The Physical Church-Turing Thesis: Modest Or Bold?1
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CS 154 NOTES Part 1. Finite Automata
CS 154 NOTES ARUN DEBRAY MARCH 13, 2014 These notes were taken in Stanford’s CS 154 class in Winter 2014, taught by Ryan Williams. I live-TEXed them using vim, and as such there may be typos; please send questions, comments, complaints, and corrections to [email protected]. Thanks to Rebecca Wang for catching a few errors. CONTENTS Part 1. Finite Automata: Very Simple Models 1 1. Deterministic Finite Automata: 1/7/141 2. Nondeterminism, Finite Automata, and Regular Expressions: 1/9/144 3. Finite Automata vs. Regular Expressions, Non-Regular Languages: 1/14/147 4. Minimizing DFAs: 1/16/14 9 5. The Myhill-Nerode Theorem and Streaming Algorithms: 1/21/14 11 6. Streaming Algorithms: 1/23/14 13 Part 2. Computability Theory: Very Powerful Models 15 7. Turing Machines: 1/28/14 15 8. Recognizability, Decidability, and Reductions: 1/30/14 18 9. Reductions, Undecidability, and the Post Correspondence Problem: 2/4/14 21 10. Oracles, Rice’s Theorem, the Recursion Theorem, and the Fixed-Point Theorem: 2/6/14 23 11. Self-Reference and the Foundations of Mathematics: 2/11/14 26 12. A Universal Theory of Data Compression: Kolmogorov Complexity: 2/18/14 28 Part 3. Complexity Theory: The Modern Models 31 13. Time Complexity: 2/20/14 31 14. More on P versus NP and the Cook-Levin Theorem: 2/25/14 33 15. NP-Complete Problems: 2/27/14 36 16. NP-Complete Problems, Part II: 3/4/14 38 17. Polytime and Oracles, Space Complexity: 3/6/14 41 18. -
Computability and Incompleteness Fact Sheets
Computability and Incompleteness Fact Sheets Computability Definition. A Turing machine is given by: A finite set of symbols, s1; : : : ; sm (including a \blank" symbol) • A finite set of states, q1; : : : ; qn (including a special \start" state) • A finite set of instructions, each of the form • If in state qi scanning symbol sj, perform act A and go to state qk where A is either \move right," \move left," or \write symbol sl." The notion of a \computation" of a Turing machine can be described in terms of the data above. From now on, when I write \let f be a function from strings to strings," I mean that there is a finite set of symbols Σ such that f is a function from strings of symbols in Σ to strings of symbols in Σ. I will also adopt the analogous convention for sets. Definition. Let f be a function from strings to strings. Then f is computable (or recursive) if there is a Turing machine M that works as follows: when M is started with its input head at the beginning of the string x (on an otherwise blank tape), it eventually halts with its head at the beginning of the string f(x). Definition. Let S be a set of strings. Then S is computable (or decidable, or recursive) if there is a Turing machine M that works as follows: when M is started with its input head at the beginning of the string x, then if x is in S, then M eventually halts, with its head on a special \yes" • symbol; and if x is not in S, then M eventually halts, with its head on a special • \no" symbol. -
COMPSCI 501: Formal Language Theory Insights on Computability Turing Machines Are a Model of Computation Two (No Longer) Surpris
Insights on Computability Turing machines are a model of computation COMPSCI 501: Formal Language Theory Lecture 11: Turing Machines Two (no longer) surprising facts: Marius Minea Although simple, can describe everything [email protected] a (real) computer can do. University of Massachusetts Amherst Although computers are powerful, not everything is computable! Plus: “play” / program with Turing machines! 13 February 2019 Why should we formally define computation? Must indeed an algorithm exist? Back to 1900: David Hilbert’s 23 open problems Increasingly a realization that sometimes this may not be the case. Tenth problem: “Occasionally it happens that we seek the solution under insufficient Given a Diophantine equation with any number of un- hypotheses or in an incorrect sense, and for this reason do not succeed. known quantities and with rational integral numerical The problem then arises: to show the impossibility of the solution under coefficients: To devise a process according to which the given hypotheses or in the sense contemplated.” it can be determined in a finite number of operations Hilbert, 1900 whether the equation is solvable in rational integers. This asks, in effect, for an algorithm. Hilbert’s Entscheidungsproblem (1928): Is there an algorithm that And “to devise” suggests there should be one. decides whether a statement in first-order logic is valid? Church and Turing A Turing machine, informally Church and Turing both showed in 1936 that a solution to the Entscheidungsproblem is impossible for the theory of arithmetic. control To make and prove such a statement, one needs to define computability. In a recent paper Alonzo Church has introduced an idea of “effective calculability”, read/write head which is equivalent to my “computability”, but is very differently defined. -
