Recursion Theory in Set Theory
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6.5 the Recursion Theorem
6.5. THE RECURSION THEOREM 417 6.5 The Recursion Theorem The recursion Theorem, due to Kleene, is a fundamental result in recursion theory. Theorem 6.5.1 (Recursion Theorem, Version 1 )Letϕ0,ϕ1,... be any ac- ceptable indexing of the partial recursive functions. For every total recursive function f, there is some n such that ϕn = ϕf(n). The recursion Theorem can be strengthened as follows. Theorem 6.5.2 (Recursion Theorem, Version 2 )Letϕ0,ϕ1,... be any ac- ceptable indexing of the partial recursive functions. There is a total recursive function h such that for all x ∈ N,ifϕx is total, then ϕϕx(h(x)) = ϕh(x). 418 CHAPTER 6. ELEMENTARY RECURSIVE FUNCTION THEORY A third version of the recursion Theorem is given below. Theorem 6.5.3 (Recursion Theorem, Version 3 ) For all n ≥ 1, there is a total recursive function h of n +1 arguments, such that for all x ∈ N,ifϕx is a total recursive function of n +1arguments, then ϕϕx(h(x,x1,...,xn),x1,...,xn) = ϕh(x,x1,...,xn), for all x1,...,xn ∈ N. As a first application of the recursion theorem, we can show that there is an index n such that ϕn is the constant function with output n. Loosely speaking, ϕn prints its own name. Let f be the recursive function such that f(x, y)=x for all x, y ∈ N. 6.5. THE RECURSION THEOREM 419 By the s-m-n Theorem, there is a recursive function g such that ϕg(x)(y)=f(x, y)=x for all x, y ∈ N. -
Topology and Descriptive Set Theory
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elsevier - Publisher Connector TOPOLOGY AND ITS APPLICATIONS ELSEVIER Topology and its Applications 58 (1994) 195-222 Topology and descriptive set theory Alexander S. Kechris ’ Department of Mathematics, California Institute of Technology, Pasadena, CA 91125, USA Received 28 March 1994 Abstract This paper consists essentially of the text of a series of four lectures given by the author in the Summer Conference on General Topology and Applications, Amsterdam, August 1994. Instead of attempting to give a general survey of the interrelationships between the two subjects mentioned in the title, which would be an enormous and hopeless task, we chose to illustrate them in a specific context, that of the study of Bore1 actions of Polish groups and Bore1 equivalence relations. This is a rapidly growing area of research of much current interest, which has interesting connections not only with topology and set theory (which are emphasized here), but also to ergodic theory, group representations, operator algebras and logic (particularly model theory and recursion theory). There are four parts, corresponding roughly to each one of the lectures. The first contains a brief review of some fundamental facts from descriptive set theory. In the second we discuss Polish groups, and in the third the basic theory of their Bore1 actions. The last part concentrates on Bore1 equivalence relations. The exposition is essentially self-contained, but proofs, when included at all, are often given in the barest outline. Keywords: Polish spaces; Bore1 sets; Analytic sets; Polish groups; Bore1 actions; Bore1 equivalence relations 1. -
Abstract Determinacy in the Low Levels of The
ABSTRACT DETERMINACY IN THE LOW LEVELS OF THE PROJECTIVE HIERARCHY by Michael R. Cotton We give an expository introduction to determinacy for sets of reals. If we are to assume the Axiom of Choice, then not all sets of reals can be determined. So we instead establish determinacy for sets according to their levels in the usual hierarchies of complexity. At a certain point ZFC is no longer strong enough, and we must begin to assume large cardinals. The primary results of this paper are Martin's theorems that Borel sets are determined, homogeneously Suslin sets are determined, and if a measurable cardinal κ exists then the complements of analytic sets are κ-homogeneously Suslin. We provide the necessary preliminaries, prove Borel determinacy, introduce measurable cardinals and ultrapowers, and then prove analytic determinacy from the existence of a measurable cardinal. Finally, we give a very brief overview of some further results which provide higher levels of determinacy relative to larger cardinals. DETERMINACY IN THE LOW LEVELS OF THE PROJECTIVE HIERARCHY A Thesis Submitted to the Faculty of Miami University in partial fulfillment of the requirements for the degree of Master of Arts Department of Mathematics by Michael R. Cotton Miami University Oxford, Ohio 2012 Advisor: Dr. Paul Larson Reader: Dr. Dennis K. Burke Reader: Dr. Tetsuya Ishiu Contents Introduction 1 0.1 Some notation and conventions . 2 1 Reals, trees, and determinacy 3 1.1 The reals as !! .............................. 3 1.2 Determinacy . 5 1.3 Borel sets . 8 1.4 Projective sets . 10 1.5 Tree representations . 12 2 Borel determinacy 14 2.1 Games with a tree of legal positions . -
