DOCSLIB.ORG

Certified Undecidability of Intuitionistic Linear Logic Via Binary Stack Machines and Minsky Machines Yannick Forster, Dominique Larchey-Wendling

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Certified Undecidability of Intuitionistic Linear Logic Via Binary Stack Machines and Minsky Machines Yannick Forster, Dominique Larchey-Wendling Certified Undecidability of Intuitionistic Linear Logic via Binary Stack Machines and Minsky Machines Yannick Forster, Dominique Larchey-Wendling To cite this version: Yannick Forster, Dominique Larchey-Wendling. Certified Undecidability of Intuitionistic Linear Logic via Binary Stack Machines and Minsky Machines. The 8th ACM SIGPLAN International Con- ference on Certified Programs and Proofs, CPP 2019, Jan 2019, Cascais, Portugal. pp.104-117, 10.1145/3293880.3294096. hal-02333390 HAL Id: hal-02333390 https://hal.archives-ouvertes.fr/hal-02333390 Submitted on 9 Nov 2020 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Certified Undecidability of Intuitionistic Linear Logic via Binary Stack Machines and Minsky Machines Yannick Forster Dominique Larchey-Wendling Saarland University Université de Lorraine, CNRS, LORIA Saarbrücken, Germany Vandœuvre-lès-Nancy, France [email protected] [email protected] Abstract machines (BSM), Minsky machines (MM) and (elementary) We formally prove the undecidability of entailment in intu- intuitionistic linear logic (both eILL and ILL): itionistic linear logic in Coq. We reduce the Post correspond- PCP ⪯ BPCP ⪯ iBPCP ⪯ BSM ⪯ MM ⪯ eILL ⪯ ILL ence problem (PCP) via binary stack machines and Minsky machines to intuitionistic linear logic. The reductions rely on Combined with the reduction Halt ⪯ PCP of [11], this yields several technically involved formalisations, amongst them a a fully formalised reduction from the halting problem of binary stack machine simulator for PCP, a verified low-level Turing machines to entailment in ILL. compiler for instruction-based languages and a soundness PCP is a problem over a finite set of cards, each having a proof for intuitionistic linear logic with respect to trivial string at the top and bottom. Such a PCP instance is solvable phase semantics. We exploit the computability of all func- if there is a non-empty finite sequence of those cards with tions definable in constructive type theory and thus do not an equal upper and lower string. We follow [11] in giving a have to rely on a concrete model of computation, enabling definition of PCP with countably many symbols. We reduce the reduction proofs to focus on correctness properties. PCP to BPCP, where symbols are restricted to Booleans and to iBPCP, where the sequence of cards is represented by a Keywords Undecidability, many-one reduction, binary stack sequence of their indices (natural numbers). machines, Minsky machines, intuitionistic linear logic, low- We describe a general framework for the semantics of level compiler, constructive type theory, Coq instruction-based machine models and explain how it can be used for compositional reasoning. We then define bin- 1 Introduction ary stack machines as an instance of such instruction-based Undecidability of advanced problems is usually established machines. BSMs have a fixed number of binary stacks and using computable (many-one) reductions from a problem programs consist of consecutively indexed PUSH and POP already known to be undecidable. Such reductions rely on instructions, the latter allowing conditional jumps based many subtle details and could thus be a prime example for on the obtained Boolean or if the stack is empty. A BSM the use of interactive theorem provers to assist ongoing re- program terminates if it jumps to a non-existing index. We search. However, formalisations of undecidability proofs are verify an iBPCP simulator — which is a BSM program ter- not common in the literature. There are two main obstacles minating if and only if a given iBPCP instance is solvable — in our eyes: (1) proofs on paper mostly omit the invariants by enumerating all possible sequences of cards. The pro- needed for the verification of the reduction; (2) they omit gram size is linear in the size of the instance and always has the computability proof, which amounts to the formal veri- more than 80 instructions. It consists of several independ- fication of a program in the chosen model of computation. ent sub-programs, making the verification crucially rely on Constructive type theory, as implemented in the proof compositional reasoning. assistant Coq [36], provides a particularly convenient set- We define Minsky machines32 [ ] with a fixed number of ting for decidability and undecidability proofs. Since every natural-number registers as another instance of instruction- function