A Lambda Model Characterizing Computational Behaviours of Terms

Μέγεθος: px
Εμφάνιση ξεκινά από τη σελίδα:

Download "A Lambda Model Characterizing Computational Behaviours of Terms"

Transcript

1 A Lambda Model Characterizing Computational Behaviours of Terms joint paper with Silvia Ghilezan RPC 01, Sendai, October 26,

2 Plan of the talk normalization properties inverse limit model Stone dualities type assignment system reducibility method replaceability 2

3 Normalization properties M N iff M β a normal form M HN iff M β λ x.yn M WN iff M β λx.n or M β xn Persistent normalization properties M PN iff MN N for all terms N in N M PHN iff MN HN for all terms N M PWN iff MN WN for all terms N 3

4 I λx.x, λx.xx, Y λf.(λx.f(xx))(λx.f(xx)), K λxy.x λx.x N, but / PWN, since (λx.x )I β / WN λx.y( ) PHN, but / N λx.x( ) HN, but / N and / PWN, since (λx.x( )) β ( ) / WN YK PWN, but / HN λx. WN, but / HN and / PWN, since (λx. )M β / WN 4

5 Λ WN PWN HN PHN N PN 5

6 Plan of the talk normalization properties inverse limit model Stone dualities type assignment system reducibility method replaceability 6

7 Functional Behaviours step function a b WN λd. if a d then b else PWN D = [D D] n IN ( }.{{.. } ) HN h ĥ h PHN ĥ ĥ N n (ĥ h) (ˆn n) PN ˆn ( ĥ) (n ˆn) n 7

8 ˆn ˆn ( ĥ) (n ˆn) ĥ n ĥ (ĥ h) (ˆn n) h ĥ h D 0 [D 0 D 0 ] i 0 (ˆn) = ( ĥ) (n ˆn) i 0 (n) = (ĥ h) (ˆn n) i 0 (ĥ) = ĥ i 0 (h) = ĥ h i 0 ( ) = 8

9 Main Theorem, Version I M PN iff [[M]] D ρˆn ˆn M N iff [[M]] D ρˆn n M PHN iff [[M]] D ρˆn ĥ M HN iff [[M]] D ρˆn h M PWN iff [[M]] D ρˆn n IN ( }.{{.. } ) n M WN iff [[M]] D ρˆn ρˆn (x) = ˆn 9

10 Plan of the talk normalization properties inverse limit model Stone dualities type assignment system reducibility method replaceability 10

11 Stone dualities topological spaces as partial orders Stone spaces as Boolean algebras (Stone, 36) Scott domains as information systems (Scott, 82) ω-algebraic complete lattices as intersection type theories (Coppo et al., 84) SFP domains as pre-locales (Abramsky, 91) 11

12 d M τ 12

13 semantics of terms d M τ 13

14 type assignment d M τ 14

15 semantics of types M d τ 15

16 Stone duality d = [[M]] d [[τ]] M M : τ d τ 16

17 We can describe an inverse limit model D by taking: the types freely generated by closing (a set of atomic types corresponding to) the elements of D 0 under the function type constructor and the intersection type constructor ; the preorder between types induced by reversing the order in D 0 and by encoding the initial projection, according to the correspondence: function type constructor step function intersection type constructor join 17

18 Plan of the talk normalization properties inverse limit model Stone dualities type assignment system reducibility method replaceability 18

19 The set T of types T = ν ˆν µ ˆµ Ω T T T T mapping m : T D m(ν) = n m(ˆν) = ˆn m(µ) = h m(ˆµ) = ĥ m(ω) = m(σ τ) = m(σ) m(τ) m(σ τ) = m(σ) m(τ). 19

20 Preorder on T ˆν ν ˆν ˆµ ν µ ˆµ µ σ Ω σ Ω Ω Ω ˆν (Ω ˆµ) (ν ˆν) ν (ˆµ µ) (ˆν ν) ˆµ Ω ˆµ µ ˆµ µ σ σ σ σ, τ τ σ σ τ τ σ σ σ σ τ, τ ζ σ ζ σ τ σ, σ τ τ σ σ, τ τ σ τ σ τ (σ τ) (σ ζ) σ τ ζ σ τ is short for σ τ and τ σ. 20

21 The set F of filters A filter is a set X T such that: Ω X if σ τ and σ X, then τ X if σ, τ X, then σ τ X F is the set of filters over T X is the filter generated by X σ is {σ} 21

22 F, is an ω-algebraic complete lattice X T (X Y ) X Y Ω Y 22

23 F and D are isomorphic as ω-algebraic complete lattices m : F D m (X) = σ X m(σ) m ( σ) = m(σ) 23

24 The type assignment system (ax) Γ, x : σ x : σ (Ω) Γ M : Ω ( E) ( I) Γ M : σ τ Γ N : σ Γ MN : τ Γ, x : σ M : τ Γ λx.m : σ τ ( I) Γ M : σ Γ M : τ Γ M : σ τ ( ) Γ M : σ, Γ M : τ σ τ 24

25 Finitary logical description For any lambda term M and environment ρ : var D [[M]] D ρ = m ({τ T Γ = ρ. Γ M : τ}) where Γ = ρ if and only if (x : σ) Γ implies m(σ) ρ(x). If Γ M : τ and M = β N, then Γ N : τ 25

26 Main Theorem, Version II M PN iff Γˆν M : ˆν M N iff Γˆν M : ν M PHN iff Γˆν M : ˆµ M HN iff Γˆν M : µ M PWN iff Γˆν M : Ω n Ω for all n IN M WN iff Γˆν M : Ω Ω Γˆν = {x : ˆν x var} 26

