DOCSLIB.ORG

A Galois Connection Calculus for Abstract Interpretation∗

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
A Galois Connection Calculus for Abstract Interpretation∗ A Galois Connection Calculus for Abstract Interpretation⇤ Patrick Cousot Radhia Cousot CIMS⇤⇤, NYU, USA [email protected] CNRS Emeritus, ENS, France s [email protected] rfru Abstract We introduce a Galois connection calculus for language independent 3. Basic GC semantics Basic GCs are primitive abstractions of specification of abstract interpretations used in programming language semantics, properties. Classical examples are the identity abstraction 1[ , formal verification, and static analysis. This Galois connection calculus and its type λ Q . Q S hC system are typed by abstract interpretation. ] , , , , the top abstraction [ , vi hC vi −− − λ − −P − − −. − −P !−!− − hC vi S > hC Categories and Subject Descriptors D.2.4 [Software/Program Verification] λ Q . J General Terms Algorithms, Languages, Reliability, Security, Theory, Verification. , ] , , > , , the join abstraction [C] vi K> hC vi −−−−−λ P . hC vi S J[ Keywords Abstract Interpretation, Galois connection, Static Analysis, Verification. −−−−−!γ} > }(}(C)), }(C), with ↵}(P ) P , γ}(Q) In Abstract , } , , 1. Galois connections in Abstract Interpretation h K ✓i −−! −−↵ h ✓i J K interpretation [3, 4, 6, 7] concrete properties (for example (e.g.) }(Q), the complement abstraction [C] , }(SC), ¬ of computations) are related to abstract properties (e.g. types). The S ¬ h ✓i −−! −¬ }(C), , the finite/infinite sequence abstraction [C] , abstract properties are always sound approximations of the con- h ◆i γ S 1 1 J K crete properties (abstract proofs/static analyzes are always correct }(C1), }(C), with ↵1(P ) , σi σ P i h ✓i ↵ −1 −− h ✓i { | 2 ^ 2 in the concrete) and are sometimes complete (proofs/analyzes of −− − −! J K dom(σ) and γ1(Q) , σ C1 i dom(σ):σi Q , the abstract properties can all be done in the abstract only). E.g. types } { 2 |8 2 2 }γ transformer abstraction [C1,C2] , }(C1 C2), are sound but incomplete [2] while abstract semantics are usually S h ⇥ ✓i −− ↵ − − −! −− complete [9]. The concrete domain , and abstract domain }(C1) [ }(C2), ˙ mapping relations to join-preserving hC vi h ! ✓i , 4 of properties are posets (partial orders being interpreted as transformers with ↵ (R)J λ X . yK x X : x, y R , hA i , { |9 2 h i2 } implication). When concrete properties all have a 4-most precise γ (g) , x, y y g( x ) , the function abstraction abstraction, the correspondence is a Galois connection (GC) , {h i| 2 { } } γ7! γ hC [C1,C2] , }(C1 C2), }(C1) }(C2), , with abstraction ↵ and concretiza- S 7! h 7! ✓i ↵ −7! −− h 7! vi −↵ hA 4i 2C7! A −− − ! ! ˙ with ↵7!(P ) λ X . f(x) f P x X , γ7!