Theory and Applications of Computability
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Computational Complexity Theory Introduction to Computational
31/03/2012 Computational complexity theory Introduction to computational complexity theory Complexity (computability) theory theory deals with two aspects: Algorithm’s complexity. Problem’s complexity. References S. Cook, « The complexity of Theorem Proving Procedures », 1971. Garey and Johnson, « Computers and Intractability, A guide to the theory of NP-completeness », 1979. J. Carlier et Ph. Chrétienne « Problèmes d’ordonnancements : algorithmes et complexité », 1988. 1 31/03/2012 Basic Notions • Some problem is a “question” characterized by parameters and needs an answer. – Parameters description; – Properties that a solutions must satisfy; – An instance is obtained when the parameters are fixed to some values. • An algorithm: a set of instructions describing how some task can be achieved or a problem can be solved. • A program : the computational implementation of an algorithm. Algorithm’s complexity (I) • There may exists several algorithms for the same problem • Raised questions: – Which one to choose ? – How they are compared ? – How measuring the efficiency ? – What are the most appropriate measures, running time, memory space ? 2 31/03/2012 Algorithm’s complexity (II) • Running time depends on: – The data of the problem, – Quality of program..., – Computer type, – Algorithm’s efficiency, – etc. • Proceed by analyzing the algorithm: – Search for some n characterizing the data. – Compute the running time in terms of n. – Evaluating the number of elementary operations, (elementary operation = simple instruction of a programming language). Algorithm’s evaluation (I) • Any algorithm is composed of two main stages: initialization and computing one • The complexity parameter is the size data n (binary coding). Definition: Let be n>0 andT(n) the running time of an algorithm expressed in terms of the size data n, T(n) is of O(f(n)) iff n0 and some constant c such that: n n0, we have T(n) c f(n). -
Computability of Fraïssé Limits
COMPUTABILITY OF FRA¨ISSE´ LIMITS BARBARA F. CSIMA, VALENTINA S. HARIZANOV, RUSSELL MILLER, AND ANTONIO MONTALBAN´ Abstract. Fra¨ıss´estudied countable structures S through analysis of the age of S, i.e., the set of all finitely generated substructures of S. We investigate the effectiveness of his analysis, considering effectively presented lists of finitely generated structures and asking when such a list is the age of a computable structure. We focus particularly on the Fra¨ıss´elimit. We also show that degree spectra of relations on a sufficiently nice Fra¨ıss´elimit are always upward closed unless the relation is definable by a quantifier-free formula. We give some sufficient or necessary conditions for a Fra¨ıss´elimit to be spectrally universal. As an application, we prove that the computable atomless Boolean algebra is spectrally universal. Contents 1. Introduction1 1.1. Classical results about Fra¨ıss´elimits and background definitions4 2. Computable Ages5 3. Computable Fra¨ıss´elimits8 3.1. Computable properties of Fra¨ıss´elimits8 3.2. Existence of computable Fra¨ıss´elimits9 4. Examples 15 5. Upward closure of degree spectra of relations 18 6. Necessary conditions for spectral universality 20 6.1. Local finiteness 20 6.2. Finite realizability 21 7. A sufficient condition for spectral universality 22 7.1. The countable atomless Boolean algebra 23 References 24 1. Introduction Computable model theory studies the algorithmic complexity of countable structures, of their isomorphisms, and of relations on such structures. Since algorithmic properties often depend on data presentation, in computable model theory classically isomorphic structures can have different computability-theoretic properties. -
Submitted Thesis
Saarland University Faculty of Mathematics and Computer Science Bachelor’s Thesis Synthetic One-One, Many-One, and Truth-Table Reducibility in Coq Author Advisor Felix Jahn Yannick Forster Reviewers Prof. Dr. Gert Smolka Prof. Dr. Markus Bläser Submitted: September 15th, 2020 ii Eidesstattliche Erklärung Ich erkläre hiermit an Eides statt, dass ich die vorliegende Arbeit selbstständig ver- fasst und keine anderen als die angegebenen Quellen und Hilfsmittel verwendet habe. Statement in Lieu of an Oath I hereby confirm that I have written this thesis on my own and that I have not used any other media or materials than the ones referred to in this thesis. Einverständniserklärung Ich bin damit einverstanden, dass meine (bestandene) Arbeit in beiden Versionen in die Bibliothek der Informatik aufgenommen und damit veröffentlicht wird. Declaration of Consent I agree to make both versions of my thesis (with a passing grade) accessible to the public by having them added to the library of the Computer Science Department. Saarbrücken, September 15th, 2020 Abstract Reducibility is an essential concept for undecidability proofs in computability the- ory. The idea behind reductions was conceived by Turing, who introduced the later so-called Turing reduction based on oracle machines. In 1944, Post furthermore in- troduced with one-one, many-one, and truth-table reductions in comparison to Tur- ing reductions more specific reducibility notions. Post then also started to analyze the structure of the different reducibility notions and their computability degrees. Most undecidable problems were reducible from the halting problem, since this was exactly the method to show them undecidable. However, Post was able to con- struct also semidecidable but undecidable sets that do not one-one, many-one, or truth-table reduce from the halting problem. -