27 Plan of the talk normalization properties inverse limit model Stone dualities type assignment system reducibility method replaceability 27

28 Proof of Reducibility method interpret types by suitable sets show soundness of the type assignment we consider Λ as the applicative structure whose domain is the set of lambda terms and where the application is just the application of terms 28

29 valuation of types [[ ]] : T 2 Λ [[ν]] = N [[ˆν]] = PN [[µ]] = HN [[ˆµ]] = PHN [[Ω]] = Λ [[σ τ]] = [[σ]] [[τ]] [[σ τ]] = [[σ]] [[τ]] = {M WN N [[σ]] MN [[τ]]} [[Ω Ω]] = {M WN N [[Ω]] MN [[Ω]]} = {M WN N Λ MN Λ} = WN n IN [[Ωn Ω]] = n IN [[Ωn Ω Ω]] = {M WN N [[Ω]] M N [[Ω Ω]]} = {M WN N Λ M N WN } = PWN 29

30 valuation of terms [[ ]] ρ : Λ Λ [[M]] ρ = M[x 1 := ρ(x 1 ),..., x n := ρ(x n )] where ρ : var Λ is a valuation of term variables in Λ FV(M) = {x 1,..., x n } 30

31 semantic satisfiability relation = ρ = M : τ iff [[M]] ρ [[τ]] ρ = Γ iff ( (x : σ) Γ) ρ = x : σ Γ = M : τ iff ( ρ = Γ) ρ = M : τ soundness Γ M : τ Γ = M : τ 31

32 Plan of the talk normalization properties inverse limit model Stone dualities type assignment system reducibility method replaceability 32

33 Proof of WN - M = β λx.n Γˆν N : Ω (Ω) Γˆν λx.n : Ω Ω ( I) 33

34 - M = β x N (ax) Γˆν x : ˆν ( ) Γˆν x : Ω Ω Ω Γˆν N : Ω Γˆν xn : Ω Ω (Ω) ( E) 34

35 PWN - for all n, there is N such that M = β λx 1... x n.n Γˆν N : Ω (Ω) Γˆν λx 1... x n.n : Ω n Ω ( I) 35

36 - M = β λ x.y N (ax) Γˆν y : ˆν ( ) Γˆν y : Ω Ω Ω (Ω) Γˆν N : Ω ( E) Γˆν yn : Ω Ω ( I) Γˆν λ x.yn : Ω Ω 36

37 HN M = β λ x.y N Γ = {z : ˆµ z x } {z : ˆν z var x } (ax) Γ y : ˆµ ( ) Γ y : Ω µ (Ω) Γ N : Ω ( E) Γ yn : µ ( I) Γ λ x.yn : ˆµ µ ( ) Γˆν λ x.yn : µ 37

38 PHN M = β λ x.yn y is free Γ = {z : Ω z x } {z : ˆν z var x } (ax) Γ y : ˆν ( ) Γ y : Ω ˆµ (Ω) Γ N : Ω ( E) Γ yn : ˆµ ( I) Γ λ x.yn : Ω ˆµ ( ) Γˆν λ x.yn : ˆµ 38

39 N - M = β x N Γˆν x : ˆν (ax) Γˆν x : ν ν. ( ) Γˆν N : ν Γˆν xn : ν (HI) ( E) 39

40 - M = β λx.n. (HI) Γˆν N : ν. ( I) (case HN ) Γˆν λx.n : ˆν ν Γˆν λx.n : µ ( I) Γˆν λx.n : (ˆν ν) µ ( ) Γˆν λx.n : ν 40

41 PN replaceable and non-replaceable variables In a normal form M: all occurrences of variables bound in the initial abstractions are replaceable all occurrences of free variables are non-replaceable if x N(λ z y.p ) is a subterm of M and the showed occurrence of x is replaceable then the occurrences of y in P are replaceable if x N(λ z y.p ) is a subterm of M and the showed occurrence of x is non-replaceable then the occurrences of y in P are non-replaceable 41

42 λx.a λy.x b λz.y z λt.c t 42

43 λx.a λy.x b λz.y z λt.c t 43

44 λx.a λy.x b λz.y z λt.c t 44

45 a normal form M PN iff in M all fathers of replaceable variables are non-replaceable λx.a : ˆν λy.x : ν b : ˆν λz.y : ˆν λt.c : ˆν z : ν t : ˆν 45

About these lecture notes. Simply Typed λ-calculus. Types

About these lecture notes. Simply Typed λ-calculus. Types About these lecture notes Simply Typed λ-calculus Akim Demaille akim@lrde.epita.fr EPITA École Pour l Informatique et les Techniques Avancées Many of these slides are largely inspired from Andrew D. Ker

Διαβάστε περισσότερα

Ordinal Arithmetic: Addition, Multiplication, Exponentiation and Limit

Ordinal Arithmetic: Addition, Multiplication, Exponentiation and Limit Ordinal Arithmetic: Addition, Multiplication, Exponentiation and Limit Ting Zhang Stanford May 11, 2001 Stanford, 5/11/2001 1 Outline Ordinal Classification Ordinal Addition Ordinal Multiplication Ordinal

Διαβάστε περισσότερα

C.S. 430 Assignment 6, Sample Solutions

C.S. 430 Assignment 6, Sample Solutions C.S. 430 Assignment 6, Sample Solutions Paul Liu November 15, 2007 Note that these are sample solutions only; in many cases there were many acceptable answers. 1 Reynolds Problem 10.1 1.1 Normal-order

