Data structures &algorithms

Data Structures &
Algorithms
NURSALEHA
SISTEM INFORMASI
SAINS DAN TEKNOLOGI
UNIVERSITAS ISLAM NEGERI SULTAN
SYARIF KASIM RIAU
http://sif.uin-suska.ac.id/
http://fst.uin-suska.ac.id/
http://www.uin-suska.ac.id/

Contents
 Textbook
 Grade
 Software

Textbook
 C & Data Structures
– P. S. Deshpande, O. G. Kakde
– CHARLES RIVER MEDIA, INC.
Hingham, Massachusetts

Grade
 Midterm test (Lab)
 Final test (Lab)
 Project (working on group)
 Multiple choice test
 How to Grade

Software: C/C++ edittor
 BC++, TC++
 C-Free is a professional C/C++ integrated
development environment (IDE) that support multi-
compilers. Use of this software, user can edit, build,
run and debug programs freely.
With C/C++ source parser included
 Lightweight C/C++ development tool.
 http://www.programarts.com/cfree_en/

C/C++ edittor: demo
 Find max of 3 numbers: a,b,c
– Using scanf, prinf (C standard)
– Using cin, cout (Cpp)

CHAPTER 0: INTRODUTION
 What is Data Structures?
– A data structure is defined by
 (1) the logical arrangement of data elements,
combined with
 (2) the set of operations we need to access the
elements.

Atomic Variables
 Atomic variables can only store one value at
a time.
int num;
float s;
 A value stored in an atomic variable cannot
be subdivided.

What is Data Structures?
 Example:library
– is composed of elements
(books)
– Accessing a particular
book requires knowledge
of the arrangement of the
books
– Users access books only
through the librarian
the logical arrangement of data elements,
combined with
the set of operations we need to access the
elements.

Basic Data Structures
 Structures include
– linked lists
– Stack, Queue
– binary trees
– …and others

What is Algorithm?
 Algorithm:
– A computable set of steps to achieve a desired
result
– Ralationship to Data Structure
 Example: Find an element
1 2 3 4 5 6 7
1
2
3
4
5
6
7

Chapter 0: C LANGUAGE
1. ADDRESS
2. POINTERS
3. ARRAYS
4. ADDRESS OF EACH ELEMENT IN AN ARRAY
5. ACCESSING & MANIPULATING AN ARRAY USING
POINTERS
6. ANOTHER CASE OF MANIPULATING AN ARRAY USING
POINTERS
7. TWO-DIMENSIONAL ARRAY
8. POINTER ARRAYS
9. STRUCTURES
10. STRUCTURE POINTERS

1. ADDRESS
For every variable there are two attributes:
address and value
cout << "Value of 'y' is: " << y << "n";
cout << "Address of 'y' is: " << &y << "nn";
In memory with address 3: value: 45.
In memory with address 2: value "Dave"

2. POINTERS
1. is a variable whose value is also an address.
2. A pointer to an integer is a variable that can
store the address of that integer
ia: value of variable
&ia: address of ia
*ia means you are printing the value at the
location specified by ia

int i; //A
int * ia; //B
cout<<"The address of i "<< &i << " value="<<i <<endl;
cout<<"The address of ia " << &ia << " value = " << ia<< endl;
i = 10; //C
ia = &i; //D
cout<<"after assigning value:"<<endl;
cout<<"The address of i "<< &i << " value="<<i <<endl;
cout<<"The address of ia " << &ia << " value = " << ia<< " point to: "<< *ia;

Points to Remember
• Pointers give a facility to access the value of
a variable indirectly.
• You can define a pointer by including a *
before the name of the variable.
• You can get the address where a variable is
stored by using &.

3. ARRAYS
1. An array is a data structure
2. used to process multiple elements with the same data
type when a number of such elements are known.
3. An array is a composite data structure; that means it
had to be constructed from basic data types such as
array integers.
1. int a[5];
2. for(int i = 0;i<5;i++)
1. {a[i]=i; }

4. ADDRESS OF EACH ELEMENT IN AN
ARRAY
Each element of the array has a memory
address.
void printdetail(int a[])
{
for(int i = 0;i<5;i++)
{
cout<< "value in array “<< a[i] <<“ at address: “ << &a[i]);
}

5. ACCESSING & MANIPULATING AN
ARRAY USING POINTERS
– You can access an array element by using a pointer.
– If an array stores integers->use a pointer to integer to
access array elements.

