(aka cs302: Discrete Mathematics II). Spring 2010. University of Virginia. David Evans. Computation is what Computers do, who needs theory?. flickr. : . gastev. [cc]. Charles Babbage’s . Difference Engine.

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Slide1

cs3102: Theory of Computation(aka cs302: Discrete Mathematics II)

Spring 2010University of VirginiaDavid EvansSlide2

Computation is what Computers do, who needs theory?

flickr

:

gastev

[cc]

Charles Babbage’s

Difference Engine

(1822, recreation)Slide3

“

Engining

” is what Engines do, who needs theory?

Hero of Alexandria’s

aeolipile

steam engine

Matthew Boulton and James Watt steam engine, 1817Slide4

Nicolas Carnot

(1796 – 1832)

“Is the potential work available from a heat source potentially unbounded?"

“Can heat engines be improved by replacing the steam with some other fluid or gas?”Slide5

Carnot’s Answer

Efficiency of an ideal engine depends only on the temperature difference between the reservoirs. Slide6

Does Theory Matter?

Theory and Construction of a Rational Heat-engine to Replace the Steam Engine and Combustion Engines Known Today,

Rudolf Diesel, 1893Slide7

Theory Drives Practice

DrivesTheorySlide8

Math Theorem vs. Science TheorySlide9

Math Theorem

Starts with a simple, well-define modelDeductive reasoning: Proven using logical deductionUseful it if provides deep insights

Scientific Theory

Starts with the complex, messy universe

Inductive reasoning:

“Proven” by lots of confirming observations and no non-conforming observations

Useful if it makes reliable predictions and helps us understand the universe

Even wrong theories are useful

This class: mostly Math Theorems, but some Scientific Theories Slide10

Key Questions

“Is the potential work available from a heat source potentially unbounded?" “Can heat engines be improved by replacing the steam with some other fluid or gas?”

Carnot’s questions about heat engines

Analogous questions about computers

“Can all problems be solved by computers?"

“Can computers solve more problems by changing their operation?”Slide11

Precise Definitions Needed

What is a problem?

What is a

computer

?

What problems can a computer solve? (Computability)

What does it mean for a computer to

solve

a problem?

What problems can a computer solve in a

reasonable time

?

(Complexity)

Two Key

Questions

How do we measure

time

?Slide12

What problems can a computer solve?

Answered (for a model) by Church and Turing (1930s)

“During the last six months I have been contriving another engine of far greater power. .. I am myself astonished at the powers I have given it.”

Charles Babbage, 1835

“It will not slice a pineapple.”

Charles Babbage, 1852

Note: Babbage wasn’t actually talking about the Analytical Engine when he said this.Slide13

There’s an app for that?Slide14

What problems can real computers solve in a

reasonable time?

I can't find an efficient algorithm, but neither can all these famous people.

Theoretical version:

(

P

=

NP

) posed by Stephen Cook in 1971

Open problem

Pragmatic version:

do all computers in our universe have these limitations?

Open problem

We (probably) won’t answer these questions in this class (but if you do you get an automatic A+!). But we will develop tools for understanding what answers might look like.Slide15

Topics in cs3102

Classes 1-18Classes 19-28

What problems can a computer solve? (Computability)

What problems can computers solve in a

reasonable time

?

(Complexity)

January - March

Problem Sets 1-5

April, May

Problem Sets 6-7Slide16

Models of Computation

Machine-likeLanguage-like

Finite Automata

(Class

2-6)

Regular ExpressionsPushdown Automata (add a stack) (Classes 7-8)

Context-free Grammar

(Classes

9-11)

Turing machine

(add an infinite tape) (Classes

12-28)

Unrestricted Grammar, Lambda Calculus

+ add

nondeterminism

to each of these!Slide17

Course OrganizationSlide18

Help Available

David EvansOffice hours (Olsson 236A): Mondays,

1:15-3pm

Thursdays (including today), right after class

Assistant:

Sonali

Parthasarathy

sp5ej@virginia.edu

Registration Survey: asks if you can make these office hoursSlide19

Course Blog: http://www.cs.virginia.edu/cs3102

Four things to do after class today:

Register

for the course blog

Complete

course registration survey

Subscribe

to Posts and Comments RSS feeds

Download

Problem Set 1

1

1

3

4Slide20

AssignmentsReading: mostly from

Sipser, some additional readings laterProblem Sets (7): PS1 is posted now, due Tuesday, Feb 2Exams (2 + final):First exam will be in-class March 2, one page of notes allowed

Second exam will (probably) be take home, April 8-13

Final examSlide21

Honor CodePlease don’t cheat!

If you’re not sure if what you are about to do is cheating, ask firstCollaboration on problem sets: “Gilligan’s Island” collaboration policy (described on PS1 handout)

Encourages discussion in groups, but ensures you understand everything yourself

Don’t use

found

solutionsExams: work aloneExam 1: in-class, one

page of notes allowedSlide22

Late PolicySlide23

Slide24

My Goals for the Course

Charles Babbage’s Brain Slide25

Definitions and ProofsSlide26

Language of Computer ScienceSets

Natural NumbersStringsLanguages

What makes a good definition?Slide27

Defining the Natural Numbers

Ellipsis definition: N

= {1, 2, 3, ...}

Theorem:

There is no largest natural number.

