Denotational semantics: proofs

Size: px
Start display at page:

Download "Denotational semantics: proofs"

Transcription

1 APPENDIX A Denotational semantics: proofs We show that every closed term M has a computable functional [[M ] as its denotation. A.1. Unification We show that for any two constructor terms one can decide whether there exists a unifier, and if so, compute a most general one. A solution of this problem has been given by Robinson (1965). In the formulation of the algorithm below we follow Martelli and Montanari (1982). By a constructor term P, Q (term for short) we mean a term built from variables x, y, z and constructors C by application. A substitution is a finite set ϑ = {P 1 /x 1,..., P n /x n } of pairs of variables and terms, such that x i x j for i j, and P i x i for all i. An element P i /x i of ϑ is called a binding (of x i to P i ). By P ϑ we denote the result of simultaneously replacing each variable x i in P by P i, and call P ϑ the instance of P induced by ϑ. We shall use ϑ, η, ζ for substitutions. Let ε be the empty substitution. For given substitutions ϑ = {P 1 /x 1,..., P n /x n } η = {Q 1 /y 1,..., Q m /y m }, the composition ϑη of ϑ and η is the substitution obtained by deleting in the set {P 1 η/x 1,..., P n η/x n, Q 1 /y 1,..., Q m /y m } all bindings P i η/x i such that P i η = x i, and also all bindings Q j /y j such that y j {x 1,..., x n }. A substitution ϑ is idempotent if ϑϑ = ϑ. A substitution ϑ is called more general than η (written η ϑ), if there is a substitution ζ such that η = ϑζ. ϑ and η are equivalent, if ϑ η ϑ. It is easy to see that (P ϑ)η = P (ϑη), and that composition is associative. We now come to the unification problem. By this we mean the question whether for two given terms P, Q there is a substitution ϑ unifying the two terms, i.e., with the property P ϑ = Qϑ. Let E denote finite equation systems, i.e., multisets {P 1 = Q 1,..., P n = Q n } 59

2 60 A. DENOTATIONAL SEMANTICS: PROOFS of equations between terms (more precisely pairs of terms). Consider { } as a (contradictory) equation system. A substitution ϑ unifies E, if for every equation P = Q in E we have P ϑ = Qϑ; no ϑ unifies { }. ϑ is a most general unifier (mgu) of E, if ϑ is a unifier of E and η ϑ for every unifier η of E. The following characterization of idempotent mgus will be useful in the proof of the Unification Theorem below. Lemma (Characterization of idempotent mgu s). Let ϑ be a unifier of E. Then ϑ is an idempotent mgu of E iff η = ϑη for all unifiers η of E. Proof. Assume that ϑ is a unifier of E.. Let ϑ be an idempotent mgu of E, and assume that η is a unifier of E. Since ϑ is a mgu of E, we have η = ϑζ for some substitution ζ. Hence η = ϑζ = ϑϑζ = ϑη.. Assume that η = ϑη for all unifiers η of E. Now let η be a unifier of E. Then η ϑ; therefore ϑ is a mgu. Since ϑ is a unifier, by assumption we have ϑ = ϑϑ. Definition (Unification algorithm). E ϑ E is defined by (a) {P = x} E ε {x = P } E, if P is not a variable. (b) {x = x} E ε E. (c) {CP 1... P n = CQ 1... Q n } E ε {P 1 = Q 1,... P n = Q n } E. (d) {CP 1... P n = C Q 1... Q n } E ε { } if C C. (e) {x = P, P 1 (x) = Q 1 (x),..., P n (x) = Q n (x)} {P/x} {P 1 (P ) = Q 1 (P ),... P n (P ) = Q n (P )} if x / FV(P ). (f) {x = P } E ε { }, if x FV(P ) and P x. Proposition. Assume E ϑ E. (a) If η is a unifier of E, then ϑη is a unifier of E. (b) If η is a unifier of E, then η = ϑη and η is a unifier of E. Proof. By cases according to the definition of E ϑ E. Clearly it suffices to treat case (e). Let η be a unifier of E. Then {P/x}η is a unifier of E. Let η be a unifier of E. Then xη = P η, hence η = {P/x}η (since both substitutions coincide on all variables), and moreover Hence η is a unifier of E. Corollary. Assume P i {P/x}η = P i η = Q i η = Q i {P/x}η. E 1 ϑ1 E 2 ϑ2... E n ϑn E n+1. (a) If ϑ is a unifier of E n+1, then ϑ 1... ϑ n ϑ is a unifier of E 1.

3 A.2. IDEALS AS DENOTATIONS OF TERMS 61 (b) If η is a unifier of E 1, then η = ϑ 1... ϑ n η and η is a unifier of E n+1. Proof. The first part clearly follows from the first part of the Proposition. The second part is proved by induction on n. For n = 0 there is nothing to show. In the step we split the assumption into E 1 ϑ1 E 2 and E 2 ϑ2... E n ϑn E n+1. By the second part of the Proposition we have that η = ϑ 1 η is a unifier of E 2. Hence by IH η = ϑ 2 ϑ n η is a unifier of E n+1. Moreover we have η = ϑ 1 η = ϑ 1 ϑ 2... ϑ n η. Unification Theorem. Let E be a finite equation system. Then every sequence E = E 1 ϑ1 E 2 ϑ2... terminates with E n+1 = or E n+1 = { }. In the first case E is unifiable, and ϑ 1... ϑ n is an idempotent mgu of E. In the second case E is not unifiable. Proof. We first show termination using the lexicographic ordering of N 3. To every E = {P 1 = Q 1,..., P n = Q n } assign a triple (n 1, n 2, n 3 ) N 3 by n 1 := number of variables in E, n 2 := number of occurrences of variables and constructors in E, n 3 := number of equations P = x in E such that P is not a variable. In every step E ϑ E the assigned triple decreases w.r.t. the lexicographic ordering of N 3. This can be verified easily by considering the different cases: For (a), n 1, n 2 remain unchanged, and n 3 decreases. For (b), (c), (d) and (f), n 2 decreases, and n 1 does not increase. For (e), n 1 decreases. Hence our given sequence E 1 ϑ1 E 2 ϑ2... terminates with E n ϑn E n+1. Then it is easy to see that either E n+1 = or E n+1 = { }. Case E n+1 =. By the Corollary ϑ 1... ϑ n is a unifier of E, and by the Proposition we have η = ϑ 1... ϑ n η for every unifier η of E. Hence by the characterization of idempotent mgu s ϑ 1... ϑ n is an idempotent mgu of E. Case E n+1 = { }. Then by the proposition E is not unifiable. A.2. Ideals as denotations of terms Recall the definition of the relation ( U, a) [λ M ] in Section 2.3 The height of a derivation of ( U, a) [λ M ] is defined as usual, by adding 1 at each rule. We define its D-height similarly, where only rules (D) count.

