Modularity of convergence in infinitary rewriting Stefan Kahrs University of Kent at Canterbury.

Slides:



Advertisements
Similar presentations
Boyce/DiPrima 9th ed, Ch 2.8: The Existence and Uniqueness Theorem Elementary Differential Equations and Boundary Value Problems, 9th edition, by William.
Advertisements

Chapter Three: Closure Properties for Regular Languages
10 October 2006 Foundations of Logic and Constraint Programming 1 Unification ­An overview Need for Unification Ranked alfabeths and terms. Substitutions.
QuickSort Average Case Analysis An Incompressibility Approach Brendan Lucier August 2, 2005.
(1) An ordered set of real numbers is called a sequence and is denoted by ( ). If the number of terms is unlimited, then the sequence is said to be an.
Normal Forms and Infinity Stefan Kahrs, Connor Smith University of Kent.
Determinization of Büchi Automata
Section 7.4: Closures of Relations Let R be a relation on a set A. We have talked about 6 properties that a relation on a set may or may not possess: reflexive,
September 12, Algorithms and Data Structures Lecture III Simonas Šaltenis Nykredit Center for Database Research Aalborg University
INFINITE SEQUENCES AND SERIES
Safety and Liveness. Defining Programs Variables with respective domain –State space of the program Program actions –Guarded commands Program computation.
1 Introduction to Computability Theory Lecture12: Reductions Prof. Amos Israeli.
1 Introduction to Computability Theory Lecture13: Mapping Reductions Prof. Amos Israeli.
INFINITE SEQUENCES AND SERIES
INFINITE SEQUENCES AND SERIES
INFINITE SEQUENCES AND SERIES
Theorems on divergent sequences. Theorem 1 If the sequence is increasing and not bounded from above then it diverges to +∞. Illustration =
13. The Weak Law and the Strong Law of Large Numbers
Monadic Predicate Logic is Decidable Boolos et al, Computability and Logic (textbook, 4 th Ed.)
APPLICATIONS OF DIFFERENTIATION 4. In Sections 2.2 and 2.4, we investigated infinite limits and vertical asymptotes.  There, we let x approach a number.
EXAMPLE 4 Graph a translated square root function Graph y = –2 x – Then state the domain and range. SOLUTION STEP 1 Sketch the graph of y = –2 x.
Theorems on continuous functions. Weierstrass’ theorem Let f(x) be a continuous function over a closed bounded interval [a,b] Then f(x) has at least one.
Index FAQ Limits of Sequences of Real Numbers Sequences of Real Numbers Limits through Rigorous Definitions The Squeeze Theorem Using the Squeeze Theorem.
Monotone Sequences Objective: To define a Monotone Sequence and determine whether it converges or not.
CAS LX 502 Semantics 3a. A formalism for meaning (cont ’ d) 3.2, 3.6.
4.2 - The Mean Value Theorem
Numerical Sequences. Why Sequences? There are six animations about limits to show the sequence in the domain and range. Problems displaying the data.
Section 1 A sequence(of real numbers) is  a list of infinitely many real numbers arranged in such a manner that it has a beginning but no end such as:
9.1 Sequences. A sequence is a list of numbers written in an explicit order. n th term Any real-valued function with domain a subset of the positive integers.
The importance of sequences and infinite series in calculus stems from Newton’s idea of representing functions as sums of infinite series.  For instance,
MATH 224 – Discrete Mathematics
LIMITS AND DERIVATIVES 2. In Sections 2.2 and 2.4, we investigated infinite limits and vertical asymptotes.  There, we let x approach a number.  The.
APPLICATIONS OF DIFFERENTIATION 4. A polynomial behaves near infinity as its term of highest degree. The polynomial behaves like the polynomial Near infinity.
10/12/20151 GC16/3C11 Functional Programming Lecture 3 The Lambda Calculus A (slightly) deeper look.
PROBABILITY AND STATISTICS FOR ENGINEERING Hossein Sameti Department of Computer Engineering Sharif University of Technology The Weak Law and the Strong.
Math Review Data Structures & File Management Computer Science Dept Va Tech July 2000 ©2000 McQuain WD 1 Summation Formulas Let N > 0, let A, B, and C.
Infinite Series Copyright © Cengage Learning. All rights reserved.
