Finite Model Theory and CSPs

Similar documents
Games and Isomorphism in Finite Model Theory. Part 1

On the Power of k-consistency

Homomorphism Preservation Theorem. Albert Atserias Universitat Politècnica de Catalunya Barcelona, Spain

Datalog and Constraint Satisfaction with Infinite Templates

On the Expressive Power of Logics on Finite Models

Infinite and Finite Model Theory Part II

Friendly Logics, Fall 2015, Lecture Notes 5

Inductive Definability & Finite-Variable Logics: From Logic to Computer Science

Fixpoint Logic vs. Infinitary Logic in Finite-Model Theory

Affine Systems of Equations and Counting Infinitary Logic

Database Theory VU , SS Ehrenfeucht-Fraïssé Games. Reinhard Pichler

On Datalog vs. LFP. Anuj Dawar and Stephan Kreutzer

1 First-order logic. 1 Syntax of first-order logic. 2 Semantics of first-order logic. 3 First-order logic queries. 2 First-order query evaluation

Dualities for Constraint Satisfaction Problems

Constraint satisfaction problems and dualities

Finite Model Theory and Graph Isomorphism. II.

RANK HIERARCHIES FOR GENERALIZED QUANTIFIERS

Finite Model Theory: First-Order Logic on the Class of Finite Models

Elementary Equivalence in Finite Structures

A Local Normal Form Theorem for Infinitary Logic with Unary Quantifiers

Finite Model Theory Tutorial. Lecture 1

Elementary Equivalence, Partial Isomorphisms, and. Scott-Karp analysis

Constraint Satisfaction. Complexity. and. Logic. Phokion G. Kolaitis. IBM Almaden Research Center & UC Santa Cruz

On Digraph Coloring Problems and Treewidth Duality

Overview of Topics. Finite Model Theory. Finite Model Theory. Connections to Database Theory. Qing Wang

Wied Pakusa. Finite Model Theory with Operators from Linear Algebra

A local normal form theorem for infinitary logic with unary quantifiers

FINITE MODELS AND FINITELY MANY VARIABLES

Connectivity. Topics in Logic and Complexity Handout 7. Proof. Proof. Consider the signature (E, <).

Bounded width problems and algebras

Random Graphs. and. The Parity Quantifier

Reflections on Finite Model Theory

A Model Theoretic Proof of Completeness of an Axiomatization of Monadic Second-Order Logic on Infinite Words

Bounded-width QBF is PSPACE-complete

Preliminaries. Introduction to EF-games. Inexpressivity results for first-order logic. Normal forms for first-order logic

Algorithmic Model Theory SS 2016

Descriptive Complexity: An overview of the field, key results, techniques, and applications.

January 25, by Albert Atserias Universitat Politècnica de Catalunya Barcelona, Spain

THERE ARE NO PURE RELATIONAL WIDTH 2 CONSTRAINT SATISFACTION PROBLEMS. Keywords: Computational Complexity, Constraint Satisfaction Problems.

Incomplete version for students of easllc2012 only. 6.6 The Model Existence Game 99

Löwenheim-Skolem Theorems, Countable Approximations, and L ω. David W. Kueker (Lecture Notes, Fall 2007)

Homomorphisms and First-Order Logic

Finite variable logics

Connectivity. Corollary. GRAPH CONNECTIVITY is not FO definable

First order logic on Galton-Watson trees

arxiv: v1 [cs.lo] 17 Apr 2017

Propositional and Predicate Logic - VII

The Computational Structure of Monotone Monadic SNP and Constraint Satisfaction: A Study through Datalog and Group Theory

Composing Schema Mappings: Second-Order Dependencies to the Rescue

1 Unifying Themes in Finite Model Theory

0-1 Laws for Fragments of SOL

Finite and Algorithmic Model Theory II: Automata-Based Methods

Ehrenfeucht-Fraïssé Games

Incomplete version for students of easllc2012 only. 94 First-Order Logic. Incomplete version for students of easllc2012 only. 6.5 The Semantic Game 93

Duplicator Spoiler Games Exposition by William Gasarch

Math 225A Model Theory. Speirs, Martin

Languages, logics and automata

Model Theory on Finite Structures

Online Appendix to The Recovery of a Schema Mapping: Bringing Exchanged Data Back

Fixed-point logics on planar graphs

Resolution and Pebbling Games

The complexity of the list homomorphism problem for graphs

A MODEL-THEORETIC PROOF OF HILBERT S NULLSTELLENSATZ

Expressiveness of predicate logic: Some motivation

The Power of Counting Logics on Restricted Classes of Finite Structures

Dynamic Programming on Trees. Example: Independent Set on T = (V, E) rooted at r V.