Module 34: Reductions and NP-Complete Proofs
Module 34: Reductions and NP-Complete Proofs This module 34 focuses on reductions and proof of NP-Complete problems. The module illustrates the ways of reducing one problem to another. Then, the module illustrates the outlines of proof of NP- Complete problems with some examples. The objectives of this module are To understand the concept of Reductions among problems To Understand proof of NP-Complete problems To overview proof of some Important NP-Complete problems. Computational Complexity There are two types of complexity theories. One is related to algorithms, known as algorithmic complexity theory and another related to complexity of problems called computational complexity theory [2,3]. Algorithmic complexity theory [3,4] aims to analyze the algorithms in terms of the size of the problem. In modules 3 and 4, we had discussed about these methods. The size is the length of the input. It can be recollected from module 3 that the size of a number n is defined to be the number of binary bits needed to write n. For example, Example: b(5) = b(1012) = 3. In other words, the complexity of the algorithm is stated in terms of the size of the algorithm. Asymptotic Analysis [1,2] refers to the study of an algorithm as the input size reaches a limit and analyzing the behaviour of the algorithms. The asymptotic analysis is also science of approximation where the behaviour of the algorithm is expressed in terms of notations such as big-oh, Big-Omega and Big- Theta. Computational complexity theory is different. Computational complexity aims to determine complexity of the problems itself. -
31 Summary of Computability Theory
CS:4330 Theory of Computation Spring 2018 Computability Theory Summary Haniel Barbosa Readings for this lecture Chapters 3-5 and Section 6.2 of [Sipser 1996], 3rd edition. A hierachy of languages n m B Regular: a b n n B Deterministic Context-free: a b n n n 2n B Context-free: a b [ a b n n n B Turing decidable: a b c B Turing recognizable: ATM 1 / 12 Why TMs? B In 1900: Hilbert posed 23 “challenge problems” in Mathematics The 10th problem: Devise a process according to which it can be decided by a finite number of operations if a given polynomial has an integral root. It became necessary to have a formal definition of “algorithms” to define their expressivity. 2 / 12 Church-Turing Thesis B In 1936 Church and Turing independently defined “algorithm”: I λ-calculus I Turing machines B Intuitive notion of algorithms = Turing machine algorithms B “Any process which could be naturally called an effective procedure can be realized by a Turing machine” th B We now know: Hilbert’s 10 problem is undecidable! 3 / 12 Algorithm as Turing Machine Definition (Algorithm) An algorithm is a decider TM in the standard representation. B The input to a TM is always a string. B If we want an object other than a string as input, we must first represent that object as a string. B Strings can easily represent polynomials, graphs, grammars, automata, and any combination of these objects. 4 / 12 How to determine decidability / Turing-recognizability? B Decidable / Turing-recognizable: I Present a TM that decides (recognizes) the language I If A is mapping reducible to -
A Bounded Jump for the Bounded Turing Degrees
A bounded jump for the bounded Turing degrees Bernard A. Anderson Barbara F. Csima ∗ Department of Pure Mathematics Department of Pure Mathematics University of Waterloo University of Waterloo [email protected] [email protected] www.math.uwaterloo.ca/∼b7anders www.math.uwaterloo.ca/∼csima Abstract b We define the bounded jump of A by A = fx 2 ! j 9i ≤ x['i(x) # A'i(x) nb ^ Φx (x)#]g and let A denote the n-th bounded jump. We demon- strate several properties of the bounded jump, including that it is strictly increasing and order preserving on the bounded Turing (bT ) degrees (also known as the weak truth-table degrees). We show that the bounded jump is related to the Ershov hierarchy. Indeed, for n ≥ 2 we have nb n nb X ≤bT ; () X is ! -c.e. () X ≤1 ; , extending the classical 0 result that X ≤bT ; () X is !-c.e.. Finally, we prove that the ana- logue of Shoenfield inversion holds for the bounded jump on the bounded b 2b Turing degrees. That is, for every X such that ; ≤bT X ≤bT ; , there b b is a Y ≤bT ; such that Y ≡bT X. 1 Introduction In computability theory, we are interested in comparing the relative computa- tional complexities of infinite sets of natural numbers. There are many ways of doing this, and which method is used often depends on the purpose of the study, or how fine a comparison is desired. Two sets of the same computational com- plexity (X ≤ Y and Y ≤ X) are said to be in the same degree. -
Decidable. (Recall It’S Not “Regular”.)