Foundations of Discrete Mathematics Chapters 5 By Dr. Dalia M. Gil, Ph.D.
The Integers. The Division Algorithms A high-school question: Compute 58/17. We can write 58 as 58 = 3 (17) + 7 This forms illustrates the answer: “3.
11.2 Series In this section, we will learn about: Various types of series. INFINITE SEQUENCES AND SERIES.
Closure Operators, Equivalences and Models a Readers’ Digest Stefan Kahrs.
In section 11.9, we were able to find power series representations for a certain restricted class of functions. Here, we investigate more general problems.
Copyright © Cengage Learning. All rights reserved. CHAPTER 8 RELATIONS.
Albert Gatt LIN3021 Formal Semantics Lecture 4. In this lecture Compositionality in Natural Langauge revisited: The role of types The typed lambda calculus.
12 INFINITE SEQUENCES AND SERIES Power Series In this section, we will learn about: Power series and testing it for convergence or divergence. INFINITE.
In this section, we investigate convergence of series that are not made up of only non- negative terms.
Naïve Set Theory. Basic Definitions Naïve set theory is the non-axiomatic treatment of set theory. In the axiomatic treatment, which we will only allude.
Discrete Structures & Algorithms More on Methods of Proof / Mathematical Induction EECE 320 — UBC.
Lecture 2 Plan: 1. Automatic Boolean Algebras 2. Automatic Linear Orders 3. Automatic Trees 4. Automatic Versions of König’s lemma 5. Intrinsic Regularity.
MTH 253 Calculus (Other Topics) Chapter 11 – Infinite Sequences and Series Section 11.1 – Sequences Copyright © 2009 by Ron Wallace, all rights reserved.
12 INFINITE SEQUENCES AND SERIES. In general, it is difficult to find the exact sum of a series.  We were able to accomplish this for geometric series.
Power Series Section 9.1a.
Section 8.2: Infinite Series. Zeno’s Paradox Can you add infinitely many numbers ?? You can’t actually get anywhere because you always have to cover half.
Copyright © Cengage Learning. All rights reserved. 11 Infinite Sequences and Series.
Revision lecture MA30041: Metric Spaces. Just to become familiar with the clicker: What day of the week is today? 1.Sunday 2.Monday 3.Tuesday 4.Wednesday.
September 29, 2009Theory of Computation Lecture 7: Primitive Recursive Functions III 1 Some Primitive Recursive Functions Example 3: h(x) = x! Here are.
Integration Newton project, Culverhay, Wednesday September 21st.
11.8 Power Series In this section, we will learn about: Power series and testing it for convergence or divergence. INFINITE SEQUENCES AND SERIES.
SEQUENCES A function whose domain is the set of all integers greater than or equal to some integer n 0 is called a sequence. Usually the initial number.
Infinite Sequences and Series 8. Sequences Sequences A sequence can be thought of as a list of numbers written in a definite order: a 1, a 2, a.
Orthogonality and Weak Convergence in Infinitary Rewriting Stefan Kahrs.
Copyright © Cengage Learning. All rights reserved.
Discrete Mathematics Lecture 6
Chapter 8 Infinite Series.
Sequences and Series of Functions
Chapter 4 Sequences.
Convergent and divergent sequences. Limit of sequence.
Determine whether the sequence converges or diverges. {image}
Lesson 5.3 What is a Function?
2.2 Fixed-Point Iteration
Presentation transcript:

Modularity of convergence in infinitary rewriting Stefan Kahrs University of Kent at Canterbury

Overview a brief recap on infinitary rewriting and convergence the problem some examples metric abstract reduction systems sketch the proof

Recap: Infinitary Term Rewriting in infinitary rewriting we permit infinite terms, and have transfinite reductions approximating them reduction sequence: a continuous function f:  Ter  (  ) such that f(n)  f(n+1) open reduction sequence f:  is a limit ordinal; it is converging if we can extend the domain to  +1, keeping it continuous f is strongly convergent if in addition redex positions are eventually deep

The problem an iTRS is (strongly) convergent iff all its open reduction sequences are are these modular properties of iTRSs? in general, or under certain conditions?