Διαβάστε περισσότερα

Formal Semantics. 1 Type Logic

Formal Semantics. 1 Type Logic Formal Semantics Principle of Compositionality The meaning of a sentence is determined by the meanings of its parts and the way they are put together. 1 Type Logic Types (a measure on expressions) The

Διαβάστε περισσότερα

From the finite to the transfinite: Λµ-terms and streams

From the finite to the transfinite: Λµ-terms and streams From the finite to the transfinite: Λµ-terms and streams WIR 2014 Fanny He f.he@bath.ac.uk Alexis Saurin alexis.saurin@pps.univ-paris-diderot.fr 12 July 2014 The Λµ-calculus Syntax of Λµ t ::= x λx.t (t)u

Διαβάστε περισσότερα

2 Composition. Invertible Mappings

2 Composition. Invertible Mappings Arkansas Tech University MATH 4033: Elementary Modern Algebra Dr. Marcel B. Finan Composition. Invertible Mappings In this section we discuss two procedures for creating new mappings from old ones, namely,

Διαβάστε περισσότερα

The λ-calculus. Lecturer: John Wickerson. Phil Wadler

The λ-calculus. Lecturer: John Wickerson. Phil Wadler The λ-calculus Lecturer: John Wickerson Phil Wadler A tiny bit of Java expr ::= expr + expr expr < expr x n block ::= cmd { cmd... cmd } cmd ::= expr; if(cmd) block else block; if(cmd) block; try{cmd}

Διαβάστε περισσότερα

The Simply Typed Lambda Calculus

The Simply Typed Lambda Calculus Type Inference Instead of writing type annotations, can we use an algorithm to infer what the type annotations should be? That depends on the type system. For simple type systems the answer is yes, and

Διαβάστε περισσότερα

Sequent Calculi for the Modal µ-calculus over S5. Luca Alberucci, University of Berne. Logic Colloquium Berne, July 4th 2008

Sequent Calculi for the Modal µ-calculus over S5. Luca Alberucci, University of Berne. Logic Colloquium Berne, July 4th 2008 Sequent Calculi for the Modal µ-calculus over S5 Luca Alberucci, University of Berne Logic Colloquium Berne, July 4th 2008 Introduction Koz: Axiomatisation for the modal µ-calculus over K Axioms: All classical

Διαβάστε περισσότερα

Example Sheet 3 Solutions

Example Sheet 3 Solutions Example Sheet 3 Solutions. i Regular Sturm-Liouville. ii Singular Sturm-Liouville mixed boundary conditions. iii Not Sturm-Liouville ODE is not in Sturm-Liouville form. iv Regular Sturm-Liouville note

Διαβάστε περισσότερα

Chapter 6: Systems of Linear Differential. be continuous functions on the interval

Chapter 6: Systems of Linear Differential. be continuous functions on the interval Chapter 6: Systems of Linear Differential Equations Let a (t), a 2 (t),..., a nn (t), b (t), b 2 (t),..., b n (t) be continuous functions on the interval I. The system of n first-order differential equations

Διαβάστε περισσότερα

EE512: Error Control Coding

EE512: Error Control Coding EE512: Error Control Coding Solution for Assignment on Finite Fields February 16, 2007 1. (a) Addition and Multiplication tables for GF (5) and GF (7) are shown in Tables 1 and 2. + 0 1 2 3 4 0 0 1 2 3

Διαβάστε περισσότερα

Reminders: linear functions

Reminders: linear functions Reminders: linear functions Let U and V be vector spaces over the same field F. Definition A function f : U V is linear if for every u 1, u 2 U, f (u 1 + u 2 ) = f (u 1 ) + f (u 2 ), and for every u U

Διαβάστε περισσότερα

Finitary proof systems for Kozen s µ

Finitary proof systems for Kozen s µ Finitary proof systems for Kozen s µ Bahareh Afshari Graham Leigh TU Wien University of Gothenburg homc & cdps 16, Singapore 1 / 17 Modal µ-calculus Syntax: p p φ ψ φ ψ φ φ x µx φ νx φ Semantics: For Kripke

Διαβάστε περισσότερα

Finite Field Problems: Solutions

Finite Field Problems: Solutions Finite Field Problems: Solutions 1. Let f = x 2 +1 Z 11 [x] and let F = Z 11 [x]/(f), a field. Let Solution: F =11 2 = 121, so F = 121 1 = 120. The possible orders are the divisors of 120. Solution: The

Διαβάστε περισσότερα

New bounds for spherical two-distance sets and equiangular lines

New bounds for spherical two-distance sets and equiangular lines New bounds for spherical two-distance sets and equiangular lines Michigan State University Oct 8-31, 016 Anhui University Definition If X = {x 1, x,, x N } S n 1 (unit sphere in R n ) and x i, x j = a

Διαβάστε περισσότερα

Fractional Colorings and Zykov Products of graphs

Fractional Colorings and Zykov Products of graphs Fractional Colorings and Zykov Products of graphs Who? Nichole Schimanski When? July 27, 2011 Graphs A graph, G, consists of a vertex set, V (G), and an edge set, E(G). V (G) is any finite set E(G) is

Διαβάστε περισσότερα

ORDINAL ARITHMETIC JULIAN J. SCHLÖDER

ORDINAL ARITHMETIC JULIAN J. SCHLÖDER ORDINAL ARITHMETIC JULIAN J. SCHLÖDER Abstract. We define ordinal arithmetic and show laws of Left- Monotonicity, Associativity, Distributivity, some minor related properties and the Cantor Normal Form.