Table of Contents
Table of Contents Vision Statement 5 Strengthening the Intellectual Community 7 Academic Review 17 Recognition of Excellence 20 New Faculty 26 Student Prizes and Scholarships 28 News from the Faculties and Schools 29 Institute for Advanced Studies 47 Saltiel Pre‐Academic Center 49 Truman Institute for the Advancement of Peace 50 The Authority for Research Students 51 The Library Authority 54 In Appreciation 56 2009 Report by the Rector Back ACADEMIC DEVELOPMENTS VISION STATEMENT The academic year 2008/2009, which is now drawing to its end, has been a tumultuous one. The continuous budgetary cuts almost prevented the opening of the academic year, and we end the year under the shadow of the current global economic crisis. Notwithstanding these difficulties, the Hebrew University continues to thrive: as the present report shows, we have maintained our position of excellence. However the name of our game is not maintenance, but rather constant change and improvement, and despite the need to cut back and save, we have embarked on several new, innovative initiatives. As I reflect on my first year as Rector of the Hebrew University, the difficulties fade in comparison to the feeling of opportunity. We are fortunate to have the ongoing support of our friends, both in Israel and abroad, which is a constant reminder to us of the significance of the Hebrew University beyond its walls. Prof. Menachem Magidor is stepping down after twelve years as President of the University: it has been an enormous privilege to work with him during this year, and benefit from his experience, wisdom and vision. -
Quantum Hypercomputation—Hype Or Computation?
Quantum Hypercomputation—Hype or Computation? Amit Hagar,∗ Alex Korolev† February 19, 2007 Abstract A recent attempt to compute a (recursion–theoretic) non–computable func- tion using the quantum adiabatic algorithm is criticized and found wanting. Quantum algorithms may outperform classical algorithms in some cases, but so far they retain the classical (recursion–theoretic) notion of computability. A speculation is then offered as to where the putative power of quantum computers may come from. ∗HPS Department, Indiana University, Bloomington, IN, 47405. [email protected] †Philosophy Department, University of BC, Vancouver, BC. [email protected] 1 1 Introduction Combining physics, mathematics and computer science, quantum computing has developed in the past two decades from a visionary idea (Feynman 1982) to one of the most exciting areas of quantum mechanics (Nielsen and Chuang 2000). The recent excitement in this lively and fashionable domain of research was triggered by Peter Shor (1994) who showed how a quantum computer could exponentially speed–up classical computation and factor numbers much more rapidly (at least in terms of the number of computational steps involved) than any known classical algorithm. Shor’s algorithm was soon followed by several other algorithms that aimed to solve combinatorial and algebraic problems, and in the last few years the- oretical study of quantum systems serving as computational devices has achieved tremendous progress. According to one authority in the field (Aharonov 1998, Abstract), we now have strong theoretical evidence that quantum computers, if built, might be used as powerful computational tool, capable of per- forming tasks which seem intractable for classical computers. -
A Probabilistic Anytime Algorithm for the Halting Problem
