1. Algorithms
Step-by-step instructions that tell a computing agent how to solve some problem using only finite resources
Resources
Memory
CPU cycles
Time/Space
Types of instructions
Sequential
Conditional
If statements
Iterative
Loops

2. Pseudocode: The Interlingua for Algorithms
an English-like description of the sequential, conditional, and iterative operations of an algorithm
no rigid syntax. As with an essay, clarity and organization are key. So is completeness.

3. Pseudocode Example Find Largest Number
Input: A list of positive numbers
Output: The largest number in the list
Procedure:
1. Set Largest to zero
2. Set Current-Number to the first in the list
3. While there are more numbers in the list
3.1 if (the Current-Number > Largest) then
Set Largest to the Current-Number
3.2 Set Current-Number to the next one in the list
4. Output Largest

4. Pseudocode Example Find Largest Number
Input: A list of positive numbers
Output: The largest number in the list
Procedure:
1. Set Largest to zero
2. Set Current-Number to the first in the list
3. While there are more numbers in the list
3.1 if (the Current-Number > Largest) then
Set Largest to the Current-Number
3.2 Set Current-Number to the next one in the list
4. Output Largest
conditional
operation

5. Pseudocode Example Find Largest Number
Input: A list of positive numbers
Output: The largest number in the list
Procedure:
1. Set Largest to zero
2. Set Current-Number to the first in the list
3. While there are more numbers in the list
3.1 if (the Current-Number > Largest) then
Set Largest to the Current-Number
3.2 Set Current-Number to the next one in the list
4. Output Largest
iterative
operation

6. Pseudocode Example Find Largest Number
Input: A list of positive numbers
Output: The largest number in the list
Procedure:
1. Set Largest to zero
2. Set Current-Number to the first in the list
3. While there are more numbers in the list
3.1 if (the Current-Number > Largest) then
Set Largest to the Current-Number
3.2 Set Current-Number to the next one in the list
4. Output Largest
Let’s “play computer” to review this algorithm…

7. Algorithms vary in efficiency
example: sum the numbers from 1 to n
1. Set sum to 0
2. Set currNum to 1
3. Repeat until currNum > n
Set sum to sum + currNum
Set currNum to currNum + 1
Algorithm I
efficiency
space= 3 memory cells
time = t(step1) +
t(step 2) +
n t(step 4) +
n t(step 5)
space requirement is constant (i.e. independent of n)
time requirement is linear (i.e. grows linearly with n). This is written “O(n)”
to see this graphically... 8

8. Algorithm Is time requirements
size of the problem
time
y = mx + b
time = m n + b
The exact equation for the line is unknown
because we lack precise values for the constants m and b.
But, we can say:
time is a linear function of the size of the problem
time = O(n) 9

9. Algorithm II for summation
First, consider a specific case: n = 100.
The “key insight”, due to Gauss:
the numbers can be grouped into
50 pairs of the form:
= 101
= 101
. . .
= 101 }
sum = 50 x 101
This algorithm
requires a single
multiplication!
Second, generalize the formula for any (even) n :
sum = (n / 2) (n + 1)
Time requirement is constant.
time = O(1) 10

10. Sequential Search: A Commonly used Algorithm
Suppose you want to a find a student in the UT directory.
It contains EIDs, names, phone numbers, lots of other information.
You want a computer application for searching the directory: given an EID, return the student’s phone number.
You want more, too, but this is a good start…

11. Sequential Search of a student database.
name EID major credit hrs.
John Smith
Paula Jones
Led Belly
Chuck Bin
JS456
PJ123
LEB900
CB1235
physics
history
music
math 36
125 72 89 1 2 n 3
algorithm
to search database
by EID :
1. ask user to enter EID to search for
2. set i to 1
3. set found to ‘no’
4. while i <= n and found = ‘no’ do
if EID = EIDi then set found to ‘yes’
else increment i by 1
7. if found = ‘no’ then print “no such student”
else < student found at array index i > 12

12. Time requirements for sequential search
amount
of work
best case (minimum amount of work):
EID found in student1
one loop iteration
worst case (maximum amount of work):
EID found in studentn
n loop iterations
average case (expected amount of work):
EID found in studentn/2
n/2 loop iterations
because the amount of work is a constant multiple of n,
the time requirement is O(n)
in the worst case and the average case. 13

13. O(n) searching is too slow
Consider searching UT’s student database using
sequential search on a computer capable of
20,000 integer comparisons per second:
n = 150,000 (students registered during past 10 years)
average case
150, comparisons seconds = seconds
,000 comparisons x
worst case
150,000 comparisons x seconds = 7.5 seconds
20,000 comparisons
Bad news for searching NYC phone book, IRS database, ... 14

14. Searching an ordered list is faster: an example of binary search.
name student major credit hrs.
John Smith
Paula Jones
Led Belly
Chuck Bin
24576
36794
42356
93687
physics
history
music
math 36
125 72 89 1 2 n 3
student
38453
41200
43756
45987
47865
49277
51243
58925
59845
60011
60367
64596
86756 4 5 6 7 8 9 10 11 12 13 14 15 16
note: the
student array
is sorted in
increasing
order
how would you
search for ?
38453 ?
46589 ?
Probe 1
Probe 3
Probe 2 15