Algorithmic Model Theory SS 2016

The Complexity of Reverse Engineering Problems for Conjunctive Queries

Durham Research Online

THE EXPRESSIVE POWER OF THE TEMPORAL QUERY LANGUAGE L H

Modal Dependence Logic

Locality of queries definable in invariant first-order logic with arbitrary built-in predicates

CMPS 277 Principles of Database Systems. Lecture #9

First-Order Logic First-Order Theories. Roopsha Samanta. Partly based on slides by Aaron Bradley and Isil Dillig

Let's begin by analyzing the complexity of the decision problem for Fval. It is easy to see that we can make an eective list A 1 ; A 2 ; : : : of nite

Undecidability and intractability results concerning Datalog programs and their persistency numbers

Pattern Logics and Auxiliary Relations

CD(4) HAS BOUNDED WIDTH

INSTITUTE OF MATHEMATICS THE CZECH ACADEMY OF SCIENCES. A trade-off between length and width in resolution. Neil Thapen

Algorithmic Meta-Theorems

FINITE MODEL THEORY (MATH 285D, UCLA, WINTER 2017) LECTURE NOTES IN PROGRESS

Graphs, Polymorphisms, and Multi-Sorted Structures

Applied Automata Theory

First-Order Logic. 1 Syntax. Domain of Discourse. FO Vocabulary. Terms

Forcing and Group Theory

The Query Containment Problem: Set Semantics vs. Bag Semantics. Phokion G. Kolaitis University of California Santa Cruz & IBM Research - Almaden

CAPTURING POLYNOMIAL TIME USING MODULAR DECOMPOSITION

4 Back and Forth Between Logic and Games

MAL TSEV CONSTRAINTS MADE SIMPLE

Mathematical Preliminaries. Sipser pages 1-28

Outline. Formale Methoden der Informatik First-Order Logic for Forgetters. Why PL1? Why PL1? Cont d. Motivation

Tutorial on the Constraint Satisfaction Problem

Predicate Calculus. Formal Methods in Verification of Computer Systems Jeremy Johnson

Splitting the Computational Universe

Finer complexity of Constraint Satisfaction Problems with Maltsev Templates

Caterpillar duality for CSP. C. Carvalho (UoD), V. Dalmau (UPF) and A. Krokhin (UoD)

A short tutorial on order-invariant first-order logic

2.2 Lowenheim-Skolem-Tarski theorems

Automata theory. An algorithmic approach. Lecture Notes. Javier Esparza

Tecniche di Verifica. Introduction to Propositional Logic

Transcription:

Finite Model Theory and CSPs Albert Atserias Universitat Politècnica de Catalunya Barcelona, Spain June 19, 2007

Part I FIRST-ORDER LOGIC, TYPES AND GAMES

Relational Structures vs. Functional Structures Structures: M = (M,R M 1,RM 2,...,f M 1,f M 2,...) I go relational: Algebraists go functional: relational structures ( no functions) algebras ( no relations).

First-Order Logic: Syntax Let x 1,x 2,... be a collection of first-order variables (intended to range over the points of the universe of a structure). Definition The collection of first-order formulas of σ (FO) is defined as: x i1 = x i2 and R i (x i1,...,x ir ) are formulas, x i1 x i2 and R i (x i1,...,x ir ) are formulas, if ϕ and ψ are formulas, so is (ϕ ψ) if ϕ and ψ are formulas, so is (ϕ ψ) if ϕ is a formula, so is ( x i )(ϕ) if ϕ is a formula, so is ( x i )(ϕ).

