Threshold logic proof systems
|
|
- Violet Horton
- 5 years ago
- Views:
Transcription
1 Threshold logic proof systems Samuel Buss Peter Clote May 19, 1995 In this note, we show the intersimulation of three threshold logics within a polynomial size and constant depth factor. The logics are PTK, PTK and FC, the latter introduced by J. Krajíče in [2]. Definition 1 Propositional threshold logic is given as follows. Formula depth and size are defined inductively by: i. a propositional variable x i, i N, is a formula of depth 0 and size 1. 1 ii. if F is a formula then F is a formula of depth 1 + dp(f ) and size 1 + size(f ). iii. if F 1,...,F n are formulas and 1 n then T n (F 1,...,F n )isaformula of depth 1 + max{depth(f i ):1 i n} and size (n + ) i n size(f i). Propositional threshold logic can be viewed as an extension of propositional logic in the connectives,,, the latter two connectives being defined by F i T1 n (F 1,...,F n ) 1 i n 1 i n F i T n n (F 1,...,F n ) A cedent is any sequence F 1,...,F n of formulas separated by commas. Cedents are sometimes designated by Γ, Δ,...(capital Gree letters). A sequent is given by Γ Δ, where Γ, Δ are arbitrary cedents. The size [resp. depth] of a cedent F 1,...,F n is 1 i n size(f i) [resp. max 1 i n (depth(f i ))]. The size [resp. depth] of a sequent Γ Δ is size(γ) + size(δ) [resp. max(depth(γ),depth(δ))]. The intended interpretation of the sequent Γ Δis Γ Δ. An initial sequent is of the form F F where F is any formula of propositional threshold logic. The rules of inference of PTK, the sequent calculus of 1 One could as well allow propositional constants 1 (true) and0(false)ofdepth0and size 1. 1
2 propositional threshold logic, are as follows. 2 By convention, T n m(a 1,...,A n )is only defined if 1 m n. structural rules wea left: Γ, Δ Γ Γ Γ, Δ Γ,A,Δ Γ wea right: Γ Γ,A,Δ contract left: Γ,A,A,Δ Γ Γ Γ,A,A,Δ Γ,A,Δ Γ contract right: Γ Γ,A,Δ permute left: cut rule Γ,A,B,Δ Γ Γ Γ,A,B,Δ Γ,B,A,Δ Γ permute right: Γ Γ,B,A,Δ Γ,A Δ Γ A, Δ Γ, Γ Δ, Δ logical rules -left: A, Γ Δ Γ A, Δ -right: Γ A, Δ A, Γ Δ A 1,...,A n, Γ Δ -left: for n 1 T n n (A 1,...,A n ), Γ Δ Γ A 1, Δ Γ A n, Δ -right: for n 1 Γ T n n (A 1,...,A n ), Δ A 1, Γ Δ A n, Γ Δ -left: for n 1 T n 1 (A 1,...A n ), Γ Δ Γ A 1,...,A n, Δ -right: for n 1 Γ T n 1 (A 1,...,A n ), Δ 2 Gentzen s original sequent calculus for first order logic was called LK (Logischer Kalül). The propositional sequent calculus with connectives,, has sometimes been called PK (propositional Kalül), so our propositional threshold Kalül is denoted PTK. 2
3 T n -left: T n 1 (A 2,...,A n ), Γ Δ A 1,T n 1 1 (A 2,...,A n ), Γ Δ T n(a 1,...,A n ), Γ Δ for 2 <n T n -right: Γ A 1,T n 1 (A 2,...,A n ), Δ Γ T n 1 1 (A 2,...,A n ), Δ Γ T n(a 1,...,A n ), Δ for 2 <n The structural rules, cut rule, rules, rules and rules are the same as for PTK. However, in place of the T n rules of PTK, PTK has the following rules. T n -left1: T n (A 1,...,A n ), Γ Δ T n +l (A 1,...,A n ), Γ Δ for 1 <+ l n T n -left2: T n (A 1,...,A n ), Γ Δ T n+m +m (A 1,...,A n,b 1,...,B m ), Γ Δ for 1 n<n+ m T n -left3: A 1,..., A n,t m (B 1,...,B m ), Γ Δ A 1,..., A n,t m+n (A 1,...,A n,b 1,...,B m ), Γ Δ for 1 m<m+ n T n -right1: Γ T n (A 1,...,A n ), Δ Γ T n+m (A 1,...,A n,b 1,...,B m ), Δ for 1 n<n+ m T n -right: Γ T n (A 1,...,A n ), Δ Γ T m l (B 1,...,B m ), Δ Γ T n+m +l (A 1,...,A n,b 1,...,B m ), Δ for 1 m<m+ n In [2], J. Krajíče introduced an extension of the Frege system F, called FC for Frege with counting. In addition to the usual connectives of F, counting connectives C n, (x 1,...,x n ) are admitted, whose interpretation is that exactly of the x i equal 1. Definition 2 FC is the propositional proof system having connectives,,,, together with infinitely many new connectives C n, (φ 1,...,φ n ), for 1 n and n. The axioms of FC are those of F (see [1]) together with the new axioms: 1. A C 1,1 (A) 2. C n,0 (A 1,...,A n ) ( A 1... A n ) 3
4 3. C n+1,+1 (A 1,...,A n+1 ) [(C n, (A 1,...,A n ) A n+1 ) (C n,+1 (A 1,...,A n ) A n+1 )] if <n 4. C n+1,n+1 (A 1,...,A n+1 ) [(C n,n (A 1,...,A n ) A n+1 )]. We intend to show the relation between FC and our threshold proof systems PTK and PTK ; namely that constant depth polynomial size FC proofs correspond to polynomial size constant depth PTK and PTK proofs, and vice versa. We begin by simulating FC within PTK. Definition 3 Translate the FC formula A by the PTK formula A as follows: FC formula PTK formula x x n i=1 A i Tn n (A 1,...,A n ) n i=1 A i T1 n (A 1,...,A n ) A B T1 2 ( A, B) A B T2 2 (A B,B A) C n, (A 1,...,A n ), 0 <<n T2 2 (T n(a 1,...,A n ), T+1 n (A 1,...,A n )) C n,n (A 1,...,A n ) Tn n (A 1,...,A n ) C n,0 (A 1,...,A n ) T1 n (A 1,...,A n ) For each axiom scheme A of FC,wesetchthePTK proof of A (usually the last few steps from the formula à proved to the equivalent A are easy and left to the reader). In our notation, C n, ( A) abbreviates C n, (A 1,...,A n ), and T n( A) abbreviates T n(a 1,...,A n ). We often abbreviate A n+1 by A, so that for instance in the first subclaim appearing in the proof of Axiom 3 below, abbreviates +1 ( A) T n ( A) A, T n +1( A) A +1 (A 1,...,A n+1 ) T n (A 1,...,A n ) A n+1,t n +1(A 1,...,A n ) A n+1 Axiom 1 x C 1,1 (x) 4