4 62 A. DENOTATIONAL SEMANTICS: PROOFS We begin with some simple consequences of this definition. The following transformations preserve D-height: (12) (13) (14) (15) V U ( U, a) [λ M ] ( V, a) [λ M ], ( U, V, a) [λ,y M ] ( U, a) [λ M ] if y / FV(M), ( U, V, a) [λ,y (My)] ( U, V, a) [λ M ] if y / FV(M), ( U, V, a) [λ, y (M( P ( y )))] ( U, P ( V ), a) [λ, z (M( z ))]. Proof. (12) and (13) are both proved by easy inductions on the respective derivations. (14). Assume ( U, V, a) [λ,y (My)]. By (A) we then have W such that ( U, V, W ) [λ,y y ] (i.e., V W ) and ( U, V, W, a) [λ,y M ]. By (12) from the latter we obtain ( U, V, V, a) [λ,y M ]. Now since y / FV(M), (13) yields ( U, V, a) [λ M ], as required. Conversely, assume ( U, V, a) [λ M ]. Since y / FV(M), (13) yields ( U, V, V, a) [λ M ]. Clearly we have ( U, V, V ) [λ,y y ]. Hence by (A) ( U, V, a) [λ,y (My)], as required. Notice that the D-height did not change in these transformations. (15). By induction on P, with a side induction on M. We distinguish cases on M. The cases x i, C and D are follow immediately from (13). In case M N the following are equivalent by induction hypothesis: ( U, V, a) [λ, y ((MN)( P ( y )))] W (( U, V, W ) [λ, y (N( P ( y )))] ( U, V, W, a) [λ, y (M( P ( y )))]) W (( U, P ( V ), W ) [λ, y (N( z ))] ( U, P ( V ), W, a) [λ, y (M( z ))]) ( U, P ( V ), a) [λ, y ((MN)( z ))]. The final case is where M is z i. Then we have to show ( U, V, a) [λ, y (P ( y ))] P ( V ) a. We distinguish cases on P ( y ). If P ( y ) is y j, then both sides are equivalent to V j a. In case P ( y ) is (C Q)( y ) the following are equivalent, using the induction hypothesis for Q( y ) ( U, V, a) [λ, y ((C Q)( y ))] ( U, V, a) [λ, y (C Q( y ))] ( U, Q( V ), a) [λ, u (C u )] ( U, Q( V ), a) [λ C] by (14) a (a = C a Q( V ) a )

5 A.2. IDEALS AS DENOTATIONS OF TERMS 63 C Q( V ) a. Let denote the equivalence relation on formal neighborhoods generated by entailment, i.e., U V means (U V ) (V U). (16) If U P ( V ), then there are W such that U P ( W ) and W V. Proof. By induction on P. The cases x and are clear, and in case P, Q we can apply the induction hypothesis. It remains to treat the case CP ( ). Since U CP ( V ) there is a b 0 such that C b 0 U. Let U i := { a a (C a U a = a i ) }. For the constructor pattern C consider C U. By definition C U = { C a a i U i if U i, and a i = otherwise }. We first show U CU. Assume C a CU. For each i, if U i, then there is an a i such that C a i U and a ii = a i, and if U i = then a i =. Hence U { C a i U i } {C b 0 } C a. Conversely assume C a U. We define Cb CU by b i = a i if a i, b i = if U i =, and otherwise (i.e., if a i = and U i ) take an arbitrary b i U i. Clearly {Cb } C a. By definition U P ( V ). Hence by induction hypothesis there are W such that U P ( W ) and W V. Therefore U CU CP ( W ). Lemma (Unification). If P 1 ( V 1 ) P n ( V n ), then P 1,..., P n are unifiable with a most general unifier ϑ and there exists W such that ( P 1 ϑ)( W ) = = ( P n ϑ)( W ) P 1 ( V 1 ) P n ( V n ). Proof. Assume P 1 ( V 1 ) P n ( V n ). Then P 1 ( V 1 ),..., P n ( V n ) are componentwise consistent and hence P 1,..., P n are unifiable with a most general unifier ϑ. We now proceed by induction on P 1,..., P n. If they are either all empty or all variables the claim is trivial. In the case ( P 1, P 1 ),..., ( P n, P n ) it follows from the linearity condition on variables that a most general unifier of ( P 1, P 1 ),..., ( P n, P n ) is the union of most general unifiers of P 1,..., P n and of P 1,..., P n. Hence the induction hypothesis applies. In the case CP 1,..., CP n the assumption CP 1 ( V 1 ) CP n ( V n ) implies P 1 ( V 1 ) P n ( V n ) and hence again the induction hypothesis applies. The remaining case is when some are variables and the other ones of the form CP i, say x, CP 2,..., CP n. By assumption V 1 C P 2 ( V 2 ) C P n ( V n ).

6 64 A. DENOTATIONAL SEMANTICS: PROOFS By induction hypothesis we obtain the required W such that ( P 2 ϑ)( W ) = = ( P n ϑ)( W ) P 2 ( V 2 ) P n ( V n ). We need a final preparation before we can tackle consistency of [λ M ]. The information systems C ρ enjoy the pleasant property of coherence, which amounts to the possibility to locate inconsistencies in two-element sets of data objects. Generally, an information system A = (A, Con, ) is coherent if it satisfies: U A is consistent if and only if all of its two-element subsets are. Lemma. Let A and B be information systems. If B is coherent, then so is A B. Proof. Let A = (A, Con A, A ) and B = (B, Con B, B ) be information systems, and consider {(U 1, b 1 ),..., (U n, b n )} Con A B. Assume 1 i<j n ({(U i, b i ), (U j, b j )} Con). We have to show {(U 1, b 1 ),..., (U n, b n )} Con. Let I {1,..., n} and i I U i Con A. We must show { b i i I } Con B. Now since B is coherent by assumption, it suffices to show that {b i, b j } Con B for all i, j I. So let i, j I. By assumption we have U i U j Con A, and hence {b i, b j } Con B. By a similar argument we can prove Lemma (Coherence). The information systems C ρ are all coherent. Proof. By induction of the height U of consistent finite sets of tokens in C ρ, as defined in parts (c) and (d) of the definition in Lemma (Consistency). [λ M ] is consistent. Proof. Let ( U i, a i ) [λ M ] for i = 1, 2. By coherence it suffices to prove that ( U 1, a 1 ) and ( U 2, a 2 ) are consistent. We shall prove this by induction on the maximum of the D-heights and a side induction on the maximum of the heights. Case (V). Let ( U 1, a 1 ), ( U 2, a 2 ) [λ x i ], and assume that U 1 and U 2 are componentwise consistent. Then U 1i a 1 and U 2i a 2. Since U 1i U 2i is consistent, a 1 and a 2 must be consistent as well. Case (C). For i = 1, 2 we have V i a i ( U i, V i, C a i ) [λ C]. Assume U 1, V 1 and U 2, V 2 are componentwise consistent. The consistency of C a 1 and C a 2 follows from V i a i and the consistency of V 1 and V 2.