April 22 7.1 Recursion Theorem
CSE 431 Theory of Computation Spring 2014 Lecture 7: April 22 Lecturer: James R. Lee Scribe: Eric Lei Disclaimer: These notes have not been subjected to the usual scrutiny reserved for formal publications. They may be distributed outside this class only with the permission of the Instructor. An interesting question about Turing machines is whether they can reproduce themselves. A Turing machine cannot be be defined in terms of itself, but can it still somehow print its own source code? The answer to this question is yes, as we will see in the recursion theorem. Afterward we will see some applications of this result. 7.1 Recursion Theorem Our end goal is to have a Turing machine that prints its own source code and operates on it. Last lecture we proved the existence of a Turing machine called SELF that ignores its input and prints its source code. We construct a similar proof for the recursion theorem. We will also need the following lemma proved last lecture. ∗ ∗ Lemma 7.1 There exists a computable function q :Σ ! Σ such that q(w) = hPwi, where Pw is a Turing machine that prints w and hats. Theorem 7.2 (Recursion theorem) Let T be a Turing machine that computes a function t :Σ∗ × Σ∗ ! Σ∗. There exists a Turing machine R that computes a function r :Σ∗ ! Σ∗, where for every w, r(w) = t(hRi; w): The theorem says that for an arbitrary computable function t, there is a Turing machine R that computes t on hRi and some input. Proof: We construct a Turing Machine R in three parts, A, B, and T , where T is given by the statement of the theorem. -
The Logic of Recursive Equations Author(S): A
The Logic of Recursive Equations Author(s): A. J. C. Hurkens, Monica McArthur, Yiannis N. Moschovakis, Lawrence S. Moss, Glen T. Whitney Source: The Journal of Symbolic Logic, Vol. 63, No. 2 (Jun., 1998), pp. 451-478 Published by: Association for Symbolic Logic Stable URL: http://www.jstor.org/stable/2586843 . Accessed: 19/09/2011 22:53 Your use of the JSTOR archive indicates your acceptance of the Terms & Conditions of Use, available at . http://www.jstor.org/page/info/about/policies/terms.jsp JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact [email protected]. Association for Symbolic Logic is collaborating with JSTOR to digitize, preserve and extend access to The Journal of Symbolic Logic. http://www.jstor.org THE JOURNAL OF SYMBOLIC LOGIC Volume 63, Number 2, June 1998 THE LOGIC OF RECURSIVE EQUATIONS A. J. C. HURKENS, MONICA McARTHUR, YIANNIS N. MOSCHOVAKIS, LAWRENCE S. MOSS, AND GLEN T. WHITNEY Abstract. We study logical systems for reasoning about equations involving recursive definitions. In particular, we are interested in "propositional" fragments of the functional language of recursion FLR [18, 17], i.e., without the value passing or abstraction allowed in FLR. The 'pure," propositional fragment FLRo turns out to coincide with the iteration theories of [1]. Our main focus here concerns the sharp contrast between the simple class of valid identities and the very complex consequence relation over several natural classes of models. -
Fractal Curves and Complexity
Perception & Psychophysics 1987, 42 (4), 365-370 Fractal curves and complexity JAMES E. CUTI'ING and JEFFREY J. GARVIN Cornell University, Ithaca, New York Fractal curves were generated on square initiators and rated in terms of complexity by eight viewers. The stimuli differed in fractional dimension, recursion, and number of segments in their generators. Across six stimulus sets, recursion accounted for most of the variance in complexity judgments, but among stimuli with the most recursive depth, fractal dimension was a respect able predictor. Six variables from previous psychophysical literature known to effect complexity judgments were compared with these fractal variables: symmetry, moments of spatial distribu tion, angular variance, number of sides, P2/A, and Leeuwenberg codes. The latter three provided reliable predictive value and were highly correlated with recursive depth, fractal dimension, and number of segments in the generator, respectively. Thus, the measures from the previous litera ture and those of fractal parameters provide equal predictive value in judgments of these stimuli. Fractals are mathematicalobjectsthat have recently cap determine the fractional dimension by dividing the loga tured the imaginations of artists, computer graphics en rithm of the number of unit lengths in the generator by gineers, and psychologists. Synthesized and popularized the logarithm of the number of unit lengths across the ini by Mandelbrot (1977, 1983), with ever-widening appeal tiator. Since there are five segments in this generator and (e.g., Peitgen & Richter, 1986), fractals have many curi three unit lengths across the initiator, the fractionaldimen ous and fascinating properties. Consider four. sion is log(5)/log(3), or about 1.47. -