Διαβάστε περισσότερα

Lecture 2. Soundness and completeness of propositional logic

Lecture 2. Soundness and completeness of propositional logic Lecture 2 Soundness and completeness of propositional logic February 9, 2004 1 Overview Review of natural deduction. Soundness and completeness. Semantics of propositional formulas. Soundness proof. Completeness

Διαβάστε περισσότερα

Dynamic types, Lambda calculus machines Section and Practice Problems Apr 21 22, 2016

Dynamic types, Lambda calculus machines Section and Practice Problems Apr 21 22, 2016 Harvard School of Engineering and Applied Sciences CS 152: Programming Languages Dynamic types, Lambda calculus machines Apr 21 22, 2016 1 Dynamic types and contracts (a) To make sure you understand the

Διαβάστε περισσότερα

Every set of first-order formulas is equivalent to an independent set

Every set of first-order formulas is equivalent to an independent set Every set of first-order formulas is equivalent to an independent set May 6, 2008 Abstract A set of first-order formulas, whatever the cardinality of the set of symbols, is equivalent to an independent

Διαβάστε περισσότερα

SOLUTIONS TO MATH38181 EXTREME VALUES AND FINANCIAL RISK EXAM

SOLUTIONS TO MATH38181 EXTREME VALUES AND FINANCIAL RISK EXAM SOLUTIONS TO MATH38181 EXTREME VALUES AND FINANCIAL RISK EXAM Solutions to Question 1 a) The cumulative distribution function of T conditional on N n is Pr T t N n) Pr max X 1,..., X N ) t N n) Pr max

Διαβάστε περισσότερα

Chap. 6 Pushdown Automata

Chap. 6 Pushdown Automata Chap. 6 Pushdown Automata 6.1 Definition of Pushdown Automata Example 6.1 L = {wcw R w (0+1) * } P c 0P0 1P1 1. Start at state q 0, push input symbol onto stack, and stay in q 0. 2. If input symbol is

Διαβάστε περισσότερα

Αλγόριθμοι και πολυπλοκότητα NP-Completeness (2)

Αλγόριθμοι και πολυπλοκότητα NP-Completeness (2) ΕΛΛΗΝΙΚΗ ΔΗΜΟΚΡΑΤΙΑ ΠΑΝΕΠΙΣΤΗΜΙΟ ΚΡΗΤΗΣ Αλγόριθμοι και πολυπλοκότητα NP-Completeness (2) Ιωάννης Τόλλης Τμήμα Επιστήμης Υπολογιστών NP-Completeness (2) x 1 x 1 x 2 x 2 x 3 x 3 x 4 x 4 12 22 32 11 13 21

Διαβάστε περισσότερα

On the Galois Group of Linear Difference-Differential Equations

On the Galois Group of Linear Difference-Differential Equations On the Galois Group of Linear Difference-Differential Equations Ruyong Feng KLMM, Chinese Academy of Sciences, China Ruyong Feng (KLMM, CAS) Galois Group 1 / 19 Contents 1 Basic Notations and Concepts

Διαβάστε περισσότερα

Chapter 3: Ordinal Numbers

Chapter 3: Ordinal Numbers Chapter 3: Ordinal Numbers There are two kinds of number.. Ordinal numbers (0th), st, 2nd, 3rd, 4th, 5th,..., ω, ω +,... ω2, ω2+,... ω 2... answers to the question What position is... in a sequence? What

Διαβάστε περισσότερα

Congruence Classes of Invertible Matrices of Order 3 over F 2

Congruence Classes of Invertible Matrices of Order 3 over F 2 International Journal of Algebra, Vol. 8, 24, no. 5, 239-246 HIKARI Ltd, www.m-hikari.com http://dx.doi.org/.2988/ija.24.422 Congruence Classes of Invertible Matrices of Order 3 over F 2 Ligong An and

Διαβάστε περισσότερα

Abstract Storage Devices

Abstract Storage Devices Abstract Storage Devices Robert König Ueli Maurer Stefano Tessaro SOFSEM 2009 January 27, 2009 Outline 1. Motivation: Storage Devices 2. Abstract Storage Devices (ASD s) 3. Reducibility 4. Factoring ASD

Διαβάστε περισσότερα

Nowhere-zero flows Let be a digraph, Abelian group. A Γ-circulation in is a mapping : such that, where, and : tail in X, head in

Nowhere-zero flows Let be a digraph, Abelian group. A Γ-circulation in is a mapping : such that, where, and : tail in X, head in Nowhere-zero flows Let be a digraph, Abelian group. A Γ-circulation in is a mapping : such that, where, and : tail in X, head in : tail in X, head in A nowhere-zero Γ-flow is a Γ-circulation such that

Διαβάστε περισσότερα

Affine Weyl Groups. Gabriele Nebe. Summerschool GRK 1632, September Lehrstuhl D für Mathematik

Affine Weyl Groups. Gabriele Nebe. Summerschool GRK 1632, September Lehrstuhl D für Mathematik Affine Weyl Groups Gabriele Nebe Lehrstuhl D für Mathematik Summerschool GRK 1632, September 2015 Crystallographic root systems. Definition A crystallographic root system Φ is a finite set of non zero

Διαβάστε περισσότερα

Homomorphism in Intuitionistic Fuzzy Automata

Homomorphism in Intuitionistic Fuzzy Automata International Journal of Fuzzy Mathematics Systems. ISSN 2248-9940 Volume 3, Number 1 (2013), pp. 39-45 Research India Publications http://www.ripublication.com/ijfms.htm Homomorphism in Intuitionistic

Διαβάστε περισσότερα

5. Choice under Uncertainty

5. Choice under Uncertainty 5. Choice under Uncertainty Daisuke Oyama Microeconomics I May 23, 2018 Formulations von Neumann-Morgenstern (1944/1947) X: Set of prizes Π: Set of probability distributions on X : Preference relation