7 A.2. IDEALS AS DENOTATIONS OF TERMS 65 Case (A). For i = 1, 2 we have ( U i, V i, a i ) [λ M ] ( U i, V i ) [λ N ] ( U. i, a i ) [λ (MN)] Assume U 1 and U 2 are componentwise consistent. By the side induction hypothesis for the right premises V 1 V 2 is consistent. Hence by the side induction hypothesis for the left hand sides a 1 and a 2 are consistent. Case (D). For i = 1, 2 we have ( U i, V i, a i ) [λ, yi M i ( y i )] Wi P i ( V i ) ( U i, W (D) i, a i ) [λ D ] for computation rules DP i ( y i ) = M i ( y i ). Assume U 1, W 1 and U 2, W 2 are componentwise consistent; we must show that a 1 and a 2 are consistent. Since W 1 W 2 P i ( V i ) for i = 1, 2, by (16) there are V 1, V 2 such that V i V i and W 1 W 2 P i ( V i ). Then by the unification lemma there are W such that ( P 1 ϑ)( W ) = ( P 2 ϑ)( W ) P i ( V i ) P i ( V i ) for i = 1, 2, where ϑ is the most general unifier of P 1 and P 2. But then also and hence by (12) we have with lesser D-height. Now (15) gives ( y i ϑ)( W ) V i, ( U i, ( y i ϑ)( W ), a i ) [λ, yi M i ( y i )] ( U i, W, a i ) [λ, z M i ( y i )ϑ] without increasing the D-height. Notice that M 1 ( y i )ϑ = M 2 ( y i )ϑ by our condition on computation rules. Hence the induction hypothesis applied to ( U 1, W, a 1 ), ( U 2, W, a 2 ) [λ, z M 1 ( y 1 )ϑ] implies the consistency of a 1 and a 2, as required. Lemma (Deductive closure). [λ M ] is deductively closed, i.e., if W [λ M ] and W ( V, b), then ( V, b) [λ M ]. Proof. By induction on the maximum of the D-heights and a side induction on the maximum of the heights of W [λ M ]. We distinguish cases on the last rule of these derivations (which is determined by M). Case (V). For all ( U, a) W we have U i a ( U, a) [λ x i ].

8 66 A. DENOTATIONAL SEMANTICS: PROOFS We must show V i b. By assumption W ( V, b), hence W V b. It suffices to prove V i W V. Let c W V ; we show V i c. There are U such that V U and ( U, c) W. But then by the above U i c, hence V i U i c. Case (A). Let W = {( U 1, a 1 ),..., ( U n, a n )}. For each ( U i, a i ) W there is U i such that ( U i, U i, a i ) [λ M ] ( U i, U i ) [λ N ] ( U. i, a i ) [λ (MN)] Define U := { U i V U i }. We first show that U is consistent. Let a, b U. There are i, j such that a U i, b U j and V U i, U j. Then U i and U j are consistent; hence by the consistency of [[λ N ] proved above a and b are consistent as well. Next we show ( V, U) [λ N ]. Let a U; we show ( V, a) [λ N ]. Fix i such that a U i and V U i, and let W i := { ( U i, b) b U i } [λ N ]. Since by the side induction hypothesis [[λ N ] is deductively closed it suffices to prove W i ( V, a), i.e., { b b U i V U i } a. But the latter set equals U i, and a U i. Finally we show ( V, U, b) [λ M ]. Let W := {( U 1, U 1, a 1 ),..., ( U n, U n, a n )} [λ M ]. By side induction hypothesis it suffices to prove that W ( V, U, b), i.e., { a i V U i U U i } b. But by definition of U the latter set equals { a i V U i }, which in turn entails b because by assumption W ( V, b). Now we can use (A) to infer ( V, b) [λ M ], as required. Case (C). Assume W [λ C ]. Then W consists of ( U, U, C a ) such that U a. Assume further W ( V, V, b). Then { C a U, U (( U, U, C a ) W V U V U ) } b. By definition of entailment b has the form C b such that W i := { a U, U, a (a = a i ( U, U, C a ) W V U V U ) } b i. We must show ( V, V, Cb ) [λ C], i.e., V b. It suffices to show V i W i, for every i. Let a W i. Then there are U, U, a such that a = a i, ( U, U, C a ) W and V U. Hence V i U i a i = a. Case (D). Let W = {( U 1, U 1, a 1),..., ( U n, U n, a n )}. For every i there is an U i such that ( Ui, U i, a i) [λ, yi M i ( y i )] U i P i ( U i ) ( U i, U i, a i) [λ D ]

9 A.3. PRESERVATION OF VALUES 67 for D P i ( y i ) = M i ( y i ) a computation rule. Assume W ( V, V, b). We must prove ( V, V, b) [λ D ]. Let I := { i 1 i n V U i V U i }. Then { a i i I } b, hence I. For i I we have V U i P i ( U i ), hence by (16) there are V i such that V P i ( V i ) and V i U i. In particular for i, j I V P i ( V i ) P j ( V j ). To simplify notation assume I = {1,..., m}. Hence by the unification lemma P 1,..., P m are unifiable with a most general unifier ϑ and there exists W such that ( P 1 ϑ)( W ) = = ( P m ϑ)( W ) P 1 ( V 1) P m ( V m). Let i, j I. Then by the conditions on computation rules M i ϑ = M j ϑ. Also ( y i ϑ)( W ) V i U i. Therefore by (12) and hence by (15) ( V, ( y i ϑ)( W ), a i ) [λ, yi M i ( y i )] ( V, W, a i ) [λ, yi M i ( y i ϑ)]. But M i ( y i ϑ) = M i ϑ = M 1 ϑ = M 1 ( y 1 ϑ) and hence for all i I ( V, W, a i ) [λ, yi M 1 ( y 1 ϑ)]. Therefore X := { ( V, W, a i ) i I } [λ, yi M 1 ( y 1 ϑ)]. Since { a i i I } b, we have X ( V, W, b) and hence the induction hypothesis implies ( V, W, b) [λ, yi M 1 ( y 1 ϑ)]. Using (15) again we obtain ( V, ( y i ϑ)( W ), b) [λ, yi M 1 ( y 1 )]. Since V P 1 ( V 1 ) P 1 (( y 1 ϑ)( W )) we obtain ( V, V, b) [λ D ], by (D). Corollary. [λ M ] is an ideal. A.3. Preservation of values We now prove that our definition above of the denotation of a term is reasonable in the sense that it is not changed by an application of the standard (β- and η-) conversions or a computation rule. For the β-conversion part of this proof it is helpful to first introduce a more standard notation, which involves variable environments. Definition. Assume that all free variables in M are among. [M ] := { b ( U, b) [λ M ] } and [[M ] u, V, y := U, U u [M ] V, y. Let

10 68 A. DENOTATIONAL SEMANTICS: PROOFS From (13) we obtain [[M ] U,V,y = [M ] U if y / FV(M), and similarly for ideals u, v instead of U, V. We have a useful monotonicity property, which follows from the deductive closure of [[λ M ]. Lemma. (a) If V U, a b and a [M ] U, then b [M ] V. (b) If v u, a b and a [M ] u, then b [M ] v. Proof. (a) V U, a b and ( U, a) [λ M ] together imply ( V, b) [λ M ], by the deductive closure of [λ M ]. (b) follows from (a). Lemma. (a) [x i ] = U i and [x i ] u = u i. (b) [λ y M ] U U,V = { (V, b) b [M ],y } and [λ y M ] u = { (V, b) b [M ] u,v,y }. (c) [MN ] = [M ] [N ] and [MN ] u = [M ] u [N ] u. Proof. (b) It suffices to prove the first part. But (V, b) [λ y M ] and U,V b [M ],y are both equivalent to ( U, V, b) [λ,y M ]. (c) For the first part we argue as follows. c [M ] U [N ] U V [[N]] U ((V, c) [M ] U ) V (( U, V ) [λ N ] ( U, V, c) [λ M ]) ( U, c) [λ (MN)] c [MN ] U. The second part is an easy consequence: by (A) c [M ] u [N ] u V [[N]] u ((V, c) [M ] u ) V [[N]] u U u ((V, c) [M ] U ) U1 u U U V [[N]] 1 U u ((V, c) [M ] ) ( ) U u V [[N]] U ((V, c) [M ] U ) U u (c [M ] U [N ] U ) U u (c [MN ] U ) c [MN ] u. by the first part Here is the proof of the equivalence marked ( ). The upward direction is obvious. For the downward direction we use monotonicity. Assume U 1 u, U V [N ] 1, U u and (V, c) [M ]. Let U 2 := U 1 U u. Then by U monotonicity V [N ] 2 and (V, c) [M ] U 2.