definable in constructive type theory is computable, based machines. MM-programs consist of INC and DEC op- one can use a synthetic approach to computability. This ap- erations, the latter allowing conditional jumps if the re- proach makes an explicit model of computation and explicit gister is zero. We describe and verify a general compiler computability proofs unnecessary, enabling the proofs to for instruction-based languages and use it to compile BSM focus on the invariants needed for the reduction. programs to MM programs. The main challenge is to re- In this paper we contribute a formalised chain of reduc- arrange jumps using a linker. For the reduction we have tions, starting at the Post correspondence problem (PCP), via to prove correctness of the compiler for both terminating binary PCP (BPCP), BPCP with indices (iBPCP), binary stack and non-terminating programs, a notable difference to other verified compilers (e.g. [6, 28]). CPP’19, January 14–15 2019, Lisbon, Portugal We formalise intuitionistic linear logic with a sequent cal- 2018. culus SILL. We introduce the elementary fragment of ILL [26, 1 CPP’19, January 14–15 2019, Lisbon, Portugal Yannick Forster and Dominique Larchey-Wendling 1 27] together with a goal directed sequent calculus GeILL a problem p can never be actually proven. However, in which is equivalent to SILL on this fragment: both define the practice, the undecidability of a problem is rarely established same entailment predicate. We give a denotational semantics directly anyway. Most proofs are by reduction from another for ILL using trivial phase semantics based on commutat- undecidable problem, and ultimately most problems are thus ive monoids and prove soundness of SILL for this semantics. proven undecidable by reduction from an initial undecidable Finally, we show that entailment in GeILL is complete for problem. In most textbook presentations, it is the halting trivial phase semantics, yielding a reduction of eILL to ILL. problem (Halt) which is seen as a seed of undecidability. Our arguments slightly generalise those of the proof in [27] The idea to exploit reductions to prove the undecidability to formalise the reduction chain MM ⪯ eILL ⪯ ILL. of problems is well-known. It was first used to formalise We conclude the paper by summarising our efforts to build undecidability proofs in a proof assistant without referring a library of undecidable problems in Coq [14] and by giving to an explicit model of computation in [11]. They define an overview of related work. The library should contain (many-one) reductions from a problem (X;p) to (Y;q) as both entry points for plugging new undecidability results a (computable) function f : X ! Y such that 8x; p x $ and tools to mechanise the corresponding reductions. We q (f x). We write p ⪯ q and say that p reduces to q if a discuss some ongoing additions to our library. reduction of (X;p) to (Y;q) exists. In the case of axiom- The axiom-free Coq formalisation of all the results in this free Coq, all such reductions are inherently computable and paper is available online and the main lemmas and theorems thus, computability can be dropped from the definition. This in the pdf version of the paper are hyper-linked with the coincides with usual practice in non-formalised papers where html version of the Coq source code: the computability of reductions is left out most of the time. https://uds-psl.github.io/ill-undecidability Actually, many-one reductions are only an instance of the more general notion of Turing-reductions (see e.g. [33]): 2 Type Theory and Formal Undecidability (X;p) is Turing-reducible to (Y;q), written p ⪯T q, if p is decidable given that q is decidable. 2.1 Definitions We write P to denote the type of propositions. The basic Lemma 2.1. If p ⪯ q then p ⪯T q. inductive data structures we use are natural numbers (n : The structure of undecidability proofs can be comprehens- N ::= 0 j S n), Booleans (b : B ::= 0 j 1), the option type ively described using an inductive predicate undec p: (O X ::= (x : X ) j ?) and lists (l : L X ::= [] j x :: l). We write ⪯ l1 ++l2 for the concatenation of two lists, jl j for the length p T q undec p of a list, l for the reversal of a list, and [ f s j s 2 l ] for a undec Halt undec q map over a list. We define l[i]: O X as the i-th element of l. The first rule establishes the halting problem for Turing Moreover, we write s 2 l if s is a member of l, and l1 ⊆ l2 if machines as a seed of undecidability. By the second rule, every member of l1 is a member of l2. We will overload the notation for lists and use it for vectors undecidability can be transferred via (Turing) reductions. as well. Given a type X, we write the type of vectors of Lemma 2.2. undec (λx: :p x) implies undec p.