15-251: Great Theoretical Ideas in Computer Science Fall 2016 Lecture 6 September 15, 2016 Turing & the Uncomputable Comparing the cardinality of sets 퐴 ≤ 퐵 if there is an injection (one-to-one map) from 퐴 to 퐵 퐴 ≥ 퐵 if there is a surjection (onto map) from 퐴 to 퐵 퐴 = 퐵 if there is a bijection from 퐴 to 퐵 퐴 > |퐵| if there is no surjection from 퐵 to 퐴 (or equivalently, there is no injection from 퐴 to 퐵) Countable and uncountable sets countable countably infinite uncountable One slide guide to countability questions You are given a set 퐴 : is it countable or uncountable 퐴 ≤ |ℕ| or 퐴 > |ℕ| 퐴 ≤ |ℕ| : • Show directly surjection from ℕ to 퐴 • Show that 퐴 ≤ |퐵| where 퐵 ∈ {ℤ, ℤ x ℤ, ℚ, Σ∗, ℚ[x], …} 퐴 > |ℕ| : • Show directly using a diagonalization argument • Show that 퐴 ≥ | 0,1 ∞| Proving sets countable using computation For example, f(n) = ‘the nth prime’. You could write a program (Turing machine) to compute f. So this is a well-defined rule. Or: f(n) = the nth rational in our listing of ℚ. (List ℤ2 via the spiral, omit the terms p/0, omit rationals seen before…) You could write a program to compute this f. Poll Let 퐴 be the set of all languages over Σ = 1 ∗ Select the correct ones: - A is finite - A is infinite - A is countable - A is uncountable Another thing to remember from last week Encoding different objects with strings Fix some alphabet Σ . We use the ⋅ notation to denote the encoding of an object as a string in Σ∗ Examples: is the encoding a TM 푀 is the encoding a DFA 퐷 is the encoding of a pair of TMs 푀1, 푀2 is the encoding a pair 푀, 푥, where 푀 is a TM, and 푥 ∈ Σ∗ is an input to 푀 Uncountable to uncomputable The real number 1/7 is “computable”. -
Computability on the Integers
Computability on the integers Laurent Bienvenu ( LIAFA, CNRS & Université de Paris 7 ) EJCIM 2011 Amiens, France 30 mars 2011 1. Basic objects of computability The formalization and study of the notion of computable function is what computability theory is about. As opposed to complexity theory, we do not care about efficiency, just about feasibility. Computable. functions What does it means for a function f : N ! N to be computable? 1. Basic objects of computability 3/79 As opposed to complexity theory, we do not care about efficiency, just about feasibility. Computable. functions What does it means for a function f : N ! N to be computable? The formalization and study of the notion of computable function is what computability theory is about. 1. Basic objects of computability 3/79 Computable. functions What does it means for a function f : N ! N to be computable? The formalization and study of the notion of computable function is what computability theory is about. As opposed to complexity theory, we do not care about efficiency, just about feasibility. 1. Basic objects of computability 3/79 But for us, it is now obvious that computable = realizable by a program / algorithm. Surprisingly, the first acceptable formalization (Turing machines) is still one of the best (if not the best) we know today. The. intuition At the time these questions were first considered (1930’s), computers did not exist (at least in the modern sense). 1. Basic objects of computability 4/79 Surprisingly, the first acceptable formalization (Turing machines) is still one of the best (if not the best) we know today. -
The Weakness of CTC Qubits and the Power of Approximate Counting
The weakness of CTC qubits and the power of approximate counting Ryan O'Donnell∗ A. C. Cem Sayy April 7, 2015 Abstract We present results in structural complexity theory concerned with the following interre- lated topics: computation with postselection/restarting, closed timelike curves (CTCs), and approximate counting. The first result is a new characterization of the lesser known complexity class BPPpath in terms of more familiar concepts. Precisely, BPPpath is the class of problems that can be efficiently solved with a nonadaptive oracle for the Approximate Counting problem. Similarly, PP equals the class of problems that can be solved efficiently with nonadaptive queries for the related Approximate Difference problem. Another result is concerned with the compu- tational power conferred by CTCs; or equivalently, the computational complexity of finding stationary distributions for quantum channels. Using the above-mentioned characterization of PP, we show that any poly(n)-time quantum computation using a CTC of O(log n) qubits may as well just use a CTC of 1 classical bit. This result essentially amounts to showing that one can find a stationary distribution for a poly(n)-dimensional quantum channel in PP. ∗Department of Computer Science, Carnegie Mellon University. Work performed while the author was at the Bo˘gazi¸ciUniversity Computer Engineering Department, supported by Marie Curie International Incoming Fellowship project number 626373. yBo˘gazi¸ciUniversity Computer Engineering Department. 1 Introduction It is well known that studying \non-realistic" augmentations of computational models can shed a great deal of light on the power of more standard models. The study of nondeterminism and the study of relativization (i.e., oracle computation) are famous examples of this phenomenon. -