11 A.3. PRESERVATION OF VALUES 69 Corollary. [λ y M ] u v = [M ] u,v,y. Proof. b [λ y M ] u v V v((v, b) [λ y M ] u ) V v (b [M ] u,v,y ) b [M ] u,v,y. by the lemma, part (b) Lemma (Substitution). [M(z)] u,[[n]] u,z = [M(N)] u. Proof. By induction on M, and cases on the form of M. Case λ y M. For readability we leave out and u. [λ y M(z)] [[N]] z The other cases are easy. = { (V, b) b [M(z)] [[N]],V z,y } = { (V, b) b [M(N)] V y } by induction hypothesis = [λ y M(N)] by the last lemma, part (b) = [(λ y M)(N)]. Lemma (Preservation of values, β). [(λ y M(y))N ] u = [M(N)] u. Proof. Again we leave out, u. By the last two lemmata and the corollary, [(λ y M(y))N ] = [λ y M(y)][[N ] = [M(y)] [[N]] y = [M(N)]. Lemma (Preservation of values, η). [λ y (My)] u = [M ] u Proof. (V, b) [λ y (My)] u U u (( U, V, b) [λ,y (My)]) U u (( U, V, b) [λ M ]) by (14) (V, b) [M ] u. if y / FV(M).

The non-logical symbols determine a specific F OL language and consists of the following sets. Σ = {Σ n } n<ω

The non-logical symbols determine a specific F OL language and consists of the following sets. Σ = {Σ n } n<ω 1 Preliminaries In this chapter we first give a summary of the basic notations, terminology and results which will be used in this thesis. The treatment here is reduced to a list of definitions. For the

More information

Math 730 Homework 6. Austin Mohr. October 14, 2009

Math 730 Homework 6. Austin Mohr. October 14, 2009 Math 730 Homework 6 Austin Mohr October 14, 2009 1 Problem 3A2 Proposition 1.1. If A X, then the family τ of all subsets of X which contain A, together with the empty set φ, is a topology on X. Proof.

More information

On the Complexity of the Reflected Logic of Proofs

On the Complexity of the Reflected Logic of Proofs On the Complexity of the Reflected Logic of Proofs Nikolai V. Krupski Department of Math. Logic and the Theory of Algorithms, Faculty of Mechanics and Mathematics, Moscow State University, Moscow 119899,

More information

3 Propositional Logic

3 Propositional Logic 3 Propositional Logic 3.1 Syntax 3.2 Semantics 3.3 Equivalence and Normal Forms 3.4 Proof Procedures 3.5 Properties Propositional Logic (25th October 2007) 1 3.1 Syntax Definition 3.0 An alphabet Σ consists

More information

Minimal logic for computable functionals

Minimal logic for computable functionals Minimal logic for computable functionals Helmut Schwichtenberg Mathematisches Institut der Universität München Contents 1. Partial continuous functionals 2. Total and structure-total functionals 3. Terms;

More information

INF3170 / INF4171 Notes on Resolution

INF3170 / INF4171 Notes on Resolution INF3170 / INF4171 Notes on Resolution Andreas Nakkerud Autumn 2015 1 Introduction This is a short description of the Resolution calculus for propositional logic, and for first order logic. We will only

More information

CHAPTER 2. Computability

CHAPTER 2. Computability CHAPTER 2 Computability At this point we leave the general setting of logic and aim to get closer to mathematics. We introduce free algebras (for example, the natural numbers) as basic data structures

More information

Applied Logic. Lecture 1 - Propositional logic. Marcin Szczuka. Institute of Informatics, The University of Warsaw

Applied Logic. Lecture 1 - Propositional logic. Marcin Szczuka. Institute of Informatics, The University of Warsaw Applied Logic Lecture 1 - Propositional logic Marcin Szczuka Institute of Informatics, The University of Warsaw Monographic lecture, Spring semester 2017/2018 Marcin Szczuka (MIMUW) Applied Logic 2018

More information

Propositional Logic Language

Propositional Logic Language Propositional Logic Language A logic consists of: an alphabet A, a language L, i.e., a set of formulas, and a binary relation = between a set of formulas and a formula. An alphabet A consists of a finite

More information

OMEGA-CATEGORIES AND CHAIN COMPLEXES. 1. Introduction. Homology, Homotopy and Applications, vol.6(1), 2004, pp RICHARD STEINER

OMEGA-CATEGORIES AND CHAIN COMPLEXES. 1. Introduction. Homology, Homotopy and Applications, vol.6(1), 2004, pp RICHARD STEINER Homology, Homotopy and Applications, vol.6(1), 2004, pp.175 200 OMEGA-CATEGORIES AND CHAIN COMPLEXES RICHARD STEINER (communicated by Ronald Brown) Abstract There are several ways to construct omega-categories

More information

Natural Deduction for Propositional Logic

Natural Deduction for Propositional Logic Natural Deduction for Propositional Logic Bow-Yaw Wang Institute of Information Science Academia Sinica, Taiwan September 10, 2018 Bow-Yaw Wang (Academia Sinica) Natural Deduction for Propositional Logic

More information

Hypersequent Calculi for some Intermediate Logics with Bounded Kripke Models

Hypersequent Calculi for some Intermediate Logics with Bounded Kripke Models Hypersequent Calculi for some Intermediate Logics with Bounded Kripke Models Agata Ciabattoni Mauro Ferrari Abstract In this paper we define cut-free hypersequent calculi for some intermediate logics semantically

More information

Soundness Theorem for System AS1

Soundness Theorem for System AS1 10 The Soundness Theorem for System AS1 1. Introduction...2 2. Soundness, Completeness, and Mutual Consistency...2 3. The Weak Soundness Theorem...4 4. The Strong Soundness Theorem...6 5. Appendix: Supporting

More information

Resolution for Predicate Logic

Resolution for Predicate Logic Logic and Proof Hilary 2016 James Worrell Resolution for Predicate Logic A serious drawback of the ground resolution procedure is that it requires looking ahead to predict which ground instances of clauses

More information

Lazy Strong Normalization

Lazy Strong Normalization Lazy Strong Normalization Luca Paolini 1,2 Dipartimento di Informatica Università di Torino (ITALIA) Elaine Pimentel 1,2 Departamento de Matemática Universidade Federal de Minas Gerais (BRASIL) Dipartimento

More information

A Weak Post s Theorem and the Deduction Theorem Retold

A Weak Post s Theorem and the Deduction Theorem Retold Chapter I A Weak Post s Theorem and the Deduction Theorem Retold This note retells (1) A weak form of Post s theorem: If Γ is finite and Γ = taut A, then Γ A and derives as a corollary the Deduction Theorem:

More information

Bisimulation for conditional modalities

Bisimulation for conditional modalities Bisimulation for conditional modalities Alexandru Baltag and Giovanni Ciná Institute for Logic, Language and Computation, University of Amsterdam March 21, 2016 Abstract We give a general definition of