Load more

Recommended publications
  • Mathematical Foundations of CS Dr. S. Rodger Section: Decidability (Handout)
    CPS 140 - Mathematical Foundations of CS Dr. S. Rodger Section: Decidability (handout) Read Section 12.1. Computability A function f with domain D is computable if there exists some TM M such that M computes f for all values in its domain. Decidability A problem is decidable if there exists a TM that can answer yes or no to every statement in the domain of the problem. The Halting Problem Domain: set of all TMs and all strings w. Question: Given coding of M and w, does M halt on w? (yes or no) Theorem The halting problem is undecidable. Proof: (by contradiction) • Assume there is a TM H (or algorithm) that solves this problem. TM H has 2 final states, qy represents yes and qn represents no. TM H has input the coding of TM M (denoted wM ) and input string w and ends in state qy (yes) if M halts on w and ends in state qn (no) if M doesn’t halt on w. (yes) halts in qy if M halts on w H(wM ;w)= 0 (no) halts in qn if M doesn thaltonw TM H always halts in a final state. Construct TM H’ from H such that H’ halts if H ends in state qn and H’ doesn’t halt if H ends in state qy. 0 0 halts if M doesn thaltonw H (wM ;w)= doesn0t halt if M halts on w 1 Construct TM Hˆ from H’ such that Hˆ makes a copy of wM and then behaves like H’. (simulates TM M on the input string that is the encoding of TM M, applies Mw to Mw).
    [Show full text]
  • Self-Referential Basis of Undecidable Dynamics: from the Liar Paradox and the Halting Problem to the Edge of Chaos
    Self-referential basis of undecidable dynamics: from The Liar Paradox and The Halting Problem to The Edge of Chaos Mikhail Prokopenko1, Michael Harre´1, Joseph Lizier1, Fabio Boschetti2, Pavlos Peppas3;4, Stuart Kauffman5 1Centre for Complex Systems, Faculty of Engineering and IT The University of Sydney, NSW 2006, Australia 2CSIRO Oceans and Atmosphere, Floreat, WA 6014, Australia 3Department of Business Administration, University of Patras, Patras 265 00, Greece 4University of Pennsylvania, Philadelphia, PA 19104, USA 5University of Pennsylvania, USA [email protected] Abstract In this paper we explore several fundamental relations between formal systems, algorithms, and dynamical sys- tems, focussing on the roles of undecidability, universality, diagonalization, and self-reference in each of these com- putational frameworks. Some of these interconnections are well-known, while some are clarified in this study as a result of a fine-grained comparison between recursive formal systems, Turing machines, and Cellular Automata (CAs). In particular, we elaborate on the diagonalization argument applied to distributed computation carried out by CAs, illustrating the key elements of Godel’s¨ proof for CAs. The comparative analysis emphasizes three factors which underlie the capacity to generate undecidable dynamics within the examined computational frameworks: (i) the program-data duality; (ii) the potential to access an infinite computational medium; and (iii) the ability to im- plement negation. The considered adaptations of Godel’s¨ proof distinguish between computational universality and undecidability, and show how the diagonalization argument exploits, on several levels, the self-referential basis of undecidability. 1 Introduction It is well-known that there are deep connections between dynamical systems, algorithms, and formal systems.