(g) tion γ satisfying P : Q : ↵(x) 4 y ✓i , { | 2 ^ 2 } , x γ(2y)A( 7!expresses C soundness8 2 andC 8 best2A abstraction). Each, fJ C1 KC2 X }(C1): x X : f(x) g(X) , v ) ( { 2 7! |8 2 8 2 2 γ⇥ } adjoint ↵/γ uniquely determines the other γ/↵.AGalois retrac- the cartesian abstraction [I,C] , }(I C), S ⇥ h 7! ✓i ↵−− tion (or insertion) has ↵ onto, so γ is one-to-one, and ↵ ◦ γ is the −−!⇥ identity. E.g. the interval abstraction [3, 4] of the power set }(C) I }(C), ˙ with ↵⇥(X) , λ i I . x C f I h 7! ✓i J K2 { 2 |9 2 7! of complete -totally ordered sets C , is I[ C, C : f[i x] X , γ⇥(Y ) , f i I : f(i) Y (i) , and I 2 } { |8 2 2 } γ [{−1 1} S h the pointwise extension ˙ of to I, etc. , , ] , }(C), I(C , , ), with ✓ ✓ −−−I i 1 1 h ✓i −− −↵ !−! h [{−1 1} J i 4. Galois connector semantics Galois connectors build a GC ↵I(X) [min X, max X], min , max , γI([a, b]) , ; , 1 ; , 1 from GCs provided as parameters. Unary Galois connectors in- x CK a x b , intervals I(C , F, ) γ , { 2 | } S [{−1 1} , clude the reduction connector R[ , , 4 ] , [a, b] a C b C a b [ , ] , γ S hC vi ! −↵ hA i { | 2 [{−1}^ 2 [{1}^ }[{1 1 } , ↵(P ) P , and the pointwise connec- and inclusion [a, b] [c, d] c a b d.AGalois isomor- ↵ 4 , J K hC vi −− −− !−! h{ γ| 2C} i λ ⇢ γ ⇢ γ ^ J . K◦ phism , , has both ↵ and γ bijective. E.g. global tor X C, A, 4 , X C, ˙ ↵ 4 −↵ λ −⇢ −. −↵ −⇢ −− hC ✓i −− − −!−! − hA i S h vi ! h i h 7! vi −− − − − −◦ −! and local invariants areF isomorphic by the right image abstraction ˙ ˙ ˙ γy X A, 4 for the pointwise orderings and 4. Binary Ga- ˙ h 7! i v γ1 y[L, ] , }(L ), L }( ), with lois connectorsJ _ include the compositionK connector , S M h ⇥M ✓i −− − ↵ − −y − !−!− − h 7! M ✓i S hC ✓i −−↵1 y y γ2 −−! ↵ (P ) , λ ` . m `, m P , γ (Q) , `, m m 1, 2, 3, 1, = 2, , b ↵ 4 , ( b ? J ˙K { |h i2 } {h i| 2 hA γ i γhA vi −−2 hA i hA i hA vi hC Q(`) , and is the pointwise extension of inclusion . 1◦ 2 −−! J 3, ⌦ (where ⌦ is a static error), the prod- } ✓ ✓ ↵ ↵ 4 : ) GC ✓i −−−−2◦ 1 hA i γ γ 2. Equivalent formalizationsγ of -based Abstract Interpre- −−−−!# 1 K 2 , , uct connector 1, 1, * 2, b 2, tation GCs ↵ 4 are Galois retracts of/Galois iso- S hC ✓i −−↵1 hA vi hC i −−↵2 hA hC vi − hA i −−!γ1 γ2 −−! ! ⇥ morphic to numerous equivalent mathematical structures [6] such 4 , 1 2, b 1 2, 4 (gen- ↵ − − −↵ −− as join-preserving maps (↵), meet-preserving maps (γ), upper- i hC ⇥CJ ✓⇥ i −− −1 −⇥ − !2 hA ⇥A v⇥ i eralizing to tuples), the higher-order functional connector 1, γ γ closures (γ ◦ ↵), Moore families ( γ(Q) Q ), Sierpi´nski 1 2 S hC { | 2A} K 1, = 2, 4 2, 6 , 1 2, topologies [5]( γ(Q) Q where is unique complemen- ✓i −−↵1 hA vi ) hC i −−↵2 hA i hC ! C {¬ | 2A} ¬ −−!λ g . γ2 g ↵1 −−! 