5 x x x T 1 1 (x) x, T 1 1 (x) x T 1 1 (x) This completes the proof of axiom 1. x x T 1 1 (x) x T 1 1 (x),x T 1 1 (x) x ( x T 1 1 (x)) ( T 1 1 (x) x) Axiom 2 C n,0 (A 1,...,A n ) A 1... A n (Recall that, associate to the left.) Claim C n,0 (A, B, C) ( A B) C Pf A T 1 1 (A) T 3 1 (A, B, C) A A T 3 1 (A, B, C) T 3 1 (A, B, C) ( A B) B B B T 1 1 (B) T 3 1 (A, B, C) B B T 3 1 (A, B, C) T 3 1 (A, B, C) ( A B) C C C C T 1 1 (C) T 3 1 (A, B, C) C C T 3 1 (A, B, C) Claim ( A B) C C 3,0 (A, B, C) Pf A, A A, A, B A, ( A B) A, ( A B), C A, ( A B C) This completes the proof of axiom 2. B B B, B B, A, B B,( A B) B,( A B), C B,( A B C) T 3 1 (A, B, C), ( A B C) A B C T 3 1 (A, B, C) C C C, C C, A, B, C C, ( A B), C C, ( A B C) Axiom 3 C n+1,+1 ( A) (C n, ( A) A n+1 ) (C n,+1 ( A, A n+1 ) A n+1 Claim PTK proves C n+1,+1 ( A) (C n, ( A) A n+1 ) (C n,+1 ( A, A n+1 ) A n+1 The claim follows from two subclaims. 5
6 Subclaim +1 ( A) T n ( A) A, T n +1 ( A) A Pf T n +1 ( A) T n +1 ( A) A, T n +1 ( A) T n +1 ( A) A, +1 ( A) T n +1 ( A) +1 ( A) T n +1 ( A), A +1 ( A) A, T n +1 ( A) A, A +1 ( A) A, T n +1 ( A) A T n ( A) T n ( A) +1 ( A) T n ( A) A, A +1 ( A) A, A +1 ( A) T+1 n ( A) A, T n( A) Combining the last lines of the previous two proofs using -right, we have which establishes the subclaim. +1 ( A) T n ( A) A, T n +1( A) A Subclaim +2 ( A) T n +2 ( A) A, T n +1 A Pf First we prove the following. T n +2 ( A) T n +2 ( A) T n +2 ( A) +2 ( A) T n +2 ( A) +2 ( A), T n ( A) T n +2 ( A), T n +1 ( A) T n +2 ( A) T n +2 ( A) T n +2 ( A) +2 ( A) +2 ( A) T n +2 ( A) +2 ( A) T n +2 ( A), T n +1 ( A),A +2 ( A) T n +2 ( A),A Second we prove the following. T n +1 ( A) T n +1 ( A) A, T n +1 ( A) T n +1 ( A) A, T n +1 ( A) +2 ( A) A, +2 ( A) T n +1 ( A) A, T n +1 ( A) T 1 1 (A) A, +2 ( A) T n +1 ( A) A +2 ( A) A, T n +1 ( A) A A, +2 A 6
7 Combining the last lines of the previous two proofs using -right, we have +2 ( A) T n +2( A) A, T n +1( A) A as desired. Now from both subclaims, it can be shown that +1 ( A) +2 ( A) T n ( A) T n +1( A) A, T n +1( A) T n +2( A) A. This establishes the claim that C n+1,+1 ( A) (C n, ( A) A n+1 ) (C n,+1 ( A) A n+1 ) Claim PTK proves the converse of the previous, i.e. This translates to (C n, ( A) A n+1 ) (C n,+1 ( A) A n+1 ) C n+1,+1 ( A) (T n ( A) T n +1( A) A) (T n +1( A) T n +2( A) A) +1 ( A) +2 ( A). The claim follows from two subclaims. Subclaim (T n( A) T+1 n ( A) A) (T+1 n ( A) T+2 n ( A) A) +1 ( A) Pf A T 1 1 (A) A, T n ( A) T 1 1 (A) A, T n ( A) +1 ( A) A, T n ( A), T n +1 ( A) +1 ( A) T n ( A) T n ( A) A, T n ( A) T n ( A) T n +1 ( A) T n +1 ( A) T n +1 ( A) +1 ( A) A, T n +1 ( A) +1 ( A) A, T n +1 ( A), T n +2 ( A) +1 ( A) Now combining the last two proofs using -left, we have (A, T n ( A), T n +1( A)) ( A, T n +1( A), T n +2( A)) +1 ( A) Subclaim (T n ( A) T n +1 ( A) A) (T n +1 ( A) T n +2 ( A) A) +2 ( A) Pf 7
8 T n +1 ( A) T n +1 ( A) +2 ( A) T n +1 ( A) T n +1 ( A) +2 ( A) A, T n ( A), T n +1 ( A) +2 ( A) T n +2 ( A) T n +2 ( A) A, T n +2 ( A) T n +2 ( A) A, +1 ( A) T n +2 ( A) A, T n +2 ( A) +2 ( A) A, T n +1 ( A), T n +2 ( A) +2 ( A) (A, T n( A), T+1 n ( A)) ( A, T+1 n ( A), T+2 n ( A)) +2 ( A) From the two subclaims, we obtain a proof of (T n ( A) T+1( n A) A) (T+1( n A) T+2( n A) A) +1 ( A) +2 ( A) which establishes (C n, ( A) A) (C n,+1 ( A) A) C n,+1 ( A). This concludes the proof of axiom 3. Axiom 4 C n+1,n+1 ( A) C n,n ( A) A Claim C n+1,n+1 ( A) C n,n ( A) A. Pf Show Tn+1 n+1 ( A) Tn n ( A) A. A 1 A 1 A n A n A 1,...,A n+1 A 1 A 1,...,A n+1 A n n+1 ( A) A 1 This completes the proof of the claim. Claim C n,n ( A) A C n+1,n+1 ( A) Pf Show T n n ( A) A n+1 ( A). n+1 ( A) A n n+1 ( A) T n n ( A) n+1 ( A) T n n ( A) A n+1 A n+1 A n+1 A 1,...,A n+1 A n+1 n+1 ( A) A n+1 A 1 A 1 A 1,...,A n+1 A 1 A n A n A 1,...,A n+1 A n A n+1 A n+1 A 1,...,A n+1 A n+1 T n n ( A),A n+1 A 1 T n n ( A),A n+1 A n T n n ( A),A n+1 A n+1 Tn n ( A),A n+1 Tn+1 n+1 ( A) This completes the proof of the claims and so establishes the provability of the translation of Axiom 4 in PTK. By depth and size of a proof in a propositional proof system such as F, FC, PTK, etc. we mean the maximum depth and size of any formula appearing in the proof (in particular, we do not mean the depth of the proof tree in a sequent calculus proof). 8
