Theory of computation
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Theory Of Computation. Dr. Adam P. Anthony Lectures 25 and 26. Overview. Computer Science: do we need computers? Computation Theory Functions Turing Machines Universal Programming Languages The Halting problem. Computer Science and Computers.

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Theory Of Computation

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Theory Of Computation

Dr. Adam P. Anthony

Lectures 25 and 26


  • Computer Science: do we need computers?

  • Computation Theory

  • Functions

  • Turing Machines

  • Universal Programming Languages

  • The Halting problem

Computer Science and Computers

Computer science is no more about computers than astronomy is about telescopes.

  • Edgser W. Dijkstra


  • Insight: the computation is separate, in concept, from the computer

  • A computer, then, is just some object that can carry out the computation

    • Humans

      • Brain, often supplemented by pencil, paper

    • Charles Babbage

      • Difference engine, analytical engine

        • Controlled using clockwork-type components

    • ENIAC

      • Controlled using vacuum tubes

    • Intel 8080

      • Controlled using micro-transistors

Strength of Computers

  • Can a simple calculator help you find your way around cleveland?

  • How about a (dumb) phone?

    • Aside from making calls

  • How about a smart phone?

  • How about a laptop?

  • How about a desktop?

  • Which of these count as computers?

Specific-Purpose vs. General Computers

  • Some ‘computers’ are designed only to achieve a limited number of specific tasks, and to do that either at high speed or at a low cost:

    • Digital Phones (Cell and otherwise)

      • Encryption chips too!

    • Various scientific measuring devices

  • Others are considered General Purpose Computers

    • Anything that can be computed, can be done on one of these machines

Theory of Computation

  • The theory of computation aims to answer the following questions:

    • What is a general purpose computer?

    • What problems can I solve with a general purpose computer?

    • Is this specific computer general purpose?

    • Given a general purpose computer, how difficult will it be to solve a specific problem?

Alan Turing: “The Father of Computer Science”

  • Successful mathematician

  • Cryptographer

  • Helped build some early (classified) computation devices

  • Many ideas predated the first computers

    • Turing Machine

    • Computability

  • Helped define what is possible on a computer, and what is not

Computable Functions

  • A function is a mapping of inputs to outputs

    • Sum(2+2) = 4

    • Feet-Centimeter(500) = 15,240

    • Sort([1,3,2,3,6,9,7,8,0]) = [0,1,2,3,3,6,7,8,9]

    • Father(Bill Smith) = Edward Smith

  • Some functions are computable

    • Given the input, an algorithmic process can always be applied to get an exact answer for the output

  • A general purpose computer can compute any computable function, and no others

Turing Machines

  • Control Unit: The actual machine

  • Tape: infinitely long memory

  • Read/Write Head: Used to read information from the tape, erase information, write new information

    • Reads one character at a time

    • Moves left/right one position at a time

  • State: Description of current situation, based on tape values

State = START

How a Turing Machine Works

  • Each new Turing Machine has an alphabet of characters that it understands, and a set of states that help it make decisions

  • Given the state the current character read by the the read/write head, and a program of execution, the Control Unitdecides to:

    • Stop running (HALT state)

    • Write over the current character

    • Move one space left/right

    • Change States

A More Complex Machine

  • Alphabet: {0,1,*}

    • A single binary number is represented as *101010*


  • Program to increase a positive binary integer by 1:

Useful Turing Machine Facts

  • Multiple Turing machines are no more powerful (though possibly faster) than a single Turing Machine

  • Any Turing Machine can ‘simulate’ another Turing Machine

  • Result: We can use unambiguous complex commands in the control unit’s program!

    • Command: “Move 5 Spaces to the left”

      • Turing Machine reads: “Execute the Turing Machine routine that moves 5 spaces to the left”

  • Theoretically speaking, one should typically demonstrate the sub-program is computable first

Church-Turing Thesis

Any function that can be computed using a Turing Machine is also computable using any other general purpose computer (i.e., the function is computable)


Who Cares?

Impact of Church-Turing Thesis

  • If Power = ‘number of functions I can compute,’ then a Turing machine is the most powerful computer imaginable

    • Or, at least, it ties with any other computer

    • It computes ALL of the computable functions!