6. ANOTHER CASE OF MANIPULATING AN
ARRAY USING POINTERS
The array limit is a pointer constant : cannot
change its value in the program.
int a[5]; int *b;
a=b; //error
b=a; //OK
It works correctly even using
a++ ???

7. TWO-DIMENSIONAL ARRAY
int a[3][2];

8. POINTER ARRAYS
 You can define a pointer array (similarly to an array of
integers).
 In the pointer array, the array elements store the
pointer that points to integer values.

9. STRUCTURES
 Structures are used when
you want to process data of
multiple data types
 But you still want to refer to
the data as a single entity
 Access data:
structurename.membernam
e

10. STRUCTURE POINTERS
Process the structure using a structure pointer

CHAPTER 2: FUNCTION & RECURSION
 1. FUNCTION
 2. THE CONCEPT OF STACK
 3. THE SEQUENCE OF EXECUTION DURING A
FUNCTION CALL
 4. PARAMETER PASSING
 5. CALL BY REFERENCE
 6. RESOLVING VARIABLE REFERENCES
 7. RECURSION
 8. STACK OVERHEADS IN RECURSION
 9. WRITING A RECURSIVE FUNCTION
 10. TYPES OF RECURSION

 1. FUNCTION
– provide modularity to the software
– divide complex tasks into small manageable tasks
– avoid duplication of work

 2. THE CONCEPT OF STACK
– A stack is memory in which values are stored and
retrieved in "last in first out" manner by using
operations called push and pop.

 3. THE SEQUENCE OF EXECUTION DURING A
FUNCTION CALL
– When the function is called, the current execution is
temporarily stopped and the control goes to the called
function. After the call, the execution resumes from the
point at which the execution is stopped.
– To get the exact point at which execution is resumed, the
address of the next instruction is stored in the stack. When
the function call completes, the address at the top of the
stack is taken.

 3. THE SEQUENCE OF EXECUTION
DURING A FUNCTION CALL
– Functions or sub-programs are implemented
using a stack.
– When a function is called, the address of the next
instruction is pushed into the stack.
– When the function is finished, the address for
execution is taken by using the pop operation.

 3. THE SEQUENCE OF
EXECUTION DURING A
FUNCTION CALL
 Result:?

 4. PARAMETER * REFERENCE PASSING
– passing by value
 the value before and after the call remains the same
– passing by reference
 changed value after the function completes

 6. RESOLVING VARIABLE REFERENCES
When a variable can
be resolved by using
multiple references,
the local definition is
given more preference

 7. RECURSION
– A method of
programming
whereby a function
directly or indirectly
calls itself
– Problems: stop
recursion?

 7. RECURSION

 7. RECURSION:
Hanoi tower

 8. STACK OVERHEADS IN RECURSION
– two important results: the depth of recursion and
the stack overheads in recursion

 9. WRITING A RECURSIVE FUNCTION
– Recursion enables us to write a program in a
natural way. The speed of a recursive program
is slower because of stack overheads.
– In a recursive program you have to specify
recursive conditions, terminating conditions, and
recursive expressions.

– LINEAR RECURSION (tuyến tính)
– TAIL RECURSION (đuôi)
– BINARY RECURSION (nhị phân)
– EXPONENTIAL RECURSION (đa tuyến)
– NESTED RECURSION (lồng)
– MUTUAL RECURSION (tương hỗ)

– LINEAR RECURSION
 only makes a single call to itself each time the
function runs


– TAIL RECURSION
 Tail recursion is a form of linear recursion.
 In tail recursion, the recursive call is the last thing
the function does. Often, the value of the recursive
call is returned.

– BINARY RECURSION
 Some recursive functions don't just have one call to
themself, they have two (or more).

– EXPONENTIAL RECURSION
 An exponential recursive function is one that, if you
were to draw out a representation of all the function
calls, would have an exponential number of calls in
relation to the size of the data set
 (exponential meaning if there were n elements, there
would be O(an
) function calls where a is a positive
number)

– EXPONENTIAL RECURSION

– NESTED RECURSION
 In nested recursion, one of the arguments to the
recursive function is the recursive function itself
 These functions tend to grow extremely fast.

Data structures &algorithms

– MUTUAL RECURSION
 A recursive function doesn't necessarily need to call
itself.
 Some recursive functions work in pairs or even larger
groups. For example, function A calls function B which
calls function C which in turn calls function A.

