3 - Ch 5 6 New.ppt - Excelsoft Technologies Network · · 2014-01-26Figure 6.15 The translation process • Lexical Analysis: – The process of recognizing which strings of symbols

1

Chapter 5

AlgorithmsAlgorithms

By:

J. BrookshearJ. Brookshear

Modified by:

Yacoub Sabatin, BEng, MScYacoub Sabatin, BEng, MSc

© 2005 Pearson Addison-Wesley. & 2014 Yacoub Sabatin. All rights reserved

2

Chapter 5: Algorithms

• 5.1 The Concept of an Algorithm

• 5.2 Algorithm Representation

• 5.3 Algorithm Discovery

• 5.4 Iterative Structures

• 5.5 Recursive Structures

• 5.6 Efficiency and Correctness

2


3

Algorithm: definition

• Before a computer can perform a task, it must be

given an algorithm telling it precisely what to do.

• An algorithm is an ordered set of unambiguous,

executable steps that defines a terminating

process.

• Requirements of an algorithm must comply with

these definition.

4

Algorithms: levels of abstraction

• Problem = motivation for algorithm

• Algorithm = procedure to solve the problem

– Often one of many possibilities

• Representation = description of algorithm

sufficient to communicate it to the desired

audience

– Always one of many possibilities (algebraic, oral, flowchart, electronic ckt, comp prog)

• Distinguish between:

– Algorithm: abstract, conceptual

– Its representation

3


5

Figure 5.2 Folding a bird from a square piece of paper


6

Figure 5.3 Origami primitives

4


7

Pseudocode primitives

• Assignment name� expression

• Conditional selection if condition then action

• Repeated execution while condition do activity

• Procedure procedure name (generic names)


8

Figure 5.4 The procedure Greetings in pseudocode

5


9

Figure 5.6 The sequential search algorithm in pseudocode


10

Problem solving steps

• 1. Understand the problem.

• 2. Get an idea how an algorithmic procedure

might solve the problem.

• 3. Formulate the algorithm and represent it as a

program.

• 4. Evaluate the program for accuracy and its

potential as a tool for solving other problems.

6


11

Techniques for “getting a foot in the door”

• Work the problem backwards

• Solve an easier related problem

– Relax some of the problem constraints

– Solve pieces of the problem first = bottom up methodology

• Stepwise refinement = top-down methodology

– Popular technique because it produces modular programs


12

Sample problem

• Person A is charged with the task of determining the ages of B’s three children.

• B tells A that the product of the children’s ages is 36.

• A replies that another clue is required.

• B tells A the sum of the children’s ages.

• A replies that another clue is needed.

• B tells A that the oldest child plays the piano.

• A tells B the ages of the three children.

• How old are the three children?

7


13

Figure 5.5


14

Figure 5.7 Components of repetitive control

8


15

Figure 5.8 The while loop structure


16

Figure 5.9 The repeat loop structure

9


17

Figure 5.10 Sorting the list Fred, Alex, Diana, Byron, and Carol alphabetically

Insertion Sort:

• Sort the list within itself.

• Take a sublist, then sort

it alphabetically.

• Details, textbook, p196


18

Figure 5.11 The insertion sort algorithm expressed in pseudocode

Insertion Sort:

• Sort the list within itself.

• Take a sublist, then sort

it alphabetically.

• Details, textbook, p196

10


19

Figure 5.12 Applying our strategy to search a list for the entry John


20

Figure 5.13 A first draft of the binary search technique

11


21

Figure 5.14 The binary search algorithm in pseudocode


22

Figure 5.15

12


23

Figure 5.16


24

Figure 5.17

13


25

Software efficiency

• Measured as number of instructions executed

• Θ notation for efficiency classes

– Example: insertion sort is Θ(n2)

• Best, worst, and average case


26

Figure 5.18 Applying the insertion sort in a worst-case situation

14


27

Figure 5.19 Graph of the worst-case analysis of the insertion sort algorithm


28

Figure 5.20 Graph of the worst-case analysis of the binary search algorithm

15


29

Software verification

• Proof of correctness

– Assertions

• Preconditions

• Loop invariants

• Testing


30

Example problem: Chain separating

• A traveler has a gold chain of seven links.

• He must stay at an isolated hotel for seven nights.

• The rent each night consists of one link from the chain.

• What is the fewest number of links that must be cut so

that the traveler can pay the hotel one link of the chain

each morning without paying for lodging in advance?

16


31

Figure 5.21 Separating the chain using only three cuts


32

Figure 5.22 Solving the problem with only one cut

17


33

Figure 5.23 The assertions associated with a typical while structure

Chapter 6

Programming

Languages

By:

J. BrookshearJ. Brookshear

Modified by:

Yacoub Sabatin, BEng, MScYacoub Sabatin, BEng, MSc

18


35

Chapter 6: Programming Languages

• 6.1 Historical Perspective

• 6.2 Traditional Programming Concepts

• 6.3 Procedural Units

• 6.4 Language Implementation

• 6.5 Object Oriented Programming

• 6.6 Programming Concurrent Activities

• 6.7 Declarative Programming


36

Figure 6.1 Generations of programming languages

19


37

Second-generation:Assembly language

• A mnemonic system for representing programs

– Mnemonic names for op-codes

– Names for all registers

– Identifiers = descriptive names for memory

locations, chosen by the programmer

• The machine language considered as the first-

generation, ex. Instruction Set (similar to

what’s in chapter 2)


