Complexity of Equivalence Relations and Preorders from 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). -
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. -
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”. -
Intrinsic Bounds on Complexity and Definability at Limit Levels*
Intrinsic bounds on complexity and definability at limit levels∗ John Chisholm Ekaterina Fokina Department of Mathematics Department of Mathematics Western Illinois University Novosibirsk State University Macomb, IL 61455 630090 Novosibirsk, Russia [email protected] [email protected] Sergey S. Goncharov Academy of Sciences, Siberian Branch Mathematical Institute 630090 Novosibirsk, Russia [email protected] Valentina S. Harizanov Julia F. Knight Department of Mathematics Department of Mathematics George Washington University University of Notre Dame [email protected] [email protected] Sara Miller Department of Mathematics University of Notre Dame [email protected] May 31, 2007 Abstract We show that for every computable limit ordinal α,thereisacom- 0 0 putable structure that is ∆α categorical, but not relatively ∆α categor- A 0 ical, i.e., does not have a formally Σα Scott family. We also show that for every computable limit ordinal α, there is a computable structure with 0 A an additional relation R that is intrinsically Σα on , but not relatively 0 A intrinsically Σ on , i.e., not definable by a computable Σα formula with α A ∗The authors gratefully acknowledge support of the Charles H. Husking endowment at the University of Notre Dame. The last five authors were also partially supported by the NSF binational grant DMS-0554841, and the fourth author by the NSF grant DMS-0704256. In addition, the second author was supported by a grant of the Russian Federation as a 2006-07 research visitor to the University of Notre Dame. 1 finitely many parameters. Earlier results in [7], [10], [8] establish the same facts for computable successor ordinals α. -
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. -
Arxiv:2010.12452V1 [Math.LO]
ORDINAL ANALYSIS OF PARTIAL COMBINATORY ALGEBRAS PAUL SHAFER AND SEBASTIAAN A. TERWIJN Abstract. For every partial combinatory algebra (pca), we define a hierarchy of extensionality rela- tions using ordinals. We investigate the closure ordinals of pca’s, i.e. the smallest ordinals where these CK relations become equal. We show that the closure ordinal of Kleene’s first model is ω1 and that the closure ordinal of Kleene’s second model is ω1. We calculate the exact complexities of the extensionality relations in Kleene’s first model, showing that they exhaust the hyperarithmetical hierarchy. We also discuss embeddings of pca’s. 1. Introduction Partial combinatory algebras (pca’s) were introduced by Feferman [9] in connection with the study of predicative systems of mathematics. Since then, they have been studied as abstract models of computation, in the same spirit as combinatory algebras (defined and studied long before, in the 1920’s, by Sch¨onfinkel and Curry) and the closely related lambda calculus, cf. Barendregt [2]. As such, pca’s figure prominently in the literature on constructive mathematics, see e.g. Beeson [5] and Troelstra and van Dalen [22]. This holds in particular for the theory of realizability, see van Oosten [24]. For example, pca’s serve as the basis of various models of constructive set theory, first defined by McCarty [14]. See Rathjen [18] for further developments using this construction. A pca is a set A equipped with a partial application operator · that has the same properties as a classical (total) combinatory algebra (see Section 2 below for precise definitions). In particular, it has the combinators K and S. -
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. -
Computable Structure Theory on Ω 1 Using Admissibility
COMPUTABLE STRUCTURE THEORY USING ADMISSIBLE RECURSION THEORY ON !1 NOAM GREENBERG AND JULIA F. KNIGHT Abstract. We use the theory of recursion on admissible ordinals to develop an analogue of classical computable model theory and effective algebra for structures of size @1, which, under our assumptions, is equal to the contin- uum. We discuss both general concepts, such as computable categoricity, and particular classes of examples, such as fields and linear orderings. 1. Introduction Our aim is to develop computable structure theory for uncountable structures. In this paper we focus on structures of size @1. The fundamental decision to be made, when trying to formulate such a theory, is the choice of computability tools that we intend to use. To discover which structures are computable, we need to first describe which subsets of the domain are computable, and which functions are computable. In this paper, we use admissible recursion theory (also known as α-recursion theory) over the domain !1. We believe that this choice yields an interesting computable structure theory. It also illuminates the concepts and techniques of classical computable structure theory by observing similarities and differences between the countable and uncountable settings. In particular, it seems that as is the case for degree theory and for the study of the lattice of c.e. sets, the difference between true finiteness and its analogue in the generalised case, namely countability in our case, is fundamental to some constructions and reveals a deep gap between classical computability and attempts to generalise it to the realm of the uncountable. In this paper, we make a sweeping simplifying assumption about our set-theoretic universe. -