Computability 7 (2018) 259–271 259 DOI 10.3233/COM-170073 IOS Press A probabilistic anytime algorithm for the halting problem Cristian S. Calude Department of Computer Science, University of Auckland, Auckland, New Zealand [email protected] www.cs.auckland.ac.nz/~cristian Monica Dumitrescu Faculty of Mathematics and Computer Science, Bucharest University, Romania [email protected] http://goo.gl/txsqpU The first author dedicates his contribution to this paper to the memory of his collaborator and friend S. Barry Cooper (1943–2015). Abstract. TheHaltingProblem,the most (in)famous undecidableproblem,has important applications in theoretical andapplied computer scienceand beyond, hencethe interest in its approximate solutions. Experimental results reportedonvarious models of computationsuggest that haltingprogramsare not uniformly distributed– running times play an important role.A reason is thataprogram whicheventually stopsbut does not halt “quickly”, stops ata timewhich is algorithmically compressible. In this paperweworkwith running times to defineaclass of computable probability distributions on theset of haltingprograms in ordertoconstructananytimealgorithmfor theHaltingproblem withaprobabilisticevaluationofthe errorofthe decision. Keywords: HaltingProblem,anytimealgorithm, running timedistribution 1. Introduction The Halting Problem asks to decide, from a description of an arbitrary program and an input, whether the computation of the program on that input will eventually stop or continue forever. In 1936 Alonzo Church, and independently Alan Turing, proved that (in Turing’s formulation) an algorithm to solve the Halting Problem for all possible program-input pairs does not exist; two equivalent models have been used to describe computa- tion by algorithms (an informal notion), the lambda calculus by Church and Turing machines by Turing. The Halting Problem is historically the first proved undecidable problem; it has many applications in mathematics, logic and theoretical as well as applied computer science, mathematics, physics, biology, etc. -
Alan Turing, Hypercomputation, Adam Smith and Next Generation Intelligent Systems
J. Intell. Syst. 21 (2012), 325–330 DOI 10.1515/jisys-2012-0017 © de Gruyter 2012 Something Old, Something New, Something Borrowed, Something Blue Part 1: Alan Turing, Hypercomputation, Adam Smith and Next Generation Intelligent Systems Mark Dougherty Abstract. In this article intelligent systems are placed in the context of accelerated Turing machines. Although such machines are not currently a reality, the very real gains in computing power made over previous decades require us to continually reevaluate the potential of intelligent systems. The economic theories of Adam Smith provide us with a useful insight into this question. Keywords. Intelligent Systems, Accelerated Turing Machine, Specialization of Labor. 2010 Mathematics Subject Classification. Primary 01-02, secondary 01A60, 01A67. 1 Introduction The editor-in-chief of this journal has asked me to write a series of shorter articles in which I try to illuminate various challenges in intelligent systems. Given space requirements they cannot be regarded as comprehensive reviews, they are intended to provide an overview and to be a point of departure for discussion and debate. The title of the series implies that I will look at both very old and rather new concepts and I will not be afraid to borrow from other subjects. Nor will I be afraid to incorporate radical “blue skies” ideas or hypotheses. By challenges I mean aspects of our subject which cannot easily be tackled by either theoretical means or through empirical enquiry. That may be because the questions raised are not researchable or because no evidence has yet been ob- served. Or it may be that work is underway but that no firm conclusions have yet been reached by the research community. -
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. -
NP-Complete Problems and Physical Reality
NP-complete Problems and Physical Reality Scott Aaronson∗ Abstract Can NP-complete problems be solved efficiently in the physical universe? I survey proposals including soap bubbles, protein folding, quantum computing, quantum advice, quantum adia- batic algorithms, quantum-mechanical nonlinearities, hidden variables, relativistic time dilation, analog computing, Malament-Hogarth spacetimes, quantum gravity, closed timelike curves, and “anthropic computing.” The section on soap bubbles even includes some “experimental” re- sults. While I do not believe that any of the proposals will let us solve NP-complete problems efficiently, I argue that by studying them, we can learn something not only about computation but also about physics. 1 Introduction “Let a computer smear—with the right kind of quantum randomness—and you create, in effect, a ‘parallel’ machine with an astronomical number of processors . All you have to do is be sure that when you collapse the system, you choose the version that happened to find the needle in the mathematical haystack.” —From Quarantine [31], a 1992 science-fiction novel by Greg Egan If I had to debate the science writer John Horgan’s claim that basic science is coming to an end [48], my argument would lean heavily on one fact: it has been only a decade since we learned that quantum computers could factor integers in polynomial time. In my (unbiased) opinion, the showdown that quantum computing has forced—between our deepest intuitions about computers on the one hand, and our best-confirmed theory of the physical world on the other—constitutes one of the most exciting scientific dramas of our time. -
