7.1 The Origin of Computer Science

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1 CS125 Lecture 7 Fall The Origin of Computer Science Alan Mathison Turing ( ) turing.jpg 170!201 pixels On Computable Numbers, with an Application to the Entscheidungsproblem : Founded computer science as graduate student : WW II hero breaking of German codes 1948: Almost qualified for UK Olympic team as marathon runner 1950: Seminal paper on AI proposed Turing test 1952: Prosecuted for homosexuality, chose chemical castration over prison 1954: Suicide 1966: Turing Award introduced as highest prize in computer science 2009: Public apology by British government 2013: Pardon by Queen of England [Dates from 7-1

2 Lecture Q: What Problem Was Turing Trying to Solve? David Hilbert Mathematical Problems 1900 Can mathematics be fully axiomatized and automated? Kurt Gödel On Formally Undecidable Propositions Alonzo Church An Unsolvable Problem of Elementary Number Theory 1936 Mathematics cannot be fully axiomatized: some true statements will be unprovable. Independently of Turing: mathematics cannot be automated cannot algorithmically distinguish provable from disprovable statements. The Cliff s Notes Version of History

3 An Unsolvable Problem of Elementary Number Theory 1936 Lecture The Basic Turing Machine The Basic Turing Machine a a b a F.C. Head can both read and write, and move in both directions Tape has unbounded length is the blank symbol. All but a finite number of tape squares are blank. F.C. = finite-state control. an both read and write, and move in both directions Def: A (deterministic) Turing Machine (TM) is a 6-tuple (Q,Σ,Γ,δ,q 0,q halt ), where: as unbounded length Q is a finite set of states, containing a start state q 0 and a halt state q halt. Σ is the input alphabet e blank symbol. All but a finite number of tape squares are blank. Γ is the tape alphabet, containing Σ and Γ Σ. δ : Q Γ Q Γ {L,R} is the transition function Interpretation: δ(q,σ) = (q,σ,r) Rewrite σ as σ in current cell Switch from state q to state q And move right δ(q,σ) = (q,σ,l) Same, but move left Unless at left end of tape, in which case stay put

4 Lecture An Example A Turing machine to determine whether a string is an even-length palindrome over alphabet Σ = {a,b}:

5 Lecture Formalizing Computation of TMs A configuration of a TM M = (Q,Σ,Γ,δ,q 0,q halt ) is denoted uqv, where q Q, u,v Γ. Tape contents = uv followed by all blanks State = q Head on first symbol of v. Equivalent to uqv, where v = v. Start configuration = q 0 w, where w is input. One step of computation (denoted C M C ): for C = uqσv with q Q \ {q halt }, u,v Γ, σ Γ, If δ(q,σ) = (q,σ,r), then C = uσ q v. If δ(q,σ) = (q,σ,l), and u = u τ for u Γ and τ Gamma, then C = u q τσ v. If δ(q,σ) = (q,σ,l), and u = ε, then C = q σ v. If q = q halt, computation halts (C = C) and the output is the contents of the tape to the left of the first blank symbol. Def: TM M = (Q,Σ,Γ,δ,q 0,q halt ) solves computational problem f : Σ 2 (Γ\{ }) if for every w Σ, there is a sequence C 0,...,C t of configurations of M such that: 1. C 0 = q 0 w 2. C i 1 M C i for i = 1,...,t 3. In C t, M is state q halt and the contents of the tape to the left of the first blank symbol is an element of f (w). Running time: Defined analogously to Word-RAM, e.g. T (n) = maximum number of steps taken by M on all inputs of length n (cf. T (n,k) for Word-RAM.) algorithms.

7 Lecture Speed of the Simulation. comparable speed. Note that the equivalence in ability to solve computational problems does not mean e.g. A standard TM can recognize palindromes in time O(n 2 ) (as we saw), and it is known that this is the best possible. But there is an O(n)-time 2-tape TM for the same problem: And this is tight the simulation above gives at most a quadratic slowdown: Theorem 7.1 If M is a multitape TM, then there is a constant c and 1-tape TM M such that on every input w Σ, M produces the same output as M (or runs forever if M does), and if M halts in time T (w) w, then M halts in time at most c T (w) RAMs vs. TMs Now we show that, even though TMs may seem much simpler and weaker than Word-RAMs, they are actually equivalent in power. Theorem 7.2 For every Word-RAM program P, there is a multitape TM M such that for every input x = (x 1,...,x n ) N, it holds that M( x ) has the same behavior as P on input x and size parameter k = max{x 1,...,x n,m} where m is the largest constant occurring in P. x denotes any reasonable encoding of k,x over the input alphabet Σ of M. For example, we can take Σ = {0,1, } and x = x 1 x n, where y denotes the binary representation of y N. If P halts with output y N, then M will halt with output y. If P does not halt, then M will also run forever. Efficiency: If P runs in time T = T (k,x) x, then M will run in time at most T 2 poly(logt,logk).