1 principal downset families ◦ ◦ J tation in the concrete domain , if any), 4˙ 1 2, 6˙ for increasing maps and C λ −f −. −↵ − −f − −γ −− ( vγ(Q) Q where vx y y x ), maximal i −− − − − −2 −◦ −◦ − !1 Z hA !1 A i K , ˙ ˙ convex{# congruences| 2A}( P # ↵(P{)=2C|↵(γ(Qv)) } Q , pointwise orderings and 6. {{ 2C| }| 2A} v soundness relations (also called abstraction relation, logical rela- 5. Galois connection calculus The GC calculus G (to specify tion, or tensor product, ↵ 4 = P, Q ↵(P ) 4 Q = P, verifiers/analyzers compositionally) is x,... X for program 1 {h i| } {h 2 Q P γ(Q) = γ− where f x, f(x) x variables, `,... L for labels, e E for elements e ::= true i| v } v ⌘{h i| 2 2 2 | dom(f) , r r0 = x, z y : x, y r y, z r0 ), 1 x ` e ..., s S for sets s ::= B Z X L } {h #i|9 h i2 ^h i2 } |1| | |− | 2 | | | | and, for powersets = }(C), = }(A), polarities of rela- e [e, e] I(s, o) s1 s s s s s s }(s) ..., C # A tions (γ(Q)= x C y Q : R(x, y) where R = x, o{ }|O for| partial orders| o| ::=[ | 7! | ⇥ | | = # { 2 |8 2 } {h 21 )|,||✓| | | y x γ( y ) ). o− o1 o2 o˙ o¨ ..., p P for posets p ::= s, o , and i| 2 { } } g G| for⇥GCs|g ::=| 1|[p] [2p, e] I[p, e, e] [s,h s] i [s] ⇤ See the auxiliary materials. ⇤⇤ Work supported in part by the CMACS NSF award 0926166. > y F [ Permission to make digital or hard copies of part or all of this work for personal or classroom use 2[s] [s] [s, s] [|s, s] [|s, s] ... | R[g] s| g | is granted without fee provided that copies are not made or distributed for profit or commercial ¬ | 1 | | 7! | ⇥ | | | | advantage and that copies bear this notice and the full citation on the first page. Copyrights for third- g g g * g g = g .... The semantics of interval sets is party components of this work must be honored. For all other uses, contact the owner/author(s). | | ) | POPL ’14, January 22–24, 2014, San Diego, CA, USA. Copyright is held by the owner/author(s). I(C, ) ( C C ? [a, b] a, b C : !) where ! S 4 , 4 ✓ ⇥ { 4 | 2 } _ ACM 978-1-4503-2544-8/14/01. http://dx.doi.org/10.1145/2535838.2537850 is# a dynamic error (maybeZ not detectable by typing). J K 6. Abstraction Papers in semantics, verification, and static Ignoring error propagation, S s1 seq S s , S s1 s2 , 7! , analysis can be understood by extracting the semantic domain S s1 S s2 , S s1 s2 S s1 S s2 , S }(s) P S s . ⇤! ⇥ , ⇤ , and GC which are used. For the interval example [3, 4, p. 247], For orders and posets, O o o, o , , , , = , J K, 2J {K)J, ✓ K } the semantic domain , }(⌃1) is that of (nonempty) sets of ˙ S OJ K , ,...,J K O o¨J , ((OKo )),J andK P Js,K o J, S sK ~ O oJ .K nonempty finite or infinite sequences of states in ⌃ , L ✓ h i ⇥M For GCs, T 1[p] , P p J KP p , T y[sL,s ] , P(S sL made of a control state in L and a memory state in X ! M , S S S ˙ T S ⇤ M 7! V JFKs ) ~ ! JsKL PJ Ks ~ J, [sK] , PJ (KP sJ )K~ mapping variables X to a complete total order , (e.g. Z, M ✓ ⇤! M ✓ [ 1 hV i h P S s , T [s] P S s P S s − , 6 or [minint, maxint], 6 ).
