FIXED-POINT EXTENSIONS of FIRST-ORDER LOGIC 0. Introduction
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Infinitary Logic and Inductive Definability Over Finite Structures
University of Pennsylvania ScholarlyCommons Technical Reports (CIS) Department of Computer & Information Science November 1991 Infinitary Logic and Inductive Definability Over Finite Structures Anuj Dawar University of Pennsylvania Steven Lindell University of Pennsylvania Scott Weinstein University of Pennsylvania Follow this and additional works at: https://repository.upenn.edu/cis_reports Recommended Citation Anuj Dawar, Steven Lindell, and Scott Weinstein, "Infinitary Logic and Inductive Definability Over Finite Structures", . November 1991. University of Pennsylvania Department of Computer and Information Science Technical Report No. MS-CIS-91-97. This paper is posted at ScholarlyCommons. https://repository.upenn.edu/cis_reports/365 For more information, please contact [email protected]. Infinitary Logic and Inductive Definability Over Finite Structures Abstract The extensions of first-order logic with a least fixed point operators (FO + LFP) and with a partial fixed point operator (FO + PFP) are known to capture the complexity classes P and PSPACE respectively in the presence of an ordering relation over finite structures. Recently, Abiteboul and Vianu [AV91b] investigated the relation of these two logics in the absence of an ordering, using a mchine model of generic computation. In particular, they showed that the two languages have equivalent expressive power if and only if P = PSPACE. These languages can also be seen as fragments of an infinitary logic where each ω formula has a bounded number of variables, L ∞ω (see, for instance, [KV90]). We present a treatment of the results in [AV91b] from this point of view. In particular, we show that we can write a formula of FO + LFP and P from ordered structures to classes of structures where every element is definable. -
CTL Model Checking
CTL Model Checking Prof. P.H. Schmitt Institut fur¨ Theoretische Informatik Fakult¨at fur¨ Informatik Universit¨at Karlsruhe (TH) Formal Systems II Prof. P.H. Schmitt CTLMC Summer 2009 1 / 26 Fixed Point Theory Prof. P.H. Schmitt CTLMC Summer 2009 2 / 26 Fixed Points Definition Definition Let f : P(G) → P(G) be a set valued function and Z a subset of G. 1. Z is called a fixed point of f if f(Z) = Z . 2. Z is called the least fixed point of f is Z is a fixed point and for all other fixed points U of f the relation Z ⊆ U is true. 3. Z is called the greatest fixed point of f is Z is a fixed point and for all other fixed points U of f the relation U ⊆ Z is true. Prof. P.H. Schmitt CTLMC Summer 2009 3 / 26 Finite Fixed Point Lemma Let G be an arbitrary set, Let P(G) denote the power set of a set G. A function f : P(G) → P(G) is called monotone of for all X, Y ⊆ G X ⊆ Y ⇒ f(X) ⊆ f(Y ) Let f : P(G) → P(G) be a monotone function on a finite set G. 1. There is a least and a greatest fixed point of f. S n 2. n≥1 f (∅) is the least fixed point of f. T n 3. n≥1 f (G) is the greatest fixed point of f. Prof. P.H. Schmitt CTLMC Summer 2009 4 / 26 Proof of part (2) of the finite fixed point Lemma Monotonicity of f yields ∅ ⊆ f(∅) ⊆ f 2(∅) ⊆ .. -
The Complexity Zoo
The Complexity Zoo Scott Aaronson www.ScottAaronson.com LATEX Translation by Chris Bourke [email protected] 417 classes and counting 1 Contents 1 About This Document 3 2 Introductory Essay 4 2.1 Recommended Further Reading ......................... 4 2.2 Other Theory Compendia ............................ 5 2.3 Errors? ....................................... 5 3 Pronunciation Guide 6 4 Complexity Classes 10 5 Special Zoo Exhibit: Classes of Quantum States and Probability Distribu- tions 110 6 Acknowledgements 116 7 Bibliography 117 2 1 About This Document What is this? Well its a PDF version of the website www.ComplexityZoo.com typeset in LATEX using the complexity package. Well, what’s that? The original Complexity Zoo is a website created by Scott Aaronson which contains a (more or less) comprehensive list of Complexity Classes studied in the area of theoretical computer science known as Computa- tional Complexity. I took on the (mostly painless, thank god for regular expressions) task of translating the Zoo’s HTML code to LATEX for two reasons. First, as a regular Zoo patron, I thought, “what better way to honor such an endeavor than to spruce up the cages a bit and typeset them all in beautiful LATEX.” Second, I thought it would be a perfect project to develop complexity, a LATEX pack- age I’ve created that defines commands to typeset (almost) all of the complexity classes you’ll find here (along with some handy options that allow you to conveniently change the fonts with a single option parameters). To get the package, visit my own home page at http://www.cse.unl.edu/~cbourke/. -
Canonical Models and the Complexity of Modal Team Logic
