1. Lecture 3 Pseudocode (S&G, §2.2)
5/4/2019
Lecture 3
Pseudocode (S&G, §2.2)
5/4/2019
CS Lecture 3
CS 100

2. Representation of Algorithms
Natural languages (English, Spanish, Chinese, …)
Formal programming languages (Java, Pascal, C++, …)
Pseudocode
“a special set of English language constructs modeled to look like the statements available in most programming languages”
compromise between natural & formal languages
5/4/2019
CS Lecture 3

3. Sequential Operations
Lecture 3
5/4/2019
Sequential Operations
computing agent
interface
input
computation
output
Note that Input xlates from physical quantities into arbitrtary media of computation,
and Output vice versa
external world
5/4/2019
CS Lecture 3
CS 100

4. Input Operation Example: Get a value for r, the radius of the circle
Lecture 3
5/4/2019
Input Operation
Example: Get a value for r, the radius of the circle
Get a value for r
25 17 25 r
Note that we are not fussy about syntax, so long as meaning is clear
Note that input can also come from a file, or a device (mouse, thermometer, etc.)
General form: Get values for “variable”, “variable”, …
“variable” means “any variable” (e.g., r)
5/4/2019
CS Lecture 3
CS 100

5. Output Operation Example: Print the value of Area
Lecture 3
5/4/2019
Output Operation
Example: Print the value of Area
Print the value of Area 26
26
Area
Note that can also print to display, write to file, control a device, etc.
General form: Print the values of “variable”, “variable”, …
5/4/2019
CS Lecture 3
CS 100

6. Print the message ‘Sorry, no answer’
Lecture 3
5/4/2019
Output Operation (2)
Example: Print the message ‘Sorry, no answer’
Print the message ‘Sorry, no answer’
Sorry, no answer
Note that can also print to display, write to file, control a device, etc.
General form: Print the message ‘message’
5/4/2019
CS Lecture 3
CS 100

7. Computational Operation
Lecture 3
5/4/2019
Computational Operation
Example: Set the value of Area to  r2
 = 3.14…
compute 3.14…  (252)
= 1963
1963
177 25
Area r
Note that we assume that we can do computer-like operations, such as arithmetic. 2
General form: Set the value of “variable” to “arithmetic expression”
5/4/2019
CS Lecture 3
CS 100

8. Computation is Physically Instantiated
Lecture 3
5/4/2019
Computation is Physically Instantiated
  (2 cm/2)2  3 cm  g/cm3
= g
2 cm
3 cm Hg
Note that “3 cm” need not be 3 cm long, nor “127.7 g” weight g
The physical quantities in real world are real, the numbers in the calculation are representations
Nevertheless, for calculation to take place, they mut be physically instantiated
(as pencil marks, for example)
127.7 g
5/4/2019
CS Lecture 3
CS 100

9. Computational Processes in General
Computation takes place in a medium
in which patterns can be represented
by means of which they can be manipulated
Example computational media:
electrical signals & charges
light intensity, fluid pressure, molecular structure, quantum state, gear position (old), abacus beads, paper, blackboard, etc.
5/4/2019
CS Lecture 3

10. Input Processes in General
An input process translates an external physical situation into the computer’s internal representational medium
Example physical situations:
position of mouse or joystick
position of key (depressed or not)
signal from light sensor
etc.
5/4/2019
CS Lecture 3

11. Output Processes in General
An output process translates the computer’s internal medium of representation into an external physical effect
Example physical effects:
characters on display screen or printer
graphics images
position of robot arm
etc.
5/4/2019
CS Lecture 3

12. Conditional Operation
Lecture 3
5/4/2019
Conditional Operation
true/false condition
do one set of operations
if true
do another set of operations
if false
Called “true branch” (or consequent) and “false branch” (or alternate)
Also, if one alternate is “do nothing” then in effect we have the choice of whether to do something or not
(We could pick which to do by tossing a coin, but that sort of non-decision can be reduced to this form)
continue
5/4/2019
CS Lecture 3
CS 100

13. Conditional Statement
Lecture 3
5/4/2019
Conditional Statement
General:
If “true/false condition” is true then
“first set of algorithmic operations”
Else (or Otherwise)
“second set of algorithmic operations”
(rest of algorithm)
Example:
If (ci  10) then
Set the value of ci to (ci – 10)
Set the value of carry to 1
Else
Set the value of carry to 0
Note indenting to make structure clearer
5/4/2019
CS Lecture 3
CS 100

14. One-Branch Conditional
Lecture 3
5/4/2019
One-Branch Conditional
true/false condition
If “true/false condition” is true then
“first set of algorithmic operations”
(rest of algorithm)
do one set of operations
if true
do nothing
if false
Called “true branch” (or consequent) and “false branch” (or alternate)
Also, if one alternate is “do nothing” then in effect we have the choice of whether to do something or not
(We could pick which to do by tossing a coin, but that sort of non-decision can be reduced to this form)
continue
5/4/2019
CS Lecture 3
CS 100

15. Computer-Inspired Conditionals in Legal Drafting
Lecture 3
5/4/2019
Computer-Inspired Conditionals in Legal Drafting
Subsection (a). IF AND ONLY IF
(1) (A) A person has threatened or attempted suicide or to inflict serious bodily harm on himself, OR
(B) The person has threatened or attempted homicide or other violent behavior, OR
(C) The person has placed others in reasonable fear of violent behavior and serious physical harm to them, OR
(D) The person is unable to avoid severe impairment or injury from specific risks, AND
(2) There is a substantial likelihood that such harm will occur,
THEN
(3) The person poses a “substantial likelihood of serious harm” for purposes of subsection (b).
This is called Normalized English
Note that this format also allows computers to process the text and answer a variety of legal questions.
— Tenn. Code Ann. sect (a) (1991)
5/4/2019
CS Lecture 3
CS 100

16. Basic Loop (Trailing Decision)
Lecture 3
5/4/2019
Basic Loop (Trailing Decision)
do loop body
if not, repeat
test if done
This is a sort of combination of the previous two:
we do something
AND then we decide to do it again OR not
Note that this is not the only kind of iterative operation
This form does the operation at least once
if done, continue
5/4/2019
CS Lecture 3
CS 100

17. Iteration (Trailing Decision)
Lecture 3
5/4/2019
Iteration (Trailing Decision)
Repeat steps i to j until “a true/false condition” becomes true
step i: operation
step i + 1: operation …
step j: operation
(rest of algorithm)
termination condition
loop body
Note indenting
If something in the loop body does not cause the termination condition to become true eventually,
then the looping will go on forever
This is a infinite loop
Violates the condition that an algorithm must return a result in finite time
5/4/2019
CS Lecture 3
CS 100

18. Example Loop Set the value of count to 1
Repeat steps 3 to 5 until (count > 100)
Set square to (count  count)
Print the values of count and square
Add 1 to count
5/4/2019
CS Lecture 3

19. Alternate Notation Repeat until “a true/false condition” becomes true
“a set of algorithmic operations”
End of loop
5/4/2019
CS Lecture 3

20. continue with rest of algorithm
Lecture 3
5/4/2019
Leading Decision Loop
test if more
and repeat
While “a true/false condition” remains true do
“a set of algorithmic operations”
End of loop
continue with rest of algorithm
if not
do loop body
if more
In this case test is called a “continuation condition”
5/4/2019
CS Lecture 3
CS 100