Load more

Recommended publications
  • GALOIS THEORY for ARBITRARY FIELD EXTENSIONS Contents 1
    GALOIS THEORY FOR ARBITRARY FIELD EXTENSIONS PETE L. CLARK Contents 1. Introduction 1 1.1. Kaplansky's Galois Connection and Correspondence 1 1.2. Three flavors of Galois extensions 2 1.3. Galois theory for algebraic extensions 3 1.4. Transcendental Extensions 3 2. Galois Connections 4 2.1. The basic formalism 4 2.2. Lattice Properties 5 2.3. Examples 6 2.4. Galois Connections Decorticated (Relations) 8 2.5. Indexed Galois Connections 9 3. Galois Theory of Group Actions 11 3.1. Basic Setup 11 3.2. Normality and Stability 11 3.3. The J -topology and the K-topology 12 4. Return to the Galois Correspondence for Field Extensions 15 4.1. The Artinian Perspective 15 4.2. The Index Calculus 17 4.3. Normality and Stability:::and Normality 18 4.4. Finite Galois Extensions 18 4.5. Algebraic Galois Extensions 19 4.6. The J -topology 22 4.7. The K-topology 22 4.8. When K is algebraically closed 22 5. Three Flavors Revisited 24 5.1. Galois Extensions 24 5.2. Dedekind Extensions 26 5.3. Perfectly Galois Extensions 27 6. Notes 28 References 29 Abstract. 1. Introduction 1.1. Kaplansky's Galois Connection and Correspondence. For an arbitrary field extension K=F , define L = L(K=F ) to be the lattice of 1 2 PETE L. CLARK subextensions L of K=F and H = H(K=F ) to be the lattice of all subgroups H of G = Aut(K=F ). Then we have Φ: L!H;L 7! Aut(K=L) and Ψ: H!F;H 7! KH : For L 2 L, we write c(L) := Ψ(Φ(L)) = KAut(K=L): One immediately verifies: L ⊂ L0 =) c(L) ⊂ c(L0);L ⊂ c(L); c(c(L)) = c(L); these properties assert that L 7! c(L) is a closure operator on the lattice L in the sense of order theory.
    [Show full text]
  • Formal Contexts, Formal Concept Analysis, and Galois Connections
    Formal Contexts, Formal Concept Analysis, and Galois Connections Jeffrey T. Denniston Austin Melton Department of Mathematical Sciences Departments of Computer Science and Mathematical Sciences Kent State University Kent State University Kent, Ohio, USA 44242 Kent, Ohio, USA 44242 [email protected] [email protected] Stephen E. Rodabaugh College of Science, Technology, Engineering, Mathematics (STEM) Youngstown State University Youngstown, OH, USA 44555-3347 [email protected] Formal concept analysis (FCA) is built on a special type of Galois connections called polarities. We present new results in formal concept analysis and in Galois connections by presenting new Galois connection results and then applying these to formal concept analysis. We also approach FCA from the perspective of collections of formal contexts. Usually, when doing FCA, a formal context is fixed. We are interested in comparing formal contexts and asking what criteria should be used when determining when one formal context is better than another formal context. Interestingly, we address this issue by studying sets of polarities. 1 Formal Concept Analysis and Order-Reversing Galois Connections We study formal concept analysis (FCA) from a “larger” perspective than is commonly done. We em- phasize formal contexts. For example, we are interested in questions such as if we are working with a given formal context K , that is, we are working with a set of objects G, a set of properties M, and a relation R ⊂ G×M, what do we do if we want to replace K with a better formal context. Of course, this raises the question: what makes one formal context better than another formal context.
    [Show full text]
  • Logics from Galois Connections
    View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elsevier - Publisher Connector International Journal of Approximate Reasoning 49 (2008) 595–606 Contents lists available at ScienceDirect International Journal of Approximate Reasoning journal homepage: www.elsevier.com/locate/ijar Logics from Galois connections Jouni Järvinen a,*, Michiro Kondo b, Jari Kortelainen c a Turku Centre for Computer Science (TUCS), University of Turku, FI-20014 Turku, Finland b School of Information Environment, Tokyo Denki University, Inzai 270-1382, Japan c Mikkeli University of Applied Sciences, P.O. Box 181, FI-50101 Mikkeli, Finland article info abstract Article history: In this paper, Information Logic of Galois Connections (ILGC) suited for approximate rea- Received 13 June 2007 soning about knowledge is introduced. In addition to the three classical propositional logic Received in revised form 29 May 2008 axioms and the inference rule of modus ponens, ILGC contains only two auxiliary rules of Accepted 16 June 2008 inference mimicking the performance of Galois connections of lattice theory, and this Available online 27 June 2008 makes ILGC comfortable to use due to the flip-flop property of the modal connectives. Kripke-style semantics based on information relations is defined for ILGC. It is also shown that ILGC is equivalent to the minimal tense logic K , and decidability and completeness of Keywords: t ILGC follow from this observation. Additionally, relationship of ILGC to the so-called clas- Rough sets Fuzzy sets sical modal logics is studied. Namely, a certain composition of Galois connection mappings Approximate reasoning forms a lattice-theoretical interior operator, and this motivates us to axiomatize a logic of Knowledge representation these compositions.