More information

1. Propositional Calculus

1. Propositional Calculus 1. Propositional Calculus Some notes for Math 601, Fall 2010 based on Elliott Mendelson, Introduction to Mathematical Logic, Fifth edition, 2010, Chapman & Hall. 2. Syntax ( grammar ). 1.1, p. 1. Given:

More information

HORSes: format, termination and confluence

HORSes: format, termination and confluence HORSes: format, termination and confluence Jean-Pierre Jouannaud INRIA-LIAMA and singhua Software Chair Joint on-going work with Jianqi Li School of Software, singhua University Project CoqLF NList Cross-discipline

More information

Entailment with Conditional Equality Constraints (Extended Version)

Entailment with Conditional Equality Constraints (Extended Version) Entailment with Conditional Equality Constraints (Extended Version) Zhendong Su Alexander Aiken Report No. UCB/CSD-00-1113 October 2000 Computer Science Division (EECS) University of California Berkeley,

More information

Harvard CS 121 and CSCI E-207 Lecture 6: Regular Languages and Countability

Harvard CS 121 and CSCI E-207 Lecture 6: Regular Languages and Countability Harvard CS 121 and CSCI E-207 Lecture 6: Regular Languages and Countability Salil Vadhan September 20, 2012 Reading: Sipser, 1.3 and The Diagonalization Method, pages 174 178 (from just before Definition

More information

Chapter 2. Unification. Foundations of Logic Programming

Chapter 2. Unification. Foundations of Logic Programming Chapter 2 1 Outline Understanding the need for unification Defining alphabets, terms, and substitutions Introducing the Martelli-Montanari Algorithm for unification Proving correctness of the algorithm

More information

5 Set Operations, Functions, and Counting

5 Set Operations, Functions, and Counting 5 Set Operations, Functions, and Counting Let N denote the positive integers, N 0 := N {0} be the non-negative integers and Z = N 0 ( N) the positive and negative integers including 0, Q the rational numbers,

More information

Boolean Algebra and Propositional Logic

Boolean Algebra and Propositional Logic Boolean Algebra and Propositional Logic Takahiro Kato September 10, 2015 ABSTRACT. This article provides yet another characterization of Boolean algebras and, using this characterization, establishes a

More information

Boolean Algebra and Propositional Logic

Boolean Algebra and Propositional Logic Boolean Algebra and Propositional Logic Takahiro Kato June 23, 2015 This article provides yet another characterization of Boolean algebras and, using this characterization, establishes a more direct connection

More information

Argumentative Characterisations of Non-monotonic Inference in Preferred Subtheories: Stable Equals Preferred

Argumentative Characterisations of Non-monotonic Inference in Preferred Subtheories: Stable Equals Preferred Argumentative Characterisations of Non-monotonic Inference in Preferred Subtheories: Stable Equals Preferred Sanjay Modgil November 17, 2017 Abstract A number of argumentation formalisms provide dialectical

More information

KRIPKE S THEORY OF TRUTH 1. INTRODUCTION

KRIPKE S THEORY OF TRUTH 1. INTRODUCTION KRIPKE S THEORY OF TRUTH RICHARD G HECK, JR 1. INTRODUCTION The purpose of this note is to give a simple, easily accessible proof of the existence of the minimal fixed point, and of various maximal fixed

More information

Chapter 4. Measure Theory. 1. Measure Spaces

Chapter 4. Measure Theory. 1. Measure Spaces Chapter 4. Measure Theory 1. Measure Spaces Let X be a nonempty set. A collection S of subsets of X is said to be an algebra on X if S has the following properties: 1. X S; 2. if A S, then A c S; 3. if

More information

Chapter 1: Systems of Linear Equations

Chapter 1: Systems of Linear Equations Chapter : Systems of Linear Equations February, 9 Systems of linear equations Linear systems Lecture A linear equation in variables x, x,, x n is an equation of the form a x + a x + + a n x n = b, where

More information

Přednáška 12. Důkazové kalkuly Kalkul Hilbertova typu. 11/29/2006 Hilbertův kalkul 1

Přednáška 12. Důkazové kalkuly Kalkul Hilbertova typu. 11/29/2006 Hilbertův kalkul 1 Přednáška 12 Důkazové kalkuly Kalkul Hilbertova typu 11/29/2006 Hilbertův kalkul 1 Formal systems, Proof calculi A proof calculus (of a theory) is given by: A. a language B. a set of axioms C. a set of

More information

Monadic Refinements for Relational Cost Analysis (Appendix)

Monadic Refinements for Relational Cost Analysis (Appendix) Monadic Refinements for Relational Cost Analysis (Appendix) Ivan Radiček Gilles Barthe Marco Gaboardi Deepak Garg Florian Zuleger Structure of the Appendix In the appendix we give material that was omitted

More information

Propositional and Predicate Logic - IV

Propositional and Predicate Logic - IV Propositional and Predicate Logic - IV Petr Gregor KTIML MFF UK ZS 2015/2016 Petr Gregor (KTIML MFF UK) Propositional and Predicate Logic - IV ZS 2015/2016 1 / 19 Tableau method (from the previous lecture)

More information

Modal Dependence Logic

Modal Dependence Logic Modal Dependence Logic Jouko Väänänen Institute for Logic, Language and Computation Universiteit van Amsterdam Plantage Muidergracht 24 1018 TV Amsterdam, The Netherlands J.A.Vaananen@uva.nl Abstract We

More information

Unification in Description Logic EL without top constructor

Unification in Description Logic EL without top constructor Fakultät Informatik EMCL Master Thesis Unification in Description Logic EL without top constructor by Nguyen Thanh Binh born on March 25 th 1984, Cao Bang, Vietnam S upervisor : Prof.Dr.-Ing. Franz Baader

More information

Computability Crib Sheet

Computability Crib Sheet Computer Science and Engineering, UCSD Winter 10 CSE 200: Computability and Complexity Instructor: Mihir Bellare Computability Crib Sheet January 3, 2010 Computability Crib Sheet This is a quick reference

More information

On Modal Logics of Partial Recursive Functions

On Modal Logics of Partial Recursive Functions arxiv:cs/0407031v1 [cs.lo] 12 Jul 2004 On Modal Logics of Partial Recursive Functions Pavel Naumov Computer Science Pennsylvania State University Middletown, PA 17057 naumov@psu.edu June 14, 2018 Abstract

More information

CS411 Notes 3 Induction and Recursion

CS411 Notes 3 Induction and Recursion CS411 Notes 3 Induction and Recursion A. Demers 5 Feb 2001 These notes present inductive techniques for defining sets and subsets, for defining functions over sets, and for proving that a property holds

More information

Cographs; chordal graphs and tree decompositions

Cographs; chordal graphs and tree decompositions Cographs; chordal graphs and tree decompositions Zdeněk Dvořák September 14, 2015 Let us now proceed with some more interesting graph classes closed on induced subgraphs. 1 Cographs The class of cographs

More information

Logic Michælmas 2003

Logic Michælmas 2003 Logic Michælmas 2003 ii Contents 1 Introduction 1 2 Propositional logic 3 3 Syntactic implication 5 3.0.1 Two consequences of completeness.............. 7 4 Posets and Zorn s lemma 9 5 Predicate logic

More information

CHAPTER 8: EXPLORING R

CHAPTER 8: EXPLORING R CHAPTER 8: EXPLORING R LECTURE NOTES FOR MATH 378 (CSUSM, SPRING 2009). WAYNE AITKEN In the previous chapter we discussed the need for a complete ordered field. The field Q is not complete, so we constructed

More information

1. Propositional Calculus

1. Propositional Calculus 1. Propositional Calculus Some notes for Math 601, Fall 2010 based on Elliott Mendelson, Introduction to Mathematical Logic, Fifth edition, 2010, Chapman & Hall. 2. Syntax ( grammar ). 1.1, p. 1. Given:

More information

Observation 4.1 G has a proper separation of order 0 if and only if G is disconnected.