On the Complexity of Team Logic and its Two-Variable Fragment Martin Lück Leibniz Universität Hannover, Germany [email protected] Abstract. We study the logic FO(∼), the extension of first-order logic with team semantics by unrestricted Boolean negation. It was recently shown axiomatizable, but otherwise has not yet received much attention in questions of computational complexity. In this paper, we consider its two-variable fragment FO2(∼) and prove that its satisfiability problem is decidable, and in fact complete for the recently introduced non-elementary class TOWER(poly). Moreover, we classify the complexity of model checking of FO(∼) with respect to the number of variables and the quantifier rank, and prove a di- chotomy between PSPACE- and ATIME-ALT(exp, poly)-completeness. To achieve the lower bounds, we propose a translation from modal team logic MTL to FO2(∼) that extends the well-known standard translation from modal logic ML to FO2. For the upper bounds, we translate to a fragment of second-order logic. Keywords: team semantics, two-variable logic, complexity, satisfiability, model checking 2012 ACM Subject Classification: Theory of computation → Complexity theory and logic; Logic; 1. Introduction In the last decades, the work of logicians has unearthed a plethora of decidable fragments of first-order logic FO. Many of these cases are restricted quantifier prefixes, such as the BSR-fragment which contains only ∃∗∀∗-sentences [30]. Others include the guarded arXiv:1804.04968v1 [cs.LO] 13 Apr 2018 fragment GF [1], the recently introduced separated fragment SF [32, 34], or the two-variable fragment FO2 [13, 27, 31]. -
User's Guide for Complexity: a LATEX Package, Version 0.80
User’s Guide for complexity: a LATEX package, Version 0.80 Chris Bourke April 12, 2007 Contents 1 Introduction 2 1.1 What is complexity? ......................... 2 1.2 Why a complexity package? ..................... 2 2 Installation 2 3 Package Options 3 3.1 Mode Options .............................. 3 3.2 Font Options .............................. 4 3.2.1 The small Option ....................... 4 4 Using the Package 6 4.1 Overridden Commands ......................... 6 4.2 Special Commands ........................... 6 4.3 Function Commands .......................... 6 4.4 Language Commands .......................... 7 4.5 Complete List of Class Commands .................. 8 5 Customization 15 5.1 Class Commands ............................ 15 1 5.2 Language Commands .......................... 16 5.3 Function Commands .......................... 17 6 Extended Example 17 7 Feedback 18 7.1 Acknowledgements ........................... 19 1 Introduction 1.1 What is complexity? complexity is a LATEX package that typesets computational complexity classes such as P (deterministic polynomial time) and NP (nondeterministic polynomial time) as well as sets (languages) such as SAT (satisfiability). In all, over 350 commands are defined for helping you to typeset Computational Complexity con- structs. 1.2 Why a complexity package? A better question is why not? Complexity theory is a more recent, though mature area of Theoretical Computer Science. Each researcher seems to have his or her own preferences as to how to typeset Complexity Classes and has built up their own personal LATEX commands file. This can be frustrating, to say the least, when it comes to collaborations or when one has to go through an entire series of files changing commands for compatibility or to get exactly the look they want (or what may be required). -
Least Fixed Points Revisited
LEAST FIXED POINTS REVISITED J.W. de Bakker Mathematical Centre, Amsterdam ABSTRACT Parameter mechanisms for recursive procedures are investigated. Con- trary to the view of Manna et al., it is argued that both call-by-value and call-by-name mechanisms yield the least fixed points of the functionals de- termined by the bodies of the procedures concerned. These functionals dif- fer, however, according to the mechanism chosen. A careful and detailed pre- sentation of this result is given, along the lines of a simple typed lambda calculus, with interpretation rules modelling program execution in such a way that call-by-value determines a change in the environment and call-by- name a textual substitution in the procedure body. KEY WORDS AND PHRASES: Semantics, recursion, least fixed points, parameter mechanisms, call-by-value, call-by-name, lambda calculus. Author's address Mathematical Centre 2e Boerhaavestraat 49 Amsterdam Netherlands 28 NOTATION Section 2 s,t,ti,t',... individual } function terms S,T,... functional p,p',... !~individual} boolean .J function terms P,... functional a~A, aeA} constants b r B, IBeB x,y,z,u X, ~,~ X] variables qcQ, x~0. J r149149 procedure symbols T(t I ..... tn), ~(tl,.-.,tn) ~ application T(TI' '~r)' P(~I' '~r)J ~x I . .xs I . .Ym.t, ~x] . .xs I . .ym.p, abstraction h~l ...~r.T, hX1 ..Xr.~ if p then t' else t"~ [ selection if p then p' else p"J n(T), <n(T),r(r)> rank 1 -< i <- n, 1 -< j -< r,) indices 1 -< h -< l, 1 _< k <- t,T,x,~, . -
Descriptive Complexity: a Logician's Approach to Computation
