Conditional Rewriting

Size: px
Start display at page:

Download "Conditional Rewriting"

Transcription

1 Conditional Rewriting Bernhard Gramlich ISR 2009, Brasilia, Brazil, June 22-26, 2009 Bernhard Gramlich Conditional Rewriting ISR 2009, July 22-26,

2 Outline Introduction Basics in Conditional Rewriting Termination Confluence Transforming CTRSs Into TRSs Systems with Extra Variables (3-Ctrss) Transforming CTRSs Into TRSs Further Topics (expressive power, modularity,... ) Bernhard Gramlich Conditional Rewriting ISR 2009, July 22-26,

3 Lecture 1 Bernhard Gramlich Conditional Rewriting ISR 2009, July 22-26,

4 Introduction Examples x z = true = x y = true, y z = true a = b = a c, a = c = a b (a = b a = c) Hence: minimal models non-unique. Consequence (here): no negation in conditions. f (x, y) g(x) = P(x, y) (built-in predicates, not here) a b = c = d a b = c d a b = c d a b = c d a b = a c x z true = x y true, y z true s(x) + y s(z) = x + y z s(x) + y s(z) = x + y z Bernhard Gramlich Conditional Rewriting ISR 2009, July 22-26,

5 Basics in Conditional Rewriting Format of CESs / CTRSs conditional (term) equational system (CES) R: axioms of shape l = r = s 1 = t 1,..., s n = t n ; leads to conditional equational logic (CEL): = R conditional (term) rewrite system (CTRS) R: rules of shape l r = s 1 t 1,..., s n t n ; interpretation of equality in conditions: = : semi-equational system = : join system = : oriented system =, all t 1 R u -irreducible ground terms: normal (join) system (where R u = {l r l r = c R}) Bernhard Gramlich Conditional Rewriting ISR 2009, July 22-26,

6 Basics in Conditional Rewriting Join CTRSs as non-left-linear normal (join) CTRSs replace every l r = s 1 t 1,..., s n t n by l r = eq(s 1, t 2 ) true,..., eq(s n, t n ) true add eq(x, x) true true fresh boolean constant, eq fresh binary predicate s R t iff eq(s, t) R true (in many-sorted setting) Syntactical properties of CTRSs R (via R u ) R u = {l r l r = c R} R left-linear, right-linear, linear, (non-)collapsing, (non-)duplicating, non-overlapping, orthogonal, overlaying,..., if u is left-linear,..., respectively advantage of these syntactic definitions: easily decidable! disadvantage (partially): semantically misleading (Ex.: f (x, y) a = Bernhard x Gramlich y)! Conditional Rewriting ISR 2009, July 22-26,

7 Basics in Conditional Rewriting Classification of CTRSs according to extra variables VAR(s) set of all variables occurring in s, l r = c R type 1: VAR(l) VAR(r) VAR(c) (no extra variables) type 2: VAR(l) VAR(r) (extra variables at most in cond.) type 3: VAR(l) VAR(c) VAR(r) (extra variables in right-hand sides must appear in conditions) type 4: no restrictions. R n-ctrs if all its rules are of type n type 2 involves existential search in conditions type 3 involves existential search in conditions and rhs s and may cause infinite branching type 4 very hard to understand / handle computationally (almost no results known for systems of this type) Bernhard Gramlich Conditional Rewriting ISR 2009, July 22-26,

8 Basics in Conditional Rewriting Induced equality for CES E Inference system for conditional equational logic (CEL): Given CES E, CEL defines conditional equational derivability = E : Reflexivity: Symmetry: Transitivity: Congruence: Application: t = t s = t t = s s = t, t = u s = u s 1 = t 1,..., s n = t n f (s,..., s n ) = f (t 1,..., t n ) s 1 σ = t 1 σ,..., s n σ = t n σ lσ = rσ if l = r = s 1 = t 1,..., s n = t n E Bernhard Gramlich Conditional Rewriting ISR 2009, July 22-26,

9 Basics in Conditional Rewriting Induced rewrite relation for CTRS R for R consisting of rules of the form l r = s 1 t 1,..., s n t n with {,, }: R 0 = R k+1 = {lσ rσ l r = c R, s i σ Rk t i σ for all s i t i in c}. = R = i 0 R i. If s R t with some concrete derivation s R n t, the latter has level n. The depth of a reduction s t is the minimal (level) n with s R n t. Relationships between different induced rewrite relations R, R, R, = R Logicality: R, = R (hence R, = R )? Bernhard Gramlich Conditional Rewriting ISR 2009, July 22-26,

10 Basics in Conditional Rewriting Examples For R = {a b = c d; e c; e d}: e R1 c, e R1 d a R2 b, a Rk b for every k 2 a R1 b, depth(a b) = 2 For R = {a b = c d; e c; e d}: c, d in normal form w.r.t. every R k a Rk b for no k, hence a R b For R = {f (x) a = f (x) x; b f (b)}: b R f (b) R a, since f (b) R b due to f (b) R b f (a) R a iff f (a) R a iff f (a) R a (since a NF), hence f (a) R a Bernhard Gramlich Conditional Rewriting ISR 2009, July 22-26,

11 Major Problems and Difficulties effectiveness in testing conditions conditions are recursively(!) evaluated entails in general a possibly non-terminating search process consequence: basic notions like one-step reduction, one-step reducibility, being a normal form etc. are undecidable in general! no modular decomposition of steps (non-locality) s R t using rule ρ does not imply s {ρ} t, because verifiation of conditions may need more rules, possibly the whole system! major obstacle in extending modularity results form TRSs to CTRSs Bernhard Gramlich Conditional Rewriting ISR 2009, July 22-26,

12 Major Problems and Difficulties confluence: variable overlaps may be critical! TRS:lσ rσ, σ σ = lσ rσ for l r R CTRS:lσ rσ, σ σ = lσ rσ for l r = c R? In general NO! s i σ t i σ, σ σ, hence: s i σ s i σ, t i σ t i σ but: s i σ t i σ? termination: may be non-effective! R = {a b = a c is terminating (has empty rewrite relation R ) but trying to apply the rule leads to a cycle Hence, from a practical point of view, R should not only be terminating, but also the evaluation of conditions (leading to stronger notions of termination) Bernhard Gramlich Conditional Rewriting ISR 2009, July 22-26,