9 Theorem 4 Suppose that P n : n 1 is a family of FC proofs, where P n is a depth d(n), sizes(n) proof of φ n. Then there exists a constant c for which there exists a family P n : n 1 of PTK proofs, where P n is a depth c + d(n), size c s(n) proof of φ n. Proof. The axioms of FC have previously been treated, and modus ponens (the only rule of inference of FC) is a special case of the cut rule of PTK. Analysis of the previous PTK proofs of the axioms of FC gives appropriate constant c. We now consider the simulation of PTK by FC. Definition 5 Translate the PTK formula A by the FC formula à as follows: PTK formula FC formula x x A à T n(a n 1,...,A n ) i= C n,i(ã1,...,ãn) A PTK sequent Γ Δ, which is equivalent to the formula is translated by the FC formula n m A i i=1 j=1 B j n m à i i=1 j=1 B j. Theorem 6 Suppose that P n : n 1 is a family of PTK proofs, where P n is a depth d(n), sizes(n) proof of φ n. Then there exists a constant c for which there exists a family P n : n 1 of FC proofs, where P n is a depth c + d(n), size s(n) c proof of φn. Proof setch By induction on the number of proof inferences. For each axiom of PTK, the translation of its sequent is easily provable in FC. Similarly, an appropriate translation of each proof rule of PTK is provable in FC. For instance, a binary rule A 1,...,A n1 B 1,...,B n2 C 1,...,C n3 D 1,...,D n4 E 1,...,E n5 F 1,...,F n6 9
10 is translated into n 1 ( i=1 Ã i n 2 i=1 n 5 ( i=1 n 3 B i ) ( Ẽ i i=1 n 6 i=1 C i To prove in FC the translation of the rule T n -left, begin with the tautology ( C n,i Γ Δ) ( C n,i Γ) Δ i n F i ) i n n 4 Using an axiom of FC, obtain ((A C n 1,i 1 ) ( A C n 1,i )) Γ Δ i n This is equivalent to the following. ((A C n 1,j ) ( A j<n i<n i=1 D i ) C n 1,i )) Γ Δ Using the translation into FC of T n (and for notational simplicity denoting the translation of formulas A by themselves), this yields the following. By distribution of this yields ((A T n 1 n 1 1 ) ( A T )) Γ Δ ((A T n 1 1 By distribution of this yields ((A T n 1 1 Γ) ( A T n 1 Γ)) Δ Γ) Δ) (( A T n 1 Γ) Δ) (T n Γ) Δ It will be shown in the proof of the next theorem that and so From this, since A T n 1 T n 1 T n 1 T n 1 1 Γ A T n 1 1 Γ (A T n 1 ) ( A T n 1 ) 10
11 it is not hard to see that there is an FC proof of the following. ((A T n 1 1 Γ) Δ) ((T n 1 Γ) Δ) (T n Γ) Δ But this is the translation of rule T n -left into FC.TheFC proofofthetranslation of T n -right is similar. Theorem 7 Suppose that P n : n 1 is a family of PTK proofs, where P n is a depth d(n), sizes(n) proof of φ n. Then there exists a constant c for which there exists a family P n : n 1 of PTK proofs, where P n is a depth c + d(n), size c s(n) proof of φ n. Proof. Note first that T n T n 1 and that T n T n 1 T n T n 1 1 T n T n T n A, T n 1 T n 1 1 T n 1 A, T n 1 1 T n 1 T n 1 Thus the n proof of Tj i T i+1 j yield a proof of and T i j+1 T i j for i<nand j<together (1) T n Case 1: T n-left1 Since T n has size O(n), there is an no(1) size proof of (1). Now T+l n T n T n, Γ Δ T+l n, Γ Δ Case 2: T n-left2 T+1 n T +1 n A, T n A T n +1 A T n,tn +1 11
12 From this, we obtain and by iteration rule T n-left2. Case 3: T n-left3 +1 T n T n +1, A A T n +1 A, T n, A +1, A A T n +1 by using the T n -left rule of PTK. Iterating this, we have the proof of the T n-left3 rule of PTK. Case 4: T n-right1 Immediate from (1). Case 5: T n-right2 Iterating the idea of proof of case 2, we can show that T n ( A) T m l ( B) T n+m +l ( A, B) From this, case 5 follows. This completes the proof of the theorem. It is not difficult to see that the simulations of FC, PTK and PTK are within a polynomial factor of the size and a constant factor of the depth. 12
13 References [1] S. A. Coo and R. Rechow. On the relative efficiency of propositional proof systems. Journal of Symbolic Logic, 44:36 50, [2] J. Krajíče. On Frege and extended Frege systems. In P. Clote and J. Remmel, editors, Feasible Mathematics II, pages Birhäuser,
Fundamentals of Logic
Fundamentals of Logic No.4 Proof Tatsuya Hagino Faculty of Environment and Information Studies Keio University 2015/5/11 Tatsuya Hagino (Faculty of Environment and InformationFundamentals Studies Keio
More information1 FUNDAMENTALS OF LOGIC NO.5 SOUNDNESS AND COMPLETENESS Tatsuya Hagino hagino@sfc.keio.ac.jp lecture URL https://vu5.sfc.keio.ac.jp/slide/ 2 So Far Propositional Logic Logical Connectives(,,, ) Truth Table
More information0.1 Equivalence between Natural Deduction and Axiomatic Systems
0.1 Equivalence between Natural Deduction and Axiomatic Systems Theorem 0.1.1. Γ ND P iff Γ AS P ( ) it is enough to prove that all axioms are theorems in ND, as MP corresponds to ( e). ( ) by induction
More information2 Deduction in Sentential Logic
2 Deduction in Sentential Logic Though we have not yet introduced any formal notion of deductions (i.e., of derivations or proofs), we can easily give a formal method for showing that formulas are tautologies:
More information5 Deduction in First-Order Logic
5 Deduction in First-Order Logic The system FOL C. Let C be a set of constant symbols. FOL C is a system of deduction for the language L # C. Axioms: The following are axioms of FOL C. (1) All tautologies.
More informationSAT and DPLL. Introduction. Preliminaries. Normal forms DPLL. Complexity. Espen H. Lian. DPLL Implementation. Bibliography.
SAT and Espen H. Lian Ifi, UiO Implementation May 4, 2010 Espen H. Lian (Ifi, UiO) SAT and May 4, 2010 1 / 59 Espen H. Lian (Ifi, UiO) SAT and May 4, 2010 2 / 59 Introduction Introduction SAT is the problem
More informationarxiv: v1 [math.lo] 24 Feb 2014
Residuated Basic Logic II. Interpolation, Decidability and Embedding Minghui Ma 1 and Zhe Lin 2 arxiv:1404.7401v1 [math.lo] 24 Feb 2014 1 Institute for Logic and Intelligence, Southwest University, Beibei
More informationSAT and DPLL. Espen H. Lian. May 4, Ifi, UiO. Espen H. Lian (Ifi, UiO) SAT and DPLL May 4, / 59
SAT and DPLL Espen H. Lian Ifi, UiO May 4, 2010 Espen H. Lian (Ifi, UiO) SAT and DPLL May 4, 2010 1 / 59 Normal forms Normal forms DPLL Complexity DPLL Implementation Bibliography Espen H. Lian (Ifi, UiO)
More informationsample-bookchapter 2015/7/7 9:44 page 1 #1 THE BINOMIAL MODEL
sample-bookchapter 2015/7/7 9:44 page 1 #1 1 THE BINOMIAL MODEL In this chapter we will study, in some detail, the simplest possible nontrivial model of a financial market the binomial model. This is a
More informationSecurity issues in contract-based computing
Security issues in contract-based computing Massimo Bartoletti 1 and Roberto Zunino 2 1 Dipartimento di Matematica e Informatica, Università degli Studi di Cagliari, Italy 2 Dipartimento di Ingegneria
More informationSemantics with Applications 2b. Structural Operational Semantics
Semantics with Applications 2b. Structural Operational Semantics Hanne Riis Nielson, Flemming Nielson (thanks to Henrik Pilegaard) [SwA] Hanne Riis Nielson, Flemming Nielson Semantics with Applications:
More informationThe Binomial Theorem and Consequences
The Binomial Theorem and Consequences Juris Steprāns York University November 17, 2011 Fermat s Theorem Pierre de Fermat claimed the following theorem in 1640, but the first published proof (by Leonhard
More informationNotes on Natural Logic
Notes on Natural Logic Notes for PHIL370 Eric Pacuit November 16, 2012 1 Preliminaries: Trees A tree is a structure T = (T, E), where T is a nonempty set whose elements are called nodes and E is a relation
More informationTABLEAU-BASED DECISION PROCEDURES FOR HYBRID LOGIC
TABLEAU-BASED DECISION PROCEDURES FOR HYBRID LOGIC THOMAS BOLANDER AND TORBEN BRAÜNER Abstract. Hybrid logics are a principled generalization of both modal logics and description logics. It is well-known
More informationLevel by Level Inequivalence, Strong Compactness, and GCH
Level by Level Inequivalence, Strong Compactness, and GCH Arthur W. Apter Department of Mathematics Baruch College of CUNY New York, New York 10010 USA and The CUNY Graduate Center, Mathematics 365 Fifth
More informationA Translation of Intersection and Union Types
A Translation of Intersection and Union Types for the λ µ-calculus Kentaro Kikuchi RIEC, Tohoku University kentaro@nue.riec.tohoku.ac.jp Takafumi Sakurai Department of Mathematics and Informatics, Chiba
More informationImplications as rules
ProDi Tübingen 26.2.2011 p. 1 Implications as rules In defence of proof-theoretic semantics Peter Schroeder-Heister Wilhelm-Schickard-Institut für Informatik Universität Tübingen ProDi Tübingen 26.2.2011
More informationStrategic Trading of Informed Trader with Monopoly on Shortand Long-Lived Information
ANNALS OF ECONOMICS AND FINANCE 10-, 351 365 (009) Strategic Trading of Informed Trader with Monopoly on Shortand Long-Lived Information Chanwoo Noh Department of Mathematics, Pohang University of Science
More informationNOTES ON FIBONACCI TREES AND THEIR OPTIMALITY* YASUICHI HORIBE INTRODUCTION 1. FIBONACCI TREES
0#0# NOTES ON FIBONACCI TREES AND THEIR OPTIMALITY* YASUICHI HORIBE Shizuoka University, Hamamatsu, 432, Japan (Submitted February 1982) INTRODUCTION Continuing a previous paper [3], some new observations
More informationQuadrant marked mesh patterns in 123-avoiding permutations
Quadrant marked mesh patterns in 23-avoiding permutations Dun Qiu Department of Mathematics University of California, San Diego La Jolla, CA 92093-02. USA duqiu@math.ucsd.edu Jeffrey Remmel Department
More informationDiscrete Mathematics for CS Spring 2008 David Wagner Final Exam
CS 70 Discrete Mathematics for CS Spring 2008 David Wagner Final Exam PRINT your name:, (last) SIGN your name: (first) PRINT your Unix account login: Your section time (e.g., Tue 3pm): Name of the person
More informationMAC Learning Objectives. Learning Objectives (Cont.)