Effective Descriptive Set Theory
Effective Descriptive Set Theory Andrew Marks December 14, 2019 1 1 These notes introduce the effective (lightface) Borel, Σ1 and Π1 sets. This study uses ideas and tools from descriptive set theory and computability theory. Our central motivation is in applications of the effective theory to theorems of classical (boldface) descriptive set theory, especially techniques which have no classical analogues. These notes have many errors and are very incomplete. Some important topics not covered include: • The Harrington-Shore-Slaman theorem [HSS] which implies many of the theorems of Section 3. • Steel forcing (see [BD, N, Mo, St78]) • Nonstandard model arguments • Barwise compactness, Jensen's model existence theorem • α-recursion theory • Recent beautiful work of the \French School": Debs, Saint-Raymond, Lecompte, Louveau, etc. These notes are from a class I taught in spring 2019. Thanks to Adam Day, Thomas Gilton, Kirill Gura, Alexander Kastner, Alexander Kechris, Derek Levinson, Antonio Montalb´an,Dean Menezes and Riley Thornton, for helpful conversations and comments on earlier versions of these notes. 1 Contents 1 1 1 1 Characterizing Σ1, ∆1, and Π1 sets 4 1 1.1 Σn formulas, closure properties, and universal sets . .4 1.2 Boldface vs lightface sets and relativization . .5 1 1.3 Normal forms for Σ1 formulas . .5 1.4 Ranking trees and Spector boundedness . .7 1 1.5 ∆1 = effectively Borel . .9 1.6 Computable ordinals, hyperarithmetic sets . 11 1 1.7 ∆1 = hyperarithmetic . 14 x 1 1.8 The hyperjump, !1 , and the analogy between c.e. and Π1 .... 15 2 Basic tools 18 2.1 Existence proofs via completeness results . -
Measures and Their Random Reals
TRANSACTIONS OF THE AMERICAN MATHEMATICAL SOCIETY Volume 367, Number 7, July 2015, Pages 5081–5097 S 0002-9947(2015)06184-4 Article electronically published on January 30, 2015 MEASURES AND THEIR RANDOM REALS JAN REIMANN AND THEODORE A. SLAMAN Abstract. We study the randomness properties of reals with respect to ar- bitrary probability measures on Cantor space. We show that every non- computable real is non-trivially random with respect to some measure. The probability measures constructed in the proof may have atoms. If one rules out the existence of atoms, i.e. considers only continuous measures, it turns out that every non-hyperarithmetical real is random for a continuous measure. On the other hand, examples of reals not random for any continuous measure can be found throughout the hyperarithmetical Turing degrees. 1. Introduction Over the past decade, the study of algorithmic randomness has produced an impressive number of results. The theory of Martin-L¨of random reals, with all its ramifications (e.g. computable or Schnorr randomness, lowness and triviality) has found deep and significant applications in computability theory, many of which are covered in recent books by Downey and Hirschfeldt [5] and Nies [23]. Usually, the measure for which randomness is considered in these studies is the uniform (1/2, 1/2)-measure on Cantor space, which is measure theoretically isomorphic to Lebesgue measure on the unit interval. However, one may ask what happens if one changes the underlying measure. It is easy to define a generalization of Martin-L¨of tests which allows for a definition of randomness with respect to arbitrary computable measures. -
Turing Oracle Machines, Online Computing, and Three Displacements in Computability Theory
Turing Oracle Machines, Online Computing, and Three Displacements in Computability Theory Robert I. Soare∗ January 3, 2009 Contents 1 Introduction 4 1.1 Terminology: Incompleteness and Incomputability . 4 1.2 The Goal of Incomputability not Computability . 5 1.3 Computing Relative to an Oracle or Database . 5 1.4 Continuous Functions and Calculus . 6 2 Origins of Computability and Incomputability 7 2.1 G¨odel'sIncompleteness Theorem . 7 2.2 Alonzo Church . 8 2.3 Herbrand-G¨odelRecursive Functions . 9 2.4 Stalemate at Princeton Over Church's Thesis . 10 2.5 G¨odel'sThoughts on Church's Thesis . 11 3 Turing Breaks the Stalemate 11 3.1 Turing Machines and Turing's Thesis . 11 3.2 G¨odel'sOpinion of Turing's Work . 13 3.2.1 G¨odel[193?] Notes in Nachlass [1935] . 14 3.2.2 Princeton Bicentennial [1946] . 15 ∗Parts of this paper were delivered in an address to the conference, Computation and Logic in the Real World, at Siena, Italy, June 18{23, 2007. Keywords: Turing ma- chine, automatic machine, a-machine, Turing oracle machine, o-machine, Alonzo Church, Stephen C. Kleene, Alan Turing, Kurt G¨odel, Emil Post, computability, incomputability, undecidability, Church-Turing Thesis, Post-Turing Thesis on relative computability, com- putable approximations, Limit Lemma, effectively continuous functions, computability in analysis, strong reducibilities. Thanks are due to C.G. Jockusch, Jr., P. Cholak, and T. Slaman for corrections and suggestions. 1 3.2.3 The Flaw in Church's Thesis . 16 3.2.4 G¨odelon Church's Thesis . 17 3.2.5 G¨odel'sLetter to Kreisel [1968] .