– MUTUAL RECURSION

Exercises 2: Recursion
 Convert number from H10->H2
7 2
1 3 2
1 1 2
01

Week3: Recursion Excercises (1)
 E1. (44/174) Write a program to compute: S
= 1 + 2 + 3 + …n using recursion.

Week3: Recursion Excercises (2-3)
 E3(a). Write a program to print a revert
number Example: input n=12345. Print out:
54321.
 E3(b). Write a program to print this number
Example: input n=12345. Print out: 12345.

 E4. Write a recursion function to find the sum
of every number in a int number. Example:
n=1980 => Sum=1+9+8+0=18.

 E4. Write a recursion function to calculate:
– S=a[0]+a[1]+…a[n-1]
 A: array of integer numbers

 E4. Write a recursion function to find an
element in an array (using linear algorithm)

 Print triangle
a b
c d

 Convert number from H10->H2
7 2
1 3 2
1 1 2
01

 Minesweeper

Week 4
CHAPTER 3: SEARCHING TECHNIQUES
1. LINEAR (SEQUENTIAL) SEARCH
2. BINARY SEARCH
3. COMPLEXITY OF ALGORITHMS

SEARCHING TECHNIQUES
 To finding out whether a particular element is
present in the list.
 2 methods: linear search, binary search
 The method we use depends on how the
elements of the list are organized
– unordered list:
 linear search: simple, slow
– an ordered list
 binary search or linear search: complex, faster

 How?
– Proceeds by sequentially comparing the key with
elements in the list
– Continues until either we find a match or the end
of the list is encountered.
– If we find a match, the search terminates
successfully by returning the index of the element
– If the end of the list is encountered without a
match, the search terminates unsuccessfully.

void lsearch(int list[],int n,int element)
{ int i, flag = 0;
for(i=0;i<n;i++)
if( list[i] == element)
{ cout<<“found at position”<<i;
flag =1;
break; }
if( flag == 0)
cout<<“ not found”;
}
flag: what for???

int lsearch(int list[],int n,int element)
{ int i, find= -1;
for(i=0;i<n;i++)
if( list[i] == element)
{find =i;
break;}
return find;
}
Another way using flag
average time: O(n)

2. BINARY SEARCH
 List must be a sorted one
 We compare the element with the element
placed approximately in the middle of the list
 If a match is found, the search terminates
successfully.
 Otherwise, we continue the search for the
key in a similar manner either in the upper
half or the lower half.

void bsearch(int list[],int n,int element)
{
int l,u,m, flag = 0;
l = 0; u = n-1;
while(l <= u)
{ m = (l+u)/2;
if( list[m] == element)
{cout<<"found:"<<m;
flag =1;
break;}
else
if(list[m] < element)
l = m+1;
else
u = m-1;
}
if( flag == 0)
cout<<"not found";
}
average time: O(log2n)

BINARY SEARCH: Recursion
int Search (int list[], int key, int left, int right)
{
if (left <= right) {
int middle = (left + right)/2;
if (key == list[middle])
return middle;
else if (key < list[middle])
return Search(list,key,left,middle-1);
else return Search(list,key,middle+1,right);
}
return -1;
}

 In Computer Science, it is important to measure the
quality of algorithms, especially the specific amount
of a certain resource an algorithm needs
 Resources: time or memory storage (PDA?)
 Different algorithms do same task with a different set
of instructions in less or more time, space or effort
than other.
 The analysis has a strong mathematical background.
 The most common way of qualifying an algorithm is
the Asymptotic Notation, also called Big O.

 It is generally written as
 Polynomial time algorithms,
– O(1) --- Constant time --- the time does not change in response to
the size of the problem.
– O(n) --- Linear time --- the time grows linearly with the size (n) of
the problem.
– O(n2
) --- Quadratic time --- the time grows quadratically with the
size (n) of the problem. In big O notation, all polynomials with the
same degree are equivalent, so O(3n2
+ 3n + 7) = O(n2
)
 Sub-linear time algorithms
– O(logn) -- Logarithmic time
 Super-polynomial time algorithms
– O(n!)
– O(2n
)

 Example1: complexity of an algorithm
void f ( int a[], int n )
{
int i;
cout<< "N = “<< n;
for ( i = 0; i < n; i++ )
cout<<a[i];
printf ( "n" );
}
?
?
2 * O(1) + O(N)
O(N)