Observation 4.1 G has a proper separation of order 0 if and only if G is disconnected. 4 Connectivity 2-connectivity Separation: A separation of G of order k is a pair of subgraphs (H, K) with H K = G and E(H K) = and V (H) V (K) = k. Such a separation is proper if V (H) \ V (K) and V (K)

More information

Laplace s Equation. Chapter Mean Value Formulas

Laplace s Equation. Chapter Mean Value Formulas Chapter 1 Laplace s Equation Let be an open set in R n. A function u C 2 () is called harmonic in if it satisfies Laplace s equation n (1.1) u := D ii u = 0 in. i=1 A function u C 2 () is called subharmonic

More information

W if p = 0; ; W ) if p 1. p times

W if p = 0; ; W ) if p 1. p times Alternating and symmetric multilinear functions. Suppose and W are normed vector spaces. For each integer p we set {0} if p < 0; W if p = 0; ( ; W = L( }... {{... } ; W if p 1. p times We say µ p ( ; W

More information

Eilenberg-Steenrod properties. (Hatcher, 2.1, 2.3, 3.1; Conlon, 2.6, 8.1, )

Eilenberg-Steenrod properties. (Hatcher, 2.1, 2.3, 3.1; Conlon, 2.6, 8.1, ) II.3 : Eilenberg-Steenrod properties (Hatcher, 2.1, 2.3, 3.1; Conlon, 2.6, 8.1, 8.3 8.5 Definition. Let U be an open subset of R n for some n. The de Rham cohomology groups (U are the cohomology groups

More information

Translative Sets and Functions and their Applications to Risk Measure Theory and Nonlinear Separation

Translative Sets and Functions and their Applications to Risk Measure Theory and Nonlinear Separation Translative Sets and Functions and their Applications to Risk Measure Theory and Nonlinear Separation Andreas H. Hamel Abstract Recently defined concepts such as nonlinear separation functionals due to

More information

Existential Second-Order Logic and Modal Logic with Quantified Accessibility Relations

Existential Second-Order Logic and Modal Logic with Quantified Accessibility Relations Existential Second-Order Logic and Modal Logic with Quantified Accessibility Relations preprint Lauri Hella University of Tampere Antti Kuusisto University of Bremen Abstract This article investigates

More information

Modal Logic: Exercises

Modal Logic: Exercises Modal Logic: Exercises KRDB FUB stream course www.inf.unibz.it/ gennari/index.php?page=nl Lecturer: R. Gennari gennari@inf.unibz.it June 6, 2010 Ex. 36 Prove the following claim. Claim 1. Uniform substitution

More information

Convexity in R N Supplemental Notes 1

Convexity in R N Supplemental Notes 1 John Nachbar Washington University November 1, 2014 Convexity in R N Supplemental Notes 1 1 Introduction. These notes provide exact characterizations of support and separation in R N. The statement of

More information

Provably Total Functions of Arithmetic with Basic Terms

Provably Total Functions of Arithmetic with Basic Terms Provably Total Functions of Arithmetic with Basic Terms Evgeny Makarov INRIA Orsay, France emakarov@gmail.com A new characterization of provably recursive functions of first-order arithmetic is described.

More information

Proseminar on Semantic Theory Fall 2013 Ling 720 Proving the Soundness and Completeness of Propositional Logic: Some Highlights 1

Proseminar on Semantic Theory Fall 2013 Ling 720 Proving the Soundness and Completeness of Propositional Logic: Some Highlights 1 Proving the Soundness and Completeness of Propositional Logic: Some Highlights 1 (1) A Summary of What We ve Done So Far for PL a. We ve given a purely syntactic characterization of valid inference in

More information

Normal Forms of Propositional Logic

Normal Forms of Propositional Logic Normal Forms of Propositional Logic Bow-Yaw Wang Institute of Information Science Academia Sinica, Taiwan September 12, 2017 Bow-Yaw Wang (Academia Sinica) Normal Forms of Propositional Logic September

More information

Relational Reasoning in Natural Language

Relational Reasoning in Natural Language 1/67 Relational Reasoning in Natural Language Larry Moss ESSLLI 10 Course on Logics for Natural Language Inference August, 2010 Adding transitive verbs the work on R, R, and other systems is joint with

More information

A Goal-Oriented Algorithm for Unification in EL w.r.t. Cycle-Restricted TBoxes

A Goal-Oriented Algorithm for Unification in EL w.r.t. Cycle-Restricted TBoxes A Goal-Oriented Algorithm for Unification in EL w.r.t. Cycle-Restricted TBoxes Franz Baader, Stefan Borgwardt, and Barbara Morawska {baader,stefborg,morawska}@tcs.inf.tu-dresden.de Theoretical Computer

More information

Axioms of separation

Axioms of separation Axioms of separation These notes discuss the same topic as Sections 31, 32, 33, 34, 35, and also 7, 10 of Munkres book. Some notions (hereditarily normal, perfectly normal, collectionwise normal, monotonically

More information

Determining Unify-Stable Presentations (long version)

Determining Unify-Stable Presentations (long version) Determining Unify-Stable Presentations (long version) Thierry Boy de la Tour 1 and Mnacho Echenim 2 1 CNRS - Laboratoire d Informatique de Grenoble, France Thierry.Boy-de-la-Tour@imag.fr 2 Dipartimento

More information

Forcing-based cut-elimination for Gentzen-style intuitionistic sequent calculus

Forcing-based cut-elimination for Gentzen-style intuitionistic sequent calculus Forcing-based cut-elimination for Gentzen-style intuitionistic sequent calculus Hugo Herbelin 1 and Gyesik Lee 2 1 INRIA & PPS, Paris Université 7 Paris, France Hugo.Herbelin@inria.fr 2 ROSAEC center,

More information

General methods in proof theory for modal logic - Lecture 1

General methods in proof theory for modal logic - Lecture 1 General methods in proof theory for modal logic - Lecture 1 Björn Lellmann and Revantha Ramanayake TU Wien Tutorial co-located with TABLEAUX 2017, FroCoS 2017 and ITP 2017 September 24, 2017. Brasilia.