Computability Theory
CSC 438F/2404F Notes (S. Cook and T. Pitassi) Fall, 2019 Computability Theory This section is partly inspired by the material in \A Course in Mathematical Logic" by Bell and Machover, Chap 6, sections 1-10. Other references: \Introduction to the theory of computation" by Michael Sipser, and \Com- putability, Complexity, and Languages" by M. Davis and E. Weyuker. Our first goal is to give a formal definition for what it means for a function on N to be com- putable by an algorithm. Historically the first convincing such definition was given by Alan Turing in 1936, in his paper which introduced what we now call Turing machines. Slightly before Turing, Alonzo Church gave a definition based on his lambda calculus. About the same time G¨odel,Herbrand, and Kleene developed definitions based on recursion schemes. Fortunately all of these definitions are equivalent, and each of many other definitions pro- posed later are also equivalent to Turing's definition. This has lead to the general belief that these definitions have got it right, and this assertion is roughly what we now call \Church's Thesis". A natural definition of computable function f on N allows for the possibility that f(x) may not be defined for all x 2 N, because algorithms do not always halt. Thus we will use the symbol 1 to mean “undefined". Definition: A partial function is a function n f :(N [ f1g) ! N [ f1g; n ≥ 0 such that f(c1; :::; cn) = 1 if some ci = 1. In the context of computability theory, whenever we refer to a function on N, we mean a partial function in the above sense. -
Independence of P Vs. NP in Regards to Oracle Relativizations. · 3 Then
Independence of P vs. NP in regards to oracle relativizations. JERRALD MEEK This is the third article in a series of four articles dealing with the P vs. NP question. The purpose of this work is to demonstrate that the methods used in the first two articles of this series are not affected by oracle relativizations. Furthermore, the solution to the P vs. NP problem is actually independent of oracle relativizations. Categories and Subject Descriptors: F.2.0 [Analysis of Algorithms and Problem Complex- ity]: General General Terms: Algorithms, Theory Additional Key Words and Phrases: P vs NP, Oracle Machine 1. INTRODUCTION. Previously in “P is a proper subset of NP” [Meek Article 1 2008] and “Analysis of the Deterministic Polynomial Time Solvability of the 0-1-Knapsack Problem” [Meek Article 2 2008] the present author has demonstrated that some NP-complete problems are likely to have no deterministic polynomial time solution. In this article the concepts of these previous works will be applied to the relativizations of the P vs. NP problem. It has previously been shown by Hartmanis and Hopcroft [1976], that the P vs. NP problem is independent of Zermelo-Fraenkel set theory. However, this same discovery had been shown by Baker [1979], who also demonstrates that if P = NP then the solution is incompatible with ZFC. The purpose of this article will be to demonstrate that the oracle relativizations of the P vs. NP problem do not preclude any solution to the original problem. This will be accomplished by constructing examples of five out of the six relativizing or- acles from Baker, Gill, and Solovay [1975], it will be demonstrated that inconsistent relativizations do not preclude a proof of P 6= NP or P = NP. -
THERE IS NO DEGREE INVARIANT HALF-JUMP a Striking
PROCEEDINGS OF THE AMERICAN MATHEMATICAL SOCIETY Volume 125, Number 10, October 1997, Pages 3033{3037 S 0002-9939(97)03915-4 THERE IS NO DEGREE INVARIANT HALF-JUMP RODNEY G. DOWNEY AND RICHARD A. SHORE (Communicated by Andreas R. Blass) Abstract. We prove that there is no degree invariant solution to Post's prob- lem that always gives an intermediate degree. In fact, assuming definable de- terminacy, if W is any definable operator on degrees such that a <W (a) < a0 on a cone then W is low2 or high2 on a cone of degrees, i.e., there is a degree c such that W (a)00 = a for every a c or W (a)00 = a for every a c. 00 ≥ 000 ≥ A striking phenomenon in the early days of computability theory was that every decision problem for axiomatizable theories turned out to be either decidable or of the same Turing degree as the halting problem (00,thecomplete computably enumerable set). Perhaps the most influential problem in computability theory over the past fifty years has been Post’s problem [10] of finding an exception to this rule, i.e. a noncomputable incomplete computably enumerable degree. The problem has been solved many times in various settings and disguises but the so- lutions always involve specific constructions of strange sets, usually by the priority method that was first developed (Friedberg [2] and Muchnik [9]) to solve this prob- lem. No natural decision problems or sets of any kind have been found that are neither computable nor complete. The question then becomes how to define what characterizes the natural computably enumerable degrees and show that none of them can supply a solution to Post’s problem.