    [Show full text]
  • The Galois Correspondence Between a Ring and Its Spectrum
    THE GALOIS CORRESPONDENCE BETWEEN A RING AND ITS SPECTRUM Let A and X be two sets equipped with a relation R ⊆ A × X. The notation is intended to remind you of the case where A is a commutative ring, X is its spectrum Spec A, and R is given by (f; x) 2 R () f(x) = 0 (i.e., f 2 px): But mathematics is full of other examples, including Galois theory (which is where the phrase \Galois correspondence" in the title comes from). For any subset S ⊆ A, define S0 ⊆ X by S0 := fx 2 X j (f; x) 2 R for all f 2 Sg : Similarly, for any subset Y ⊆ X, define Y 0 ⊆ A by Y 0 := ff 2 A j (f; x) 2 R for all x 2 Y g : In our canonical example, S0 = V (S) (the \zero set" of S), and Y 0 = I(Y ) := T 0 y2Y py. In more intuitive language, Y is the ideal of functions that vanish on Y . These \prime" operations are order reversing: S ⊆ T =) T 0 ⊆ S0; and similarly for subsets of X. Any subset of X of the form S0 will be called closed. (This gives the Zariski topology in our canonical example.) Similarly, any subset of A of the form Y 0 will be called closed. (So the closed subsets of A are the radical ideals in our canonical example.) Note that we always have S ⊆ S00 and Y ⊆ Y 00 for S ⊆ A and Y ⊆ X. I claim that equality holds for closed sets.
    [Show full text]
  • Galois Connections
    1 Galois Connections Roland Backhouse 3rd December, 2002 2 Fusion Many problems are expressed in the form evaluate generate ◦ where generate generates a (possibly infinite) candidate set of solutions, and evaluate selects a best solution. Examples: shortest path ; ◦ (x ) L: 2 ◦ Solution method is to fuse the generation and evaluation processes, eliminating the need to generate all candidate solutions. 3 Conditions for Fusion Fusion is made possible when evaluate is an adjoint in a Galois connection, • generate is expressed as a fixed point. • Solution method typically involves generalising the problem. 4 Definition Suppose = (A; ) and = (B; ) are partially ordered sets and A v B suppose F A B and G B A . Then (F;G) is a Galois connection 2 2 of and iff, for all x B and y A, A B 2 2 F:x y x G:y : v ≡ F is called the lower adjoint. G is the upper adjoint. 5 Examples | Propositional Calculus :p q p :q ≡ p ^ q r q (p r) ) ≡ ( p _ q r (p r) ^ (q r) ) ≡ ) ) p q _ r p ^ :q r ) ≡ ) ) ) ) Examples | Set Theory :S T S :T ⊆ ≡ ⊇ S T U T :S U \ ⊆ ≡ ⊆ [ S T U S U ^ T U [ ⊆ ≡ ⊆ ⊆ S T U S :T U ⊆ [ ≡ \ ⊆ 6 Examples | Number Theory -x y x -y ≤ ≡ ≥ x+y z y z-x ≤ ≡ ≤ x n x n d e ≤ ≡ ≤ x y z x z ^ y z ≤ ≡ ≤ ≤ x y z x z=y for all y > 0 ×" ≤ ≡ ≤ 7 Examples | predicates even:m b (if b then 2 else 1) n m ≡ odd:m b (if b then 1 else 2) n m ( ≡ x S b S if b then fxg else φ 2 ) ≡ ⊇ x S b S if b then U else Unfxg 2 ( ≡ ⊆ S = φ b S if b then φ else U ) ≡ ⊆ S = φ b S if b then U else φ 6 ( ≡ ⊆ ) 8 Examples | programming algebra Languages ("factors") L M N L N=M · ⊆ ≡
    [Show full text]
  • An Algebraic Theory of Complexity for Discrete Optimization Davis Cohen, Martin Cooper, Paidi Creed, Peter Jeavons, Stanislas Zivny