More information

3 COUNTABILITY AND CONNECTEDNESS AXIOMS

3 COUNTABILITY AND CONNECTEDNESS AXIOMS 3 COUNTABILITY AND CONNECTEDNESS AXIOMS Definition 3.1 Let X be a topological space. A subset D of X is dense in X iff D = X. X is separable iff it contains a countable dense subset. X satisfies the first

More information

where Σ is a finite discrete Gal(K sep /K)-set unramified along U and F s is a finite Gal(k(s) sep /k(s))-subset

where Σ is a finite discrete Gal(K sep /K)-set unramified along U and F s is a finite Gal(k(s) sep /k(s))-subset Classification of quasi-finite étale separated schemes As we saw in lecture, Zariski s Main Theorem provides a very visual picture of quasi-finite étale separated schemes X over a henselian local ring

More information

Math 541 Fall 2008 Connectivity Transition from Math 453/503 to Math 541 Ross E. Staffeldt-August 2008

Math 541 Fall 2008 Connectivity Transition from Math 453/503 to Math 541 Ross E. Staffeldt-August 2008 Math 541 Fall 2008 Connectivity Transition from Math 453/503 to Math 541 Ross E. Staffeldt-August 2008 Closed sets We have been operating at a fundamental level at which a topological space is a set together

More information

Definitions. Notations. Injective, Surjective and Bijective. Divides. Cartesian Product. Relations. Equivalence Relations

Definitions. Notations. Injective, Surjective and Bijective. Divides. Cartesian Product. Relations. Equivalence Relations Page 1 Definitions Tuesday, May 8, 2018 12:23 AM Notations " " means "equals, by definition" the set of all real numbers the set of integers Denote a function from a set to a set by Denote the image of

More information

7 RC Simulates RA. Lemma: For every RA expression E(A 1... A k ) there exists a DRC formula F with F V (F ) = {A 1,..., A k } and

7 RC Simulates RA. Lemma: For every RA expression E(A 1... A k ) there exists a DRC formula F with F V (F ) = {A 1,..., A k } and 7 RC Simulates RA. We now show that DRC (and hence TRC) is at least as expressive as RA. That is, given an RA expression E that mentions at most C, there is an equivalent DRC expression E that mentions

More information

The Complexity of Entailment Problems over Conditional Equality Constraints

The Complexity of Entailment Problems over Conditional Equality Constraints The Complexity of Entailment Problems over Conditional Equality Constraints Zhendong Su Department of Computer Science, University of California, Davis, CA 95616-8562 +1 530 7545376 (phone) +1 530 7524767

More information

Consequence Relations and Natural Deduction

Consequence Relations and Natural Deduction Consequence Relations and Natural Deduction Joshua D. Guttman Worcester Polytechnic Institute September 9, 2010 Contents 1 Consequence Relations 1 2 A Derivation System for Natural Deduction 3 3 Derivations

More information

A Discrete Duality Between Nonmonotonic Consequence Relations and Convex Geometries

A Discrete Duality Between Nonmonotonic Consequence Relations and Convex Geometries A Discrete Duality Between Nonmonotonic Consequence Relations and Convex Geometries Johannes Marti and Riccardo Pinosio Draft from April 5, 2018 Abstract In this paper we present a duality between nonmonotonic

More information

A Generic Approach to Coalition Formation

A Generic Approach to Coalition Formation A Generic Approach to Coalition Formation Krzysztof R. Apt and Andreas Witzel Abstract We propose an abstract approach to coalition formation by focusing on partial preference relations between partitions

More information

c i r i i=1 r 1 = [1, 2] r 2 = [0, 1] r 3 = [3, 4].

c i r i i=1 r 1 = [1, 2] r 2 = [0, 1] r 3 = [3, 4]. Lecture Notes: Rank of a Matrix Yufei Tao Department of Computer Science and Engineering Chinese University of Hong Kong taoyf@cse.cuhk.edu.hk 1 Linear Independence Definition 1. Let r 1, r 2,..., r m

More information

Course 212: Academic Year Section 1: Metric Spaces

Course 212: Academic Year Section 1: Metric Spaces Course 212: Academic Year 1991-2 Section 1: Metric Spaces D. R. Wilkins Contents 1 Metric Spaces 3 1.1 Distance Functions and Metric Spaces............. 3 1.2 Convergence and Continuity in Metric Spaces.........

More information

The density of rational points on non-singular hypersurfaces, I

The density of rational points on non-singular hypersurfaces, I The density of rational points on non-singular hypersurfaces, I T.D. Browning 1 and D.R. Heath-Brown 2 1 School of Mathematics, Bristol University, Bristol BS8 1TW 2 Mathematical Institute,24 29 St. Giles,Oxford

More information

A NICE PROOF OF FARKAS LEMMA

A NICE PROOF OF FARKAS LEMMA A NICE PROOF OF FARKAS LEMMA DANIEL VICTOR TAUSK Abstract. The goal of this short note is to present a nice proof of Farkas Lemma which states that if C is the convex cone spanned by a finite set and if

More information

General Patterns for Nonmonotonic Reasoning: From Basic Entailments to Plausible Relations

General Patterns for Nonmonotonic Reasoning: From Basic Entailments to Plausible Relations General Patterns for Nonmonotonic Reasoning: From Basic Entailments to Plausible Relations OFER ARIELI AND ARNON AVRON, Department of Computer Science, School of Mathematical Sciences, Tel-Aviv University,

More information

Isomorphisms between pattern classes

Isomorphisms between pattern classes Journal of Combinatorics olume 0, Number 0, 1 8, 0000 Isomorphisms between pattern classes M. H. Albert, M. D. Atkinson and Anders Claesson Isomorphisms φ : A B between pattern classes are considered.

More information

Solution. 1 Solution of Homework 7. Sangchul Lee. March 22, Problem 1.1

Solution. 1 Solution of Homework 7. Sangchul Lee. March 22, Problem 1.1 Solution Sangchul Lee March, 018 1 Solution of Homework 7 Problem 1.1 For a given k N, Consider two sequences (a n ) and (b n,k ) in R. Suppose that a n b n,k for all n,k N Show that limsup a n B k :=

More information

Safety Analysis versus Type Inference

Safety Analysis versus Type Inference Information and Computation, 118(1):128 141, 1995. Safety Analysis versus Type Inference Jens Palsberg palsberg@daimi.aau.dk Michael I. Schwartzbach mis@daimi.aau.dk Computer Science Department, Aarhus

More information

Models of Computation,

Models of Computation, Models of Computation, 2010 1 Induction We use a lot of inductive techniques in this course, both to give definitions and to prove facts about our semantics So, it s worth taking a little while to set

More information

An Abstract Decision Procedure for a Theory of Inductive Data Types

An Abstract Decision Procedure for a Theory of Inductive Data Types An Abstract Decision Procedure for a Theory of Inductive Data Types lark Barrett Igor Shikanian Department of omputer Science ourant Institute of Mathematical Sciences New York University esare Tinelli

More information

LECTURE 3 Functional spaces on manifolds

LECTURE 3 Functional spaces on manifolds LECTURE 3 Functional spaces on manifolds The aim of this section is to introduce Sobolev spaces on manifolds (or on vector bundles over manifolds). These will be the Banach spaces of sections we were after

More information

Chapter 3: Propositional Calculus: Deductive Systems. September 19, 2008

Chapter 3: Propositional Calculus: Deductive Systems. September 19, 2008 Chapter 3: Propositional Calculus: Deductive Systems September 19, 2008 Outline 1 3.1 Deductive (Proof) System 2 3.2 Gentzen System G 3 3.3 Hilbert System H 4 3.4 Soundness and Completeness; Consistency

More information

SUBLATTICES OF LATTICES OF ORDER-CONVEX SETS, III. THE CASE OF TOTALLY ORDERED SETS

SUBLATTICES OF LATTICES OF ORDER-CONVEX SETS, III. THE CASE OF TOTALLY ORDERED SETS SUBLATTICES OF LATTICES OF ORDER-CONVEX SETS, III. THE CASE OF TOTALLY ORDERED SETS MARINA SEMENOVA AND FRIEDRICH WEHRUNG Abstract. For a partially ordered set P, let Co(P) denote the lattice of all order-convex

