theory of computation lecture 01

Theory of Computation

Mahmoud Ali Ahmed (PhD) Dean

Faculty of Mathematical Sciences

U. of K.

email: mali@uofk.edu

What is Computation?Computation is a general term for any type of information

processing that can be represented as an algorithm

precisely (mathematically).

Examples:• Adding two numbers in our brains, on a piece of paper or using a calculator.

• Converting a decimal number to its binary presentation or vise versa.• Finding the greatest common divisors of two numbers.

• …

What is Theory of Computation

• A very fundamental and traditional branch

of Theory of Computation seeks:

1. A more tangible definition for the intuitive

notion of algorithm which results in a more

concrete definition for computation.

2. Finding the boundaries (limitations) of

computation.

"Theoretical Computer Science (TCS) studies the

inherent powers and limitations of computation,

that is broadly defined to include both current and

future, man-made and naturally arising

computing phenomena."

Why Computation

What can be computed with very limited memory?

Such memory restricted devices are all around us

Many applications within CS ranging from compiler construction

and text search to computer challenges in video games

Computability

What can be computed at all?

and any future “computers”

There are fundamental problems that cannot be solved.

ComplexityWhat can be computed efficiently?

Understanding the fundamental limits of computation

including the limits of and any future “computers”

Some problems seems to require more resources to solve than others,

for example coming up with a correct proof for a mathematical

statement seems to take more time than verifying the correctness of

a proof

Theory of Computation tries to answer the

following questions :

o What are the fundamental capabilities and

limitations of computers?

o Which problems can be solved by computers

and which ones cannot?

o What makes some problems computationally

hard and others easy?

Based on this discussion, the course provides an

introduction to the theory of computation.

Traditionally, the study of theory of computation

comprises three central areas:

o Automata.

o Computability, and

o Complexity.

Objective of this CourseThe aim of this course is to introduce several apparently different

formalizations of the informal notion of algorithm; to show that

they are equivalent; and to use them to demonstrate that there are

uncomputable functions and algorithmically undecidable

problems, and enables the student to be aware about:

• Automata theory (Dealing with solving simple decision

problems (solvable and unsolvable problems)),

• Computability (Computable problems: Turing machine), and

• Complexity (dealing with solvable problems: Time Complexity

(P and NP, and NP-complete) and Space Complexity

(PSPACE, NL)).

Course Outlines and Planning:

Historical Perspective

Review: Sets, Mathematical Notations, Graphs and Algorithms.

Machines: Register Machine, Automat, Turing Machine

Complexity Classes.

Course Outlines

Course Planning:Week No. Topics

1 Orientations, Review and Introduction

2,3,4,5,6,

7,8,9

Machines Models: Register Machine and

Automata theory:- (solvability and decidability)

10,11,12,

13

Computability Model :Turing machine

14 Complexity theory: Complexity Classes

Final Exam

Student Evaluation

The student will be evaluated (Assessed) based on the

following:

Course work

Final exam

30%

70%

References- Michael Sipser, Introduction to the Theory of Computation, PWS

Publishing Company, 1997.

- Mikhail J. Atallah and Mariana Blanton (Eds.), Algorithms and

Theory of Computation Handbook: General Concepts and Techniques,

CRC Press, New York, 2009 (2nd Edition).

 Thomas Cormen, Charles Leiserson, Ronald Rivest, and Cliff Stein,

Introduction to Algorithms, McGraw Hill Publishing Company and

MIT Press, 2009 (3rd Edition).

Peter Linz, An Introduction to Formal Languages and Automata,

Jones and Bartlett Publishers, 2006.

- Other open sources (Internet)

Historical PerspectivesEuclid (350 B.C.) : ElementsLeonhard Euler (1707-1783) : Graph theory

Alan Turing (1912-1954) : Computability

Alonzo Church (1903-1995) : Lambda-calculusJohn von Neumann (1903-1957) : Stored program

Claude Shannon (1916-2001) : Information theory

Noam Chomsky (1928-) : Formal languages

John Backus (1924-) : Functional programming

Edsger Dijkstra (1930-2002) : Structured programming

E N D

Review: Sets and Mathematical Notations

DefinitionsSET: It is an unordered collection of elements.

A= {1, 2, 3}

C={x|x is positive integer greater than or equal to x2}

B ={hi, there}Example:

D = {3, 3.1, 3.14, 3.141, 3.1415, 3.14159, …}

A, B and C are finite sets while D is an infinite.

Set construction| or means “such that”

Example:E = {k | 0<k<4}F = {k | k is a perfect square}

Set membership

means “belong to” or “ not belong to”,7 {p | p prime}q {0, 2, 4, 6,...}

Example:

Sets can contain other sets

Example:G = {2, {5}}

H = {{{0}}} {0} 0 S = {1, 2, 3, {1}, {{2}}}

Common SetsNaturals: N = {1, 2, 3, 4, ...}

Integers: Z = {..,-2, -1, 0, 1, 2,..}Rationals: Q = {a/b| a,b Z, b 0}Reals: = {x | x a real #}

Empty set: Ø = {}z+ = non-negative integers

= non-positive real

MultisetsE N D

a set with repeated elements allowed, (i.e., each element has “multiplier”).