Descriptive Complexity a Logicians Approach to Computation Neil Immerman Computer Science Dept University of Massachusetts Amherst MA immermancsumassedu App eared in Notices of the American Mathematical Society A basic issue in computer science is the complexity of problems If one is doing a calculation once on a mediumsized input the simplest algorithm may b e the b est metho d to use even if it is not the fastest However when one has a subproblem that will havetobesolved millions of times optimization is imp ortant A fundamental issue in theoretical computer science is the computational complexity of problems Howmuch time and howmuch memory space is needed to solve a particular problem Here are a few examples of such problems Reachability Given a directed graph and two sp ecied p oints s t determine if there is a path from s to t A simple lineartime algorithm marks s and then continues to mark every vertex at the head of an edge whose tail is marked When no more vertices can b e marked t is reachable from s i it has b een marked Mintriangulation Given a p olygon in the plane and a length L determine if there is a triangulation of the p olygon of total length less than or equal to L Even though there are exp onentially many p ossible triangulations a dynamic programming algorithm can nd an optimal one in O n steps ThreeColorability Given an undirected graph determine whether its vertices can b e colored using three colors with no two adjacentvertices having the same color Again there are exp onentially many p ossibilities -
Domain Theory: an Introduction
Domain Theory: An Introduction Robert Cartwright Rebecca Parsons Rice University This monograph is an unauthorized revision of “Lectures On A Mathematical Theory of Computation” by Dana Scott [3]. Scott’s monograph uses a formulation of domains called neighborhood systems in which finite elements are selected subsets of a master set of objects called “tokens”. Since tokens have little intuitive significance, Scott has discarded neighborhood systems in favor of an equivalent formulation of domains called information systems [4]. Unfortunately, he has not rewritten his monograph to reflect this change. We have rewritten Scott’s monograph in terms of finitary bases (see Cartwright [2]) instead of information systems. A finitary basis is an information system that is closed under least upper bounds on finite consistent subsets. This convention ensures that every finite answer is represented by a single basis object instead of a set of objects. 1 The Rudiments of Domain Theory Motivation Programs perform computations by repeatedly applying primitive operations to data values. The set of primitive operations and data values depends on the particular programming language. Nearly all languages support a rich collection of data values including atomic objects, such as booleans, integers, characters, and floating point numbers, and composite objects, such as arrays, records, sequences, tuples, and infinite streams. More advanced languages also support functions and procedures as data values. To define the meaning of programs in a given language, we must first define the building blocks—the primitive data values and operations—from which computations in the language are constructed. Domain theory is a comprehensive mathematical framework for defining the data values and primitive operations of a programming language. -
Arxiv:1301.2793V2 [Math.LO] 18 Jul 2014 Uaeacnetof Concept a Mulate Fpwrbet Uetepwre Xo) N/Rmk S Ff of Use Make And/Or Axiom), Presupp Principles
ON TARSKI’S FIXED POINT THEOREM GIOVANNI CURI To Orsola Abstract. A concept of abstract inductive definition on a complete lattice is formulated and studied. As an application, a constructive version of Tarski’s fixed point theorem is obtained. Introduction The fixed point theorem referred to in this paper is the one asserting that every monotone mapping on a complete lattice L has a least fixed point. The proof, due to A. Tarski, of this result, is a simple and most significant example of a proof that can be carried out on the base of intuitionistic logic (e.g. in the intuitionistic set theory IZF, or in topos logic), and that yet is widely regarded as essentially non-constructive. The reason for this fact is that Tarski’s construction of the fixed point is highly impredicative: if f : L → L is a monotone map, its least fixed point is given by V P , with P ≡ {x ∈ L | f(x) ≤ x}. Impredicativity here is found in the fact that the fixed point, call it p, appears in its own construction (p belongs to P ), and, indirectly, in the fact that the complete lattice L (and, as a consequence, the collection P over which the infimum is taken) is assumed to form a set, an assumption that seems only reasonable in an intuitionistic setting in the presence of strong impredicative principles (cf. Section 2 below). In concrete applications (e.g. in computer science and numerical analysis) the monotone operator f is often also continuous, in particular it preserves suprema of non-empty chains; in this situation, the least fixed point can be constructed taking the supremum of the ascending chain ⊥,f(⊥),f(f(⊥)), ..., given by the set of finite iterations of f on the least element ⊥. -
Descriptive Complexity Theories
