introduction to algorithms lecture 1. introduction the methods of algorithm design form one of the...
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Introduction to AlgorithmsIntroduction to Algorithms
Lecture 1
IntroductionIntroduction
• The methods of algorithm design form one of the core practical technologies of computer science.
• The main aim of this lecture is to familiarize the student with the framework we shall use through the course about the design and analysis of algorithms.
• We start with a discussion of the algorithms needed to
solve computational problems. The problem of sorting is used as a running example.
• We introduce a pseudocode to show how we shall
specify the algorithms.
AlgorithmsAlgorithms
• The word algorithm comes from the name of a Persian mathematician Abu Ja’far Mohammed ibn-i Musa al Khowarizmi.
• In computer science, this word refers to a special method useable by a computer for solution of a problem. The statement of the problem specifies in general terms the desired input/output relationship.
• For example, sorting a given sequence of numbers into nondecreasing order provides fertile ground for introducing many standard design techniques and analysis tools.
The problem of sortingThe problem of sorting
Insertion SortInsertion Sort
Example of Insertion SortExample of Insertion Sort
Example of Insertion SortExample of Insertion Sort
Example of Insertion SortExample of Insertion Sort
Example of Insertion SortExample of Insertion Sort
Example of Insertion SortExample of Insertion Sort
Example of Insertion SortExample of Insertion Sort
Example of Insertion SortExample of Insertion Sort
Example of Insertion SortExample of Insertion Sort
Example of Insertion SortExample of Insertion Sort
Example of Insertion SortExample of Insertion Sort
Example of Insertion SortExample of Insertion Sort
Analysis of algorithmsAnalysis of algorithms
The theoretical study of computer-programperformance and resource usage.
What’s more important than performance?• modularity
• correctness• maintainability• functionality• robustness
• user-friendliness• programmer time
• simplicity• extensibility
• reliability
Analysis of algorithmsAnalysis of algorithms
Why study algorithms and performance?
• Algorithms help us to understand scalability.
• Performance often draws the line between what is feasible and what is impossible.
• Algorithmic mathematics provides a language for talking about program behavior.
• The lessons of program performance generalize to other computing resources.
• Speed is fun!
Running TimeRunning Time
• The running time depends on the input: an already sorted sequence is easier to sort.
• Parameterize the running time by the size of the input, since short sequences are easier to sort than long ones.
• Generally, we seek upper bounds on the running time, because everybody likes a guarantee.
Kinds of analysesKinds of analyses
Worst-case: (usually)• T(n) = maximum time of algorithm on any input of size n.
Average-case: (sometimes)• T(n) = expected time of algorithm over all inputs of size n.• Need assumption of statistical distribution of inputs.
Best-case:• Cheat with a slow algorithm that works fast on some input.
Machine-Machine-IIndependent timendependent time
The RAM Model
Machine independent algorithm design depends on a hypothetical computer called Random Acces Machine (RAM). Assumptions:• Each simple operation such as +, -, if ...etc takes exactly one time step.• Loops and subroutines are not considered simple operations.• Each memory acces takes exactly one time step.
Machine-independent timeMachine-independent time
What is insertion sort’s worst-case time?
• It depends on the speed of our computer,• relative speed (on the same machine),• absolute speed (on different machines).
BIG IDEA:• Ignore machine-dependent constants.• Look at growth of “Asymptotic Analysis”
nnT as )(
Machine-independent time: An exampleMachine-independent time: An example
A pseudocode for insertion sort ( INSERTION SORT ). INSERTION-SORT(A)
1 for j 2 to length [A]2 do key A[ j] 3 Insert A[j] into the sortted sequence A[1,..., j-1].4 i j – 15 while i > 0 and A[i] > key6 do A[i+1] A[i]7 i i – 18 A[i +1] key
Analysis of INSERTION-SORT(contd.)Analysis of INSERTION-SORT(contd.)
1]1[8
)1(17
)1(][]1[6
][05
114
10]11[ sequence
sorted theinto][Insert 3
1][2
][21
timescost SORT(A)-INSERTION
8
27
26
25
4
2
1
nckeyiA
tcii
tciAiA
tckeyiAandi
ncji
njA
jA
ncjAkey
ncAlengthj
nj j
nj j
nj j
do
while
do
tofor
Analysis of INSERTION-SORT(contd.)Analysis of INSERTION-SORT(contd.)
)1()1()1()(2
62
5421
n
jj
n
jj tctcncnccnT
).1()1( 82
7
nctcn
jj
The total running time is
Analysis of INSERTION-SORT(contd.)Analysis of INSERTION-SORT(contd.)
The best case: The array is already sorted. (tj =1 for j=2,3, ...,n)
)1()1()1()1()( 85421 ncncncncncnT
).()( 854285421 ccccnccccc
Analysis of INSERTION-SORT(contd.)Analysis of INSERTION-SORT(contd.)