    An Algebraic Theory of Complexity for Discrete Optimization Davis Cohen, Martin Cooper, Paidi Creed, Peter Jeavons, Stanislas Zivny To cite this version: Davis Cohen, Martin Cooper, Paidi Creed, Peter Jeavons, Stanislas Zivny. An Algebraic Theory of Complexity for Discrete Optimization. SIAM Journal on Computing, Society for Industrial and Applied Mathematics, 2013, vol. 42 (n° 5), pp. 1915-1939. 10.1137/130906398. hal-01122748 HAL Id: hal-01122748 https://hal.archives-ouvertes.fr/hal-01122748 Submitted on 4 Mar 2015 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Open Archive TOULOUSE Archive Ouverte (OATAO) OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible. This is an author-deposited version published in : http://oatao.univ-toulouse.fr/ Eprints ID : 12741 To link to this article : DOI :10.1137/130906398 URL : http://dx.doi.org/10.1137/130906398 To cite this version : Cohen, Davis and Cooper, Martin C. and Creed, Paidi and Jeavons, Peter and Zivny, Stanislas An Algebraic Theory of Complexity for Discrete Optimization. (2013) SIAM Journal on Computing, vol. 42 (n° 5).
    [Show full text]
  • Constructive Galois Connections
    ZU064-05-FPR main 23 July 2018 12:36 1 Constructive Galois Connections DAVID DARAIS University of Vermont, USA (e-mail: [email protected]) DAVID VAN HORN University of Maryland, College Park, USA (e-mail: [email protected]) Abstract Galois connections are a foundational tool for structuring abstraction in semantics and their use lies at the heart of the theory of abstract interpretation. Yet, mechanization of Galois connections using proof assistants remains limited to restricted modes of use, preventing their general application in mechanized metatheory and certified programming. This paper presents constructive Galois connections, a variant of Galois connections that is effective both on paper and in proof assistants; is complete with respect toa large subset of classical Galois connections; and enables more general reasoning principles, including the “calculational” style advocated by Cousot. To design constructive Galois connections we identify a restricted mode of use of classical ones which is both general and amenable to mechanization in dependently-typed functional programming languages. Crucial to our metatheory is the addition of monadic structure to Galois connections to control a “specification effect.” Effectful calculations may reason classically, while pure calculations have extractable computational content. Explicitly moving between the worlds of specification and implementation is enabled by our metatheory. To validate our approach, we provide two case studies in mechanizing existing proofs from the literature: the first uses calculational abstract interpretation to design a static analyzer; the second forms a semantic basis for gradual typing. Both mechanized proofs closely follow their original paper-and-pencil counterparts, employ reasoning principles arXiv:1807.08711v1 [cs.PL] 23 Jul 2018 not captured by previous mechanization approaches, support the extraction of verified algorithms, and are novel.