    [Show full text]
  • Notes on Unprovable Statements
    Stanford University | CS154: Automata and Complexity Handout 4 Luca Trevisan and Ryan Williams 2/9/2012 Notes on Unprovable Statements We prove that in any \interesting" formalization of mathematics: • There are true statements that cannot be proved. • The consistency of the formalism cannot be proved within the system. • The problem of checking whether a given statement has a proof is undecidable. The first part and second parts were proved by G¨odelin 1931, the third part was proved by Church and Turing in 1936. The Church-Turing result also gives a different way of proving G¨odel'sresults, and we will present these proofs instead of G¨odel'soriginal ones. By \formalization of mathematics" we mean the description of a formal language to write mathematical statements, a precise definition of what it means for a statement to be true, and a precise definition of what is a valid proof for a given statement. A formal system is consistent, or sound, if no false statement has a valid proof. We will only consider consistent formal systems. A formal system is complete if every true statement has a valid proof; we will argue that no interesting formal system can be consistent and complete. We say that a (consistent) formalization of mathematics is \interesting" if it has the following properties: 1. Mathematical statements that can be precisely described in English should be expressible in the system. In particular, we will assume that, given a Turing machine M and an input w, it is possible to construct a statement SM;w in the system that means \the Turing machine M accepts the input w".
    [Show full text]
  • Introduction to the Theory of Computation Computability, Complexity, and the Lambda Calculus Some Notes for CIS262
    Introduction to the Theory of Computation Computability, Complexity, And the Lambda Calculus Some Notes for CIS262 Jean Gallier and Jocelyn Quaintance Department of Computer and Information Science University of Pennsylvania Philadelphia, PA 19104, USA e-mail: [email protected] c Jean Gallier Please, do not reproduce without permission of the author April 28, 2020 2 Contents Contents 3 1 RAM Programs, Turing Machines 7 1.1 Partial Functions and RAM Programs . 10 1.2 Definition of a Turing Machine . 15 1.3 Computations of Turing Machines . 17 1.4 Equivalence of RAM programs And Turing Machines . 20 1.5 Listable Languages and Computable Languages . 21 1.6 A Simple Function Not Known to be Computable . 22 1.7 The Primitive Recursive Functions . 25 1.8 Primitive Recursive Predicates . 33 1.9 The Partial Computable Functions . 35 2 Universal RAM Programs and the Halting Problem 41 2.1 Pairing Functions . 41 2.2 Equivalence of Alphabets . 48 2.3 Coding of RAM Programs; The Halting Problem . 50 2.4 Universal RAM Programs . 54 2.5 Indexing of RAM Programs . 59 2.6 Kleene's T -Predicate . 60 2.7 A Non-Computable Function; Busy Beavers . 62 3 Elementary Recursive Function Theory 67 3.1 Acceptable Indexings . 67 3.2 Undecidable Problems . 70 3.3 Reducibility and Rice's Theorem . 73 3.4 Listable (Recursively Enumerable) Sets . 76 3.5 Reducibility and Complete Sets . 82 4 The Lambda-Calculus 87 4.1 Syntax of the Lambda-Calculus . 89 4.2 β-Reduction and β-Conversion; the Church{Rosser Theorem . 94 4.3 Some Useful Combinators .
    [Show full text]
  • Undecidable Problems: a Sampler (.Pdf)
    UNDECIDABLE PROBLEMS: A SAMPLER BJORN POONEN Abstract. After discussing two senses in which the notion of undecidability is used, we present a survey of undecidable decision problems arising in various branches of mathemat- ics. 1. Introduction The goal of this survey article is to demonstrate that undecidable decision problems arise naturally in many branches of mathematics. The criterion for selection of a problem in this survey is simply that the author finds it entertaining! We do not pretend that our list of undecidable problems is complete in any sense. And some of the problems we consider turn out to be decidable or to have unknown decidability status. For another survey of undecidable problems, see [Dav77]. 2. Two notions of undecidability There are two common settings in which one speaks of undecidability: 1. Independence from axioms: A single statement is called undecidable if neither it nor its negation can be deduced using the rules of logic from the set of axioms being used. (Example: The continuum hypothesis, that there is no cardinal number @0 strictly between @0 and 2 , is undecidable in the ZFC axiom system, assuming that ZFC itself is consistent [G¨od40,Coh63, Coh64].) The first examples of statements independent of a \natural" axiom system were constructed by K. G¨odel[G¨od31]. 2. Decision problem: A family of problems with YES/NO answers is called unde- cidable if there is no algorithm that terminates with the correct answer for every problem in the family. (Example: Hilbert's tenth problem, to decide whether a mul- tivariable polynomial equation with integer coefficients has a solution in integers, is undecidable [Mat70].) Remark 2.1.
