Computability, Reverse Mathematics and Combinatorics
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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. -
Arxiv:2011.02915V1 [Math.LO] 3 Nov 2020 the Otniy Ucin Pnst Tctr)Ue Nti Ae R T Are Paper (C 1.1
REVERSE MATHEMATICS OF THE UNCOUNTABILITY OF R: BAIRE CLASSES, METRIC SPACES, AND UNORDERED SUMS SAM SANDERS Abstract. Dag Normann and the author have recently initiated the study of the logical and computational properties of the uncountability of R formalised as the statement NIN (resp. NBI) that there is no injection (resp. bijection) from [0, 1] to N. On one hand, these principles are hard to prove relative to the usual scale based on comprehension and discontinuous functionals. On the other hand, these principles are among the weakest principles on a new com- plimentary scale based on (classically valid) continuity axioms from Brouwer’s intuitionistic mathematics. We continue the study of NIN and NBI relative to the latter scale, connecting these principles with theorems about Baire classes, metric spaces, and unordered sums. The importance of the first two topics re- quires no explanation, while the final topic’s main theorem, i.e. that when they exist, unordered sums are (countable) series, has the rather unique property of implying NIN formulated with the Cauchy criterion, and (only) NBI when formulated with limits. This study is undertaken within Ulrich Kohlenbach’s framework of higher-order Reverse Mathematics. 1. Introduction The uncountability of R deals with arbitrary mappings with domain R, and is therefore best studied in a language that has such objects as first-class citizens. Obviousness, much more than beauty, is however in the eye of the beholder. Lest we be misunderstood, we formulate a blanket caveat: all notions (computation, continuity, function, open set, et cetera) used in this paper are to be interpreted via their higher-order definitions, also listed below, unless explicitly stated otherwise. -
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 -
On the Reverse Mathematics of General Topology Phd Dissertation, August 2005
The Pennsylvania State University The Graduate School Department of Mathematics ON THE REVERSE MATHEMATICS OF GENERAL TOPOLOGY A Thesis in Mathematics by Carl Mummert Copyright 2005 Carl Mummert Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy August 2005 The thesis of Carl Mummert was reviewed and approved* by the following: Stephen G. Simpson Professor of Mathematics Thesis Advisor Chair of Committee Dmitri Burago Professor of Mathematics John D. Clemens Assistant Professor of Mathematics Martin F¨urer Associate Professor of Computer Science Alexander Nabutovsky Professor of Mathematics Nigel Higson Professor of Mathematics Chair of the Mathematics Department *Signatures are on file in the Graduate School. Abstract This thesis presents a formalization of general topology in second-order arithmetic. Topological spaces are represented as spaces of filters on par- tially ordered sets. If P is a poset, let MF(P ) be the set of maximal fil- ters on P . Let UF(P ) be the set of unbounded filters on P . If X is MF(P ) or UF(P ), the topology on X has a basis {Np | p ∈ P }, where Np = {F ∈ X | p ∈ F }. Spaces of the form MF(P ) are called MF spaces; spaces of the form UF(P ) are called UF spaces. A poset space is either an MF space or a UF space; a poset space formed from a countable poset is said to be countably based. The class of countably based poset spaces in- cludes all complete separable metric spaces and many nonmetrizable spaces including the Gandy–Harrington space. All poset spaces have the strong Choquet property. -
Reverse Mathematics Carl Mummert Communicated by Daniel J
B OOKREVIEW Reverse Mathematics Carl Mummert Communicated by Daniel J. Velleman the theorem at hand? This question is a central motiva- Reverse Mathematics: Proofs from the Inside Out tion of the field of reverse mathematics in mathematical By John Stillwell logic. Princeton University Press Mathematicians have long investigated the problem Hardcover, 200 pages ISBN: 978-1-4008-8903-7 of the Parallel Postulate in geometry: which theorems require it, and which can be proved without it? Analo- gous questions arose about the Axiom of Choice: which There are several monographs on aspects of reverse math- theorems genuinely require the Axiom of Choice for their ematics, but none can be described as a “general audi- proofs? ence” text. Simpson’s Subsystems of Second Order Arith- In each of these cases, it is easy to see the importance metic [3], rightly regarded as a classic, makes substantial of the background theory. After all, what use is it to prove assumptions about the reader’s background in mathemat- a theorem “without the Axiom of Choice” if the proof ical logic. Hirschfeldt’s Slicing the Truth [2] is more ac- uses some other axiom that already implies the Axiom cessible but also makes assumptions beyond an upper- of Choice? To address the question of necessity, we must level undergraduate background and focuses more specif- begin by specifying a precise set of background axioms— ically on combinatorics. The field has been due for a gen- our base theory. This allows us to answer the question of eral treatment accessible to undergraduates and to math- whether an additional axiom is necessary for a particular ematicians in other areas looking for an easily compre- hensible introduction to the field. -
