21-algorithms (1).ppt

1
Data Structures
and Algorithms
ICT 006

2
What is an algorithm?
An algorithm is “a finite set of precise
instructions for performing a computation or for
solving a problem”
 A program is one type of algorithm
All programs are algorithms
Not all algorithms are programs!
 Directions to somebody’s house is an algorithm
 A recipe for cooking a cake is an algorithm
 The steps to compute the cosine of 90° is an
algorithm

3
Some algorithms are harder than
others
Some algorithms are easy
 Finding the largest (or smallest) value in a list
 Finding a specific value in a list
Some algorithms are a bit harder
 Sorting a list
Some algorithms are very hard
 Finding the shortest path between Miami and Seattle
Some algorithms are essentially impossible
 Factoring large composite numbers
In section 2.2, we’ll see how to rate how “hard”
algorithms are

4
Algorithm 1: Maximum element
Given a list, how do we find the maximum
element in the list?
To express the algorithm, we’ll use
pseudocode
 Pseudocode is kinda like a programming
language, but not really

5
Algorithm for finding the maximum
element in a list:
procedure max (a1, a2, …, an: integers)
max := a1
for i := 2 to n
if max < ai then max := ai
{max is the largest element}

6
procedure max (a1, a2, …, an: integers)
max := a1
for i := 2 to n
max := a1
for i := 2 to n
4 1 7 0 5 2 9 3 6 8
a1 a2 a3 a4 a5 a6 a7 a8 a9 a10
max
i 2
3
4
5
6
7
8
9
10
4
7
9

7
Maximum element running time
How long does this take?
If the list has n elements, worst case
scenario is that it takes n “steps”
 Here, a step is considered a single step
through the list

8
Properties of algorithms
Algorithms generally share a set of properties:
 Input: what the algorithm takes in as input
 Output: what the algorithm produces as output
 Definiteness: the steps are defined precisely
 Correctness: should produce the correct output
 Finiteness: the steps required should be finite
 Effectiveness: each step must be able to be
performed in a finite amount of time
 Generality: the algorithm should be applicable to all
problems of a similar form

9
Searching algorithms
Given a list, find a specific element in the
list
We will see two types
 Linear search
a.k.a. sequential search
 Binary search

10
Algorithm 2: Linear search
Given a list, find a specific element in the list
 List does NOT have to be sorted!
procedure linear_search (x: integer; a1, a2, …, an:
integers)
i := 1
while ( i ≤ n and x ≠ ai )
i := i + 1
if i ≤ n then location := i
else location := 0
{location is the subscript of the term that equals x, or it
is 0 if x is not found}

11
procedure linear_search (x: integer; a1, a2, …, an: integers)
i := 1
i := i + 1
else location := 0
i := 1
i := i + 1
else location := 0
Algorithm 2: Linear search, take 1
4 1 7 0 5 2 9 3 6 8
a1 a2 a3 a4 a5 a6 a7 a8 a9 a10
i 2
3
4
5
6
7
8
1
x 3
location 8

12
procedure linear_search (x: integer; a1, a2, …, an: integers)
i := 1
i := i + 1
else location := 0
i := 1
i := i + 1
else location := 0
Algorithm 2: Linear search, take 2
4 1 7 0 5 2 9 3 6 8
a1 a2 a3 a4 a5 a6 a7 a8 a9 a10
i 2
3
4
5
6
7
8
9
10
1
x 11
location 0
11

13
Linear search running time
How long does this take?
If the list has n elements, worst case
scenario is that it takes n “steps”
 Here, a step is considered a single step
through the list

14
Algorithm 3: Binary search
Given a list, find a specific element in the list
 List MUST be sorted!
Each time it iterates through, it cuts the list in half
procedure binary_search (x: integer; a1, a2, …, an: increasing integers)
i := 1 { i is left endpoint of search interval }
j := n { j is right endpoint of search interval }
while i < j
begin
m := (i+j)/2 { m is the point in the middle }
if x > am then i := m+1
else j := m
end
if x = ai then location := i
else location := 0
{location is the subscript of the term that equals x, or it is 0 if x is not found}