First-Order Logic: Semantics Let ϕ(x) be a formula with free variables x = (x 1,...,x r ), let A be a structure, and let a = (a 1,...,a r ) A r. A = ϕ(x 1 /a 1,...,x r /a n )

First-Order Logic: Semantics Let ϕ(x) be a formula with free variables x = (x 1,...,x r ), let A be a structure, and let a = (a 1,...,a r ) A r. A = ϕ(x 1 /a 1,...,x r /a n ) Example ϕ(x) := ( y)( z)(e(x,z) E(y,z)). a G = ϕ(x/a)

Fragments of First-Order Logic Fragments: full (FO), existential ( FO), existential positive ( FO + ), quantifier-free, atomic and negated atomic, atomic. x i1 = x i2 and R i (x i1,...,x ir ) are formulas, x i1 x i2 and R i (x i1,...,x ir ) are formulas, if ϕ and ψ are formulas, so is (ϕ ψ) if ϕ and ψ are formulas, so is (ϕ ψ), if ϕ is a formula, so is ( x i )(ϕ) if ϕ is a formula, so is ( x i )(ϕ).

Fragments of First-Order Logic Fragments: full (FO), existential ( FO), existential positive ( FO + ), quantifier-free, atomic and negated atomic, atomic. x i1 = x i2 and R i (x i1,...,x ir ) are formulas, x i1 x i2 and R i (x i1,...,x ir ) are formulas, if ϕ and ψ are formulas, so is (ϕ ψ) if ϕ and ψ are formulas, so is (ϕ ψ), if ϕ is a formula, so is ( x i )(ϕ) if ϕ is a formula, so is ( x i )(ϕ).

Fragments of First-Order Logic Fragments: full (FO), existential ( FO), existential positive ( FO + ), quantifier-free, atomic and negated atomic, atomic. x i1 = x i2 and R i (x i1,...,x ir ) are formulas, x i1 x i2 and R i (x i1,...,x ir ) are formulas, if ϕ and ψ are formulas, so is (ϕ ψ) if ϕ and ψ are formulas, so is (ϕ ψ), if ϕ is a formula, so is ( x i )(ϕ) if ϕ is a formula, so is ( x i )(ϕ).

Fragments of First-Order Logic Fragments: full (FO), existential ( FO), existential positive ( FO + ), quantifier-free, atomic and negated atomic, atomic. x i1 = x i2 and R i (x i1,...,x ir ) are formulas, x i1 x i2 and R i (x i1,...,x ir ) are formulas, if ϕ and ψ are formulas, so is (ϕ ψ) if ϕ and ψ are formulas, so is (ϕ ψ), if ϕ is a formula, so is ( x i )(ϕ) if ϕ is a formula, so is ( x i )(ϕ).

Fragments of First-Order Logic Fragments: full (FO), existential ( FO), existential positive ( FO + ), quantifier-free, atomic and negated atomic, atomic. x i1 = x i2 and R i (x i1,...,x ir ) are formulas, x i1 x i2 and R i (x i1,...,x ir ) are formulas, if ϕ and ψ are formulas, so is (ϕ ψ) if ϕ and ψ are formulas, so is (ϕ ψ), if ϕ is a formula, so is ( x i )(ϕ) if ϕ is a formula, so is ( x i )(ϕ).

Fragments of First-Order Logic Fragments: full (FO), existential ( FO), existential positive ( FO + ), quantifier-free, atomic and negated atomic, atomic. x i1 = x i2 and R i (x i1,...,x ir ) are formulas, x i1 x i2 and R i (x i1,...,x ir ) are formulas, if ϕ and ψ are formulas, so is (ϕ ψ) if ϕ and ψ are formulas, so is (ϕ ψ), if ϕ is a formula, so is ( x i )(ϕ) if ϕ is a formula, so is ( x i )(ϕ).

Types Definition Let A be a structure, let a = (a 1,...,a r ) an r-tuple in A r, and let L be a collection of first-order formulas: 1. tp L (A,a) = {ϕ(x 1,...,x r ) L : A = ϕ(x 1 /a 1,...,x r /a r )} 2. tp L (A) = {ϕ L : A = ϕ} Intuitively: if tp L (A,a) tp L (B,b), then every L-expressible property satisfied by a in A is also satisfied by b in B. We write A,a L B,b

Examples of Types a b G,a FO H,b because In G, every point has a common neighbor with a In H, not every point has a common neighbor with b ϕ(x) := ( y)( z)(e(x,z) E(y,z))