    [Show full text]
  • Lattice Theory
    Chapter 1 Lattice theory 1.1 Partial orders 1.1.1 Binary Relations A binary relation R on a set X is a set of pairs of elements of X. That is, R ⊆ X2. We write xRy as a synonym for (x, y) ∈ R and say that R holds at (x, y). We may also view R as a square matrix of 0’s and 1’s, with rows and columns each indexed by elements of X. Then Rxy = 1 just when xRy. The following attributes of a binary relation R in the left column satisfy the corresponding conditions on the right for all x, y, and z. We abbreviate “xRy and yRz” to “xRyRz”. empty ¬(xRy) reflexive xRx irreflexive ¬(xRx) identity xRy ↔ x = y transitive xRyRz → xRz symmetric xRy → yRx antisymmetric xRyRx → x = y clique xRy For any given X, three of these attributes are each satisfied by exactly one binary relation on X, namely empty, identity, and clique, written respectively ∅, 1X , and KX . As sets of pairs these are respectively the empty set, the set of all pairs (x, x), and the set of all pairs (x, y), for x, y ∈ X. As square X × X matrices these are respectively the matrix of all 0’s, the matrix with 1’s on the leading diagonal and 0’s off the diagonal, and the matrix of all 1’s. Each of these attributes holds of R if and only if it holds of its converse R˘, defined by xRy ↔ yRx˘ . This extends to Boolean combinations of these attributes, those formed using “and,” “or,” and “not,” such as “reflexive and either not transitive or antisymmetric”.
    [Show full text]
  • Galois Connections
    Modern Algebra 2 (MATH 6140) HANDOUT 6 (April 14, 2008) GALOIS CONNECTIONS Let S and T be classes, and let R ⊆ S × T be a relation. For each subset U ⊆ S define U ? = U R := ft 2 T j 8u 2 U((u; t) 2 R)g and for each V ⊆ T define V ? = RV := fs 2 S j 8v 2 V ((s; v) 2 R)g: These are two mappings, ?: P(S) !P(T ) and ?: P(T ) !P(S). It is immediate from the definition of ? that: Lemma 1. U × V ⊆ R () U ⊆ V ? () V ⊆ U ?. The last bi-implication in Lemma 1 motivates the following definition. Definition 2. A Galois connection (GC) between classes S and T is a pair of map- pings ?: P(S) !P(T ) and ?: P(T ) !P(S) such that for all U ⊆ S and V ⊆ T it is the case that U ⊆ V ? () V ⊆ U ? Lemma 3. A Galois connection between S and T arises from a uniquely determined relation in the manner described before Lemma 1. Proof. If a GC between S and T arises from a relation R ⊆ S × T , then, according to Lemma 1, (u; v) 2 R iff fug ⊆ fvg? iff fvg ⊆ fug?. So, given an arbitrary GC, the natural candidate for the associated relation is R = f(u; v) 2 S × T j v 2 fug?g. To see that the GC induced by this relation R is the same as the starting GC we must show that U R = U ? for all U ⊆ S. (Since everything is symmetric so far, the same argument will show RV = V ?.) Note that R is defined so that the desired statement is true for singleton sets: fugR = fug?.
    [Show full text]
  • Order Theory, Galois Connections and Abstract Interpretation
    Order theory Galois Connections GC and Abstract Interpretation Examples Order Theory, Galois Connections and Abstract Interpretation David Henriques Carnegie Mellon University November 7th 2011 fsu-logo David Henriques Order Theory 1/ 40 Order theory Galois Connections GC and Abstract Interpretation Examples Order Theory fsu-logo David Henriques Order Theory 2/ 40 It's not easy, we need a formal treatment of order! Actually, there is an intruder in this list. Can you spot it? Order theory Galois Connections GC and Abstract Interpretation Examples Orders are everywhere 23 I 0 ≤ 1 and 1 ≤ 10 I Two cousins have a common grandfather I 22=7 is a worse approximation of π than 3:141592654 I aardvark comes before zyzzyva I a seraphim ranks above an angel I rock beats scissors I neither f1; 2; 4g or f2; 3; 5g are subsets of one another, but both are subsets of f1; 2; 3; 4; 5g fsu-logo David Henriques Order Theory 3/ 40 It's not easy, we need a formal treatment of order! Order theory Galois Connections GC and Abstract Interpretation Examples Orders are everywhere 23 I 0 ≤ 1 and 1 ≤ 10 I Two cousins have a common grandfather I 22=7 is a worse approximation of π than 3:141592654 I aardvark comes before zyzzyva I a seraphim ranks above an angel I rock beats scissors I neither f1; 2; 4g or f2; 3; 5g are subsets of one another, but both are subsets of f1; 2; 3; 4; 5g Actually, there is an intruder in this list. Can you spot it? fsu-logo David Henriques Order Theory 3/ 40 Order theory Galois Connections GC and Abstract Interpretation Examples Orders are everywhere 23 I 0 ≤ 1 and 1 ≤ 10 I Two cousins have a common grandfather I 22=7 is a worse approximation of π than 3:141592654 I aardvark comes before zyzzyva I a seraphim ranks above an angel I rock beats scissors I neither f1; 2; 4g or f2; 3; 5g are subsets of one another, but both are subsets of f1; 2; 3; 4; 5g Actually, there is an intruder in this list.