 Example2: complexity of an algorithm
void f ( int a[], int n )
{ int i;
cout<< "N = “<< n;
for ( i = 0; i < n; i++ )
for (int j=0;j<n;j++)
cout<<a[i]<<a[j];
for ( i = 0; i < n; i++ )
cout<<a[i];
printf ( "n" );
}
?
?
2 * O(1) + O(N)+O(N2
)
O(N2)

 Linear Search
– O(n).
 Binary Search
– O(log2 N)

Week 4
CHAPTER 4: SORTING TECHNIQUES
1. BUBBLE SORT
2. INSERTION SORT
3. SELECTION SORT
4. QUICK SORT

SORTING TECHNIQUES
We need to do sorting for the following reasons :
a) By keeping a data file sorted, we can do binary
search on it.
b) Doing certain operations, like matching data in
two different files, become much faster.
There are various methods for sorting: Bubble sort,
Insertion sort, Selection sort, Quick sort, Heap sort,
Merge sort…. They having different average and
worst case behaviours:

1. BUBBLE SORT
Introduction:
Bubble sorting is a simple sorting technique in
which we arrange the elements of the list by
forming pairs of adjacent elements. That
means we form the pair of the ith and (i+1)th
element. If the order is ascending, we
interchange the elements of the pair if the
first element of the pair is greater than the
second element.

1. BUBBLE SORT
Bubble sort: beginning of first pass

1. BUBBLE SORT
Bubble sort: end of First pass

1. BUBBLE SORT
void bsort(int list[], int n)
{
int i,j;
for(i=0;i<(n-1);i++)
for(j=0;j<(n-(i+1));j++)
if(list[j] > list[j+1])
swap(&list[j],&list[j+1]);
}

2. INSERTION SORT
Introduction:
Basic Idea: Insert a record R into a
sequence of ordered records: R1,R2,.., Ri
with keys K1 <= K2 <= ... <= Ki , such that,
the resulting sequence of size i+1 is also
ordered with respect to key values.

2. INSERTION SORT
Algorithm Insertion_Sort; (* Assume Ro has Ko = -maxint *)
void InsertionSort( Item &list[])
{ // Insertion_Sort
Item r;
int i,j;
list[0].key = -maxint;
for (j=2; j<=n; j++)
{ r=list[j];
i=j-1;
while ( r.key < list[i].key )
{// move greater entries to the right
list[i+1]:=list[i];
i:=i-1;
};
list[i+1] = r // insert into it's place
}

3. SELECTION SORT
Selection sort is a simplicity sorting algorithm. It
works as its name as it is. Here are basic
steps of selection sort algorithm:
1. Find the minimum element in the list
2. Swap it with the element in the first position
of the list
3. Repeat the steps above for all remainder
elements of the list starting at the second
position.

3. SELECTION SORT
void selection_sort(int list[], int n){
int i, j, min; for (i = 0; i < n - 1; i++) {
min = i;
for (j = i+1; j < n; j++) {
if (list[j] < list[min]) {
min = j;
}
}
swap(&list[i], &list[min]);
}

4. QUICK SORT
Chọn ngẫu nhiên một phần tử X của dãy (chẳng
hạn phần tử đầu tiên) và cố gắng phân chia dãy
này thành 3 dãy con liên tiếp nhau:
+ Dãy 1: Gồm những phần tử nhỏ hơn X.
+ Dãy 2: Gồm những phần tử bằng X.
+ Dãy 3: Gồm những phần tử lớn hơn X.
Sau đó áp dụng lại giải thuật này cho dãy con thứ
nhất và dãy con thứ ba (dãy con này có số phần
tử lớn hơn 1).

Week 6
CHAPTER 6: LINKED LISTS

Introduction
When dealing with many problems we need a dynamic
list, dynamic in the sense that the size requirement
need not be known at compile time. Thus, the list
may grow or shrink during runtime. A linked list is a
data structure that is used to model such a dynamic
list of data items, so the study of the linked lists as
one of the data structures is important.

Data Structures
struct node
{
int data;
struct node *link;
};

Data Structures
struct node *insert(struct node *p, int n) {
struct node *temp;
if(p==NULL){
p=new node;
if(p==NULL){
printf("Errorn");
exit(0);
}
p-> data = n;
p-> link = p;
}
else{
temp = p;
while (temp-> link != p)
temp = temp-> link;
temp-> link = new node;
if(temp -> link == NULL){
printf("Errorn");
exit(0);
}
temp = temp-> link;
temp-> data = n;
temp-> link = p;
}
return (p);

INSERTING A NODE BY USING
RECURSIVE PROGRAMS

Data structures &algorithms

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