Meaning of Types What does A,a L B,b mean? when L = {all atomic formulas}, it means the mapping (a i b i : i = 1,...,r) is a homomorphism between the substructures induced by a and b when L = {all atomic and negated atomic formulas}, it means the mapping (a i b i : i = 1,...,r) is an isomorphism between the substructures induced by a and b

Meaning of Types What does A,a L B,b mean? when L = {all formulas with at most one quantifier}, it means the substructures induced by a and b are isomorphic and have the same types of extensions by one point when L = {all formulas with at most two quantifiers}, it means the substructures induced by... note that A,a FO B,b iff B,b FO A,a. We write A,a L B,b

Ehrenfeucht-Fraïssé Games Two players: Spoiler and Duplicator Two structures: A and B Unlimited pebbles: p 1,p 2,... and q 1,q 2,... An initial position: a A r and b B r Rounds: a b Referee: Spoiler wins if at any round the mapping p i q i is not a partial isomorphism. Otherwise, Duplicator wins.

Ehrenfeucht-Fraïssé Games Two players: Spoiler and Duplicator Two structures: A and B Unlimited pebbles: p 1,p 2,... and q 1,q 2,... An initial position: a A r and b B r Rounds: a b Referee: Spoiler wins if at any round the mapping p i q i is not a partial isomorphism. Otherwise, Duplicator wins.

Ehrenfeucht-Fraïssé Games Two players: Spoiler and Duplicator Two structures: A and B Unlimited pebbles: p 1,p 2,... and q 1,q 2,... An initial position: a A r and b B r Rounds: a b Referee: Spoiler wins if at any round the mapping p i q i is not a partial isomorphism. Otherwise, Duplicator wins.

Ehrenfeucht-Fraïssé Games Two players: Spoiler and Duplicator Two structures: A and B Unlimited pebbles: p 1,p 2,... and q 1,q 2,... An initial position: a A r and b B r Rounds: a b Referee: Spoiler wins if at any round the mapping p i q i is not a partial isomorphism. Otherwise, Duplicator wins.

Ehrenfeucht-Fraïssé Games Two players: Spoiler and Duplicator Two structures: A and B Unlimited pebbles: p 1,p 2,... and q 1,q 2,... An initial position: a A r and b B r Rounds: a b Referee: Spoiler wins if at any round the mapping p i q i is not a partial isomorphism. Otherwise, Duplicator wins.

Back-and-Forth Systems Definition (Fraïssé) An n-round winning strategy for the Duplicator on A,a and B,b is a sequence of non-empty sets of partial isomorphisms (F i : i < n) such that (a b) F 0 and 1. Retract For every i < n, every f F i and every g f, we have g F i, 2. Forth For every i < n 1, every f F i, and every a A, there exists g F i+1 with a Dom(g) and f g. 3. Back For every i < n 1, every f F i, and every b B, there exists g F i+1 with b Rng(g) and f g. A,a EF B,b: there is an n-round winning strategy for every n

Indistinguishability vs Games Theorem (Ehrenfeucht, Fraïssé) A,a FO B,b if and only if A,a EF B,b

Ehrenfeucht-Fraïssé Games for Fragments Two players: Spoiler and Duplicator Two structures: A and B Unlimited pebbles: p 1,p 2,... and q 1,q 2,... Rounds: a b Referee: Spoiler wins if at any round the mapping p i q i is not a partial isomorphism (resp. homomorphism). EF, EF, EF+ : winning strategy for every n.

Indistinguishability vs Games Theorem (Ehrenfeucht and Fraïssé) A,a FO B,b if and only if A,a EF B,b A,a FO B,b if and only if A,a EF B,b A,a FO+ B,b if and only if A,a EF+ B,b

Indistinguishability is too Strong for Finite Structures For finite structures, these concepts add nothing: A FO B A = B A FO+ B A B where A = B : there is an isomorphism between A and B A B : there is a homomorphism from A to B

Indistinguishability is too Strong for Finite Structures Why? Canonical formulas: For every finite A, there exists an FO-sentence ϕ A such that B = ϕ A A = B For every finite A, there exists an FO + -sentence ψ A such that B = ψ A A B. Second has a name: the canonical conjunctive query of A [Chandra and Merlin]