    [Show full text]
  • Arxiv:1111.3965V3 [Quant-Ph] 25 Jun 2012 Setting
    Quantum measurement occurrence is undecidable J. Eisert,1 M. P. Muller,¨ 2 and C. Gogolin1 1Qmio Group, Dahlem Center for Complex Quantum Systems, Freie Universitat¨ Berlin, 14195 Berlin, Germany 2Perimeter Institute for Theoretical Physics, 31 Caroline Street North, Waterloo, Ontario N2L 2Y5, Canada In this work, we show that very natural, apparently simple problems in quantum measurement theory can be undecidable even if their classical analogues are decidable. Undecidability hence appears as a genuine quantum property here. Formally, an undecidable problem is a decision problem for which one cannot construct a single algorithm that will always provide a correct answer in finite time. The problem we consider is to determine whether sequentially used identical Stern-Gerlach-type measurement devices, giving rise to a tree of possible outcomes, have outcomes that never occur. Finally, we point out implications for measurement-based quantum computing and studies of quantum many-body models and suggest that a plethora of problems may indeed be undecidable. At the heart of the field of quantum information theory is the insight that the computational complexity of similar tasks in quantum and classical settings may be crucially different. Here we present an extreme example of this phenomenon: an operationally defined problem that is undecidable in the quan- tum setting but decidable in an even slightly more general classical analog. While the early focus in the field was on the assessment of tasks of quantum information processing, it has become increasingly clear that studies in computational complexity are also very fruitful when approaching problems FIG. 1. (Color online) The setting of sequential application of Stern- Gerlach-type devices considered here, gives rise to a tree of possible outside the realm of actual information processing, for exam- outcomes.
    [Show full text]
  • The Gödel Incompleteness Theorems (1931) by the Axiom of Choice
    The G¨odel incompleteness theorems (1931) by the axiom of choice Vasil Penchev, [email protected] Bulgarian Academy of Sciences: Institute of philosophy and Sociology: Dept. of Logic and Philosophy of Science Abstract. Those incompleteness theorems mean the relation of (Peano) arithmetic and (ZFC) set theory, or philosophically, the relation of arithmetical finiteness and actual infinity. The same is managed in the framework of set theory by the axiom of choice (respectively, by the equivalent well-ordering "theorem'). One may discuss that incompleteness form the viewpoint of set theory by the axiom of choice rather than the usual viewpoint meant in the proof of theorems. The logical corollaries from that "nonstandard" viewpoint the relation of set theory and arithmetic are demonstrated. Key words: choice, arithmetic, set theory, well-ordering, information Many efforts address the constructiveness of the proof in the so-called G¨odel first incompleteness theorem (G¨odel, 1931). One should use the axiom of choice in- stead of this and abstract from that the theorem is formulated about statements, which will be replaced by the arbitrary elements of any infinite countable set. Furthermore all four theorems ( G¨odel, 1931; L¨ob, 1955; Rogers, 1967; Jeroslow, 1971) about the so-called metamathematical fixed points induced by the G¨odel incompleteness theorems will be considered jointly and in a generalized way. The strategy is the following: An arbitrary infinite countable set "A" and another set "B" so that their intersection is empty are given. One constitutes their union "C=A[B", which will be an infinite set whatever "B" is.