•The worst case: The array is reverse sorted
(tj =j for j=2,3, ...,n).
)12/)1(()1()( 521 nncncncnT
)1()2/)1(()2/)1(( 876 ncnncnnc
ncccccccnccc )2/2/2/()2/2/2/( 87654212
765
2
)1(1
nnj
n
j
cbnannT 2)(
Growth of FunctionsGrowth of Functions
Although we can sometimes determine the exact running time of an algorithm, the extra precision is not usually worth the effort of computing it.
For large inputs, the multiplicative constants and lower order terms of an exact running time are dominated by the effects of the input size itself.
Asymptotic NotationAsymptotic Notation
The notation we use to describe the asymptotic running time of an algorithm are defined in terms of functions whose domains are the set of natural numbers
...,2,1,0N
O-notationO-notation
• For a given function , we denote by the set of functions
• We use O-notation to give an asymptotic upper bound of a function, to within a constant factor.
• means that there existes some constant c s.t. is always for large enough n.
)(ng ))(( ngO
0
0
allfor )()(0
s.t.and constants positiveexist there:)())((
nnncgnf
ncnfngO
))(()( ngOnf
)(ncg)(nf
ΩΩ--Omega Omega notationnotation
• For a given function , we denote by the set of functions
• We use Ω-notation to give an asymptotic lower bound on a function, to within a constant factor.
• means that there exists some constant c s.t.
is always for large enough n.
)(ng ))(( ng
0
0
allfor )()(0
s.t.and constants positiveexist there:)())((
nnnfncg
ncnfng
))(()( ngnf
)(nf )(ncg
--Theta Theta notationnotation
• For a given function , we denote by the set of functions
• A function belongs to the set if there exist positive constants and such that it can be “sand- wiched” between and or sufficienly large n.
• means that there exists some constant c1 and c2 s.t. for large enough n.
)(ng ))(( ng
021
021
allfor )()()(c0
s.t.and,, constants positiveexist there:)())((
nnngcnfng
nccnfng
)(nf ))(( ng1c 2c
)(1 ngc )(2 ngc
Θ
))(()( ngnf )()()( 21 ngcnfngc
Asymptotic notationAsymptotic notation
Graphic examples of and . ,, O
22
221 3
2
1ncnnnc
213
2
1c
nc
Example 1. Example 1.
Show that
We must find c1 and c2 such that
Dividing bothsides by n2 yields
For
)(32
1)( 22 nnnnf
)(32
1,7 22
0 nnnn
Theorem Theorem
• For any two functions and , we have
if and only if
)(ng
))(()( ngnf
)(nf
)).(()( and ))(()( ngnfngOnf
Because :
)2(5223 nnn
Example 2.Example 2.
)2(5223)( nnnnf
)2(5223 nOnn
Example 3. Example 3.
610033,3forsince)(61003 2222 nnncnOnn
Example 3. Example 3.
3when61003,1forsince)(61003
610033,3forsince)(610032332
2222
nnnncnOnn
nnncnOnn
Example 3. Example 3.
cnncncnOnn
nnnncnOnn
nnncnOnn
when3,any forsince)(61003
3when61003,1forsince)(61003
610033,3forsince)(61003
22
2332
2222
Example 3. Example 3.
100when610032,2forsince)(61003
when3,any forsince)(61003
3when61003,1forsince)(61003
610033,3forsince)(61003
2222
22
2332
2222
nnnncnnn
cnncncnOnn
nnnncnOnn
nnncnOnn
Example 3. Example 3.
3when61003,3forsince)(61003
100when610032,2forsince)(61003
when3,any forsince)(61003
3when61003,1forsince)(61003
610033,3forsince)(61003
3232
2222
22
2332
2222
nnnncnnn
nnnncnnn
cnncncnOnn
nnnncnOnn
nnncnOnn
Example 3. Example 3.
100when61003,any forsince)(61003
3when61003,3forsince)(61003
100when610032,2forsince)(61003
when3,any forsince)(61003
3when61003,1forsince)(61003
610033,3forsince)(61003
22
3232
2222
22
2332
2222
nnncncnnn
nnnncnnn
nnnncnnn
cnncncnOnn
nnnncnOnn
nnncnOnn
Example 3. Example 3.
apply. and both since)(61003
100when61003,any forsince)(61003
3when61003,3forsince)(61003
100when610032,2forsince)(61003
when3,any forsince)(61003
3when61003,1forsince)(61003
610033,3forsince)(61003
22
22
3232
2222
22
2332
2222
Onnn
nnncncnnn
nnnncnnn
nnnncnnn
cnncncnOnn
nnnncnOnn
nnncnOnn
Example 3. Example 3.