Examples of Canonical Formulas Let A be: ϕ A = ( x)( y)( z) (x y y z x z ( u)(u = x u = y u = z) E(x,y) E(y,z) E(z,x) E(y,x) E(z,y) E(x,z) E(x,x) E(y,y) E(z,z)). ψ A = ( x)( y)( z) (E(x,y) E(y,z) E(z,x))

First-Order Logic: k-variable Fragments Let us limit the set of first-order variables to x 1,...,x k. FO k : k-variable fragment of FO FO k : k-variable fragment of FO FO +,k : k-variable fragment of FO + Note: Variables may be reused! Example: path 5 (x,y) := ( z)(e(x,z) ( x)(e(z,x) ( z)(e(x,z) ( x)(e(z,x) E(x,y))))).

k-pebble EF-Games Two players: Spoiler and Duplicator Two structures: A and B Limited pebbles: p 1,...,p k and q 1,...,q k An initial position: a A r and b B r Rounds: a b Referee: Spoiler wins if at any round the mapping p i q i is not a partial isomorphism (resp. homomorphism). Otherwise, Duplicator wins. EFk, EFk, EF+,k : a strategy for every n.

Indistinguishability vs k-pebbles Games Theorem (Barwise, Immerman, Kolaitis and Vardi) A,a FOk B,b if and only if A,a EFk B,b A,a EFk B,b if and only if A,a FOk B,b A,a FO+,k B,b if and only if A,a EF+,k B,b

Fundamental Questions Obviously, A = B = A FOk B A B = A FO+,k B k-width Problem: For what A s do we have A FOk B = A = B A FO+,k B = A B Width-k Problem: For what B s do we have A FOk B = A = B A FO+,k B = A B

Why Are These Questions Relevant for Us? Theorem (Kolaitis and Vardi) For finite A and B, the following are equivalent: A FOk,+ B the (strong) k-consistency algorithm run on the CSP instance given by the scopes in A and the constraint relations in B does not detect a contradiction. Note: The k-consistency algorithm runs in polynomial time for every fixed k.

Why Are These Questions Relevant for Us? The k-width/width-k problems (for homomorphisms) aim for a classification of the scopes/templates that are solvable by a widely used algorithm.

Part II ON THE k-width PROBLEM

Some Sufficient Conditions for FO k Theorem (Lindell) If G is a tree of degree d, then for all H we have G FOd+2 H = G = H Theorem (Grohe) If G is a 3-connected planar graph, then for all H we have G FO30 H = G = H

Proof of Lindell s Theorem Proof by Example:

Proof of Lindell s Theorem Proof by Example:

Proof of Lindell s Theorem Proof by Example:

Proof of Lindell s Theorem Proof by Example:

Proof of Lindell s Theorem Proof by Example:

Proof of Lindell s Theorem Proof by Example:

Treewidth Definition K k+1 is a k-tree, if G is a k-tree, then adding a vertex connected to all vertices of a K k -subgraph of G is a k-tree. Definition (Robertson) A graph

Treewidth Definition K k+1 is a k-tree, if G is a k-tree, then adding a vertex connected to all vertices of a K k -subgraph of G is a k-tree. Definition (Robertson) A graph

Treewidth Definition K k+1 is a k-tree, if G is a k-tree, then adding a vertex connected to all vertices of a K k -subgraph of G is a k-tree. Definition (Robertson) A graph

Treewidth Definition K k+1 is a k-tree, if G is a k-tree, then adding a vertex connected to all vertices of a K k -subgraph of G is a k-tree. Definition (Robertson) A graph

Treewidth Definition K k+1 is a k-tree, if G is a k-tree, then adding a vertex connected to all vertices of a K k -subgraph of G is a k-tree. Definition (Robertson) A graph

Treewidth Definition K k+1 is a k-tree, if G is a k-tree, then adding a vertex connected to all vertices of a K k -subgraph of G is a k-tree. Definition (Robertson) A graph

Treewidth Definition K k+1 is a k-tree, if G is a k-tree, then adding a vertex connected to all vertices of a K k -subgraph of G is a k-tree. Definition (Robertson) A graph

Treewidth Definition K k+1 is a k-tree, if G is a k-tree, then adding a vertex connected to all vertices of a K k -subgraph of G is a k-tree. Definition (Robertson) A graph

Treewidth Definition K k+1 is a k-tree, if G is a k-tree, then adding a vertex connected to all vertices of a K k -subgraph of G is a k-tree. Definition (Robertson) A graph

Treewidth Definition K k+1 is a k-tree, if G is a k-tree, then adding a vertex connected to all vertices of a K k -subgraph of G is a k-tree. Definition (Robertson and Seymour) A graph has treewidth at most k if it is the subgraph of a k-tree.