Διαβάστε περισσότερα

k A = [k, k]( )[a 1, a 2 ] = [ka 1,ka 2 ] 4For the division of two intervals of confidence in R +

k A = [k, k]( )[a 1, a 2 ] = [ka 1,ka 2 ] 4For the division of two intervals of confidence in R + Chapter 3. Fuzzy Arithmetic 3- Fuzzy arithmetic: ~Addition(+) and subtraction (-): Let A = [a and B = [b, b in R If x [a and y [b, b than x+y [a +b +b Symbolically,we write A(+)B = [a (+)[b, b = [a +b

Διαβάστε περισσότερα

A Note on Intuitionistic Fuzzy. Equivalence Relation

A Note on Intuitionistic Fuzzy. Equivalence Relation International Mathematical Forum, 5, 2010, no. 67, 3301-3307 A Note on Intuitionistic Fuzzy Equivalence Relation D. K. Basnet Dept. of Mathematics, Assam University Silchar-788011, Assam, India dkbasnet@rediffmail.com

Διαβάστε περισσότερα

Exercises 10. Find a fundamental matrix of the given system of equations. Also find the fundamental matrix Φ(t) satisfying Φ(0) = I. 1.

Exercises 10. Find a fundamental matrix of the given system of equations. Also find the fundamental matrix Φ(t) satisfying Φ(0) = I. 1. Exercises 0 More exercises are available in Elementary Differential Equations. If you have a problem to solve any of them, feel free to come to office hour. Problem Find a fundamental matrix of the given

Διαβάστε περισσότερα

Chapter 6: Systems of Linear Differential. be continuous functions on the interval

Chapter 6: Systems of Linear Differential. be continuous functions on the interval Chapter 6: Systems of Linear Differential Equations Let a (t), a 2 (t),..., a nn (t), b (t), b 2 (t),..., b n (t) be continuous functions on the interval I. The system of n first-order differential equations

Διαβάστε περισσότερα

Cyclic or elementary abelian Covers of K 4

Cyclic or elementary abelian Covers of K 4 Cyclic or elementary abelian Covers of K 4 Yan-Quan Feng Mathematics, Beijing Jiaotong University Beijing 100044, P.R. China Summer School, Rogla, Slovenian 2011-06 Outline 1 Question 2 Main results 3

Διαβάστε περισσότερα

Generating Set of the Complete Semigroups of Binary Relations

Generating Set of the Complete Semigroups of Binary Relations Applied Mathematics 06 7 98-07 Published Online January 06 in SciRes http://wwwscirporg/journal/am http://dxdoiorg/036/am067009 Generating Set of the Complete Semigroups of Binary Relations Yasha iasamidze

Διαβάστε περισσότερα

Mean-Variance Analysis

Mean-Variance Analysis Mean-Variance Analysis Jan Schneider McCombs School of Business University of Texas at Austin Jan Schneider Mean-Variance Analysis Beta Representation of the Risk Premium risk premium E t [Rt t+τ ] R1

Διαβάστε περισσότερα

Foundations of Computer Science ENGR 3520 Fall 2013 Thursday, Nov 21, 2013

Foundations of Computer Science ENGR 3520 Fall 2013 Thursday, Nov 21, 2013 Foundations of Computer Science Lecture Notes ENGR 3520 Fall 2013 Thursday, Nov 21, 2013 λ-calculus λ-terms. A λ-term is either: A variable x, y, z,... λx.m M N (M) (where x is a variable and M a λ-term)

Διαβάστε περισσότερα

Tridiagonal matrices. Gérard MEURANT. October, 2008

Tridiagonal matrices. Gérard MEURANT. October, 2008 Tridiagonal matrices Gérard MEURANT October, 2008 1 Similarity 2 Cholesy factorizations 3 Eigenvalues 4 Inverse Similarity Let α 1 ω 1 β 1 α 2 ω 2 T =......... β 2 α 1 ω 1 β 1 α and β i ω i, i = 1,...,

Διαβάστε περισσότερα

Homomorphism of Intuitionistic Fuzzy Groups

Homomorphism of Intuitionistic Fuzzy Groups International Mathematical Forum, Vol. 6, 20, no. 64, 369-378 Homomorphism o Intuitionistic Fuzz Groups P. K. Sharma Department o Mathematics, D..V. College Jalandhar Cit, Punjab, India pksharma@davjalandhar.com

Διαβάστε περισσότερα

SCITECH Volume 13, Issue 2 RESEARCH ORGANISATION Published online: March 29, 2018

SCITECH Volume 13, Issue 2 RESEARCH ORGANISATION Published online: March 29, 2018 Journal of rogressive Research in Mathematics(JRM) ISSN: 2395-028 SCITECH Volume 3, Issue 2 RESEARCH ORGANISATION ublished online: March 29, 208 Journal of rogressive Research in Mathematics www.scitecresearch.com/journals

Διαβάστε περισσότερα

MINIMAL CLOSED SETS AND MAXIMAL CLOSED SETS

MINIMAL CLOSED SETS AND MAXIMAL CLOSED SETS MINIMAL CLOSED SETS AND MAXIMAL CLOSED SETS FUMIE NAKAOKA AND NOBUYUKI ODA Received 20 December 2005; Revised 28 May 2006; Accepted 6 August 2006 Some properties of minimal closed sets and maximal closed

Διαβάστε περισσότερα

Lecture 2: Dirac notation and a review of linear algebra Read Sakurai chapter 1, Baym chatper 3