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. -
A Short History of Computational Complexity
The Computational Complexity Column by Lance FORTNOW NEC Laboratories America 4 Independence Way, Princeton, NJ 08540, USA [email protected] http://www.neci.nj.nec.com/homepages/fortnow/beatcs Every third year the Conference on Computational Complexity is held in Europe and this summer the University of Aarhus (Denmark) will host the meeting July 7-10. More details at the conference web page http://www.computationalcomplexity.org This month we present a historical view of computational complexity written by Steve Homer and myself. This is a preliminary version of a chapter to be included in an upcoming North-Holland Handbook of the History of Mathematical Logic edited by Dirk van Dalen, John Dawson and Aki Kanamori. A Short History of Computational Complexity Lance Fortnow1 Steve Homer2 NEC Research Institute Computer Science Department 4 Independence Way Boston University Princeton, NJ 08540 111 Cummington Street Boston, MA 02215 1 Introduction It all started with a machine. In 1936, Turing developed his theoretical com- putational model. He based his model on how he perceived mathematicians think. As digital computers were developed in the 40's and 50's, the Turing machine proved itself as the right theoretical model for computation. Quickly though we discovered that the basic Turing machine model fails to account for the amount of time or memory needed by a computer, a critical issue today but even more so in those early days of computing. The key idea to measure time and space as a function of the length of the input came in the early 1960's by Hartmanis and Stearns. -
Computability Theory
Computability Theory With a Short Introduction to Complexity Theory – Monograph – Karl-Heinz Zimmermann Computability Theory – Monograph – Hamburg University of Technology Prof. Dr. Karl-Heinz Zimmermann Hamburg University of Technology 21071 Hamburg Germany This monograph is listed in the GBV database and the TUHH library. All rights reserved ©2011-2019, by Karl-Heinz Zimmermann, author https://doi.org/10.15480/882.2318 http://hdl.handle.net/11420/2889 urn:nbn:de:gbv:830-882.037681 For Gela and Eileen VI Preface A beautiful theory with heartbreaking results. Why do we need a formalization of the notion of algorithm or effective computation? In order to show that a specific problem is algorithmically solvable, it is sufficient to provide an algorithm that solves it in a sufficiently precise manner. However, in order to prove that a problem is in principle not computable by an algorithm, a rigorous formalism is necessary that allows mathematical proofs. The need for such a formalism became apparent in the studies of David Hilbert (1900) on the foundations of mathematics and Kurt G¨odel (1931) on the incompleteness of elementary arithmetic. The first investigations in this field were conducted by the logicians Alonzo Church, Stephen Kleene, Emil Post, and Alan Turing in the early 1930s. They have provided the foundation of computability theory as a branch of theoretical computer science. The fundamental results established Turing com- putability as the correct formalization of the informal idea of effective calculation. The results have led to Church’s thesis stating that ”everything computable is computable by a Turing machine”. The the- ory of computability has grown rapidly from its beginning. -
Theory and Applications of Computability