13 Major Problems and Difficulties realization / implementation of conditional rewriting how to realize / implement rewriting with conditional rewrite rules? strategies for sequentialization / backtracking? how to avoid complicated rewrite machine architecture? why CTRSs instead of transformed unconditional TRSs? conceptually more adequate, intuitive any encoding / transformation entails a loss of structural information! appropriate encodings / transformations into TRSs are not easy! Bernhard Gramlich Conditional Rewriting ISR 2009, July 22-26,

14 More tomorrow! Bernhard Gramlich Conditional Rewriting ISR 2009, July 22-26,

15 Lecture 2 Bernhard Gramlich Conditional Rewriting ISR 2009, July 22-26,

16 Termination operational termination problems effective computation (reduction, normalization) example: a b = a c confluence criteria for TRSs do not directly generalize to CTRSs (see later) idea for recovery: require well-founded decrease not only from l to r, but also from l to u, u condition term in c, l r = c R yields stronger notions of termination Bernhard Gramlich Conditional Rewriting ISR 2009, July 22-26,

17 Termination strengthened approaches [Kaplan 84-87, Dershowitz et al ] R simplifying if there exists simplification ordering (rewrite ordering with subterm property) s.t. l r, s i, t i for all l r = s 1 t 1,..., s n t n R R reductive if there exists reduction ordering s.t. l r, s i, t i for all l r = s 1 t 1,..., s n t n R R decreasing if there exists well-founded term ordering s.t. R lσ st s 1 σ, t 1 σ,..., s n σ, t n σ for all l r = s 1 t 1,..., s n t n R ( st = ( ) + ) Bernhard Gramlich Conditional Rewriting ISR 2009, July 22-26,

18 Termination strengthened relationships simplifying = reductive = decreasing = terminating all implications are proper: f (f (x)) a = f (g(f (x))) b reductive, but not simplifying f (b) f (a); a c = b d decreasing, but not reductive a b = a c terminating, but not decreasing Bernhard Gramlich Conditional Rewriting ISR 2009, July 22-26,

19 Termination strengthened remarks oriented + normal CTRSs: for decreasingness e.g.: lσ st s 1 σ,..., s n σ instead of lσ st s 1 σ, t 1 σ,..., s n σ, t n σ decreasingness depends on interpretation of = in conditions ( or ) simplifyingness, reductivity and decreasingness can be refined: check conditions sequentially e.g. from left to right, instead of simultaneously) 2-CTRSs with extra variables in conditions cannot be simplifying,... 3-CTRSs with extra variables in right-hand sides: sequentialization becomes essential to make sense (see later)! Bernhard Gramlich Conditional Rewriting ISR 2009, July 22-26,

20 Confluence criteria for TRSs without termination orthogonality implies confluence (CR) with termination Critical Pair Theorem: local confluence (WCR) joinability of CPs (JCP) Newman s Lemma: SN = (WCR CR) hence: SN = (JCP CR) critical pairs (peaks), CPs l 1 σ[r 2 σ] p l 1 σ[l 2 σ] p ɛ r 1 σ where l i r i R, i = 1, 2, variable disjoint l1 p F (no overlap into variables) σ mgu of l1 p and l 2 Bernhard Gramlich Conditional Rewriting ISR 2009, July 22-26,

21 Confluence conditional critical pairs (peaks), CCPs l 1 σ[r 2 σ] p l 1 σ[l 2 σ] p ɛ r 1 σ with l 1 r 1 = c 1 R, σ at ɛ and l 2 r 2 = c 2 R, σ at p l 1 p F σ mgu of l1 p and l 2 yielding CCP l 1 σ[r 2 σ] = r 1 σ = c 1 σ, c 2 σ joinability: s = t = c CCP(R) joinable if σ : sσ t σ = cσ (in)feasibility: s = t = c feasible if there exists σ with cσ even for terminating systems: (in)feasibility and joinability of CCPs undecidable R non-overlapping CTRS (via R u ) syntactic criterion for non-overlappingness R has only infeasible CCPs semantic criterion for non-overlappingness Bernhard Gramlich Conditional Rewriting ISR 2009, July 22-26,

22 Confluence of (Join 2-)CTRSs criteria without termination orthogonality? No! b f (b); f (x) a = f (x) x is orthogonal (left-linear and non-overlapping) b f (b) a, due to f (b) b due to f (b) b, hence: f (a) f (b) a, but f (a) a: f (a) a f (a) a f (a) a... No! sufficient criterion orthogonality + normality example: b f (b); f (x) a = f (x) c proof idea: show that m commutes over n, via induction on m+n (here: m = Rm, m = Rm ) Bernhard Gramlich Conditional Rewriting ISR 2009, July 22-26,

23 Confluence of (Join 2-)CTRSs criteria with termination for Newman s Lemma we need: WCR does WCR JCP hold for CTRSs? No! counterexample R: (1) h(x) k(b) = k(x) h(b) (2) k(a) h(a) (3) a b CCPs: k(b) (3) k(a) (2) h(a), due to k(a) h(b) via k(a) h(a) h(b) joinable via: k(b) h(a) however, the variable overlap h(b) h(a) k(b) is NOT joinable anymore! properties of R: decreasing, normal, overlaying, shallow joinable Bernhard Gramlich Conditional Rewriting ISR 2009, July 22-26,

24 Confluence of (Join 2-)CTRSs strengthened notions of confluence for CTRSs shallow joinable CCP t s u = c: σ with cσ : tσ m sσ n uσ implies tσ n v m uσ for some v (notation: n = Rn ) shallow confluence: m n n m level confluence: n n n n shallow confluence = level confluence = confluence Bernhard Gramlich Conditional Rewriting ISR 2009, July 22-26,

25 Confluence of (Join 2-)CTRSs confluence criteria with termination [Dershowitz et al ] A terminating (join 2-) CTRS with joinable CCPs is confluent if it is (a) decreasing; or (b) left-linear, normal, shallow joinable; or (c) overlaying proof ideas (a) by well-founded induction (as for TRSs), but using the stronger decreasing order (b) by proving shallow confluence (c) by using + as induction order and exploiting the overlay property Bernhard Gramlich Conditional Rewriting ISR 2009, July 22-26,