MAC 1140 Module 12 Introduction to Sequences, Counting, The Binomial Theorem, and Mathematical Induction Learning Objectives Upon completing this module, you should be able to 1. represent sequences. 2.
More informationOrthogonality to the value group is the same as generic stability in C-minimal expansions of ACVF
Orthogonality to the value group is the same as generic stability in C-minimal expansions of ACVF Will Johnson February 18, 2014 1 Introduction Let T be some C-minimal expansion of ACVF. Let U be the monster
More informationLecture 2: The Simple Story of 2-SAT
0510-7410: Topics in Algorithms - Random Satisfiability March 04, 2014 Lecture 2: The Simple Story of 2-SAT Lecturer: Benny Applebaum Scribe(s): Mor Baruch 1 Lecture Outline In this talk we will show that
More informationDESCENDANTS IN HEAP ORDERED TREES OR A TRIUMPH OF COMPUTER ALGEBRA
DESCENDANTS IN HEAP ORDERED TREES OR A TRIUMPH OF COMPUTER ALGEBRA Helmut Prodinger Institut für Algebra und Diskrete Mathematik Technical University of Vienna Wiedner Hauptstrasse 8 0 A-00 Vienna, Austria
More informationAlgorithmic Game Theory and Applications. Lecture 11: Games of Perfect Information
Algorithmic Game Theory and Applications Lecture 11: Games of Perfect Information Kousha Etessami finite games of perfect information Recall, a perfect information (PI) game has only 1 node per information
More informationThe Real Numbers. Here we show one way to explicitly construct the real numbers R. First we need a definition.
The Real Numbers Here we show one way to explicitly construct the real numbers R. First we need a definition. Definitions/Notation: A sequence of rational numbers is a funtion f : N Q. Rather than write
More informationClassifying Descents According to Parity
Classifying Descents According to Parity arxiv:math/0508570v1 [math.co] 29 Aug 2005 Sergey Kitaev Reyjaví University Ofanleiti 2 IS-103 Reyjaví, Iceland sergey@ru.is Jeffrey Remmel Department of Mathematics
More informationA relation on 132-avoiding permutation patterns
Discrete Mathematics and Theoretical Computer Science DMTCS vol. VOL, 205, 285 302 A relation on 32-avoiding permutation patterns Natalie Aisbett School of Mathematics and Statistics, University of Sydney,
More informationInversion Formulae on Permutations Avoiding 321
Inversion Formulae on Permutations Avoiding 31 Pingge Chen College of Mathematics and Econometrics Hunan University Changsha, P. R. China. chenpingge@hnu.edu.cn Suijie Wang College of Mathematics and Econometrics
More informationLECTURE 3: FREE CENTRAL LIMIT THEOREM AND FREE CUMULANTS
LECTURE 3: FREE CENTRAL LIMIT THEOREM AND FREE CUMULANTS Recall from Lecture 2 that if (A, φ) is a non-commutative probability space and A 1,..., A n are subalgebras of A which are free with respect to
More information3 The Model Existence Theorem
3 The Model Existence Theorem Although we don t have compactness or a useful Completeness Theorem, Henkinstyle arguments can still be used in some contexts to build models. In this section we describe
More informationMartingales. by D. Cox December 2, 2009
Martingales by D. Cox December 2, 2009 1 Stochastic Processes. Definition 1.1 Let T be an arbitrary index set. A stochastic process indexed by T is a family of random variables (X t : t T) defined on a
More informationAn effective perfect-set theorem
An effective perfect-set theorem David Belanger, joint with Keng Meng (Selwyn) Ng CTFM 2016 at Waseda University, Tokyo Institute for Mathematical Sciences National University of Singapore The perfect
More informationA Syntactic Realization Theorem for Justification Logics
A Syntactic Realization Theorem for Justification Logics Kai Brünnler, Remo Goetschi, and Roman Kuznets 1 Institut für Informatik und angewandte Mathematik, Universität Bern Neubrückstrasse 10, CH-3012
More informationDecidability and Recursive Languages
Decidability and Recursive Languages Let L (Σ { }) be a language, i.e., a set of strings of symbols with a finite length. For example, {0, 01, 10, 210, 1010,...}. Let M be a TM such that for any string
More informationIntroduction An example Cut elimination. Deduction Modulo. Olivier Hermant. Tuesday, December 12, Deduction Modulo
Tuesday, December 12, 2006 Deduction and Computation Sequent calculus The cut rule The rewrite rules Sequent calculus The cut rule The rewrite rules Deduction system: Gentzen s sequent calculus Γ, P P
More informationThe reverse self-dual serial cost-sharing rule M. Josune Albizuri, Henar Díez and Amaia de Sarachu. April 17, 2012
The reverse self-dual serial cost-sharing rule M. Josune Albizuri, Henar Díez and Amaia de Sarachu April 17, 01 Abstract. In this study we define a cost sharing rule for cost sharing problems. This rule
More informationδ j 1 (S j S j 1 ) (2.3) j=1
Chapter The Binomial Model Let S be some tradable asset with prices and let S k = St k ), k = 0, 1,,....1) H = HS 0, S 1,..., S N 1, S N ).) be some option payoff with start date t 0 and end date or maturity
More informationDiscrete Random Variables and Probability Distributions. Stat 4570/5570 Based on Devore s book (Ed 8)
3 Discrete Random Variables and Probability Distributions Stat 4570/5570 Based on Devore s book (Ed 8) Random Variables We can associate each single outcome of an experiment with a real number: We refer
More informationNotes on the symmetric group
Notes on the symmetric group 1 Computations in the symmetric group Recall that, given a set X, the set S X of all bijections from X to itself (or, more briefly, permutations of X) is group under function
More informationGAME THEORY. Department of Economics, MIT, Follow Muhamet s slides. We need the following result for future reference.