15. The binary search algorithm
assuming that the entries in student are sorted in increasing order,
1. ask user to input studentNum to search for
2. set found to ‘no’
3. while not done searching and found = ‘no’
set middle to the index counter at the middle of the student list
5. if studentNum = studentmiddle then set found to ‘yes’
6. if studentNum < studentmiddle then chop off the last half
of the student list
If studentNum > studentmiddle then chop off the first half
8. if found = ‘no’ then print “no such student”
else <studentNum found at array index middle>
What does this mean?
How?
How? 16

16. The binary search algorithm
assuming that the entries in student are sorted in increasing order,
1. ask user to input studentNum to search for
2. set beginning to 1
3. set end to n
4. set found to ‘no’
5. while beginning <= end and found = ‘no’
6. set middle to (beginning + end) / 2 {round down to nearest integer}
7. if studentNum = studentmiddle then set found to ‘yes’
8. if studentNum < studentmiddle then set end to middle - 1
9. if studentNum > studentmiddle then set beginning to middle + 1
10.if found = ‘no’ then print “no such student”
else <studentNum found at array index middle> 17

17. Time requirements for binary search
At each iteration of the loop, the algorithm cuts the list
(i.e. the list called student) in half.
In the worst case (i.e. when studentNum is not in the list called student)
how many times will this happen?
n = 16
1st iteration 16/2 = 8
2nd iteration 8/2 = 4
3rd iteration 4/2 = 2
4th iteration 2/2 = 1
the number of times a number n
can be cut in half and not go below 1
is log2 n.
Said another way:
log2 n = m is equivalent to 2m = n
In the average case and the worst case, binary search is O(log2 n) 18

18. This is a major improvement
n sequential search binary search
O(n) O(log2 n)
150, ,
20,000, ,000,
27=128
218=262,144
225 is about
33,000,000
number of comparisons
needed in the worst case
150,000 comparisons x second = 7.5 seconds
20,000 comparisons
in terms of seconds...
comparisons x second > seconds
sequential search:
binary search:
vs. 19

19. Sorting a list
First, an algorithm that’s easy to write, but is badly inefficient...
unsorted
sorted
35467
67854
46781
13528
87341 1 2 3 4 5
initially
unsorted
sorted
35467
67854
46781
13528
87341 1 2 3 4 5
1st iteration
unsorted
sorted
35467
67854
46781
13528
87341 1 2 3 4 5
2nd iteration
unsorted
sorted
35467
67854
46781
13528
87341 1 2 3 4 5
etc.
3rd iteration 20

20. The “Simple Sort” Algorithm
given a list of positive numbers, unsorted1, …, unsortedn and
another list, sorted1, …, sortedn, with all values initially set to zero
1. set i to 1
2. repeat until i > n
set indexForSmallest to the index of the smallest
positive value in unsorted
set sortedi to unsortedindexForSmallest
set unsortedindexForSmallest to 0
increment i

21. This algorithm is expensive!
Time requirement
total time = n iterations x time per iteration
time per iteration = time to find smallest value in a list of length n
= O(n)
total time = n x O(n)
= O(n2)
Space requirement
total space = space for unsorted + space for sorted
= O(2n) 22

22. Creating Algorithms is the Challenge of Computer Science
It’s not easy; try this one:
The Traveling Salesperson problem:
A salesperson wants to visit 25 cities while minimizing the total number of miles driven, visiting each city exactly once, and returning home again. Which route is best?

23. Simplify the Problem to get an intuition about it
C D
four cities connected by roads
Q: Starting at A, what’s the shortest route that meets the requirements?
A: Obvious to anyone who looks at the entire map. Not so obvious to an
algorithm that “sees”, at any one time, only one city and its roads
One algorithm to answer the question:
1. generate all possible routes of length 5
2. check each path to determine whether it meets the requirement
start at A,
visit B, C, and D in some order,
then return to A
How much time does this algorithm require? 24

24. All Paths from A of length 5
A D A D A D A D A D A D A D A D
B C B C B C B C
A D A D
B C
A B
C D
Number of paths to generate and check is 24 = 16. 25

25. Can you Improve the Algorithm?
Prune bad routes as soon as possible. What’s a “bad route?”
Look for good solutions, even if they’re sub-optimal. What’s a “good solution?”
Bad routes have cycles.
Also a route is bad if its opening subroute is more costly than the best known complete route.

26. This gets real bad, real fast!
In general, the algorithm’s time requirement is: (the number of roads in&out of a city) number of cities
Assuming the number of roads is only 2, the time requirement is 2number of cities , given by the powers of 2 table.
Assuming a computer could evaluate 10 million routes per second, finding the best route for 25 cities would require about 3.5 seconds. No problem!
However, finding the best route for 64 cities would require about seconds, or hours!
20 million
5000

27. Comparing the time requirements
work 2n n2
order ,000 35 30 25 20 15 10 5
log2n n
n min
2n x forget it!
years centuries n
log2n
time requirements for algorithms
of various orders of magnitude.
Time is in seconds, unless stated otherwise n 28

28. Conclusions Algorithms are the key to creating computing solutions.
The design of an algorithm can make the difference between useful and impractical.
Algorithms often have a trade-off between space (memory) and time.