26 Confluence of (Join 2-)CTRSs beyond overlay systems consider R given by a b f (a, a) b f (b, x) f (x, x) = f (x, x) x f (x, b) f (x, x) = f (x, x) x CCPs (the non-trivial ones) joinable: f (b, a) f (a, a) b: f (b, a) f (a, a) b f (a, b) f (a, a) b: f (a, b) f (a, a) b but: f (b, b) f (a, a) a not joinable anymore shared parallel critical peaks play a crucial role this can be exploited for generalizing the overlay confluence criterion (c) beyond overlay systems [Gramlich/Wirth 96] Bernhard Gramlich Conditional Rewriting ISR 2009, July 22-26,

27 Exercises A Confluence of CTRS Test the following join (1-) CTRSs for confluence (via constructing conditional critical pairs and applying known confluence criteria): (a) (b) (c) { } h(g(x), y) h(g(x), g(y)) = h(x, x) a R = h(a, g(y)) y R = R = f (x) k(x) = h(x) x h(a) a g(f (x)) b g(k(a)) b g(k(h(x))) g(k(x)) ins(x, c(y, )) c(x, c(y, l)) = x y true ins(x, c(y, )) c(y, ins(x, l)) = x y false 0 y true s(x) 0 false s(x) s(y) false Bernhard Gramlich Conditional Rewriting ISR 2009, July 22-26,

28 Lecture 3 Bernhard Gramlich Conditional Rewriting ISR 2009, July 22-26,

29 Lecture 4 Bernhard Gramlich Conditional Rewriting ISR 2009, July 22-26,

30 Selected literature on conditional rewriting Bernhard Gramlich Conditional Rewriting ISR 2009, July 22-26,

2 Deduction in Sentential Logic

2 Deduction in Sentential Logic 2 Deduction in Sentential Logic Though we have not yet introduced any formal notion of deductions (i.e., of derivations or proofs), we can easily give a formal method for showing that formulas are tautologies:

More information

Matching of Meta-Expressions with Recursive Bindings

Matching of Meta-Expressions with Recursive Bindings Matching of Meta-Expressions with Recursive Bindings David Sabel Goethe-University Frankfurt am Main, Germany UNIF 2017, Oxford, UK Research supported by the Deutsche Forschungsgemeinschaft (DFG) under

More information

CS792 Notes Henkin Models, Soundness and Completeness

CS792 Notes Henkin Models, Soundness and Completeness CS792 Notes Henkin Models, Soundness and Completeness Arranged by Alexandra Stefan March 24, 2005 These notes are a summary of chapters 4.5.1-4.5.5 from [1]. 1 Review indexed family of sets: A s, where

More information

Unary PCF is Decidable

Unary PCF is Decidable Unary PCF is Decidable Ralph Loader Merton College, Oxford November 1995, revised October 1996 and September 1997. Abstract We show that unary PCF, a very small fragment of Plotkin s PCF [?], has a decidable

More information

CS 4110 Programming Languages and Logics Lecture #2: Introduction to Semantics. 1 Arithmetic Expressions

CS 4110 Programming Languages and Logics Lecture #2: Introduction to Semantics. 1 Arithmetic Expressions CS 4110 Programming Languages and Logics Lecture #2: Introduction to Semantics What is the meaning of a program? When we write a program, we represent it using sequences of characters. But these strings

More information

Harvard School of Engineering and Applied Sciences CS 152: Programming Languages

Harvard School of Engineering and Applied Sciences CS 152: Programming Languages Harvard School of Engineering and Applied Sciences CS 152: Programming Languages Lecture 3 Tuesday, January 30, 2018 1 Inductive sets Induction is an important concept in the theory of programming language.

More information

Integrating rational functions (Sect. 8.4)

Integrating rational functions (Sect. 8.4) Integrating rational functions (Sect. 8.4) Integrating rational functions, p m(x) q n (x). Polynomial division: p m(x) The method of partial fractions. p (x) (x r )(x r 2 ) p (n )(x). (Repeated roots).

More information

Strong normalisation and the typed lambda calculus

Strong normalisation and the typed lambda calculus CHAPTER 9 Strong normalisation and the typed lambda calculus In the previous chapter we looked at some reduction rules for intuitionistic natural deduction proofs and we have seen that by applying these

More information

Notes on the symmetric group

Notes on the symmetric group Notes on the symmetric group 1 Computations in the symmetric group Recall that, given a set X, the set S X of all bijections from X to itself (or, more briefly, permutations of X) is group under function

More information

Lecture 2: The Simple Story of 2-SAT

Lecture 2: The Simple Story of 2-SAT 0510-7410: Topics in Algorithms - Random Satisfiability March 04, 2014 Lecture 2: The Simple Story of 2-SAT Lecturer: Benny Applebaum Scribe(s): Mor Baruch 1 Lecture Outline In this talk we will show that

More information

Brief Notes on the Category Theoretic Semantics of Simply Typed Lambda Calculus

Brief Notes on the Category Theoretic Semantics of Simply Typed Lambda Calculus University of Cambridge 2017 MPhil ACS / CST Part III Category Theory and Logic (L108) Brief Notes on the Category Theoretic Semantics of Simply Typed Lambda Calculus Andrew Pitts Notation: comma-separated

More information

Notes on Natural Logic

Notes on Natural Logic Notes on Natural Logic Notes for PHIL370 Eric Pacuit November 16, 2012 1 Preliminaries: Trees A tree is a structure T = (T, E), where T is a nonempty set whose elements are called nodes and E is a relation

More information

Lecture Notes on Type Checking

Lecture Notes on Type Checking Lecture Notes on Type Checking 15-312: Foundations of Programming Languages Frank Pfenning Lecture 17 October 23, 2003 At the beginning of this class we were quite careful to guarantee that every well-typed