Example 1: collapsing rules F(x)  x; G(y)  y each rule on its own convergent, but not together t=F(G(t)), u=G(F(u)) t  u  t ... note: the presence of a collapsing rule always breaks strong convergence

Example 2: one collapsing rule F(x)  x; G(H(x))  G(x) each rule on its own convergent, but not together t=F(H(t)), u=H(F(u)) G(t)  G(u)  G(t) ...

Revised Problem is convergence modular for non-collapsing iTRS?

Example 3: weird stuff F(x,x,y)  F(x,y,x) ; 0  S(0) both are individually convergent, and they are together as well; but notice: F(0,0,0)  F(0,0,1)  F(0,1,0)  F(1,1,0)  F( 1,0,1)  F(1,1,1) ... if we project the second argument we get the sequence 0, 0, 1, 1, 0, 1, 1, 2, 2, 1, 2, 2, 3, 3, 2,... not a reduction sequence!

Example 4: more weird stuff A  H(A), A  Z, H(Z)  S(Z), H(S(x))  S(S(x)) we have A  w S n (Z) but we do not have A  w S  J(K(x,y))  J(y) t=K(A,t), u=K(S ,u) we have J(t)  w J(u)...but the blue subterms of J(t) do not reduce to the blue subterms of J(u)

Metric abstract reduction systems a MARS is an ARS with a metric (M, ,d) sequences: ordinal-indexed continuous functions weak reduction sequences: reduction sequences of the MARS (M,  w,d) theorem: (M, ,d) is convergent iff (M,  w,d) is

Focussed Sequences a sequence f:  M is focussed, if there is a  < , such that for all , there is a  : f(  )  w f(  ) for all ,  >  in words: elements sufficiently far down the sequence reduce to all elements sufficiently far down the sequence theorem: a MARS is convergent iff all its focussed sequences are

Replacing principal subterms write t[n  u] for replacing all principal subterms, n ranks from the root, by the fixed term u this operation preserves (reflexive) reduction steps we also use it for sequences, applying it pointwise

Set up in the following let R and S be two non- collapsing and convergent iTRSs let f be an open reduction sequence of the combined system observe that f[1  x] must converge, by assumption corollary: strong convergence is modular

Proof idea if l  r in the system at the root (and l  r) of the sequence then f[1  l] must be convergent it must remain convergent if we reduce some of the l's to r's this tells us something about f

Predicate sequence a predicate sequence is a function that tells us whether we should replace a term (a principal subterm) with l or r, depending on how far we are in the sequence we need to ensure that reductions are preserved: –once we replace t with r, we need to do this as well further down the sequence –if we replace t with r, and t  w s then we replace s with r as well

Modified Sequence each rewrite step of the original sequence is split into two halves first half: time is moved on, and so some l's will be rewritten to r's; this also models rewriting below principal subterm positions second half: the original rewrite step is performed (if situated in top-rank)

2 particular ones k t : a term is not a reduct of t f p : a reduct of the term will appear in position p "in the future"

Observations by modifying f with predicate sequence f p we can show that all principal subterms in position p sufficiently far down the sequence have a reduct further down by modifying it with k t one can show that the sequence of subterms at p is focussed

Overall proof if f is divergent then it is divergent with diameter  thus it suffices to look at f[n  x] with 2 (-n) <  then we can prove the property by induction on the rank, using the previous observation, and the earlier theorem that convergence of focussed sequences coincides with convergence of reduction sequences

Conclusion convergence is modular for non-collapsing iTRSs strong convergence is modular (note: for left-linear systems this result is due to Simonsen)