Descriptive Complexity Theories Joerg FLUM ABSTRACT: In this article we review some of the main results of descriptive complexity theory in order to make the reader familiar with the nature of the investigations in this area. We start by presenting the characterization of automata recognizable languages by monadic second-order logic. Afterwards we explain the characteri- zation of various logics by fixed-point logics. We assume familiarity with logic but try to keep knowledge of complexity theory to a minimum. Keywords: Computational complexity theory, complexity classes, descriptive characterizations, monadic second-order logic, fixed-point logic, Turing machine. Complexity theory or more precisely, computational complexity theory (cf. Papadimitriou 1994), tries to classify problems according to the complexity of algorithms solving them. Of course, we can think of various ways of measuring the complexity of an al- gorithm, but the most important and relevant ones are time and space. That is, we think we have a type of machine capable, in principle, to carry out any algorithm, a so-called general purpose machine (or, general purpose computer), and we measure the com- plexity of an algorithm in terms of the time or the number of steps needed to carry out this algorithm. By space, we mean the amount of memory the algorithm uses. Time bounds yield (time) complexity classes consisting of all problems solvable by an algorithm keeping to the time bound. Similarly, space complexity classes are obtained. It turns out that these definitions are quite robust in the sense that, for reasonable time or space bounds, the corresponding complexity classes do not depend on the special type of machine model chosen. -
On the Descriptive Complexity of Color Coding
On the Descriptive Complexity of Color Coding Max Bannach Institute for Theoretical Computer Science, Universität zu Lübeck, Germany [email protected] Till Tantau Institute for Theoretical Computer Science, Universität zu Lübeck, Germany [email protected] Abstract Color coding is an algorithmic technique used in parameterized complexity theory to detect “small” structures inside graphs. The idea is to derandomize algorithms that first randomly color a graph and then search for an easily-detectable, small color pattern. We transfer color coding to the world of descriptive complexity theory by characterizing – purely in terms of the syntactic structure of describing formulas – when the powerful second-order quantifiers representing a random coloring can be replaced by equivalent, simple first-order formulas. Building on this result, we identify syntactic properties of first-order quantifiers that can be eliminated from formulas describing parameterized problems. The result applies to many packing and embedding problems, but also to the long path problem. Together with a new result on the parameterized complexity of formula families involving only a fixed number of variables, we get that many problems lie in fpt just because of the way they are commonly described using logical formulas. 2012 ACM Subject Classification Theory of computation → Finite Model Theory; Theory of computation → Fixed parameter tractability Keywords and phrases color coding, descriptive complexity, fixed-parameter tractability, quantifier elimination, para-AC0 Digital Object Identifier 10.4230/LIPIcs.STACS.2019.11 Related Version https://arxiv.org/abs/1901.03364 1 Introduction Descriptive complexity provides a powerful link between logic and complexity theory: We use a logical formula to describe a problem and can then infer the computational complexity of the problem just from the syntactic structure of the formula. -
Hilog: a Foundation for Higher-Order Logic Programming
J. LOGIC PROGRAMMING 1993:15:187-230 187 HILOG: A FOUNDATION FOR HIGHER-ORDER LOGIC PROGRAMMING WEIDONG CHEN,* MICHAEL KIFER, AND DAVID S. WARREN D We describe a novel logic, called HiLog, and show that it provides a more suitable basis for logic programming than does traditional predicate logic. HiLog has a higher-order syntax and allows arbitrary terms to appear in places where predicates, functions, and atomic formulas occur in predicate calculus. But its semantics is first-order and admits a sound and complete proof procedure. Applications of HiLog are discussed, including DCG grammars, higher-order and modular logic programming, and deductive databases. a 1. PREFACE Manipulating predicates, functions, and even atomic formulas is commonplace in logic programming. For example, Prolog combines predicate calculus, higher-order, and meta-level programming in one working system, allowing programmers routine use of generic predicate definitions (e.g., transitive closure, sorting) where predi- cates can be passed as parameters and returned as values [7]. Another well-known useful feature is the “call” meta-predicate of Prolog. Applications of higher-order constructs in the database context have been pointed out in many works, including [24, 29, 411. Although predicate calculus provides the basis for Prolog, it does not have the wherewithal to support any of the above features, which, consequently, have an ad hoc status in logic programming. In this paper, we investigate the fundamental principles underlying higher-order logic programming and, in particular, shed new light on why and how these Prolog features appear to work in practice. We propose *Present address: Computer Science and Engineering, Southern Methodist University, Dallas, TX 75275.