More information

Lecture Notes on Bidirectional Type Checking

Lecture Notes on Bidirectional Type Checking Lecture Notes on Bidirectional Type Checking 15-312: Foundations of Programming Languages Frank Pfenning Lecture 17 October 21, 2004 At the beginning of this class we were quite careful to guarantee that

More information

Harvard School of Engineering and Applied Sciences CS 152: Programming Languages

Harvard School of Engineering and Applied Sciences CS 152: Programming Languages Harvard School of Engineering and Applied Sciences CS 152: Programming Languages Lecture 2 Thursday, January 30, 2014 1 Expressing Program Properties Now that we have defined our small-step operational

More information

TABLEAU-BASED DECISION PROCEDURES FOR HYBRID LOGIC

TABLEAU-BASED DECISION PROCEDURES FOR HYBRID LOGIC TABLEAU-BASED DECISION PROCEDURES FOR HYBRID LOGIC THOMAS BOLANDER AND TORBEN BRAÜNER Abstract. Hybrid logics are a principled generalization of both modal logics and description logics. It is well-known

More information

Introduction An example Cut elimination. Deduction Modulo. Olivier Hermant. Tuesday, December 12, Deduction Modulo

Introduction An example Cut elimination. Deduction Modulo. Olivier Hermant. Tuesday, December 12, Deduction Modulo Tuesday, December 12, 2006 Deduction and Computation Sequent calculus The cut rule The rewrite rules Sequent calculus The cut rule The rewrite rules Deduction system: Gentzen s sequent calculus Γ, P P

More information

Harvard School of Engineering and Applied Sciences CS 152: Programming Languages

Harvard School of Engineering and Applied Sciences CS 152: Programming Languages Harvard School of Engineering and Applied Sciences CS 152: Programming Languages Lecture 3 Tuesday, February 2, 2016 1 Inductive proofs, continued Last lecture we considered inductively defined sets, and

More information

Syllogistic Logics with Verbs

Syllogistic Logics with Verbs Syllogistic Logics with Verbs Lawrence S Moss Department of Mathematics Indiana University Bloomington, IN 47405 USA lsm@csindianaedu Abstract This paper provides sound and complete logical systems for

More information

Arborescent Architecture for Decentralized Supervisory Control of Discrete Event Systems

Arborescent Architecture for Decentralized Supervisory Control of Discrete Event Systems Arborescent Architecture for Decentralized Supervisory Control of Discrete Event Systems Ahmed Khoumsi and Hicham Chakib Dept. Electrical & Computer Engineering, University of Sherbrooke, Canada Email:

More information

Semantics with Applications 2b. Structural Operational Semantics

Semantics with Applications 2b. Structural Operational Semantics Semantics with Applications 2b. Structural Operational Semantics Hanne Riis Nielson, Flemming Nielson (thanks to Henrik Pilegaard) [SwA] Hanne Riis Nielson, Flemming Nielson Semantics with Applications:

More information

In this lecture, we will use the semantics of our simple language of arithmetic expressions,

In this lecture, we will use the semantics of our simple language of arithmetic expressions, CS 4110 Programming Languages and Logics Lecture #3: Inductive definitions and proofs In this lecture, we will use the semantics of our simple language of arithmetic expressions, e ::= x n e 1 + e 2 e

More information

A CATEGORICAL FOUNDATION FOR STRUCTURED REVERSIBLE FLOWCHART LANGUAGES: SOUNDNESS AND ADEQUACY

A CATEGORICAL FOUNDATION FOR STRUCTURED REVERSIBLE FLOWCHART LANGUAGES: SOUNDNESS AND ADEQUACY Logical Methods in Computer Science Vol. 14(3:16)2018, pp. 1 38 https://lmcs.episciences.org/ Submitted Oct. 12, 2017 Published Sep. 05, 2018 A CATEGORICAL FOUNDATION FOR STRUCTURED REVERSIBLE FLOWCHART

More information

Yao s Minimax Principle

Yao s Minimax Principle Complexity of algorithms The complexity of an algorithm is usually measured with respect to the size of the input, where size may for example refer to the length of a binary word describing the input,

More information

The Role of Human Creativity in Mechanized Verification. J Strother Moore Department of Computer Science University of Texas at Austin

The Role of Human Creativity in Mechanized Verification. J Strother Moore Department of Computer Science University of Texas at Austin The Role of Human Creativity in Mechanized Verification J Strother Moore Department of Computer Science University of Texas at Austin 1 John McCarthy(Sep 4, 1927 Oct 23, 2011) 2 Contributions Lisp, mathematical

More information

Binary Decision Diagrams

Binary Decision Diagrams Binary Decision Diagrams Hao Zheng Department of Computer Science and Engineering University of South Florida Tampa, FL 33620 Email: zheng@cse.usf.edu Phone: (813)974-4757 Fax: (813)974-5456 Hao Zheng

More information

CATEGORICAL SKEW LATTICES

CATEGORICAL SKEW LATTICES CATEGORICAL SKEW LATTICES MICHAEL KINYON AND JONATHAN LEECH Abstract. Categorical skew lattices are a variety of skew lattices on which the natural partial order is especially well behaved. While most

More information

A Syntactic Realization Theorem for Justification Logics

A Syntactic Realization Theorem for Justification Logics A Syntactic Realization Theorem for Justification Logics Kai Brünnler, Remo Goetschi, and Roman Kuznets 1 Institut für Informatik und angewandte Mathematik, Universität Bern Neubrückstrasse 10, CH-3012

More information

Binary Decision Diagrams

Binary Decision Diagrams Binary Decision Diagrams Hao Zheng Department of Computer Science and Engineering University of South Florida Tampa, FL 33620 Email: zheng@cse.usf.edu Phone: (813)974-4757 Fax: (813)974-5456 Hao Zheng

More information

Finite Memory and Imperfect Monitoring

Finite Memory and Imperfect Monitoring Federal Reserve Bank of Minneapolis Research Department Finite Memory and Imperfect Monitoring Harold L. Cole and Narayana Kocherlakota Working Paper 604 September 2000 Cole: U.C.L.A. and Federal Reserve

More information

Development Separation in Lambda-Calculus

Development Separation in Lambda-Calculus WoLLIC 2005 Preliminary Version Development Separation in Lambda-Calculus Hongwei Xi 1,2 Computer Science Department Boston University Boston, Massachusetts, USA Abstract We present a proof technique in

