computing the running times of iterative
constructs (loops). (CLRS – Appendix A)
Example: Maximum Subvector
Given an array A[1…n] of numeric values (can be
positive, zero, and negative) determine the
subvector A[i…j] (1 i j n) whose sum of
elements is maximum over all subvectors.
1 -2 2 2
32. Comp 122
ymp - 32
Review on Summations
MaxSubvector(A, n)
maxsum 0;
for i 1 to n
do for j = i to n
sum 0
for k i to j
do sum += A[k]
maxsum max(sum, maxsum)
return maxsum
n n j
T(n) = 1
i=1 j=i k=i
NOTE: This is not a simplified solution. What is the final answer?
33. Comp 122
ymp - 33
Review on Summations
Constant Series: For integers a and b, a b,
Linear Series (Arithmetic Series): For n 0,
Quadratic Series: For n 0,
b
a
i
a
b 1
1
2
)
1
(
2
1
1
n
n
n
i
n
i
n
i
n
n
n
n
i
1
2
2
2
2
6
)
1
2
)(
1
(
2
1
34. Comp 122
ymp - 34
Review on Summations
Cubic Series: For n 0,
Geometric Series: For real x 1,
For |x| < 1,
n
i
n
n
n
i
1
2
2
3
3
3
3
4
)
1
(
2
1
n
k
n
n
k
x
x
x
x
x
x
0
1
2
1
1
1
0 1
1
k
k
x
x
35. Comp 122
ymp - 35
Review on Summations
Linear-Geometric Series: For n 0, real c 1,
Harmonic Series: nth harmonic number, nI+
,
n
i
n
n
n
i
c
c
nc
c
n
nc
c
c
ic
1
2
2
1
2
)
1
(
)
1
(
2
n
Hn
1
3
1
2
1
1
n
k
O
n
k
1
)
1
(
)
ln(
1
36. Comp 122
ymp - 36
Review on Summations
Telescoping Series:
Differentiating Series: For |x| < 1,
n
k
n
k
k a
a
a
a
1
0
1
0
2
1
k
k
x
x
kx
37. Comp 122
ymp - 37
Review on Summations
Approximation by integrals:
For monotonically increasing f(n)
For monotonically decreasing f(n)
How?
n
m
n
m
k
n
m
dx
x
f
k
f
dx
x
f
1
1
)
(
)
(
)
(
1
1
)
(
)
(
)
(
n
m
n
m
k
n
m
dx
x
f
k
f
dx
x
f
38. Comp 122
ymp - 38
Review on Summations
nth harmonic number
n
k
n
n
x
dx
k
1
1
1
)
1
ln(
1
n
k
n
n
x
dx
k
2 1
ln
1
n
k
n
k
1
1
ln
1
![Comp 122
ymp - 17
o-notation
f(n) becomes insignificant relative to g(n) as n
approaches infinity:
lim [f(n) / g(n)] = 0
n
g(n) is an upper bound for f(n) that is not
asymptotically tight.
Observe the difference in this definition from previous
ones. Why?
o(g(n)) = {f(n): c > 0, n0 > 0 such that
n n0, we have 0 f(n) < cg(n)}.
For a given function g(n), the set little-o:](https://image.slidesharecdn.com/02-asymp-250713113026-23bb5846/85/02-asymp-ppt01_Intro-ppt-algorithm-for-preperation-stu-used-17-320.jpg)
![Comp 122
ymp - 18
(g(n)) = {f(n): c > 0, n0 > 0 such that
n n0, we have 0 cg(n) < f(n)}.
-notation
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.
For a given function g(n), the set little-omega:](https://image.slidesharecdn.com/02-asymp-250713113026-23bb5846/85/02-asymp-ppt01_Intro-ppt-algorithm-for-preperation-stu-used-18-320.jpg)
![Comp 122
ymp - 20
Limits
lim [f(n) / g(n)] = 0 f(n) (g(n))
n
lim [f(n) / g(n)] < f(n) (g(n))
n
0 < lim [f(n) / g(n)] < f(n) (g(n))
n
0 < lim [f(n) / g(n)] f(n) (g(n))
n
lim [f(n) / g(n)] = f(n) (g(n))
n
lim [f(n) / g(n)] undefined can’t say
n](https://image.slidesharecdn.com/02-asymp-250713113026-23bb5846/85/02-asymp-ppt01_Intro-ppt-algorithm-for-preperation-stu-used-20-320.jpg)
![Comp 122
ymp - 31
Review on Summations
Why do we need summation formulas?
For computing the running times of iterative
constructs (loops). (CLRS – Appendix A)
Example: Maximum Subvector
Given an array A[1…n] of numeric values (can be
positive, zero, and negative) determine the
subvector A[i…j] (1 i j n) whose sum of
elements is maximum over all subvectors.
1 -2 2 2](https://image.slidesharecdn.com/02-asymp-250713113026-23bb5846/85/02-asymp-ppt01_Intro-ppt-algorithm-for-preperation-stu-used-31-320.jpg)
![Comp 122
ymp - 32
Review on Summations
MaxSubvector(A, n)
maxsum 0;
for i 1 to n
do for j = i to n
sum 0
for k i to j
do sum += A[k]
maxsum max(sum, maxsum)
return maxsum
n n j
T(n) = 1
i=1 j=i k=i
NOTE: This is not a simplified solution. What is the final answer?](https://image.slidesharecdn.com/02-asymp-250713113026-23bb5846/85/02-asymp-ppt01_Intro-ppt-algorithm-for-preperation-stu-used-32-320.jpg)