14.126 GAME THEORY MIHAI MANEA Department of Economics, MIT, 1. Existence and Continuity of Nash Equilibria Follow Muhamet s slides. We need the following result for future reference. Theorem 1. Suppose
More informationMTH6154 Financial Mathematics I Interest Rates and Present Value Analysis
16 MTH6154 Financial Mathematics I Interest Rates and Present Value Analysis Contents 2 Interest Rates 16 2.1 Definitions.................................... 16 2.1.1 Rate of Return..............................
More informationHandout 4: Deterministic Systems and the Shortest Path Problem
SEEM 3470: Dynamic Optimization and Applications 2013 14 Second Term Handout 4: Deterministic Systems and the Shortest Path Problem Instructor: Shiqian Ma January 27, 2014 Suggested Reading: Bertsekas
More informationSupplementary Material for Combinatorial Partial Monitoring Game with Linear Feedback and Its Application. A. Full proof for Theorems 4.1 and 4.
Supplementary Material for Combinatorial Partial Monitoring Game with Linear Feedback and Its Application. A. Full proof for Theorems 4.1 and 4. If the reader will recall, we have the following problem-specific
More informationYao s Minimax Principle
Complexity of algorithms The complexity of an algorithm is usually measured with respect to the size of the input, where size may for example refer to the length of a binary word describing the input,
More informationPermutation Factorizations and Prime Parking Functions
Permutation Factorizations and Prime Parking Functions Amarpreet Rattan Department of Combinatorics and Optimization University of Waterloo Waterloo, ON, Canada N2L 3G1 arattan@math.uwaterloo.ca June 10,
More informationLECTURE 2: MULTIPERIOD MODELS AND TREES
LECTURE 2: MULTIPERIOD MODELS AND TREES 1. Introduction One-period models, which were the subject of Lecture 1, are of limited usefulness in the pricing and hedging of derivative securities. In real-world
More informationDRAFT. 1 exercise in state (S, t), π(s, t) = 0 do not exercise in state (S, t) Review of the Risk Neutral Stock Dynamics
Chapter 12 American Put Option Recall that the American option has strike K and maturity T and gives the holder the right to exercise at any time in [0, T ]. The American option is not straightforward
More informationGeneralising the weak compactness of ω
Generalising the weak compactness of ω Andrew Brooke-Taylor Generalised Baire Spaces Masterclass Royal Netherlands Academy of Arts and Sciences 22 August 2018 Andrew Brooke-Taylor Generalising the weak
More informationBINOMIAL OPTION PRICING AND BLACK-SCHOLES
BINOMIAL OPTION PRICING AND BLACK-CHOLE JOHN THICKTUN 1. Introduction This paper aims to investigate the assumptions under which the binomial option pricing model converges to the Blac-choles formula.
More informationTHE NUMBER OF UNARY CLONES CONTAINING THE PERMUTATIONS ON AN INFINITE SET
THE NUMBER OF UNARY CLONES CONTAINING THE PERMUTATIONS ON AN INFINITE SET MICHAEL PINSKER Abstract. We calculate the number of unary clones (submonoids of the full transformation monoid) containing the
More informationCut-free sequent calculi for algebras with adjoint modalities
Cut-free sequent calculi for algebras with adjoint modalities Roy Dyckhoff (University of St Andrews) and Mehrnoosh Sadrzadeh (Universities of Oxford & Southampton) TANCL Conference, Oxford, 8 August 2007
More informationCIS 500 Software Foundations Fall October. CIS 500, 6 October 1
CIS 500 Software Foundations Fall 2004 6 October CIS 500, 6 October 1 Midterm 1 is next Wednesday Today s lecture will not be covered by the midterm. Next Monday, review class. Old exams and review questions
More informationBinomial Coefficient
Binomial Coefficient This short text is a set of notes about the binomial coefficients, which link together algebra, combinatorics, sets, binary numbers and probability. The Product Rule Suppose you are
More informationLecture 4: Divide and Conquer
Lecture 4: Divide and Conquer Divide and Conquer Merge sort is an example of a divide-and-conquer algorithm Recall the three steps (at each level to solve a divideand-conquer problem recursively Divide
More informationHarvard School of Engineering and Applied Sciences CS 152: Programming Languages
Harvard School of Engineering and Applied Sciences CS 152: Programming Languages Lecture 3 Tuesday, January 30, 2018 1 Inductive sets Induction is an important concept in the theory of programming language.