    [Show full text]
  • Lecture 12: Undecidability
    Lecture 12: Undecidability Instructor: Ketan Mulmuley Scriber: Yuan Li February 17, 2015 1 Hilbert's Question German mathematician David Hilbert proposed the following question as one of the main goals in his program: Can truth be decided? In order to formalize this question, we need to answer • What is truth? • What does it mean to be decided? The first question (about truth) is answered in logic, which is usually different from the answer given by your grandmother. The second question (about decidability) is answered in Theory of Computation. At the time of Hilbert, there is no notion of \algorithm" or \Turing machine", he uses the phrase \mechanical procedure" to actually mean algorithm. 2 What is Truth In order to formalize \truth", we need to choose a model including axioms and rules of inference from which all mathematical truths could in principle be proven. There are (at least) two options: set theory or number theory. 1 For example, based on set theory, we can define 0 := fg 1 := f0g = ffgg 2 := f0; 1g = ffg; ffggg n := f0; 1; 2; : : : ; n − 2; n − 1g: Alfred Whitehead and Bertrand Russell have written a book called Principia Mathematica, which attempted to give a set of axioms and inference rules based on set theory and symbolic logic. For different models, there are corresponding operators. For example, +, −, ∗, <, >, =, ::: for number theory, and [, \, =, ::: for set theory. From now on, let us fix the model to be number theory, which we have learned in kindergartens. Let the domain be (N; 0; succ; <; +; ∗), where N is the set of natural num- bers, succ is the successor function.
    [Show full text]
  • PHILOSOPHY PHIL 379 — “Logic II” Winter Term 2016 Course Outline
    FACULTY OF ARTS DEPARTMENT OF PHILOSOPHY PHIL 379 — “Logic II” Winter Term 2016 Course Outline Lectures: TuTh 12:30–13:45, Professional Faculties 118 Instructor: Richard Zach Oce: Social Sciences 1248A Email: [email protected] Phone: (403) 220–3170 Oce Hours: Tu 2–3, Th 11–12, or by appointment Course Description Formal logic has many applications both within philosophy and outside (especially in math- ematics, computer science, and linguistics). This second course will introduce you to the concepts, results, and methods of formal logic necessary to understand and appreciate these applications as well as the limitations of formal logic. It will be mathematical in that you will be required to master abstract formal concepts and to prove theorems about logic (not just in logic the way you did in Phil 279); but it does not presuppose any (advanced) knowledge of mathematics. We will start from the basics. We will begin by studying some basic formal concepts: sets, relations, and functions, and the sizes of infinite sets. We will then consider the language, semantics, and proof theory of first-order logic (FOL), and ways in which we can use first-order logic to formalize facts and reasoning abouts some domains of interest to philosophers, computer scientists, and logicians. In the second part of the course, we will begin to investigate the meta-theory of first-order logic. We will concentrate on a few central results: the completeness theorem, which re- lates the proof theory and semantics of first-order logic, and the compactness theorem and Löwenheim-Skolem theorems, which concern the existence and size of first-order structures.
    [Show full text]
  • Computability and Undecidability
    A talk given at Huawei Company (Oct. 31, 2019) and Shenzhen Univ. (Nov. 28, 2019) Computability and Undecidability Zhi-Wei Sun Nanjing University Nanjing 210093, P. R. China [email protected] http://math.nju.edu.cn/∼zwsun Nov. 28, 2019 Abstract In this talk we introduce the theory of computability and the undecidability results on Hilbert's Tenth Problem. We will also mention some algorithms concerning primes. 2 / 39 Part I. The Theory of Computability 3 / 39 Primitive recursive functions Let N = f0; 1; 2;:::g and call each n 2 N a natural number. Three Basic Functions: Zero Function: O(x) = 0 (for all x 2 N). Successor Function: S(x) = x + 1. Projection Function: Ink (x1;:::; xn) = xk (1 6 k 6 n) Composition: f (x1;:::; xn) = g(h1(x1;:::; xn);:::; hm(x1;:::; xn)) Primitive Recursion: f (x1;:::; xn; 0) =g(x1;:::; xn); f (x1;:::; xn; y + 1) =h(x1;:::; xn; y; f (x1;:::; xn; y)) Primitive recursive functions are the basic functions and those obtained from the basic functions by applying composition and primitive recursion a finite number of times. 4 / 39 Acknermann's function It is easy to see that all primitive recursive functions are computable by our intuition. Skolem's Claim (1924): All intuitively computable number-theoretic functions are primitive recursive functions. In 1928 Ackermann showed that the following Ackermann function A(m; n) is computable but not primitively recursive. A(0; n) =n + 1; A(m + 1; 0) =A(m; 1) A(m + 1; n + 1) =A(m; A(m + 1; n)): For example, A(1; 2) =A(0; A(1; 1)) = A(0; A(0; A(1; 0))) =A(0; A(0; A(0; 1))) = A(0; A(0; 2)) =A(0; 3) = 4: 5 / 39 Partial recursive functions µ-operator: f (x1;:::; xn) = µy(g(x1;:::; xn; y) = 0) means that f (x1;:::; xn) is the least natural number y such that g(x1;:::; xn; y) = 0.