Theory and Applications of Computability Springer book series in cooperation with the association Computability in Europe Series Overview Books published in this series will be of interest to the research community and graduate students, with a unique focus on issues of computability. The perspective of the series is multidisciplinary, recapturing the spirit of Turing by linking theoretical and real-world concerns from computer science, mathematics, biology, physics, and the philosophy of science. The series includes research monographs, advanced and graduate texts, and books that offer an original and informative view of computability and computational paradigms. Series Editors . Laurent Bienvenu (University of Montpellier) . Paola Bonizzoni (Università Degli Studi di Milano-Bicocca) . Vasco Brattka (Universität der Bundeswehr München) . Elvira Mayordomo (Universidad de Zaragoza) . Prakash Panangaden (McGill University) Series Advisory Board Samson Abramsky, University of Oxford Ming Li, University of Waterloo Eric Allender, Rutgers, The State University of NJ Wolfgang Maass, Technische Universität Graz Klaus Ambos-Spies, Universität Heidelberg Grzegorz Rozenberg, Leiden University and Giorgio Ausiello, Università di Roma, "La Sapienza" University of Colorado, Boulder Jeremy Avigad, Carnegie Mellon University Alan Selman, University at Buffalo, The State Samuel R. Buss, University of California, San Diego University of New York Rodney G. Downey, Victoria Univ. of Wellington Wilfried Sieg, Carnegie Mellon University Sergei S. Goncharov, Novosibirsk State University Jan van Leeuwen, Universiteit Utrecht Peter Jeavons, University of Oxford Klaus Weihrauch, FernUniversität Hagen Nataša Jonoska, Univ. of South Florida, Tampa Philip Welch, University of Bristol Ulrich Kohlenbach, Technische Univ. Darmstadt Series Information http://www.springer.com/series/8819 Latest Book in Series Title: The Incomputable – Journeys Beyond the Turing Barrier Authors: S. -
An Introduction to Computability Theory
AN INTRODUCTION TO COMPUTABILITY THEORY CINDY CHUNG Abstract. This paper will give an introduction to the fundamentals of com- putability theory. Based on Robert Soare's textbook, The Art of Turing Computability: Theory and Applications, we examine concepts including the Halting problem, properties of Turing jumps and degrees, and Post's Theo- rem.1 In the paper, we will assume the reader has a conceptual understanding of computer programs. Contents 1. Basic Computability 1 2. The Halting Problem 2 3. Post's Theorem: Jumps and Quantifiers 3 3.1. Arithmetical Hierarchy 3 3.2. Degrees and Jumps 4 3.3. The Zero-Jump, 00 5 3.4. Post's Theorem 5 Acknowledgments 8 References 8 1. Basic Computability Definition 1.1. An algorithm is a procedure capable of being carried out by a computer program. It accepts various forms of numerical inputs including numbers and finite strings of numbers. Computability theory is a branch of mathematical logic that focuses on algo- rithms, formally known in this area as computable functions, and studies the degree, or level of computability that can be attributed to various sets (these concepts will be formally defined and elaborated upon below). This field was founded in the 1930s by many mathematicians including the logicians: Alan Turing, Stephen Kleene, Alonzo Church, and Emil Post. Turing first pioneered computability theory when he introduced the fundamental concept of the a-machine which is now known as the Turing machine (this concept will be intuitively defined below) in his 1936 pa- per, On computable numbers, with an application to the Entscheidungsproblem[7]. -
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 . -
What Is Mathematics: Gödel's Theorem and Around. by Karlis
1 Version released: January 25, 2015 What is Mathematics: Gödel's Theorem and Around Hyper-textbook for students by Karlis Podnieks, Professor University of Latvia Institute of Mathematics and Computer Science An extended translation of the 2nd edition of my book "Around Gödel's theorem" published in 1992 in Russian (online copy). Diploma, 2000 Diploma, 1999 This work is licensed under a Creative Commons License and is copyrighted © 1997-2015 by me, Karlis Podnieks. This hyper-textbook contains many links to: Wikipedia, the free encyclopedia; MacTutor History of Mathematics archive of the University of St Andrews; MathWorld of Wolfram Research. Are you a platonist? Test yourself. Tuesday, August 26, 1930: Chronology of a turning point in the human intellectua l history... Visiting Gödel in Vienna... An explanation of “The Incomprehensible Effectiveness of Mathematics in the Natural Sciences" (as put by Eugene Wigner). 2 Table of Contents References..........................................................................................................4 1. Platonism, intuition and the nature of mathematics.......................................6 1.1. Platonism – the Philosophy of Working Mathematicians.......................6 1.2. Investigation of Stable Self-contained Models – the True Nature of the Mathematical Method..................................................................................15 1.3. Intuition and Axioms............................................................................20 1.4. Formal Theories....................................................................................27