More informationB8.3 Week 2 summary 2018
S p VT u = f(su ) S T = S u V t =? S t S t e r(t t) 1 p VT d = f(sd ) S T = S d t T time Figure 1: Underlying asset price in a one-step binomial model B8.3 Week 2 summary 2018 The simplesodel for a random
More informationIntroduction to Probability Theory and Stochastic Processes for Finance Lecture Notes
Introduction to Probability Theory and Stochastic Processes for Finance Lecture Notes Fabio Trojani Department of Economics, University of St. Gallen, Switzerland Correspondence address: Fabio Trojani,
More informationSTOCHASTIC CALCULUS AND BLACK-SCHOLES MODEL
STOCHASTIC CALCULUS AND BLACK-SCHOLES MODEL YOUNGGEUN YOO Abstract. Ito s lemma is often used in Ito calculus to find the differentials of a stochastic process that depends on time. This paper will introduce
More informationLecture 2: The Neoclassical Growth Model
Lecture 2: The Neoclassical Growth Model Florian Scheuer 1 Plan Introduce production technology, storage multiple goods 2 The Neoclassical Model Three goods: Final output Capital Labor One household, with
More informationSeparable Preferences Ted Bergstrom, UCSB
Separable Preferences Ted Bergstrom, UCSB When applied economists want to focus their attention on a single commodity or on one commodity group, they often find it convenient to work with a twocommodity
More informationFinding Equilibria in Games of No Chance
Finding Equilibria in Games of No Chance Kristoffer Arnsfelt Hansen, Peter Bro Miltersen, and Troels Bjerre Sørensen Department of Computer Science, University of Aarhus, Denmark {arnsfelt,bromille,trold}@daimi.au.dk
More informationChapter 5. Sampling Distributions
Lecture notes, Lang Wu, UBC 1 Chapter 5. Sampling Distributions 5.1. Introduction In statistical inference, we attempt to estimate an unknown population characteristic, such as the population mean, µ,
More informationfig 3.2 promissory note
Chapter 4. FIXED INCOME SECURITIES Objectives: To set the price of securities at the specified moment of time. To simulate mathematical and real content situations, where the values of securities need
More informationLecture l(x) 1. (1) x X
Lecture 14 Agenda for the lecture Kraft s inequality Shannon codes The relation H(X) L u (X) = L p (X) H(X) + 1 14.1 Kraft s inequality While the definition of prefix-free codes is intuitively clear, we
More informationStrong normalisation and the typed lambda calculus
CHAPTER 9 Strong normalisation and the typed lambda calculus In the previous chapter we looked at some reduction rules for intuitionistic natural deduction proofs and we have seen that by applying these
More informationOptimal Satisficing Tree Searches
Optimal Satisficing Tree Searches Dan Geiger and Jeffrey A. Barnett Northrop Research and Technology Center One Research Park Palos Verdes, CA 90274 Abstract We provide an algorithm that finds optimal
More informationHarvard School of Engineering and Applied Sciences CS 152: Programming Languages
Harvard School of Engineering and Applied Sciences CS 152: Programming Languages Lecture 2 Thursday, January 30, 2014 1 Expressing Program Properties Now that we have defined our small-step operational
More informationAsymptotic Notation. Instructor: Laszlo Babai June 14, 2002
Asymptotic Notation Instructor: Laszlo Babai June 14, 2002 1 Preliminaries Notation: exp(x) = e x. Throughout this course we shall use the following shorthand in quantifier notation. ( a) is read as for
More informationMaximally Consistent Extensions
Chapter 4 Maximally Consistent Extensions Throughout this chapter we require that all formulae are written in Polish notation and that the variables are amongv 0,v 1,v 2,... Recall that by the PRENEX NORMAL
More informationOn the Number of Permutations Avoiding a Given Pattern
On the Number of Permutations Avoiding a Given Pattern Noga Alon Ehud Friedgut February 22, 2002 Abstract Let σ S k and τ S n be permutations. We say τ contains σ if there exist 1 x 1 < x 2
More informationOn Packing Densities of Set Partitions
On Packing Densities of Set Partitions Adam M.Goyt 1 Department of Mathematics Minnesota State University Moorhead Moorhead, MN 56563, USA goytadam@mnstate.edu Lara K. Pudwell Department of Mathematics
More informationNon replication of options
Non replication of options Christos Kountzakis, Ioannis A Polyrakis and Foivos Xanthos June 30, 2008 Abstract In this paper we study the scarcity of replication of options in the two period model of financial
More informationProof Techniques for Operational Semantics. Questions? Why Bother? Mathematical Induction Well-Founded Induction Structural Induction
Proof Techniques for Operational Semantics Announcements Homework 1 feedback/grades posted Homework 2 due tonight at 11:55pm Meeting 10, CSCI 5535, Spring 2010 2 Plan Questions? Why Bother? Mathematical
More informationHorn-formulas as Types for Structural Resolution
Horn-formulas as Types for Structural Resolution Peng Fu, Ekaterina Komendantskaya University of Dundee School of Computing 2 / 17 Introduction: Background Logic Programming(LP) is based on first-order
More information10. Discrete-time models
Pricing Options with Mathematical Models 10. Discrete-time models Some of the content of these slides is based on material from the book Introduction to the Economics and Mathematics of Financial Markets
More informationBETA DISTRIBUTION ON ARITHMETICAL SEMIGROUPS
Annales Univ Sci Budapest Sect Comp 47 (2018) 147 154 BETA DISTRIBUTION ON ARITHMETICAL SEMIGROUPS Gintautas Bareikis and Algirdas Mačiulis (Vilnius Lithuania) Communicated by Imre Kátai (Received February
More informationAmerican Option Pricing Formula for Uncertain Financial Market
American Option Pricing Formula for Uncertain Financial Market Xiaowei Chen Uncertainty Theory Laboratory, Department of Mathematical Sciences Tsinghua University, Beijing 184, China chenxw7@mailstsinghuaeducn
More informationHomework #4. CMSC351 - Spring 2013 PRINT Name : Due: Thu Apr 16 th at the start of class