    [Show full text]
  • From Hilbert to Turing and Beyond
    From Hilbert to Turing and beyond ... Leopoldo Bertossi Carleton University School of Computer Science Ottawa, Canada 2 An Old Problem Revisited yes? X^2 + 2XY^3 - 4Y^5Z -3 = 0 no? solution in Z? yes X^2 + Y^2 -2 = 0 X^2 + 1 =0 no Example: Hilbert's 10th problem (1900): Is there a general me- chanical procedure to decide if an arbitrary diophantine equation has integer roots? 3 David Hilbert (1862 - 1943) It was an open problem to Hilbert's time 4 5 6 Hilbert does not use the word \algorithm", but procedure (Ver- fahren) with the usual requirements He does use explicitly \decision" (Entscheidung) There had been \algorithms" since long before, e.g. for the gcd Even decision algorithms, e.g. deciding if a number is prime However, the term comes from the name of the Persian sci- entist and mathematician Abdullah Muhammad bin Musa al- Khwarizmi (9th century) In the 12th century one of his books was translated into Latin, where his name was rendered in Latin as \Algorithmi" His treatise \al-Kitab al-mukhtasar ¯ hisab al-jabr wal-muqabala" was translated into Latin in the 12th century as \Algebra et Al- mucabal" (the origin of the term algebra) 7 And Even More Hilbert ... In the context of his far reaching program on foundations of mathematics, Hilbert identi¯es the following as crucial: Das Entscheidungsproblem: (The Decision Problem) Determine if the set of all the formulas of ¯rst-order logic that are universally valid (tautological) is decidable or not 8x(P (x) ! P (c)) and (P (c) ! 9xP (x)) are valid (always true) 8x9y(Q(x) ^ P (x; y)) is not (it is false in some structures) Explicitly formulated in the 20's in the the context of the \engere FunktionenkalkÄul"(the narrower function calculus) Hilbert, D.
    [Show full text]
  • Mathematical Background
    Appendix A Mathematical Background In this appendix, we review the basic notions, concepts and facts of logic, set theory, algebra, analysis, and formal language theory that are used throughout this book. For further details see, for example, [105, 134, 138] for logic, [61, 56, 88] for set theory, [40, 74, 119] for algebra, [51] for formal languages, and [77, 142] for mathematical analysis. Propositional Calculus P Syntax • An expression of P is a finite sequence of symbols. Each symbol denotes either an individ- ual constant or an individual variable, or it is a logic connective or a parenthesis. Individual constants are denoted by a,b,c,... (possibly indexed). Individual variables are denoted by x,y,z,... (possibly indexed). Logic connectives are ∨,∧,⇒,⇔ and ¬; they are called the dis- junction, conjunction, implication, equivalence, and negation, respectively. Punctuation marks are parentheses. • Not every expression of P is well-formed. An expression of P is well formed if it is either 1) an individual-constant or individual-variable symbol, or 2) one of the expressions F ∨ G, F ∧ G, F ⇒ G, F ⇔ G, and ¬F, where F and G are well-formed expressions of P. A well-formed expression of P is called a sentence. Semantic • The intended meanings of the logic connectives are: “or” (∨), “and” (∧), “implies” (⇒), “if and only if” (⇔), “not” (¬). • Let {,,⊥} be a set. The elements , and ⊥ are called logic values and stand for “true” and “false,” respectively. Often, 1 and 0 are used instead of , and ⊥, respectively. • Any sentence has either the truth value , or ⊥. A sentence is said to be true if its truth value is ,, and false if its truth value is ⊥.
    [Show full text]