Lecture 2: Dirac notation and a review of linear algebra Read Sakurai chapter 1, Baym chatper 3 Lecture 2: Dirac notation and a review of linear algebra Read Sakurai chapter 1, Baym chatper 3 1 State vector space and the dual space Space of wavefunctions The space of wavefunctions is the set of all

Διαβάστε περισσότερα

SCHOOL OF MATHEMATICAL SCIENCES G11LMA Linear Mathematics Examination Solutions

SCHOOL OF MATHEMATICAL SCIENCES G11LMA Linear Mathematics Examination Solutions SCHOOL OF MATHEMATICAL SCIENCES GLMA Linear Mathematics 00- Examination Solutions. (a) i. ( + 5i)( i) = (6 + 5) + (5 )i = + i. Real part is, imaginary part is. (b) ii. + 5i i ( + 5i)( + i) = ( i)( + i)

Διαβάστε περισσότερα

Divergence for log concave functions

Divergence for log concave functions Divergence or log concave unctions Umut Caglar The Euler International Mathematical Institute June 22nd, 2013 Joint work with C. Schütt and E. Werner Outline 1 Introduction 2 Main Theorem 3 -divergence

Διαβάστε περισσότερα

Practice Exam 2. Conceptual Questions. 1. State a Basic identity and then verify it. (a) Identity: Solution: One identity is csc(θ) = 1

Practice Exam 2. Conceptual Questions. 1. State a Basic identity and then verify it. (a) Identity: Solution: One identity is csc(θ) = 1 Conceptual Questions. State a Basic identity and then verify it. a) Identity: Solution: One identity is cscθ) = sinθ) Practice Exam b) Verification: Solution: Given the point of intersection x, y) of the

Διαβάστε περισσότερα

Intuitionistic Fuzzy Ideals of Near Rings

Intuitionistic Fuzzy Ideals of Near Rings International Mathematical Forum, Vol. 7, 202, no. 6, 769-776 Intuitionistic Fuzzy Ideals of Near Rings P. K. Sharma P.G. Department of Mathematics D.A.V. College Jalandhar city, Punjab, India pksharma@davjalandhar.com

Διαβάστε περισσότερα

Statistics 104: Quantitative Methods for Economics Formula and Theorem Review

Statistics 104: Quantitative Methods for Economics Formula and Theorem Review Harvard College Statistics 104: Quantitative Methods for Economics Formula and Theorem Review Tommy MacWilliam, 13 tmacwilliam@college.harvard.edu March 10, 2011 Contents 1 Introduction to Data 5 1.1 Sample

Διαβάστε περισσότερα

Homework 8 Model Solution Section

Homework 8 Model Solution Section MATH 004 Homework Solution Homework 8 Model Solution Section 14.5 14.6. 14.5. Use the Chain Rule to find dz where z cosx + 4y), x 5t 4, y 1 t. dz dx + dy y sinx + 4y)0t + 4) sinx + 4y) 1t ) 0t + 4t ) sinx

Διαβάστε περισσότερα

Uniform Convergence of Fourier Series Michael Taylor

Uniform Convergence of Fourier Series Michael Taylor Uniform Convergence of Fourier Series Michael Taylor Given f L 1 T 1 ), we consider the partial sums of the Fourier series of f: N 1) S N fθ) = ˆfk)e ikθ. k= N A calculation gives the Dirichlet formula

Διαβάστε περισσότερα

Matrices and Determinants

Matrices and Determinants Matrices and Determinants SUBJECTIVE PROBLEMS: Q 1. For what value of k do the following system of equations possess a non-trivial (i.e., not all zero) solution over the set of rationals Q? x + ky + 3z

Διαβάστε περισσότερα

Overview. Transition Semantics. Configurations and the transition relation. Executions and computation

Overview. Transition Semantics. Configurations and the transition relation. Executions and computation Overview Transition Semantics Configurations and the transition relation Executions and computation Inference rules for small-step structural operational semantics for the simple imperative language Transition

Διαβάστε περισσότερα

Lecture 21: Properties and robustness of LSE

Lecture 21: Properties and robustness of LSE Lecture 21: Properties and robustness of LSE BLUE: Robustness of LSE against normality We now study properties of l τ β and σ 2 under assumption A2, i.e., without the normality assumption on ε. From Theorem

Διαβάστε περισσότερα

SOLUTIONS TO MATH38181 EXTREME VALUES AND FINANCIAL RISK EXAM

SOLUTIONS TO MATH38181 EXTREME VALUES AND FINANCIAL RISK EXAM SOLUTIONS TO MATH38181 EXTREME VALUES AND FINANCIAL RISK EXAM Solutions to Question 1 a) The cumulative distribution function of T conditional on N n is Pr (T t N n) Pr (max (X 1,..., X N ) t N n) Pr (max

Διαβάστε περισσότερα

Numerical Analysis FMN011

Numerical Analysis FMN011 Numerical Analysis FMN011 Carmen Arévalo Lund University carmen@maths.lth.se Lecture 12 Periodic data A function g has period P if g(x + P ) = g(x) Model: Trigonometric polynomial of order M T M (x) =

Διαβάστε περισσότερα

Other Test Constructions: Likelihood Ratio & Bayes Tests

Other Test Constructions: Likelihood Ratio & Bayes Tests Other Test Constructions: Likelihood Ratio & Bayes Tests Side-Note: So far we have seen a few approaches for creating tests such as Neyman-Pearson Lemma ( most powerful tests of H 0 : θ = θ 0 vs H 1 :

Διαβάστε περισσότερα

Inverse trigonometric functions & General Solution of Trigonometric Equations. ------------------ ----------------------------- -----------------