38

Assembly language characteristics

• One-to-one correspondence between machine

instructions and assembly instructions

– Programmer must think like the machine

• Inherently machine-dependent

• Converted to machine language by a program

called an assembler

20


39

Assembly language example

Machine language

156C

166D

5056

30CE

C000

Assembly language

LD R5, Price

LD R6, ShippingCharge

ADDI R0, R5 R6

ST R0, TotalCost

HLT


40

Third generation language

• Uses high-level primitives

– Similar to our pseudo-code in Chapter 5

• Machine independent (mostly)

• Examples: C, Basic, FORTRAN, COBOL

• Each primitive corresponds to a short sequence of machine language instructions

• Converted to machine language by a program called a compiler

• Some languages are ‘interpreted’ i.e. require ‘Interpreter’, ex. Basic.

21


41

Figure 6.2 The evolution of programming paradigms


42

Figure 6.3 A function for checkbook balancing constructed from simpler functions

22


43

Figure 6.4 The composition of a typical imperative program or program unit


44

Figure 6.5 Variable declarations in C, C++, C#, and Java

23


45

Figure 6.6 A two-dimensional array with two rows and nine columns


46

Figure 6.7 Declaration of heterogeneous array

24


47

Figure 6.8Control structures and their representations in C, C++, C#, and Java


48

Figure 6.9 The for loop structure and its representation in C++, C#, and Java

25


49

Figure 6.10 The flow of control involving a procedure


50

Figure 6.11 The procedure ProjectPopulation written in the programming language C

26


51

Figure 6.12Executing the procedure Demo and passing parameters by value


52

Figure 6.13Executing the procedure Demo and passing parameters by reference

27


53

Figure 6.14 The function CylinderVolume written in the programming language C


54

Figure 6.21 The complete program preparation process

28


55

Figure 6.15 The translation process

• Lexical Analysis:

– The process of recognizing which strings of symbols represent a single

entity.

– Analyzer reads the source symbol by symbol & identify which group of

symbols are related.

• Parser: Views program in terms of lexical units (tokens) rather

than individual symbols, to form a statement in the language.

• Code Generation:Machine-language constructing to implement

the statements recognized by the parser.


56

Figure 6.16 A syntax diagram of our if-then-else pseudocode statement

29

57

Figure 6.17 Syntax diagrams describing the structure of a simple algebraic expression


58

Figure 6.18 The parse tree for the string x + y x z based on the syntax diagrams in Figure 6.17

30


59

Figure 6.19Two distinct parse trees for the statement if B1 then if B2 then S1 else S2


60

Figure 6.20 An object-oriented approach to the translation process

31


61

Objects and Classes

• Object = active program unit containing both

data and procedures

• Class = a template for all objects of the same

type (blueprint)

An Object is often called an instance of the class.


62

Components of an object

• Instance variable = variable within an object

• Method = function or procedure within an

object

– Can manipulate the object’s instance variables

• Constructor = special method to initialize a

new object instance

32


63

Encapsulation

• Encapsulation = a way of restricting access to

the internal components of an object

– Private

– Public


64

Additional object-oriented concepts

• Inheritance: allows new classes to be defined in

terms of previously defined classes

• Polymorphism: allows method calls to be

interpreted by the object that receives the call

33


65

Figure 6.22 The structure of a class describing a laser weapon in a computer game


66

Figure 6.23 A class with a constructor

34


67

Figure 6.24 Our LaserClass definition using encapsulation as it would appear in a Java or C# program


68

Programming concurrent activities

• Parallel or concurrent processing =

simultaneous execution of multiple processes

– True concurrent processing requires multiple CPUs

– Can be simulated using time-sharing with a single

CPU

35


69

Figure 6.25 Spawning processes


70

Interaction between processes

• Mutual exclusion = a method for ensuring that

data can be accessed by only one process at a

time

• Monitor = a data item augmented with the

ability to control access to itself

36


71

Declarative programming

• Resolution = combining two or more statements to

produce a new, logically equivalent statement

– Example: (P OR Q) AND (R OR ¬Q)

resolves to (P OR R)

– Resolvent = a new statement deduced by resolution

– Clause form = statement whose elementary components are

connected by the Boolean operation OR

• Unification = assigning a value to a variable in a

statement


72

Figure 6.26 Resolving the statements (P OR Q) and (R OR ¬Q) to produce (P OR R)

37


73

Figure 6.27 Resolving the statements (P OR Q), (R OR ¬Q), ¬R, and ¬P


74

Prolog

• Fact = predicateName(arguments).

– Example: parent(bill, mary).

• Rule = conclusion :- premise.

– :- means “if”

– Example: wise(X) :- old(X).

– Example: faster(X,Z) :- faster(X,Y), faster(Y,Z).

• All statements must be fact or rules.

Documents

3 - Ch 5 6 New.ppt - Excelsoft Technologies Network · · 2014-01-26Figure 6.15 The translation process • Lexical Analysis: – The process of recognizing which strings of symbols