More information

CS 3331 Numerical Methods Lecture 2: Functions of One Variable. Cherung Lee

CS 3331 Numerical Methods Lecture 2: Functions of One Variable. Cherung Lee CS 3331 Numerical Methods Lecture 2: Functions of One Variable Cherung Lee Outline Introduction Solving nonlinear equations: find x such that f(x ) = 0. Binary search methods: (Bisection, regula falsi)

More information

HW 1 Reminder. Principles of Programming Languages. Lets try another proof. Induction. Induction on Derivations. CSE 230: Winter 2007

HW 1 Reminder. Principles of Programming Languages. Lets try another proof. Induction. Induction on Derivations. CSE 230: Winter 2007 CSE 230: Winter 2007 Principles of Programming Languages Lecture 4: Induction, Small-Step Semantics HW 1 Reminder Due next Tue Instructions about turning in code to follow Send me mail if you have issues

More information

Concurrency Semantics in Continuation-Passing Style The Companion Technical Report

Concurrency Semantics in Continuation-Passing Style The Companion Technical Report Concurrency Semantics in Continuation-Passing Style The Companion Technical Report Eneia Nicolae Todoran Technical University of Cluj-Napoca Department of Computer Science Baritiu Str. 28, 400027, Cluj-Napoca,

More information

Math-Stat-491-Fall2014-Notes-V

Math-Stat-491-Fall2014-Notes-V Math-Stat-491-Fall2014-Notes-V Hariharan Narayanan December 7, 2014 Martingales 1 Introduction Martingales were originally introduced into probability theory as a model for fair betting games. Essentially

More information

Development Separation in Lambda-Calculus

Development Separation in Lambda-Calculus Development Separation in Lambda-Calculus Hongwei Xi Boston University Work partly funded by NSF grant CCR-0229480 Development Separation in Lambda-Calculus p.1/26 Motivation for the Research To facilitate

More information

Approximating the Transitive Closure of a Boolean Affine Relation

Approximating the Transitive Closure of a Boolean Affine Relation Approximating the Transitive Closure of a Boolean Affine Relation Paul Feautrier ENS de Lyon Paul.Feautrier@ens-lyon.fr January 22, 2012 1 / 18 Characterization Frakas Lemma Comparison to the ACI Method

More information

2 Modeling Credit Risk

2 Modeling Credit Risk 2 Modeling Credit Risk In this chapter we present some simple approaches to measure credit risk. We start in Section 2.1 with a short overview of the standardized approach of the Basel framework for banking

More information

Isabelle/FOL First-Order Logic

Isabelle/FOL First-Order Logic Isabelle/FOL First-Order Logic Larry Paulson and Markus Wenzel October 8, 2017 Contents 1 Intuitionistic first-order logic 2 1.1 Syntax and axiomatic basis................... 2 1.1.1 Equality..........................

More information

arxiv: v1 [math.lo] 24 Feb 2014

arxiv: v1 [math.lo] 24 Feb 2014 Residuated Basic Logic II. Interpolation, Decidability and Embedding Minghui Ma 1 and Zhe Lin 2 arxiv:1404.7401v1 [math.lo] 24 Feb 2014 1 Institute for Logic and Intelligence, Southwest University, Beibei

More information

A Consistent Semantics of Self-Adjusting Computation

A Consistent Semantics of Self-Adjusting Computation A Consistent Semantics of Self-Adjusting Computation Umut A. Acar 1 Matthias Blume 1 Jacob Donham 2 December 2006 CMU-CS-06-168 School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213

More information

A Translation of Intersection and Union Types

A Translation of Intersection and Union Types A Translation of Intersection and Union Types for the λ µ-calculus Kentaro Kikuchi RIEC, Tohoku University kentaro@nue.riec.tohoku.ac.jp Takafumi Sakurai Department of Mathematics and Informatics, Chiba

More information

Algorithmic Game Theory and Applications. Lecture 11: Games of Perfect Information

Algorithmic Game Theory and Applications. Lecture 11: Games of Perfect Information Algorithmic Game Theory and Applications Lecture 11: Games of Perfect Information Kousha Etessami finite games of perfect information Recall, a perfect information (PI) game has only 1 node per information

More information

Modular and Distributive Lattices

Modular and Distributive Lattices CHAPTER 4 Modular and Distributive Lattices Background R. P. DILWORTH Imbedding problems and the gluing construction. One of the most powerful tools in the study of modular lattices is the notion of the

More information

LECTURE 3: FREE CENTRAL LIMIT THEOREM AND FREE CUMULANTS

LECTURE 3: FREE CENTRAL LIMIT THEOREM AND FREE CUMULANTS LECTURE 3: FREE CENTRAL LIMIT THEOREM AND FREE CUMULANTS Recall from Lecture 2 that if (A, φ) is a non-commutative probability space and A 1,..., A n are subalgebras of A which are free with respect to

More information

Comparing Goal-Oriented and Procedural Service Orchestration

Comparing Goal-Oriented and Procedural Service Orchestration Comparing Goal-Oriented and Procedural Service Orchestration M. Birna van Riemsdijk 1 Martin Wirsing 2 1 Technische Universiteit Delft, The Netherlands m.b.vanriemsdijk@tudelft.nl 2 Ludwig-Maximilians-Universität

More information

Maximum Contiguous Subsequences

Maximum Contiguous Subsequences Chapter 8 Maximum Contiguous Subsequences In this chapter, we consider a well-know problem and apply the algorithm-design techniques that we have learned thus far to this problem. While applying these

More information

CIS 500 Software Foundations Fall October. CIS 500, 6 October 1

CIS 500 Software Foundations Fall October. CIS 500, 6 October 1 CIS 500 Software Foundations Fall 2004 6 October CIS 500, 6 October 1 Midterm 1 is next Wednesday Today s lecture will not be covered by the midterm. Next Monday, review class. Old exams and review questions