I Want to Start My Story in Germany, in 1877, with a Mathematician Named Georg Cantor
LISTENING - Ron Eglash is talking about his project http://www.ted.com/talks/ 1) Do you understand these words? iteration mission scale altar mound recursion crinkle 2) Listen and answer the questions. 1. What did Georg Cantor discover? What were the consequences for him? 2. What did von Koch do? 3. What did Benoit Mandelbrot realize? 4. Why should we look at our hand? 5. What did Ron get a scholarship for? 6. In what situation did Ron use the phrase “I am a mathematician and I would like to stand on your roof.” ? 7. What is special about the royal palace? 8. What do the rings in a village in southern Zambia represent? 3)Listen for the second time and decide whether the statements are true or false. 1. Cantor realized that he had a set whose number of elements was equal to infinity. 2. When he was released from a hospital, he lost his faith in God. 3. We can use whatever seed shape we like to start with. 4. Mathematicians of the 19th century did not understand the concept of iteration and infinity. 5. Ron mentions lungs, kidney, ferns, and acacia trees to demonstrate fractals in nature. 6. The chief was very surprised when Ron wanted to see his village. 7. Termites do not create conscious fractals when building their mounds. 8. The tiny village inside the larger village is for very old people. I want to start my story in Germany, in 1877, with a mathematician named Georg Cantor. And Cantor decided he was going to take a line and erase the middle third of the line, and take those two resulting lines and bring them back into the same process, a recursive process. -
Lab 8: Recursion and Fractals
Lab 8: Recursion and Fractals In this lab you’ll get practice creating fractals with recursion. You will create a class that has will draw (at least) two types of fractals. Once completed, submit your .java file via Moodle. To make grading easier, please set up your class so that both fractals are drawn automatically when the constructor is executed. Create a Sierpinski triangle Step 1: In your class’s constructor, ask the user how large a canvas s/he wants. Step 2: Write a method drawTriangle that draws a triangle on the screen. This method will take the x,y coordinates of three points as well as the color of the triangle. For now, start with Step 3: In a method createSierpinski, determine the largest triangle that can fit on the canvas (given the canvas’s dimensions supplied by the user). Step 4: Create a method findMiddlePoints. This is the recursive method. It will take three sets of x,y coordinates for the outer triangle. (The first time the method is called, it will be called with the coordinates determined by the createSierpinski method.) The base case of the method will be determined by the minimum size triangle that can be displayed. The recursive case will be to calculate the three midpoints, defined by the three inputs. Then, by using the six coordinates (3 passed in and 3 calculated), the method will recur on the three interior triangles. Once these recursive calls have finished, use drawTriangle to draw the triangle defined by the three original inputs to the method. -
Descriptive Set Theory
Descriptive Set Theory David Marker Fall 2002 Contents I Classical Descriptive Set Theory 2 1 Polish Spaces 2 2 Borel Sets 14 3 E®ective Descriptive Set Theory: The Arithmetic Hierarchy 27 4 Analytic Sets 34 5 Coanalytic Sets 43 6 Determinacy 54 7 Hyperarithmetic Sets 62 II Borel Equivalence Relations 73 1 8 ¦1-Equivalence Relations 73 9 Tame Borel Equivalence Relations 82 10 Countable Borel Equivalence Relations 87 11 Hyper¯nite Equivalence Relations 92 1 These are informal notes for a course in Descriptive Set Theory given at the University of Illinois at Chicago in Fall 2002. While I hope to give a fairly broad survey of the subject we will be concentrating on problems about group actions, particularly those motivated by Vaught's conjecture. Kechris' Classical Descriptive Set Theory is the main reference for these notes. Notation: If A is a set, A<! is the set of all ¯nite sequences from A. Suppose <! σ = (a0; : : : ; am) 2 A and b 2 A. Then σ b is the sequence (a0; : : : ; am; b). We let ; denote the empty sequence. If σ 2 A<!, then jσj is the length of σ. If f : N ! A, then fjn is the sequence (f(0); : : :b; f(n ¡ 1)). If X is any set, P(X), the power set of X is the set of all subsets X. If X is a metric space, x 2 X and ² > 0, then B²(x) = fy 2 X : d(x; y) < ²g is the open ball of radius ² around x. Part I Classical Descriptive Set Theory 1 Polish Spaces De¯nition 1.1 Let X be a topological space. -