    [Show full text]
  • Galois Connections for Phylogenetic Networks and Their Polytopes
    GALOIS CONNECTIONS FOR PHYLOGENETIC NETWORKS AND THEIR POLYTOPES STEFAN FORCEY AND DREW SCALZO Abstract. We describe Galois connections which arise between two kinds of combinatorial structures, both of which generalize trees with labelled leaves, and then apply those connections to a family of polytopes. The graphs we study can be imbued with metric properties or associated to vectors. Famous exam- ples are the Billera-Holmes-Vogtmann metric space of phylogenetic trees, and the Balanced Minimal Evolution polytopes of phylogenetic trees described by Eickmeyer, Huggins, Pachter and Yoshida. Recently the space of trees has been expanded to split networks by Devadoss and Petti, while the def- inition of phylogenetic polytopes has been generalized to encompass 1-nested phylogenetic networks, by Durell and Forcey. The first Galois connection we describe is a reflection between the (unweighted) circular split networks and the 1-nested phylogenetic networks. Another Galois connection exists be- tween certain metric versions of these structures. Reflection between the purely combinatorial posets becomes a coreflection in the geometric case. Our chief contributions here, beyond the discovery of the Galois connections, are: a translation between approaches using PC-trees and networks, a new way to look at weightings on networks, and a fuller characterization of faces of the phylogenetic polytopes. 1. Introduction The simplest structure we consider here is a partition of a finite set into two parts. A tree with labeled leaves represents a collection of such bipartitions, also known as splits, since removing any edge of the tree partitions the leaves via separating the tree into two components. A collection of splits that can be thus displayed by a tree is called pairwise compatible.
    [Show full text]
  • Galois Connections and Applications Mathematics and Its Applications
    Galois Connections and Applications Mathematics and Its Applications Managi ng Editor: M. HAZEWINKEL Centre for Mathematics and Computer Science, Amsterdam, The Netherlands Volume 565 Galois Connections and Applications Edited by K. Denecke University of Potsdam, Potsdam, Germany M. Erne University of Hannover, Hannover, Germany and S. L. Wismath University of Lethbridge, Lethbridge, Canada Springer Science+Business Media, B.V. A C. J.P. Catalogue record for this book is available from the Library of Congress. ISBN 978-90-481-6540-7 ISBN 978-1-4020-1898-5 (eBook) DOI 10.1007/978-1-4020-1898-5 Printed on acid-free paper All Rights Reserved © 2004 Springer Science+Business Media Dordrecht Originally published by Kluwer Academic Publishers in 2004. Softcover reprint of the hardcover 1st edition 2004 No part of this work may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, microtilming, recording or otherwise, without written permission from the Publisher, with the exception of any material supplied specitically for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work Contents Preface . Vll M. Erne Adjunctions and Galois Connections: Origins, History and Devel- opment . 1 G. Janelidze Categorical Galois Theory: Revision and Some Recent Develop- ments ................................ 139 M. Erne The Polarity between Approximation and Distribution . 173 K. Denecke, S. l. Wismath Galois Connections and Complete Sublattices .... ..... 211 R. Poschel Galois Connections for Operations and Relations . 231 K. Kaarli Galois Connections and Polynomial Completeness . 259 K. Gtazek, St. Niwczyk Q-Independence and Weak Automorphisms ..........
    [Show full text]