More information

MAT 4250: Lecture 1 Eric Chung

MAT 4250: Lecture 1 Eric Chung 1 MAT 4250: Lecture 1 Eric Chung 2Chapter 1: Impartial Combinatorial Games 3 Combinatorial games Combinatorial games are two-person games with perfect information and no chance moves, and with a win-or-lose

More information

Ch4. Variance Reduction Techniques

Ch4. Variance Reduction Techniques Ch4. Zhang Jin-Ting Department of Statistics and Applied Probability July 17, 2012 Ch4. Outline Ch4. This chapter aims to improve the Monte Carlo Integration estimator via reducing its variance using some

More information

Lecture 7: Bayesian approach to MAB - Gittins index

Lecture 7: Bayesian approach to MAB - Gittins index Advanced Topics in Machine Learning and Algorithmic Game Theory Lecture 7: Bayesian approach to MAB - Gittins index Lecturer: Yishay Mansour Scribe: Mariano Schain 7.1 Introduction In the Bayesian approach

More information

Gödel algebras free over finite distributive lattices

Gödel algebras free over finite distributive lattices TANCL, Oxford, August 4-9, 2007 1 Gödel algebras free over finite distributive lattices Stefano Aguzzoli Brunella Gerla Vincenzo Marra D.S.I. D.I.COM. D.I.C.O. University of Milano University of Insubria

More information

Global Joint Distribution Factorizes into Local Marginal Distributions on Tree-Structured Graphs

Global Joint Distribution Factorizes into Local Marginal Distributions on Tree-Structured Graphs Teaching Note October 26, 2007 Global Joint Distribution Factorizes into Local Marginal Distributions on Tree-Structured Graphs Xinhua Zhang Xinhua.Zhang@anu.edu.au Research School of Information Sciences

More information

The Design of Arbitrage-Free Data Pricing Schemes

The Design of Arbitrage-Free Data Pricing Schemes The Design of Arbitrage-Free Data Pricing Schemes Shaleen Deep 1 and Paraschos Koutris 2 1 University of Wisconsin-Madison, Madison, WI, USA shaleen@cs.wisc.edu 2 University of Wisconsin-Madison, Madison,

More information

A DNC function that computes no effectively bi-immune set

A DNC function that computes no effectively bi-immune set A DNC function that computes no effectively bi-immune set Achilles A. Beros Laboratoire d Informatique de Nantes Atlantique, Université de Nantes July 5, 204 Standard Definitions Definition f is diagonally

More information

SAT and DPLL. Espen H. Lian. May 4, Ifi, UiO. Espen H. Lian (Ifi, UiO) SAT and DPLL May 4, / 59

SAT and DPLL. Espen H. Lian. May 4, Ifi, UiO. Espen H. Lian (Ifi, UiO) SAT and DPLL May 4, / 59 SAT and DPLL Espen H. Lian Ifi, UiO May 4, 2010 Espen H. Lian (Ifi, UiO) SAT and DPLL May 4, 2010 1 / 59 Normal forms Normal forms DPLL Complexity DPLL Implementation Bibliography Espen H. Lian (Ifi, UiO)

More information

Proof Techniques for Operational Semantics

Proof Techniques for Operational Semantics Proof Techniques for Operational Semantics Wei Hu Memorial Lecture I will give a completely optional bonus survey lecture: A Recent History of PL in Context It will discuss what has been hot in various

More information

A Knowledge-Theoretic Approach to Distributed Problem Solving

A Knowledge-Theoretic Approach to Distributed Problem Solving A Knowledge-Theoretic Approach to Distributed Problem Solving Michael Wooldridge Department of Electronic Engineering, Queen Mary & Westfield College University of London, London E 4NS, United Kingdom

More information

α-structural Recursion and Induction

α-structural Recursion and Induction α-structural Recursion and Induction AndrewPitts UniversityofCambridge ComputerLaboratory TPHOLs 2005, - p. 1 Overview TPHOLs 2005, - p. 2 N.B. binding and non-binding constructs are treated just the same

More information

X ln( +1 ) +1 [0 ] Γ( )

X ln( +1 ) +1 [0 ] Γ( ) Problem Set #1 Due: 11 September 2014 Instructor: David Laibson Economics 2010c Problem 1 (Growth Model): Recall the growth model that we discussed in class. We expressed the sequence problem as ( 0 )=

More information

Grainless Semantics without Critical Regions

Grainless Semantics without Critical Regions Grainless Semantics without Critical Regions John C. Reynolds Department of Computer Science Carnegie Mellon University April 11, 2007 (corrected April 27, 2007) (Work in progress, jointly with Ruy Ley-Wild)

More information

Short-time-to-expiry expansion for a digital European put option under the CEV model. November 1, 2017

Short-time-to-expiry expansion for a digital European put option under the CEV model. November 1, 2017 Short-time-to-expiry expansion for a digital European put option under the CEV model November 1, 2017 Abstract In this paper I present a short-time-to-expiry asymptotic series expansion for a digital European

More information

OPPA European Social Fund Prague & EU: We invest in your future.

OPPA European Social Fund Prague & EU: We invest in your future. OPPA European Social Fund Prague & EU: We invest in your future. Cooperative Game Theory Michal Jakob and Michal Pěchouček Agent Technology Center, Dept. of Computer Science and Engineering, FEE, Czech

More information

Proof Techniques for Operational Semantics. Questions? Why Bother? Mathematical Induction Well-Founded Induction Structural Induction

Proof Techniques for Operational Semantics. Questions? Why Bother? Mathematical Induction Well-Founded Induction Structural Induction Proof Techniques for Operational Semantics Announcements Homework 1 feedback/grades posted Homework 2 due tonight at 11:55pm Meeting 10, CSCI 5535, Spring 2010 2 Plan Questions? Why Bother? Mathematical

More information

MTH6154 Financial Mathematics I Stochastic Interest Rates

MTH6154 Financial Mathematics I Stochastic Interest Rates MTH6154 Financial Mathematics I Stochastic Interest Rates Contents 4 Stochastic Interest Rates 45 4.1 Fixed Interest Rate Model............................ 45 4.2 Varying Interest Rate Model...........................