Homework #4 CMSC351 - Spring 2013 PRINT Name : Due: Thu Apr 16 th at the start of class o Grades depend on neatness and clarity. o Write your answers with enough detail about your approach and concepts
More informationTableau Theorem Prover for Intuitionistic Propositional Logic
Tableau Theorem Prover for Intuitionistic Propositional Logic Portland State University CS 510 - Mathematical Logic and Programming Languages Motivation Tableau for Classical Logic If A is contradictory
More informationCS792 Notes Henkin Models, Soundness and Completeness
CS792 Notes Henkin Models, Soundness and Completeness Arranged by Alexandra Stefan March 24, 2005 These notes are a summary of chapters 4.5.1-4.5.5 from [1]. 1 Review indexed family of sets: A s, where
More informationTableau Theorem Prover for Intuitionistic Propositional Logic
Tableau Theorem Prover for Intuitionistic Propositional Logic Portland State University CS 510 - Mathematical Logic and Programming Languages Motivation Tableau for Classical Logic If A is contradictory
More informationL 2 -theoretical study of the relation between the LIBOR market model and the HJM model Takashi Yasuoka
Journal of Math-for-Industry, Vol. 5 (213A-2), pp. 11 16 L 2 -theoretical study of the relation between the LIBOR market model and the HJM model Takashi Yasuoka Received on November 2, 212 / Revised on
More informationTR : Knowledge-Based Rational Decisions
City University of New York (CUNY) CUNY Academic Works Computer Science Technical Reports Graduate Center 2009 TR-2009011: Knowledge-Based Rational Decisions Sergei Artemov Follow this and additional works
More informationClass Notes: On the Theme of Calculators Are Not Needed
Class Notes: On the Theme of Calculators Are Not Needed Public Economics (ECO336) November 03 Preamble This year (and in future), the policy in this course is: No Calculators. This is for two constructive
More informationA Property Equivalent to n-permutability for Infinite Groups
Journal of Algebra 221, 570 578 (1999) Article ID jabr.1999.7996, available online at http://www.idealibrary.com on A Property Equivalent to n-permutability for Infinite Groups Alireza Abdollahi* and Aliakbar
More informationBrief Notes on the Category Theoretic Semantics of Simply Typed Lambda Calculus
University of Cambridge 2017 MPhil ACS / CST Part III Category Theory and Logic (L108) Brief Notes on the Category Theoretic Semantics of Simply Typed Lambda Calculus Andrew Pitts Notation: comma-separated
More informationarxiv: v1 [math.co] 31 Mar 2009
A BIJECTION BETWEEN WELL-LABELLED POSITIVE PATHS AND MATCHINGS OLIVIER BERNARDI, BERTRAND DUPLANTIER, AND PHILIPPE NADEAU arxiv:0903.539v [math.co] 3 Mar 009 Abstract. A well-labelled positive path of
More informationComputing Unsatisfiable k-sat Instances with Few Occurrences per Variable
Computing Unsatisfiable k-sat Instances with Few Occurrences per Variable Shlomo Hoory and Stefan Szeider Department of Computer Science, University of Toronto, shlomoh,szeider@cs.toronto.edu Abstract.
More informationMSc Financial Engineering CHRISTMAS ASSIGNMENT: MERTON S JUMP-DIFFUSION MODEL. To be handed in by monday January 28, 2013
MSc Financial Engineering 2012-13 CHRISTMAS ASSIGNMENT: MERTON S JUMP-DIFFUSION MODEL To be handed in by monday January 28, 2013 Department EMS, Birkbeck Introduction The assignment consists of Reading
More informationHarvard School of Engineering and Applied Sciences CS 152: Programming Languages
Harvard School of Engineering and Applied Sciences CS 152: Programming Languages Lecture 3 Tuesday, February 2, 2016 1 Inductive proofs, continued Last lecture we considered inductively defined sets, and
More informationRight-cancellability of a family of operations on binary trees
Right-cancellability of a family of operations on binary trees Philippe Dchon LaBRI, U.R.A. CNRS 1304, Université Bordeax 1, 33405 Talence, France We prove some new reslts on a family of operations on
More informationEssays on Some Combinatorial Optimization Problems with Interval Data
Essays on Some Combinatorial Optimization Problems with Interval Data a thesis submitted to the department of industrial engineering and the institute of engineering and sciences of bilkent university
More informationCSE 21 Winter 2016 Homework 6 Due: Wednesday, May 11, 2016 at 11:59pm. Instructions
CSE 1 Winter 016 Homework 6 Due: Wednesday, May 11, 016 at 11:59pm Instructions Homework should be done in groups of one to three people. You are free to change group members at any time throughout the
More informationA1: American Options in the Binomial Model
Appendix 1 A1: American Options in the Binomial Model So far we were dealing with options which can be excercised only at a fixed time, at their maturity date T. These are european options. In a complete
More informationCHAPTER 14: REPEATED PRISONER S DILEMMA
CHAPTER 4: REPEATED PRISONER S DILEMMA In this chapter, we consider infinitely repeated play of the Prisoner s Dilemma game. We denote the possible actions for P i by C i for cooperating with the other
More informationManaging Default Contagion in Financial Networks
Managing Default Contagion in Financial Networks Nils Detering University of California, Santa Barbara with Thilo Meyer-Brandis, Konstantinos Panagiotou, Daniel Ritter (all LMU) CFMAR 10th Anniversary
More informationDepartment of Mathematics. Mathematics of Financial Derivatives
Department of Mathematics MA408 Mathematics of Financial Derivatives Thursday 15th January, 2009 2pm 4pm Duration: 2 hours Attempt THREE questions MA408 Page 1 of 5 1. (a) Suppose 0 < E 1 < E 3 and E 2
More informationAn Adaptive Characterization of Signed Systems for Paraconsistent Reasoning
An Adaptive Characterization of Signed Systems for Paraconsistent Reasoning Diderik Batens, Joke Meheus, Dagmar Provijn Centre for Logic and Philosophy of Science University of Ghent, Belgium {Diderik.Batens,Joke.Meheus,Dagmar.Provijn}@UGent.be
More information