Inverse trigonometric functions & General Solution of Trigonometric Equations. ------------------ ----------------------------- ----------------- Inverse trigonometric functions & General Solution of Trigonometric Equations. 1. Sin ( ) = a) b) c) d) Ans b. Solution : Method 1. Ans a: 17 > 1 a) is rejected. w.k.t Sin ( sin ) = d is rejected. If sin

Διαβάστε περισσότερα

Second Order Partial Differential Equations

Second Order Partial Differential Equations Chapter 7 Second Order Partial Differential Equations 7.1 Introduction A second order linear PDE in two independent variables (x, y Ω can be written as A(x, y u x + B(x, y u xy + C(x, y u u u + D(x, y

Διαβάστε περισσότερα

Commutative Monoids in Intuitionistic Fuzzy Sets

Commutative Monoids in Intuitionistic Fuzzy Sets Commutative Monoids in Intuitionistic Fuzzy Sets S K Mala #1, Dr. MM Shanmugapriya *2 1 PhD Scholar in Mathematics, Karpagam University, Coimbatore, Tamilnadu- 641021 Assistant Professor of Mathematics,

Διαβάστε περισσότερα

λρ-calculus 1. each λ-variable is a λρ-term, called an atom or atomic term; 2. if M and N are λρ-term then (MN) is a λρ-term called an application;

λρ-calculus 1. each λ-variable is a λρ-term, called an atom or atomic term; 2. if M and N are λρ-term then (MN) is a λρ-term called an application; λρ-calculus Yuichi Komori komori@math.s.chiba-u.ac.jp Department of Mathematics, Faculty of Sciences, Chiba University Arato Cho aratoc@g.math.s.chiba-u.ac.jp Department of Mathematics, Faculty of Sciences,

Διαβάστε περισσότερα

3.4 SUM AND DIFFERENCE FORMULAS. NOTE: cos(α+β) cos α + cos β cos(α-β) cos α -cos β

3.4 SUM AND DIFFERENCE FORMULAS. NOTE: cos(α+β) cos α + cos β cos(α-β) cos α -cos β 3.4 SUM AND DIFFERENCE FORMULAS Page Theorem cos(αβ cos α cos β -sin α cos(α-β cos α cos β sin α NOTE: cos(αβ cos α cos β cos(α-β cos α -cos β Proof of cos(α-β cos α cos β sin α Let s use a unit circle

Διαβάστε περισσότερα

Type Theory and Coq. Herman Geuvers. Principal Types and Type Checking

Type Theory and Coq. Herman Geuvers. Principal Types and Type Checking Type Theory and Coq Herman Geuvers Principal Types and Type Checking 1 Overview of todays lecture Simple Type Theory à la Curry (versus Simple Type Theory à la Church) Principal Types algorithm Type checking

Διαβάστε περισσότερα

Quadratic Expressions

Quadratic Expressions Quadratic Expressions. The standard form of a quadratic equation is ax + bx + c = 0 where a, b, c R and a 0. The roots of ax + bx + c = 0 are b ± b a 4ac. 3. For the equation ax +bx+c = 0, sum of the roots

Διαβάστε περισσότερα

Phys460.nb Solution for the t-dependent Schrodinger s equation How did we find the solution? (not required)

Phys460.nb Solution for the t-dependent Schrodinger s equation How did we find the solution? (not required) Phys460.nb 81 ψ n (t) is still the (same) eigenstate of H But for tdependent H. The answer is NO. 5.5.5. Solution for the tdependent Schrodinger s equation If we assume that at time t 0, the electron starts

Διαβάστε περισσότερα

Main source: "Discrete-time systems and computer control" by Α. ΣΚΟΔΡΑΣ ΨΗΦΙΑΚΟΣ ΕΛΕΓΧΟΣ ΔΙΑΛΕΞΗ 4 ΔΙΑΦΑΝΕΙΑ 1

Main source: Discrete-time systems and computer control by Α. ΣΚΟΔΡΑΣ ΨΗΦΙΑΚΟΣ ΕΛΕΓΧΟΣ ΔΙΑΛΕΞΗ 4 ΔΙΑΦΑΝΕΙΑ 1 Main source: "Discrete-time systems and computer control" by Α. ΣΚΟΔΡΑΣ ΨΗΦΙΑΚΟΣ ΕΛΕΓΧΟΣ ΔΙΑΛΕΞΗ 4 ΔΙΑΦΑΝΕΙΑ 1 A Brief History of Sampling Research 1915 - Edmund Taylor Whittaker (1873-1956) devised a

Διαβάστε περισσότερα

4.6 Autoregressive Moving Average Model ARMA(1,1)

4.6 Autoregressive Moving Average Model ARMA(1,1) 84 CHAPTER 4. STATIONARY TS MODELS 4.6 Autoregressive Moving Average Model ARMA(,) This section is an introduction to a wide class of models ARMA(p,q) which we will consider in more detail later in this

Διαβάστε περισσότερα

Math221: HW# 1 solutions

Math221: HW# 1 solutions Math: HW# solutions Andy Royston October, 5 7.5.7, 3 rd Ed. We have a n = b n = a = fxdx = xdx =, x cos nxdx = x sin nx n sin nxdx n = cos nx n = n n, x sin nxdx = x cos nx n + cos nxdx n cos n = + sin

Διαβάστε περισσότερα

Απόκριση σε Μοναδιαία Ωστική Δύναμη (Unit Impulse) Απόκριση σε Δυνάμεις Αυθαίρετα Μεταβαλλόμενες με το Χρόνο. Απόστολος Σ.