More information

1 Overview. 2 The Gradient Descent Algorithm. AM 221: Advanced Optimization Spring 2016

1 Overview. 2 The Gradient Descent Algorithm. AM 221: Advanced Optimization Spring 2016 AM 22: Advanced Optimization Spring 206 Prof. Yaron Singer Lecture 9 February 24th Overview In the previous lecture we reviewed results from multivariate calculus in preparation for our journey into convex

More information

Cut-free sequent calculi for algebras with adjoint modalities

Cut-free sequent calculi for algebras with adjoint modalities Cut-free sequent calculi for algebras with adjoint modalities Roy Dyckhoff (University of St Andrews) and Mehrnoosh Sadrzadeh (Universities of Oxford & Southampton) TANCL Conference, Oxford, 8 August 2007

More information

Topics in Contract Theory Lecture 3

Topics in Contract Theory Lecture 3 Leonardo Felli 9 January, 2002 Topics in Contract Theory Lecture 3 Consider now a different cause for the failure of the Coase Theorem: the presence of transaction costs. Of course for this to be an interesting

More information

The Traveling Salesman Problem. Time Complexity under Nondeterminism. A Nondeterministic Algorithm for tsp (d)

The Traveling Salesman Problem. Time Complexity under Nondeterminism. A Nondeterministic Algorithm for tsp (d) The Traveling Salesman Problem We are given n cities 1, 2,..., n and integer distances d ij between any two cities i and j. Assume d ij = d ji for convenience. The traveling salesman problem (tsp) asks

More information

Sublinear Time Algorithms Oct 19, Lecture 1

Sublinear Time Algorithms Oct 19, Lecture 1 0368.416701 Sublinear Time Algorithms Oct 19, 2009 Lecturer: Ronitt Rubinfeld Lecture 1 Scribe: Daniel Shahaf 1 Sublinear-time algorithms: motivation Twenty years ago, there was practically no investigation

More information

The finite lattice representation problem and intervals in subgroup lattices of finite groups

The finite lattice representation problem and intervals in subgroup lattices of finite groups The finite lattice representation problem and intervals in subgroup lattices of finite groups William DeMeo Math 613: Group Theory 15 December 2009 Abstract A well-known result of universal algebra states:

More information

Multiproduct Pricing Made Simple

Multiproduct Pricing Made Simple Multiproduct Pricing Made Simple Mark Armstrong John Vickers Oxford University September 2016 Armstrong & Vickers () Multiproduct Pricing September 2016 1 / 21 Overview Multiproduct pricing important for:

More information

Chapter 4. Cardinal Arithmetic.

Chapter 4. Cardinal Arithmetic. Chapter 4. Cardinal Arithmetic. 4.1. Basic notions about cardinals. We are used to comparing the size of sets by seeing if there is an injection from one to the other, or a bijection between the two. Definition.

More information

An Adaptive Characterization of Signed Systems for Paraconsistent Reasoning

An Adaptive Characterization of Signed Systems for Paraconsistent Reasoning An Adaptive Characterization of Signed Systems for Paraconsistent Reasoning Diderik Batens, Joke Meheus, Dagmar Provijn Centre for Logic and Philosophy of Science University of Ghent, Belgium {Diderik.Batens,Joke.Meheus,Dagmar.Provijn}@UGent.be

More information

SAT and DPLL. Introduction. Preliminaries. Normal forms DPLL. Complexity. Espen H. Lian. DPLL Implementation. Bibliography.

SAT and DPLL. Introduction. Preliminaries. Normal forms DPLL. Complexity. Espen H. Lian. DPLL Implementation. Bibliography. SAT and Espen H. Lian Ifi, UiO Implementation May 4, 2010 Espen H. Lian (Ifi, UiO) SAT and May 4, 2010 1 / 59 Espen H. Lian (Ifi, UiO) SAT and May 4, 2010 2 / 59 Introduction Introduction SAT is the problem

More information

THE TRAVELING SALESMAN PROBLEM FOR MOVING POINTS ON A LINE

THE TRAVELING SALESMAN PROBLEM FOR MOVING POINTS ON A LINE THE TRAVELING SALESMAN PROBLEM FOR MOVING POINTS ON A LINE GÜNTER ROTE Abstract. A salesperson wants to visit each of n objects that move on a line at given constant speeds in the shortest possible time,

More information

The Turing Definability of the Relation of Computably Enumerable In. S. Barry Cooper

The Turing Definability of the Relation of Computably Enumerable In. S. Barry Cooper The Turing Definability of the Relation of Computably Enumerable In S. Barry Cooper Computability Theory Seminar University of Leeds Winter, 1999 2000 1. The big picture Turing definability/invariance

More information

A semantics for concurrent permission logic. Stephen Brookes CMU

A semantics for concurrent permission logic. Stephen Brookes CMU A semantics for concurrent permission logic Stephen Brookes CMU Cambridge, March 2006 Traditional logic Owicki/Gries 76 Γ {p} c {q} Resource-sensitive partial correctness Γ specifies resources ri, protection

More information

Tug of War Game. William Gasarch and Nick Sovich and Paul Zimand. October 6, Abstract

Tug of War Game. William Gasarch and Nick Sovich and Paul Zimand. October 6, Abstract Tug of War Game William Gasarch and ick Sovich and Paul Zimand October 6, 2009 To be written later Abstract Introduction Combinatorial games under auction play, introduced by Lazarus, Loeb, Propp, Stromquist,

More information

Partial Fractions. A rational function is a fraction in which both the numerator and denominator are polynomials. For example, f ( x) = 4, g( x) =

Partial Fractions. A rational function is a fraction in which both the numerator and denominator are polynomials. For example, f ( x) = 4, g( x) = Partial Fractions A rational function is a fraction in which both the numerator and denominator are polynomials. For example, f ( x) = 4, g( x) = 3 x 2 x + 5, and h( x) = x + 26 x 2 are rational functions.

More information

10.1 Elimination of strictly dominated strategies

10.1 Elimination of strictly dominated strategies Chapter 10 Elimination by Mixed Strategies The notions of dominance apply in particular to mixed extensions of finite strategic games. But we can also consider dominance of a pure strategy by a mixed strategy.