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. -
Computability Computability Computability Turing, Gödel, Church, and Beyond Turing, Gödel, Church, and Beyond Edited by B
computer science/philosophy Computability Computability Computability turing, Gödel, Church, and beyond turing, Gödel, Church, and beyond edited by b. Jack Copeland, Carl J. posy, and oron Shagrir edited by b. Jack Copeland, Carl J. posy, and oron Shagrir Copeland, b. Jack Copeland is professor of philosophy at the ContributorS in the 1930s a series of seminal works published by university of Canterbury, new Zealand, and Director Scott aaronson, Dorit aharonov, b. Jack Copeland, martin Davis, Solomon Feferman, Saul alan turing, Kurt Gödel, alonzo Church, and others of the turing archive for the History of Computing. Kripke, Carl J. posy, Hilary putnam, oron Shagrir, Stewart Shapiro, Wilfried Sieg, robert established the theoretical basis for computability. p Carl J. posy is professor of philosophy and member irving Soare, umesh V. Vazirani editors and Shagrir, osy, this work, advancing precise characterizations of ef- of the Centers for the Study of rationality and for lan- fective, algorithmic computability, was the culmina- guage, logic, and Cognition at the Hebrew university tion of intensive investigations into the foundations of Jerusalem. oron Shagrir is professor of philoso- of mathematics. in the decades since, the theory of phy and Former Chair of the Cognitive Science De- computability has moved to the center of discussions partment at the Hebrew university of Jerusalem. He in philosophy, computer science, and cognitive sci- is currently the vice rector of the Hebrew university. ence. in this volume, distinguished computer scien- tists, mathematicians, logicians, and philosophers consider the conceptual foundations of comput- ability in light of our modern understanding. Some chapters focus on the pioneering work by turing, Gödel, and Church, including the Church- turing thesis and Gödel’s response to Church’s and turing’s proposals. -
Biological Computation: a Road
918 Biological Computation: A Road to Complex Engineered Systems Nelson Alfonso Gómez-Cruz Modeling and Simulation Laboratory Universidad del Rosario Bogotá, Colombia [email protected] Carlos Eduardo Maldonado Research Professor Universidad del Rosario Bogotá, Colombia [email protected] Provided that there is no theoretical frame for complex engineered systems (CES) as yet, this paper claims that bio-inspired engineering can help provide such a frame. Within CES bio- inspired systems play a key role. The disclosure from bio-inspired systems and biological computation has not been sufficiently worked out, however. Biological computation is to be taken as the processing of information by living systems that is carried out in polynomial time, i.e., efficiently; such processing however is grasped by current science and research as an intractable problem (for instance, the protein folding problem). A remark is needed here: P versus NP problems should be well defined and delimited but biological computation problems are not. The shift from conventional engineering to bio-inspired engineering needs bring the subject (or problem) of computability to a new level. Within the frame of computation, so far, the prevailing paradigm is still the Turing-Church thesis. In other words, conventional 919 engineering is still ruled by the Church-Turing thesis (CTt). However, CES is ruled by CTt, too. Contrarily to the above, we shall argue here that biological computation demands a more careful thinking that leads us towards hypercomputation. Bio-inspired engineering and CES thereafter, must turn its regard toward biological computation. Thus, biological computation can and should be taken as the ground for engineering complex non-linear systems.