Treewidth Definition K k+1 is a k-tree, if G is a k-tree, then adding a vertex connected to all vertices of a K k -subgraph of G is a k-tree. Definition (Robertson and Seymour) A graph has treewidth at most k if it is the subgraph of a k-tree.

Sufficient Condition for FO +,k Theorem (Dalmau, Kolaitis, and Vardi) If the treewidth of the Gaifman graph of the core of A is less than k, then for all B be have A FO+,k B = A B

Proof of DKV s Theorem Proof by Example:

Proof of DKV s Theorem Proof by Example:

Proof of DKV s Theorem Proof by Example:

Proof of DKV s Theorem Proof by Example:

Proof of DKV s Theorem Proof by Example:

Proof of DKV s Theorem Proof by Example:

Proof of DKV s Theorem Proof by Example:

Proof of DKV s Theorem Proof by Example:

Proof of DKV s Theorem Proof by Example:

Proof of DKV s Theorem Proof by Example:

Proof of DKV s Theorem Proof by Example:

Necessary Conditions? Solving the k-width problem for FOk looks like an extraordinarily difficult question (undecidable?) But, perhaps surprisingly, for FO+,k it is doable. Theorem (A..., Bulatov, and Dalmau) The DKV condition is also necessary. Corollary The following are equivalent: 1. the treewidth of the Gaifman graph of the core of A is less than k 2. A FO+,k B = A B for every B.

Part III INDUCTIVE DEFINITIONS AND DATALOG

Inductive Definitions: Example There is a path from x to y: P (0) (x,y) := false P (n+1) (x,y) := x = y ( z)(e(x,z) P (n) (z,y)). P(x,y) n P (n) (x,y)

Inductive Definitions: General Form Let ϕ(x,x) be a formula with r variables and an r-ary second order variable X that appears positively. We form the iterates: ϕ (0) (x) := false ϕ (n+1) (x) := ϕ(x,x/ϕ (n) ) The union U = n ϕ(n) is a fixed point, in fact the least one: Theorem (Knaster-Tarski) On every finite structure, U(x) ϕ(x,x/u) if X(x) ϕ(x,x), then U(x) X(x).

Fixed-Point Logics Least Fixed Point Logic (LFP): closure of FO under inductive definitions. Existential LFP ( LFP): closure of FO under inductive definitions. Existential-Positive LFP ( LFP + ): closure of FO + under inductive definitions. And the k-variable fragments: LFP k, LFP k, and LFP +,k

Fixed-Point Logics vs. Datalog Datalog is just convenient syntax for LFP + : P(x,y) : P(x,y) : x = y ( z)(e(x,z) P(z,y)) We can do the same for LFP. Example: II I I II II Game reachability. II I P(x,y) : P(x,y) : P(x,y) : I(x) E(x,y) I(x) ( z)(e(x,z) P(z,y)) II(x) ( z)(e(x,z) P(z,y))

Infinitary Logics Infinitary Logic (L ω ): closure of FO under infinitary conjunctions and disjunctions Existential L ω ( L ω ): closure of FO under infinitary conjunctions and disjunctions Existential-Positive L ω ( L + ω ): closure of FO + under infinitary conjunctions and disjunctions And the k-variable fragments: L k ω, L k ω, and L +,k ω

Fixed-Point Logics and Infinitary Logics Carefully reusing variables we have [Barwise, Kolaitis and Vardi]: FO k LFP k L k ω Also for fragments [Kolaitis and Vardi]: FO k LFP k L k ω FO +,k LFP +,k L +,k ω

Infinitary Logic and First-Order Logic in the Finite Theorem (Kolaitis and Vardi) For finite A and B, the following are equivalent: 1. A FOk B (resp. A FOk B and A FO+,k B) 2. A Lk ω B (resp. A L k ω B and A L +,k ω B) Proof sketch (only for FO +,k vs L +,k ω): The appropriate game for L +,k ω goes on for infinitely (ω) rounds. But after A k B k + 1 rounds, some configuration must repeat, so if Spoiler has not won yet, Duplicator can survive forever. Q.E.D.