15
Algorithm 3: Binary search, take 1
2 4 6 8 10 12 14 16 18 20
a1 a2 a3 a4 a5 a6 a7 a8 a9 a10
i j
m
i := 1
j := n
while i < j
begin
m := (i+j)/2
else j := m
end
else location := 0
i := 1
j := n
while i < j
begin
m := (i+j)/2
else j := m
end
1
x 14
10
5
6 8 8
7 7
6
7
location 7

16
Algorithm 3: Binary search, take 2
2 4 6 8 10 12 14 16 18 20
a1 a2 a3 a4 a5 a6 a7 a8 a9 a10
i j
m
i := 1
j := n
while i < j
begin
m := (i+j)/2
else j := m
end
else location := 0
i := 1
j := n
while i < j
begin
m := (i+j)/2
else j := m
end
if x = ai then location := I
else location := 0
1
x 15
10
5
6 8 8
7
location 0
8

17
Algorithm 3: Binary search
A somewhat alternative view of what a
binary search does…

18
Binary search running time
How long does this take (worst case)?
If the list has 8 elements
 It takes 3 steps
If the list has n elements
 It takes log2 n steps

19
Sorting algorithms
Given a list, put it into some order
 Numerical, lexicographic, etc.
We will see two types
 Bubble sort
 Insertion sort

20
Algorithm 4: Bubble sort
One of the most simple sorting algorithms
 Also one of the least efficient
It takes successive elements and “bubbles” them
up the list
procedure bubble_sort (a1, a2, …, an)
for i := 1 to n-1
for j := 1 to n-i
if aj > aj+1
then interchange aj and aj+1
{ a1, …, an are in increasing order }

22
Algorithm 4: Bubble sort
An example using physical objects…

23
Bubble sort running time
Bubble sort algorithm:
for i := 1 to n-1
for j := 1 to n-i
if aj > aj+1
then interchange aj and aj+1
Outer for loop does n-1 iterations
Inner for loop does
 n-1 iterations the first time
 n-2 iterations the second time
 …
 1 iteration the last time
Total: (n-1) + (n-2) + (n-3) + … + 2 + 1 = (n2-n)/2
 We can say that’s “about” n2 time

25
Algorithm 5: Insertion sort
Another simple (and inefficient) algorithm
It starts with a list with one element, and inserts new elements into
their proper place in the sorted part of the list
procedure insertion_sort (a1, a2, …, an)
for j := 2 to n
begin
i := 1
while aj > ai
i := i +1
m := aj
for k := 0 to j-i-1
aj-k := aj-k-1
ai := m
end { a1, a2, …, an are sorted }
take successive elements in the list
find where that element should be
in the sorted portion of the list
move all elements in the sorted
portion of the list that are greater
than the current element up by one
put the current element into it’s proper place in the sorted portion of the list

26
Insertion sort running time
for j := 2 to n begin
i := 1
while aj > ai
i := i +1
m := aj
for k := 0 to j-i-1
aj-k := aj-k-1
ai := m
end { a1, a2, …, an are sorted }
Outer for loop runs n-1 times
In the inner for loop:
 Worst case is when the while keeps i at 1, and the for loop runs lots of
times
 If i is 1, the inner for loop runs 1 time (k goes from 0 to 0) on the first
iteration, 1 time on the second, up to n-2 times on the last iteration
Total is 1 + 2 + … + n-2 = (n-1)(n-2)/2
 We can say that’s “about” n2 time

27
Comparison of running times
Searches
 Linear: n steps
 Binary: log2 n steps
 Binary search is about as fast as you can get
Sorts
 Bubble: n2 steps
 Insertion: n2 steps
 There are other, more efficient, sorting techniques
In principle, the fastest are heap sort, quick sort, and merge
sort
These each take take n * log2 n steps
In practice, quick sort is the fastest, followed by merge sort

21-algorithms (1).ppt

Recommended

More Related Content

Similar to 21-algorithms (1).ppt (20)

Recently uploaded (20)

21-algorithms (1).ppt