  • If a Turing machine can’t solve a problem, then neither can a real computer, no matter how ‘powerful’ it is

  • Modern Computers As Turing Machines

    • Control Unit = Processor

    • Alphabet = {0,1}

    • States = Op Codes

    • Read/Write Head = BUS

    • Programs = Software

    • Tape = RAM

      • Infinite?????

      • No, but for most purposes it is long enough to solve the problem

    • Tape = External Storage

      • Only limited by the number of natural resources we can obtain from the entire universe (so, probably infinite!)

    Bare-Bones Computer Language

    • Programming languages usually market their ‘features’

      • Meant to make programming easier

    • Bare-Bones Language:

      • Only includes features that are 100% necessary to be equivalent to a Turing machine:

        • Variable names: all variables are in binary

        • clear statement: set a variable = 0 (clear X;)

        • incrstatement: increase a variable by 1 ( incr X; )

        • decrstatement: decrease a variable by 1 (decr X;)

        • While/end: continue execution until a variable = 0

          • while X not 0 do;



    Group Work!

    • Can you use Bare-Bones to:

      • Set the variable Z = 4?

      • Add X + Y = Z? (use one variable each for X,Y,Z)

      • Copy the value of X into Y?

    About the Bare-Bones Language

    • Computer scientists have proven that any computer that can execute the Bare-Bones language is equivalent in power to a Turing Machine

      • Heaven forbid!

    • Useful conclusion:

      • Any programming language does at least the same as Bare-Bones (hopefully more!) will also be Turing Equivalent

      • The extra features are just for convenience

    Where We’ve Been

    • Computers are just tools for completing computations

    • Theory of computation: what is possible/impossible for all computers? What is computable?

    • Turing Machine: imaginary ‘all powerful’ computer

      • Church-Turing thesis states no computer can do better

    • Modern computers are equivalent to Turing Machines

    • Any algorithm we implement on a computer is computable

    Where We’re headed

    • It’d be nice to know, before we start if a problem is noncomputable

      • Halting problem as an example

    • Even if a problem is computable, it would be nice to know in advance if it is easy or hard to solve

    • Even if we can solve a problem, it would be nice to know how long it will take to solve it

      • Save effort in solving complex problems

      • Take advantage of complexity

    The Halting Problem

    • Some problems can’t be solved.

    • Consider: Given the source code for any computer program, can you analyze the code and decide if it will it ever stop running?

    The Halting Problem

    Does this program halt?

    int X = 3

    while( X > 0)

    x = x -1

    The Halting Problem

    Does this program halt?

    int X = 3


    x = x +1;

    until x = 0

    The Halting Problem

    How about this program?

    virtual void estimate_sigmas(){

    sigmas = std::vector< std::vector<Matrix> >(num_clusters);

    for(inti = 0; i<num_clusters; i++){

    sigmas[i] = std::vector<Matrix>(num_clusters);


    for(inti = 0; i<num_clusters; i++){

    for(int j = 0; j<num_clusters; j++){

    sigmas[i][j] = zero_matrix<double>(proper_size,proper_size);



    Vector temp_v(proper_size);



    for(tie(ebg,end) = edges(data); ebg!=end; ++ebg){

    if(data[*ebg].type == edge_type && data[*ebg].exists){

    c_i = data[source(*ebg,data)].clustering();

    c_j = data[target(*ebg,data)].clustering();

    temp_v = get_edge_vector(*ebg) - ic_means[c_i][c_j]

    sigmas[c_i][c_j] = sigmas[c_i][c_j] + outer_prod(temp_v,trans(temp_v))/observed_edge_prob(c_i,c_j);




    Computability Explained

    • Sometimes, we can work out answers for simple, example inputs of hard problems, but:

      • What algorithm did you use to decide for the first two programs?

      • Can you generalize it to the third?

    • To prove something is not computable, we’ll use the following strategy:

      • Assume that there is an algorithm that can solve the problem all the time

      • Show that, regardless of how the algorithm works, that there is at least one case where the algorithm will fail

        • Contradicts part 1, which claimed it ‘always works’

    The Halting Problem Is Not Computable (PROOF!)