Part IV ON THE WIDTH-k PROBLEM

Structures Having Width- FO +,k Theorem (Kolaitis and Vardi, Feder and Vardi) The following are equivalent: 1. A FO+,k B implies A B for every A 2. CSP(B) is LFP +,k -definable 3. CSP(B) is L +,k ω-definable Where CSP(B) is definable in L means that there exists a sentence ϕ in L such that A = ϕ iff A B.

Structures Having Width- FO +,k Theorem (Kolaitis and Vardi, Feder and Vardi) The following are equivalent: 1. A FO+,k B implies A B for every A 2. CSP(B) is LFP +,k -definable 3. CSP(B) is L +,k ω-definable 4. CSP(B) is LFP k -definable 5. CSP(B) is L k ω -definable Where CSP(B) is definable in L means that there exists a sentence ϕ in L such that A = ϕ iff A B.

Proof of (1) (5) Suppose there exist A FO+,k B such that A B. So CSP(B) is not L k ω-definable. Q.E.D.

Proof of (1) (5) Suppose there exist A FO+,k B such that A B. Claim: A FO+,k B A FOk A B So CSP(B) is not L k ω-definable. Q.E.D.

Proof of (1) (5) Suppose there exist A FO+,k B such that A B. A FO+,k B Claim: A FOk A B Strategy for Duplicator: copy move on A on first component, use the h from the strategy for A FO+,k B for the second. So CSP(B) is not L k ω-definable. Q.E.D.

Proof of (1) (5) Suppose there exist A FO+,k B such that A B. A FO+,k B Claim: A FOk A B Strategy for Duplicator: copy move on A on first component, use the h from the strategy for A FO+,k B for the second. Why does it work? a R A implies (a,h(a)) R A B because h : A B, a R A implies (a,h(a)) R A B by the definition of A B. So CSP(B) is not L k ω-definable. Q.E.D.

Proof of (1) (5) Suppose there exist A FO+,k B such that A B. A FO+,k B Claim: A FOk A B Strategy for Duplicator: copy move on A on first component, use the h from the strategy for A FO+,k B for the second. Why does it work? a R A implies (a,h(a)) R A B because h : A B, a R A implies (a,h(a)) R A B by the definition of A B. But then any L k ω formula that holds on A also holds on A B. So CSP(B) is not L k ω-definable. Q.E.D.

How far can we take this? Questions: Can we add LFP to the list? Can we add L k ω to the list? What are the LFP-definable CSP(B) s? (resp. L k ω ) A partial answer: Theorem (A..., Bulatov, and Dawar) If CSP(B) is definable in L k ω, then the variety of the algebra of B omits types 1 and 2. This strengthens a result of Larose and Zádori (who had bounded width instead).

Roadmap of the Proof: I Part 1: Systems of equations in any non-trivial Abelian group is not L k ω-definable. To do that, we construct two systems of equations A 1 and A 2 such that 1. A 1 is satisfiable 2. A 2 is unsatisfiable 3. A 1 FOk A 2 Ideas borrowed from: A result of Cai, Fürer and Immerman in finite model theory. A construction of Tseitin in propositional proof complexity. Treewidth and the robber cop games of Thomas and Seymour in structural graph theory.

Roadmap of the Proof: II Part 2: Definability of CSP(B) in fragments that are closed under Datalog-reductions (such as LFP and beyond) implies that the CSPs with an algebra having a reduct in the variety of the algebra of B are definable. This required formalizing the appropriate reductions as Datalog-reductions: homomorphic images, powers, subalgebras. See also [Larose and Zádori, Larose and Tesson].

Roadmap of the Proof: III Part 3: Algebraic: if the variety does not omit type 1 or 2, then it has the reduct of a module.

Part V CLOSING REMARKS

Further Directions Are there digraphs with width-k, for some k > 3 but not width-3? Prove that the width-k problem for FOk is undecidable. Does LFP HOM = LFP +?