More information

M17 MAT25-21 HOMEWORK 6

M17 MAT25-21 HOMEWORK 6 M17 MAT25-21 HOMEWORK 6 DUE 10:00AM WEDNESDAY SEPTEMBER 13TH 1. To Hand In Double Series. The exercises in this section will guide you to complete the proof of the following theorem: Theorem 1: Absolute

More information

Logic: Propositional Logic Truth Tables

Logic: Propositional Logic Truth Tables Logic: Propositional Logic Truth Tables Raffaella Bernardi bernardi@inf.unibz.it P.zza Domenicani 3, Room 2.28 Faculty of Computer Science, Free University of Bolzano-Bozen http://www.inf.unibz.it/~bernardi/courses/logic06

More information

Closure operators on sets and algebraic lattices

Closure operators on sets and algebraic lattices Closure operators on sets and algebraic lattices Sergiu Rudeanu University of Bucharest Romania Closure operators are abundant in mathematics; here are a few examples. Given an algebraic structure, such

More information

Clausal Presentation of Theories in Deduction Modulo

Clausal Presentation of Theories in Deduction Modulo Gao JH. Clausal presentation of theories in deduction modulo. JOURNAL OF COMPUTER SCIENCE AND TECHNOL- OGY 28(6): 1085 1096 Nov. 2013. DOI 10.1007/s11390-013-1399-0 Clausal Presentation of Theories in

More information

ITERATING ALONG A PRIKRY SEQUENCE

ITERATING ALONG A PRIKRY SEQUENCE ITERATING ALONG A PRIKRY SEQUENCE SPENCER UNGER Abstract. In this paper we introduce a new method which combines Prikry forcing with an iteration between the Prikry points. Using our method we prove from

More information

Overview. I Review of natural deduction. I Soundness and completeness. I Semantics of propositional formulas. I Soundness proof. I Completeness proof.

Overview. I Review of natural deduction. I Soundness and completeness. I Semantics of propositional formulas. I Soundness proof. I Completeness proof. Overview I Review of natural deduction. I Soundness and completeness. I Semantics of propositional formulas. I Soundness proof. I Completeness proof. Propositional formulas Grammar: ::= p j (:) j ( ^ )

More information

Hartogs Theorem: separate analyticity implies joint Paul Garrett garrett/

Hartogs Theorem: separate analyticity implies joint Paul Garrett  garrett/ (February 9, 25) Hartogs Theorem: separate analyticity implies joint Paul Garrett garrett@math.umn.edu http://www.math.umn.edu/ garrett/ (The present proof of this old result roughly follows the proof

More information

A Linear Round Lower Bound for Lovasz-Schrijver SDP Relaxations of Vertex Cover

A Linear Round Lower Bound for Lovasz-Schrijver SDP Relaxations of Vertex Cover A Linear Round Lower Bound for Lovasz-Schrijver SDP Relaxations of Vertex Cover Grant Schoenebeck Luca Trevisan Madhur Tulsiani Abstract We study semidefinite programming relaxations of Vertex Cover arising

More information

Canonical Calculi: Invertibility, Axiom expansion and (Non)-determinism

Canonical Calculi: Invertibility, Axiom expansion and (Non)-determinism Canonical Calculi: Invertibility, Axiom expansion and (Non)-determinism A. Avron 1, A. Ciabattoni 2, and A. Zamansky 1 1 Tel-Aviv University 2 Vienna University of Technology Abstract. We apply the semantic

More information

Part II Logic and Set Theory

Part II Logic and Set Theory Part II Logic and Set Theory Theorems Based on lectures by I. B. Leader Notes taken by Dexter Chua Lent 2015 These notes are not endorsed by the lecturers, and I have modified them (often significantly)

More information

NICTA Advanced Course. Theorem Proving Principles, Techniques, Applications

NICTA Advanced Course. Theorem Proving Principles, Techniques, Applications NICTA Advanced Course Theorem Proving Principles, Techniques, Applications λ 1 CONTENT Intro & motivation, getting started with Isabelle Foundations & Principles Lambda Calculus Higher Order Logic, natural

More information

On closures of lexicographic star-free languages. E. Ochmański and K. Stawikowska

On closures of lexicographic star-free languages. E. Ochmański and K. Stawikowska On closures of lexicographic star-free languages E. Ochmański and K. Stawikowska Preprint No 7/2005 Version 1, posted on April 19, 2005 On closures of lexicographic star-free languages Edward Ochma ski

More information

Supplementary Notes on Inductive Definitions

Supplementary Notes on Inductive Definitions Supplementary Notes on Inductive Definitions 15-312: Foundations of Programming Languages Frank Pfenning Lecture 2 August 29, 2002 These supplementary notes review the notion of an inductive definition

More information

Topological properties

Topological properties CHAPTER 4 Topological properties 1. Connectedness Definitions and examples Basic properties Connected components Connected versus path connected, again 2. Compactness Definition and first examples Topological

More information

Interpolation in Logics with Constructors

Interpolation in Logics with Constructors Interpolation in Logics with Constructors Daniel Găină Japan Advanced Institute of Science and Technology School of Information Science Abstract We present a generic method for establishing the interpolation

More information

VISCOSITY SOLUTIONS. We follow Han and Lin, Elliptic Partial Differential Equations, 5.

VISCOSITY SOLUTIONS. We follow Han and Lin, Elliptic Partial Differential Equations, 5. VISCOSITY SOLUTIONS PETER HINTZ We follow Han and Lin, Elliptic Partial Differential Equations, 5. 1. Motivation Throughout, we will assume that Ω R n is a bounded and connected domain and that a ij C(Ω)

More information

arxiv: v4 [math.lo] 6 Apr 2018

arxiv: v4 [math.lo] 6 Apr 2018 Complexity of the interpretability logic IL arxiv:1710.05599v4 [math.lo] 6 Apr 2018 Luka Mikec luka.mikec@math.hr Fedor Pakhomov pakhfn@mi.ras.ru Monday 2 nd April, 2018 Abstract Mladen Vuković vukovic@math.hr

More information

Introduction to lambda calculus Part 6

Introduction to lambda calculus Part 6 Introduction to lambda calculus Part 6 Antti-Juhani Kaijanaho 2017-02-16 1 Untyped lambda calculus 2 Typed lambda calculi 2.1 Dynamically typed lambda calculus with integers 2.2 A model of Lisp 2.3 Simply

More information

From Frame Properties to Hypersequent Rules in Modal Logics

From Frame Properties to Hypersequent Rules in Modal Logics From Frame Properties to Hypersequent Rules in Modal Logics Ori Lahav School of Computer Science Tel Aviv University Tel Aviv, Israel Email: orilahav@post.tau.ac.il Abstract We provide a general method

More information

FUNCTIONAL ANALYSIS LECTURE NOTES: COMPACT SETS AND FINITE-DIMENSIONAL SPACES. 1. Compact Sets

FUNCTIONAL ANALYSIS LECTURE NOTES: COMPACT SETS AND FINITE-DIMENSIONAL SPACES. 1. Compact Sets FUNCTIONAL ANALYSIS LECTURE NOTES: COMPACT SETS AND FINITE-DIMENSIONAL SPACES CHRISTOPHER HEIL 1. Compact Sets Definition 1.1 (Compact and Totally Bounded Sets). Let X be a metric space, and let E X be

More information