    • Assume, for the sake of contradiction, that there exists a computer program that, given any other computer program as input, can tell us if it stops:

      • STOPS(Program)

    • Make a new program:

      • Opposite(X):

        • If STOPS(X), then run forever.

        • Otherwise, stop!

    • Opposite is a program, which itself accepts program code as input. What happens when we try to run

      • Opposite(Oppisite)?

        • If STOPS(Opposite), then Opposite will run forever

        • Otherwise, stop!

    Halting Problem Implications

    • Existence proof: Since there’s one program that exists which we can’t compute if it halts, then there may be (probably are) others

    • If there’s one problem that seems computable, but is not, then there are others

      • Look up Wang Tiles for an interesting example!

    • Program Analysis: “Does my program compute X?”

      • Any place in the code where X is computed, add a HALT command

        • Changes to “Does my program ever halt?”

    Algorithmic Complexity

    • Algorithmic Complexity refers to how many resources (time and memory) a computer will need to solve a problem

      • How long will it take to process all the data?

      • How much space (Memory) will we need?

      • If we use more space, will it take less time?

      • Are some problems harder to solve than others?

        • Can we figure that out before we try to solve them?

      • How can we take advantage of complexity?

    Problems Vs. Solutions

    • It’s one thing to take a specific algorithm and say it’s complex (or not):

      PROCEDURE Add(X,Y):

      sum = X + Y

      RETURN sum

    • Because solving the problem, and doing so efficiently, are two different things:

      PROCEDURE BadAdd(X,Y):

      Z = 1000000000

      sum = 0


      Z = Z - 1

      UNTIL Z = 0

      sum = X + Y

      RETURN sum

    • To say that a PROBLEM is difficult, you need to prove that there are no easy ways to solve it

    Run-Time Complexity

    • Looked at briefly in chapter 5

    • Principal method for analyzing algorithm complexity:

      • How many steps does it take to complete the entire algorithm?

    • Steps are often based on the size of the input:

      PROCEDURE add-all(L):

      sum = 0

      count = 0

      WHILE count < length(L):

      sum = sum + L[count]

      RETURN sum

      • How many steps to add a list with 10 numbers? 1000 numbers?

    Space Complexity

    • Many algorithms only need enough space to hold the input data

      • Procedure add-all (L) Only needs enough space to store L

    • Others, because the problem is more difficult, use supplementary data

      • EX: Binary Search Trees

    • Still others use extra memory to be faster

      • EX: Dynamic Programming Fibonacci sequence

    Complexity Classes

    • The most reoccurring algorithmic runtimes are (in order) constant, log(N), N, N*log(N), N2, N3, and an

    • A polynomial problem is any problem for which the best known algorithm for solving it has time complexity that is no worse than a polynomial function f(N) = Nd where d can be any number and N is the size of the input.

    • All problems that we can solve with an exact solution is a reasonable amount of time are in the polynomial class

    • Problems outside this class are referred to as intractible

    • For short, we refer to the entire set of all polynomial problems in the world a the set P

    Complexity Classes, continued.

    • A Nondeterministic machine: is a theoretical machine that just knows how to solve a problem, no matter how hard it may be

    • A Nondeterministic Polynomial Problem is a problem for which the best known algorithm for solving has a polynomial runtime, but its execution would require a nondeterministic machine

    • Another intuition: these are problems for which finding the solution is hard, but checking the solution for correctness is easy

    • We refer to the set of problems in this domain as NP




    • In General, the class of problems in NP consists of problems that are difficult, but useful

      • Traveling Salesman example—best solution is exponential

    • Within a single class, some problems are harder than others

      • In P, it is harder to sort a list than it is to add two numbers

    • In the class NP, we identify a set of problems that are the most difficult to solve in the entire set

      • Called NP-Complete problems

      • The speed at which we can solve these problems determines how fast we can solve the lesser problems

    P vs. NP

    • All problems that are in the set P must also be in the set NP

      • Why?

    • Big, unknown question in Computer Science:

      • DOES P = NP???

    • What does it mean if P = NP?

    • One approach: Find a polynomial solution for an NP-Complete problem

      • Thousands have tried, all have failed

    • Most people believe, but can’t prove P  NP

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