8 Lecture Simulating a Word RAM Program by a Multitape TM Tape 1: \$M[0] M[1] M[S 1] (sequence of w-bit binary strings). Tape 2: S (in binary) Tape 3: w (in unary sequence of w ones) Tape 4: i {0,...,S 1} such that TM head on tape 1 is within M[i] (in binary) Tape 5: j {0,...,w 1} such that TM head on tape 1 is at the j th bit of M[i] (in unary) Tapes 6 (r+5): Registers R[0],...,R[r 1] (in binary) Tape 7: scratch tape Keep track of program counter l in state of M The TM can carry out each of the following instructions in time poly(w) where w is the current word size: P l = R[i] R[ j] op R[k] for each of the word operations op we allow. If P l = IF R[i] = 0, GOTO l HALT Each of the following can be implemented in time O(S w), where S is the current space bound: R[i] M[R[ j]] M[R[i]] R[ j] MALLOC Throughout the computation S T (here we use T n) and w logmax{k + 1,S} logmax{k + 1,T }.

9 Lecture The Church Turing Thesis Many other models of computation are equivalent in power to Turing Machines: TMs with 2-dimensional tapes Word RAMs Integer RAMs General Grammars 2-counter machines Church s λ-calculus (µ-recursive functions) Markov algorithms Your favorite programming language (C, Python, OCaml,...) The equivalence of each to the others is a mathematical theorem. That these formal models of algorithms capture our intuitive notion of algorithms is the Church Turing Thesis. (Church s thesis = partial recursive functions, Turing s thesis = TMs.) The Church Turing Thesis is an extramathematical proposition, not subject to formal proof. The Extended Church-Turing Thesis: Every reasonable model of computation can be simulated on a Turing machine with only a polynomial slowdown. Counterexamples? Integer RAMs. Randomized computation. Parallel computation. Analog computers. DNA computers. Quantum computers. Extended C-T Thesis needs to be qualified with sequential and deterministic and bounded work per step.

10 Lecture Languages and Complexity Classes In classifying computational problems as solvable vs. efficiently solvable vs. unsolvable, it is convenient to restriction attention to decision problems ones where the answer is YES or NO. These correspond to computing functions f : Σ {0,1} (where 1=YES), or equivalently to deciding whether the input string is a particular language L Σ (namely, the set of strings where the answer is YES). Motivations for focus on decision problems: Don t need to worry about intractability because of f (x) being long. Many computational problems of interest can be reformulated as an essentially equivalent decision problem. Some Languages: PALINDROMES = {x Σ : x = x R }. PRIMES = { N : N is a prime number}. MINIMUMSPANNINGTREE = { G,w : G a weighted graph with a minimum spanning tree of weight at most w}. RANK = { x 1,...,x n,l : x 1 is among the smallest l items in x 1,...,x n }. = any reasonable encoding as a string. Which choices of encoding can make the difference between a computational problem being solvable or not? What about solvable in polynomial time? Graph as adjacency matrix vs. list of edges? Numbers in binary vs. base 10? Numbers in binary vs. unary? Solvable Problems: A function f : Σ Σ is called recursive or computable if there is an algorithm that computes f. A language L Σ is called recursive or decidable if the characteristic function of L is recursive. An algorithm that computes the characteristic function of L is also said to decide (membership in) the language L. R = {L : L is recursive}.

11 Lecture Does not depend on the choice of computational model (TMs vs. Word RAMs vs. Lambda Calculus)! Efficiently Solvable Problems: L TIME(T (n)) if there is an algorithm deciding L that runs in time O(T (n)). Depends on the model of computation! So we should really write TIME RAM (T (n)), TIME TM (T (n)), etc. TIME TM ( ) conventionally refers to multitape TMs. Polynomial Time: P = c TIME TM (n c ) = c TIME RAM (n c ) Same for all reasonable models of computation and reasonable encodings of inputs. Coarse approximation to efficiently solvable. We have CF P R 2 Σ, where CF is the class of context-free languages (i.e. languages that can be generated by grammars as in the PROP programming problem from HW3). Questions: Are there non-recursive languages? Are there recursive languages not in P? Are there natural languages not in R? In R \ P? Is FACTORING in P? Is MST TIME RAM (O(n))? Is MST TIME TM (O(n))?

12 Lecture Can MULTIPLICATION be done in time O(n) on a Word-RAM? On a multitape TM?. 7.9 Describing Algorithms Formal Description Word-RAM code or TM 6-tuple or TM state diagram Only when we ask for it! Implementation/Pseudocode Description Prose description of tape/memory contents, head movements (in case of TMs) High-level pseudocode and/or prose description of head movements, Omit details of states, transition functions, low-level RAM code (but do convince yourself that a TM/Word- RAM can do what you re describing!) Like our proofs that a TMs can simulate Multitape TMs can simulate Word-RAMs. High-Level Description Most of the algorithms descriptions we ve seen so far. Freely use other algorithms we ve seen as subroutines. Provide enough detail to be convinced of correctness and runtime on a Word RAM (possibly using some implementation-level specification or pseudocode to make things precise). Track number of executions of high-level steps, time for high-level steps (depends on size of data being manipulated).

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