Example H = {0, 1, 2, 2, 2, 5, 5}

sequencesDefinition: it an ordered list of elements

(0, 1, 2, 5) “4-tuple”Example:

(1,2) “2-tuple”

Universal & Existential Quantification

“for all”12 xxxExample:

“there exists”x| 2 xxExample:

Combinations:

yxyx

Boolean Operations

E N D

"" AND"" OR

"" NOT

"" XOR

NAND

NOR

Logical Implication"" implies

331 yxyx Example:

"" if and only if (iff)

"" equivalent

)]()[()( ABBABA or

ABBABA

Example:)(),max(),min( yxyxyx

))()( PQQP

Subsets"" Subset notation

)( TxSxTS "" Proper Subset

))()(( TSTSTS ))()(( TSSTTS

SS SSS

Union

Example:

Example:

TxSxxTS Intersection

TxSxxTS

Set difference: S - T

Example: TxSxxTS

Example:

TSTS

TxSxxTS

Symmetric difference: S T

Universal set: U (everything)

Set Complement SorS

Disjoint sets:

Example

SUSxxS Example:

TSTSTS

SSSS

Set Identities• Commutative Law:Example

STTS STTS

• Associative Law:

VTSVTS )()(VTSVTS )()(

Example

Example:

• Associative Law:

)()()( VSTSVTS )()()( VSTSVTS

Example:• Absorption Law:

STSS )(STSS )(

E N D

• DeMorgan's Laws:Example

TSTS )(TSTS )(

Proof Types• Construction• Contradiction

• Induction

• Counter-example

GraphsDefinition: Used to represent and model a problem with special kind of relationship such as:

• Common relationships• Communication networks

• Dependency constraints

• Reachability information• + many more practical applications!

Definition:Graph : is the combination of a set of vertices , and a set of edges VVE V

),( EVG

Pictorially: nodes & lines

Undirected GraphsDefinition: it the graph whose edges have no direction.

a

b

c e

d

V={a, b, c, d, e}

E={(c, a), (c, b), (c, d), (c, e), (a, b), (b, d), (d, e)}

Example

Directed GraphsDefinition: it the graph whose edges have direction.

Example

a

b

c e

d

V={a, b, c, d, e}

E={(a, b), (a, c), (b, c), (b, d), (d, c), (d, e), (c, e)}

Graph TerminologyGraph VVE ),,( EVG node vertexedge arc

a

b

c e

d

f

For any pair of vertices (u, v) V are said to be neighbors in G (u, v) or (v, u) is an edge of G.

a & b are neighbors. a & e are not neighbors.

Undirected Node Degree

Degree in undirected graphs:Degree of any undirected node (vertex) of the graphs is denoted by deg(v):

deg(v) = # of adjacent (incident) edges to vertex v in G

Example

deg(c)=4 deg(f)=0

directed Node Degree

Degree in directed graphs:

For any directed node (vertex) of the graph G there are two types of degree.

In-deg(v): = # of incoming edges

Out-deg(v): = # of outgoing edges

Example

a

b

c e

d

f

in-deg(c)=3 out-deg(c)=1

in-deg(f)=0 out-deg(f)=0

Complete Graph

Graph : is said to be complete if it contains all edges, i. e., vuVVvuE ,

),( EVKn

a

b

c e

d

f

Transition GraphDefinition: a transition graph is the graph that can be defined by vertices representing states (s) and directed lines from each state (sk) to itself (loop) or to another state (sj)

S1 S2

Graph Representation

a

b

c

d

Adjacency list:

1: (a) b d 2: (a) c d

Adjacency matrix:

a b c da 0 1 1 0b 1 0 0 1c 1 0 0 0d 0 1 0 0

AlgorithmAn algorithm is a set of unambiguous computational

procedures or steps that produce an output given an input.

Implementing an algorithm means transforming the set of

steps specified in the algorithm into a computer program.

Algorithms can be used to search, sort, and select data.

Data structures such as stacks, queues, arrays, and

trees can all have algorithms applied to them.

• The study and analysis of algorithms is

important as algorithms play a large role in

solving real world problems particularly in

business and other sectors where computing

has had a large impact.

Why it is important to study & analyze an algorithms?

• Different algorithms that accomplish the

same task can vary in their efficiency.

• This may not be important when used on a

small scale but when applied to large-scale

problems, efficiency becomes ever more

important.

• Algorithm analysis involves measuring how

long an algorithm will take to reach a result and

terminate.

What is an algorithm analysis?

• This may be measured by comparison with

other algorithms or it can be measured by

determining of what "order" the algorithm is.

Algorithm Complexity

Complexity is measured in the form of "Big-O"

notation. For example, "O(1)" means "order 1"

and describes programs where statements are

simply executed one after the other.

These programs/methods are described as "constant time" methods .

Where loops are involved in the program,

this gives the program the order of "O(N)"

and these programs are known as having

"linear time".

Quadratic time methods involve loops being

nested within another loop and is given the

notation "O(N^2)".

By doubling the input of programs with

methods of complexity O(N^2), the number

of steps needed to execute the code to

completion will be multiplied by four.

Based on the complexity (the time needed to execute

the algorithm), algorithms can be classified into

several classes such as

• Linear Time Algorithms

• Exponential Time Algorithms

• Open Time (never complete) Algorithms

Running Time Running time can be estimated in a more general

manner by using Pseudocode to represent the

algorithm as a set of fundamental operations

which can then be counted.

Pseudocode gives a high-level description of an

algorithm without the ambiguity associated with

plain text but also without the need to know the

syntax of a particular programming language.

Pseudocode contains all of the following

features:

Conventional loops: If... then... else...

While... do... Repeat... until... For... do...

Method declaration: Method name: Algorithm methodName(args) Input description: Input... Output description: Output...

Method calls: var.methodName(args) Return values: return... Expressions:

Assignment: < -- Equality comparison: ==

In a pseudo code description of an algorithm, each line can be viewed as a certain number of operations which can be counted and added up to find a total value (order of algorithm) for the algorithm. Some examples are shown below: • varNumber <-- X[1] (2 operations - 1 array value retrieval, 1 assignment).

• For a <-- 1 to n do... (n operations - any operations within this loop will then be multiplied by n also as they will be carried out n times).• return varNumber (1 operations).