More information

Financial Mathematics III Theory summary

Financial Mathematics III Theory summary Financial Mathematics III Theory summary Table of Contents Lecture 1... 7 1. State the objective of modern portfolio theory... 7 2. Define the return of an asset... 7 3. How is expected return defined?...

More information

Fundamental Theorems of Asset Pricing. 3.1 Arbitrage and risk neutral probability measures

Fundamental Theorems of Asset Pricing. 3.1 Arbitrage and risk neutral probability measures Lecture 3 Fundamental Theorems of Asset Pricing 3.1 Arbitrage and risk neutral probability measures Several important concepts were illustrated in the example in Lecture 2: arbitrage; risk neutral probability

More information

Laurence Boxer and Ismet KARACA

Laurence Boxer and Ismet KARACA SOME PROPERTIES OF DIGITAL COVERING SPACES Laurence Boxer and Ismet KARACA Abstract. In this paper we study digital versions of some properties of covering spaces from algebraic topology. We correct and

More information

Virtual Demand and Stable Mechanisms

Virtual Demand and Stable Mechanisms Virtual Demand and Stable Mechanisms Jan Christoph Schlegel Faculty of Business and Economics, University of Lausanne, Switzerland jschlege@unil.ch Abstract We study conditions for the existence of stable

More information

5 Deduction in First-Order Logic

5 Deduction in First-Order Logic 5 Deduction in First-Order Logic The system FOL C. Let C be a set of constant symbols. FOL C is a system of deduction for the language L # C. Axioms: The following are axioms of FOL C. (1) All tautologies.

More information

Best-Reply Sets. Jonathan Weinstein Washington University in St. Louis. This version: May 2015

Best-Reply Sets. Jonathan Weinstein Washington University in St. Louis. This version: May 2015 Best-Reply Sets Jonathan Weinstein Washington University in St. Louis This version: May 2015 Introduction The best-reply correspondence of a game the mapping from beliefs over one s opponents actions to

More information

Matching [for] the Lambda Calculus of Objects

Matching [for] the Lambda Calculus of Objects Matching [for] the Lambda Calculus of Objects Viviana Bono 1 Dipartimento di Informatica, Università di Torino C.so Svizzera 185, I-10149 Torino, Italy e-mail: bono@di.unito.it Michele Bugliesi Dipartimento

More information

Untyped Lambda Calculus

Untyped Lambda Calculus Chapter 2 Untyped Lambda Calculus We assume the existence of a denumerable set VAR of (object) variables x 0,x 1,x 2,..., and use x,y,z to range over these variables. Given two variables x 1 and x 2, we

More information

Syllogistic Logics with Verbs

Syllogistic Logics with Verbs Syllogistic Logics with Verbs Lawrence S Moss Department of Mathematics Indiana University Bloomington, IN 47405 USA lsm@csindianaedu Abstract This paper provides sound and complete logical systems for

More information

Chapter 8. Markowitz Portfolio Theory. 8.1 Expected Returns and Covariance

Chapter 8. Markowitz Portfolio Theory. 8.1 Expected Returns and Covariance Chapter 8 Markowitz Portfolio Theory 8.1 Expected Returns and Covariance The main question in portfolio theory is the following: Given an initial capital V (0), and opportunities (buy or sell) in N securities

More information

Non replication of options

Non replication of options Non replication of options Christos Kountzakis, Ioannis A Polyrakis and Foivos Xanthos June 30, 2008 Abstract In this paper we study the scarcity of replication of options in the two period model of financial

More information

Martingales. by D. Cox December 2, 2009

Martingales. by D. Cox December 2, 2009 Martingales by D. Cox December 2, 2009 1 Stochastic Processes. Definition 1.1 Let T be an arbitrary index set. A stochastic process indexed by T is a family of random variables (X t : t T) defined on a

More information

0.1 Equivalence between Natural Deduction and Axiomatic Systems

0.1 Equivalence between Natural Deduction and Axiomatic Systems 0.1 Equivalence between Natural Deduction and Axiomatic Systems Theorem 0.1.1. Γ ND P iff Γ AS P ( ) it is enough to prove that all axioms are theorems in ND, as MP corresponds to ( e). ( ) by induction

More information

Sy D. Friedman. August 28, 2001

Sy D. Friedman. August 28, 2001 0 # and Inner Models Sy D. Friedman August 28, 2001 In this paper we examine the cardinal structure of inner models that satisfy GCH but do not contain 0 #. We show, assuming that 0 # exists, that such

More information

First-Order Logic in Standard Notation Basics

First-Order Logic in Standard Notation Basics 1 VOCABULARY First-Order Logic in Standard Notation Basics http://mathvault.ca April 21, 2017 1 Vocabulary Just as a natural language is formed with letters as its building blocks, the First- Order Logic

More information

Lecture Notes 6. Assume F belongs to a family of distributions, (e.g. F is Normal), indexed by some parameter θ.

Lecture Notes 6. Assume F belongs to a family of distributions, (e.g. F is Normal), indexed by some parameter θ. Sufficient Statistics Lecture Notes 6 Sufficiency Data reduction in terms of a particular statistic can be thought of as a partition of the sample space X. Definition T is sufficient for θ if the conditional

More information

Maximizing the Spread of Influence through a Social Network Problem/Motivation: Suppose we want to market a product or promote an idea or behavior in

Maximizing the Spread of Influence through a Social Network Problem/Motivation: Suppose we want to market a product or promote an idea or behavior in Maximizing the Spread of Influence through a Social Network Problem/Motivation: Suppose we want to market a product or promote an idea or behavior in a society. In order to do so, we can target individuals,

More information

Decidability and Recursive Languages

Decidability and Recursive Languages Decidability and Recursive Languages Let L (Σ { }) be a language, i.e., a set of strings of symbols with a finite length. For example, {0, 01, 10, 210, 1010,...}. Let M be a TM such that for any string

More information

Hierarchical Exchange Rules and the Core in. Indivisible Objects Allocation

Hierarchical Exchange Rules and the Core in. Indivisible Objects Allocation Hierarchical Exchange Rules and the Core in Indivisible Objects Allocation Qianfeng Tang and Yongchao Zhang January 8, 2016 Abstract We study the allocation of indivisible objects under the general endowment

More information