Proof:

The meaning of “...” goes on forever.Slide28

Defining the Natural Numbers

Recursive definition:Base: 1 is a natural number

Induction: if

i

is a natural number,

i

+1

is a natural number

Theorem:

There is no largest natural number.

Proof:

Suppose there is some largest natural number

x

.

By the induction part of the definition,

x

+1

is a natural number. Since

x

+1 >

x

, no such

x

exists.Slide29

What is a Proof?An argument that a statement is true that is convincing to a “reasonable” person

Mathematical proofs are convincing if they follow established techniques:Proof by ContradictionProof by ConstructionProof by Induction

Proof by ReductionSlide30

Proof by Contradiction

Assume the logical opposite of the statement.Show it leads to a contradiction.

Theorem:

There is no largest natural number.

Proof by Contradiction:

Suppose there is some largest natural number

x

.

By the induction part of the definition,

x

+1

is a natural number. Since

x

+1 >

x

, no such

x

exists.

What are all the (unstated) assumptions in this proof?Slide31

Proof by Induction

To show something is true for an infinite set of objects:Define the set recursively (often assumed, but important to be explicit)Show the property is true for the base case

Show that the

induction case

preserves the property:

assume it holds for incoming objects prove it holds for created objects

This is a form of Deductive reasoning, not Inductive reasoning!Slide32

Theorem:

The sum of two natural numbers is a natural number.Slide33

Natural Numbers:

Base: 1 is a natural numberInduction: if

i

is a natural number,

i

+1

is a natural

number

Theorem:

The sum of two natural numbers is a natural number.

The

sum of two natural numbers,

A + B

, is a natural number.

Proof:

By

induction on the value of

B

:

Base case:

B

= 1

.

By the definition, since

A

is a natural number,

A

+1

is a natural number.

Induction case:

B

=

i

+ 1

for some natural number

i

.

Induction

hypothesis:

A +

i

is a natural number.

Since

A +

i

is a natural number,

A +

i

+ 1 =

A

+

B

is a natural number.Slide34

Set A group of objects.

Base: (the empty set) is a set

Induction:

if

S

is a set, adding one object to S produces a set.Alphabet

A finite set of symbols

String

A sequence of symbols from an alphabet,

Base:

(the empty string)

is a

string

Induction:

if

s

is a string, and

a

,

sa

is a string

Language

A set of strings

In CS theory, this is the definition of a language.Slide35

Proofs about Strings and Languages

Prove there is no longest string.Prove the set of strings is closed under concatenation.

Prove these two languages are the same:

A

= [

ab

]*

B

is defined by:

B

if

s

B

then

s

a

B

and

s

b

BSlide36

ChargeBefore Sunday:

Register for course blog, submit survey, subscribe to RSS feedsBefore Tuesday: Read Sipser Chapter 0 and Section 1.1

Start Problem Set 1

I have office hours now.

aka cs302 Discrete Mathematics II Spring 2010 University of Virginia David Evans Computation is what Computers do who needs theory flickr gastev cc Charles Babbages Difference Engine ID: 532490 Download Presentation

Class 24: . NP-Completeness. Spring 2010. University of Virginia. David Evans. Menu. Dilbert and Decidability. P = NP . Recap. Proving a problem is in . NP. NP-Complete. If you haven’t yet, read PS7 today! It is different from previous PS and you should start thinking about it. You are not expect to wait until PS6 is due to start PS7..

(aka cs302: Discrete Mathematics II). Spring 2010. University of Virginia. David Evans. Computation is what Computers do, who needs theory?. flickr. : . gastev. [cc]. Charles Babbage’s . Difference Engine.

Class . 4: . Nondeterminism. Spring 2010. University of Virginia. David Evans. TexPoint fonts used in EMF. . Read the TexPoint manual before you delete this box.: . A. A. A. A. A. A. A. A. A. A. A. A.

Class 22: . Introducing Complexity. Spring 2010. University of Virginia. David Evans. Exam 2: due now. Read Chapter 7. (we skip Ch 6). Classes 1-13. s. All Languages. Regular Languages. a. Finite Languages.

CS3231, 2010-2011. First Semester. Rahul. Jain. TexPoint fonts used in EMF. . Read the TexPoint manual before you delete this box.: . A. A. A. A. A. A. A. A. Why do I care about Theory ?. It provides solid foundations..

Class 5: . Non-Regular Languages. Spring 2010. University of Virginia. David Evans. TexPoint fonts used in EMF. . Read the TexPoint manual before you delete this box.: . A. A. A. A. A. A. A. A. A. A.

Class 6: . Pushdown Automata. Spring 2010. University of Virginia. David Evans. TexPoint fonts used in EMF. . Read the TexPoint manual before you delete this box.: . A. A. A. A. A. A. Menu. Revisiting and Reversing the Pumping Lemma.

Class 11: . Moore, Mealy, and Markov Models. Spring 2010. University of Virginia. David Evans. Menu. Exam Review. Variations on DFAs:. Moore Machine. : states produce output. Mealy Machine. : edges produce output.

Class 7: . Context-Free Languages. Spring 2010. University of Virginia. David Evans. Menu. Nondeterministic PDAs. Reviewing Machine Models of Computing. Linguistic Models of Computing. DPDA Recap: DFA/.

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