Abstract Recursion and Intrinsic Complexity
ABSTRACT RECURSION AND INTRINSIC COMPLEXITY Yiannis N. Moschovakis Department of Mathematics University of California, Los Angeles [email protected] October 2018 iv Abstract recursion and intrinsic complexity was first published by Cambridge University Press as Volume 48 in the Lecture Notes in Logic, c Association for Symbolic Logic, 2019. The Cambridge University Press catalog entry for the work can be found at https://www.cambridge.org/us/academic/subjects/mathematics /logic-categories-and-sets /abstract-recursion-and-intrinsic-complexity. The published version can be purchased through Cambridge University Press and other standard distribution channels. This copy is made available for personal use only and must not be sold or redistributed. This final prepublication draft of ARIC was compiled on November 30, 2018, 22:50 CONTENTS Introduction ................................................... .... 1 Chapter 1. Preliminaries .......................................... 7 1A.Standardnotations................................ ............. 7 Partial functions, 9. Monotone and continuous functionals, 10. Trees, 12. Problems, 14. 1B. Continuous, call-by-value recursion . ..................... 15 The where -notation for mutual recursion, 17. Recursion rules, 17. Problems, 19. 1C.Somebasicalgorithms............................. .................... 21 The merge-sort algorithm, 21. The Euclidean algorithm, 23. The binary (Stein) algorithm, 24. Horner’s rule, 25. Problems, 25. 1D.Partialstructures............................... ...................... -
Determinacy and Large Cardinals
Determinacy and Large Cardinals Itay Neeman∗ Abstract. The principle of determinacy has been crucial to the study of definable sets of real numbers. This paper surveys some of the uses of determinacy, concentrating specifically on the connection between determinacy and large cardinals, and takes this connection further, to the level of games of length ω1. Mathematics Subject Classification (2000). 03E55; 03E60; 03E45; 03E15. Keywords. Determinacy, iteration trees, large cardinals, long games, Woodin cardinals. 1. Determinacy Let ωω denote the set of infinite sequences of natural numbers. For A ⊂ ωω let Gω(A) denote the length ω game with payoff A. The format of Gω(A) is displayed in Diagram 1. Two players, denoted I and II, alternate playing natural numbers forming together a sequence x = hx(n) | n < ωi in ωω called a run of the game. The run is won by player I if x ∈ A, and otherwise the run is won by player II. I x(0) x(2) ...... II x(1) x(3) ...... Diagram 1. The game Gω(A). A game is determined if one of the players has a winning strategy. The set A is ω determined if Gω(A) is determined. For Γ ⊂ P(ω ), det(Γ) denotes the statement that all sets in Γ are determined. Using the axiom of choice, or more specifically using a wellordering of the reals, it is easy to construct a non-determined set A. det(P(ωω)) is therefore false. On the other hand it has become clear through research over the years that det(Γ) is true if all the sets in Γ are definable by some concrete means. -
Borel Hierarchy and Omega Context Free Languages Olivier Finkel
Borel Hierarchy and Omega Context Free Languages Olivier Finkel To cite this version: Olivier Finkel. Borel Hierarchy and Omega Context Free Languages. Theoretical Computer Science, Elsevier, 2003, 290 (3), pp.1385-1405. hal-00103679v2 HAL Id: hal-00103679 https://hal.archives-ouvertes.fr/hal-00103679v2 Submitted on 18 Jan 2011 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. BOREL HIERARCHY AND OMEGA CONTEXT FREE LANGUAGES Olivier Finkel ∗ Equipe de Logique Math´ematique CNRS et Universit´eParis 7, U.F.R. de Math´ematiques 2 Place Jussieu 75251 Paris cedex 05, France. Abstract We give in this paper additional answers to questions of Lescow and Thomas [Logi- cal Specifications of Infinite Computations, In:”A Decade of Concurrency”, Springer LNCS 803 (1994), 583-621], proving topological properties of omega context free lan- guages (ω-CFL) which extend those of [O. Finkel, Topological Properties of Omega Context Free Languages, Theoretical Computer Science, Vol. 262 (1-2), 2001, p. 669-697]: there exist some ω-CFL which are non Borel sets and one cannot decide whether an ω-CFL is a Borel set. We give also an answer to a question of Niwin- ski [Problem on ω-Powers Posed in the Proceedings of the 1990 Workshop ”Logics and Recognizable Sets”] and of Simonnet [Automates et Th´eorie Descriptive, Ph.D.