applies. only since)(61003
apply. and both since)(61003
100when61003,any forsince)(61003
3when61003,3forsince)(61003
100when610032,2forsince)(61003
when3,any forsince)(61003
3when61003,1forsince)(61003
610033,3forsince)(61003
32
22
22
3232
2222
22
2332
2222
Onnn
Onnn
nnncncnnn
nnnncnnn
nnnncnnn
cnncncnOnn
nnnncnOnn
nnncnOnn
Example 3. Example 3.
applies. only since)(61003
applies. only since)(61003
apply. and both since)(61003
100when61003,any forsince)(61003
3when61003,3forsince)(61003
100when610032,2forsince)(61003
when3,any forsince)(61003
3when61003,1forsince)(61003
610033,3forsince)(61003
2
32
22
22
3232
2222
22
2332
2222
nnn
Onnn
Onnn
nnncncnnn
nnnncnnn
nnnncnnn
cnncncnOnn
nnnncnOnn
nnncnOnn
oo-notation-notation
• We use (small o) o-notation to denote an upper bound that is not asymptotically tight.
• We formally define as the set))(( ngo
0
0
allfor )()(0
s.t.0 constants aexist there
0constant positiveany for :)(
))((
nnncgnf
n
cnf
ngo
0)(
)(lim
ng
nf
n
Example 4. Example 4.
)(2)(
But
)(2)(
isThat
ally tightasymptoticnot Is:)(2)(
ally tightAsymptotic:)(2)(
22
2
2
22
nonnf
nonnf
nOnnf
nOnnf
ωω-notation-notation
• We use ω-notation to denote an upper bound that is not asymptotically tight.
• We formally define as the set))(( ng
0
0
allfor )()(0
s.t.0 constants aexist there
0constant positiveany for :)(
))((
nnnfncg
n
cnf
ng
∞=
=relation The
∞→ g(n)
f(n)lim
tmplies thaω(g(n)) i f(n)
n
Example Example
)(2
)(
But
)(2
)(
22
2
nn
nf
nn
nf
Standard notations and common functionsStandard notations and common functions
• Floors and ceilings
11 xxxxx
Standard notations and common functionsStandard notations and common functions
• Modular arithmetic
For any integer a and positive integer n
nnaana /mod
Standard notations and common functionsStandard notations and common functions
• Polynomials:
Given a nonnegative integer d, a polynomial in n of degree d is
d
i
i
i nanp0
)(
Standard notations and common functionsStandard notations and common functions
• Exponentials:
!3!2
132 xx
xe x
Standard notations and common functionsStandard notations and common functions
• Logarithms:
)lg(lglglg
)(loglog
logln
loglg 2
nn
nn
nn
nn
kk
e
Standard notations and common functionsStandard notations and common functions
• Logarithms:
For all real a>0, b>0, c>0, and n
b
aa
ana
baab
ba
c
cb
b
n
b
ccc
ab
log
loglog
loglog
loglog)(log
log
Standard notations and common functionsStandard notations and common functions
• Logarithms:
ba
ca
aa
a
b
ac
bb
bb
log
1log
log)/1(logloglog
Standard notations and common functionsStandard notations and common functions
• Series expansion:
For
For
5432
)1ln(5432 xxxx
xx
1x
1x
xxx
x
)1ln(
)1(
Standard notations and common functionsStandard notations and common functions
• Factorials
For the Stirling approximation:
ne
nnn
n1
12!
0n
)lg()!lg(
)2(!
)(!
nnn
n
nonn
n
Designing algorithms Designing algorithms
There are many ways to design algorithms:
• Insertion sort uses an incremental approach• Merge sort uses divide-and-conquer approach
Insertion sort analysisInsertion sort analysis
Merge SortMerge Sort
Merge SortMerge Sort
MERGE_SORT(A,p,r)
1if p<r2 then q← (p+r)/2 3 MERGE_SORT(A,p,q)4 MERGE_SORT(A,q+1, r)5 MERGE(A,p,q,r)
Merging two sorted arraysMerging two sorted arrays
Merging two sorted arraysMerging two sorted arrays
Merging two sorted arraysMerging two sorted arrays
Merging two sorted arraysMerging two sorted arrays
Merging two sorted arraysMerging two sorted arrays
Merging two sorted arraysMerging two sorted arrays
Merging two sorted arraysMerging two sorted arrays
Merging two sorted arraysMerging two sorted arrays
Merging two sorted arraysMerging two sorted arrays
Merging two sorted arraysMerging two sorted arrays
Merging two sorted arraysMerging two sorted arrays
Merging two sorted arraysMerging two sorted arrays
Merging two sorted arraysMerging two sorted arrays
Analyzing merge sortAnalyzing merge sort
Recurrence for merge sortRecurrence for merge sort
Recursion treeRecursion tree
Recursion treeRecursion tree
Recursion treeRecursion tree
Recursion treeRecursion tree
Recursion treeRecursion tree
Recursion treeRecursion tree
Recursion treeRecursion tree
Recursion treeRecursion tree
Recursion treeRecursion tree
Recursion treeRecursion tree