Απόκριση σε Μοναδιαία Ωστική Δύναμη (Unit Impulse) Απόκριση σε Δυνάμεις Αυθαίρετα Μεταβαλλόμενες με το Χρόνο. Απόστολος Σ. Απόκριση σε Δυνάμεις Αυθαίρετα Μεταβαλλόμενες με το Χρόνο The time integral of a force is referred to as impulse, is determined by and is obtained from: Newton s 2 nd Law of motion states that the action

Διαβάστε περισσότερα

A Bonus-Malus System as a Markov Set-Chain. Małgorzata Niemiec Warsaw School of Economics Institute of Econometrics

A Bonus-Malus System as a Markov Set-Chain. Małgorzata Niemiec Warsaw School of Economics Institute of Econometrics A Bonus-Malus System as a Markov Set-Chain Małgorzata Niemiec Warsaw School of Economics Institute of Econometrics Contents 1. Markov set-chain 2. Model of bonus-malus system 3. Example 4. Conclusions

Διαβάστε περισσότερα

The ε-pseudospectrum of a Matrix

The ε-pseudospectrum of a Matrix The ε-pseudospectrum of a Matrix Feb 16, 2015 () The ε-pseudospectrum of a Matrix Feb 16, 2015 1 / 18 1 Preliminaries 2 Definitions 3 Basic Properties 4 Computation of Pseudospectrum of 2 2 5 Problems

Διαβάστε περισσότερα

Trace Semantics for Polymorphic References. GaLoP 16 April 3rd 2016

Trace Semantics for Polymorphic References. GaLoP 16 April 3rd 2016 Trace Semantics for Polymorphic References Guilhem Jaber & Nikos Tzevelekos PPS, IRIF, Université Paris Diderot Queen Mary University of London GaLoP 16 April 3rd 2016 1 / 19 Goals Build an intensional

Διαβάστε περισσότερα

ST5224: Advanced Statistical Theory II

ST5224: Advanced Statistical Theory II ST5224: Advanced Statistical Theory II 2014/2015: Semester II Tutorial 7 1. Let X be a sample from a population P and consider testing hypotheses H 0 : P = P 0 versus H 1 : P = P 1, where P j is a known

Διαβάστε περισσότερα

Bounding Nonsplitting Enumeration Degrees

Bounding Nonsplitting Enumeration Degrees Bounding Nonsplitting Enumeration Degrees Thomas F. Kent Andrea Sorbi Università degli Studi di Siena Italia July 18, 2007 Goal: Introduce a form of Σ 0 2-permitting for the enumeration degrees. Till now,

Διαβάστε περισσότερα

Second Order RLC Filters

Second Order RLC Filters ECEN 60 Circuits/Electronics Spring 007-0-07 P. Mathys Second Order RLC Filters RLC Lowpass Filter A passive RLC lowpass filter (LPF) circuit is shown in the following schematic. R L C v O (t) Using phasor

Διαβάστε περισσότερα

1. Introduction and Preliminaries.

1. Introduction and Preliminaries. Faculty of Sciences and Mathematics, University of Niš, Serbia Available at: http://www.pmf.ni.ac.yu/filomat Filomat 22:1 (2008), 97 106 ON δ SETS IN γ SPACES V. Renuka Devi and D. Sivaraj Abstract We

Διαβάστε περισσότερα

Fourier Series. MATH 211, Calculus II. J. Robert Buchanan. Spring Department of Mathematics

Fourier Series. MATH 211, Calculus II. J. Robert Buchanan. Spring Department of Mathematics Fourier Series MATH 211, Calculus II J. Robert Buchanan Department of Mathematics Spring 2018 Introduction Not all functions can be represented by Taylor series. f (k) (c) A Taylor series f (x) = (x c)

Διαβάστε περισσότερα

Dr. D. Dinev, Department of Structural Mechanics, UACEG

Dr. D. Dinev, Department of Structural Mechanics, UACEG Lecture 4 Material behavior: Constitutive equations Field of the game Print version Lecture on Theory of lasticity and Plasticity of Dr. D. Dinev, Department of Structural Mechanics, UACG 4.1 Contents

Διαβάστε περισσότερα

Jesse Maassen and Mark Lundstrom Purdue University November 25, 2013

Jesse Maassen and Mark Lundstrom Purdue University November 25, 2013 Notes on Average Scattering imes and Hall Factors Jesse Maassen and Mar Lundstrom Purdue University November 5, 13 I. Introduction 1 II. Solution of the BE 1 III. Exercises: Woring out average scattering

Διαβάστε περισσότερα

Verification. Lecture 12. Martin Zimmermann

Verification. Lecture 12. Martin Zimmermann Verification Lecture 12 Martin Zimmermann Plan for today LTL FairnessinLTL LTL Model Checking Review: Syntax modal logic over infinite sequences[pnueli 1977] Propositional logic a AP ϕandϕ ψ Temporal operators

Διαβάστε περισσότερα

Introduction to Type Theory February 2008 Alpha Lernet Summer School Piriapolis, Uruguay. Herman Geuvers Nijmegen & Eindhoven, NL

Introduction to Type Theory February 2008 Alpha Lernet Summer School Piriapolis, Uruguay. Herman Geuvers Nijmegen & Eindhoven, NL Introduction to Type Theory February 2008 Alpha Lernet Summer School Piriapolis, Uruguay Herman Geuvers Nijmegen & Eindhoven, NL Lecture 3: Polymorphic Type Theory: Full polymorphism and ML style polymorphism

Διαβάστε περισσότερα

THE SECOND ISOMORPHISM THEOREM ON ORDERED SET UNDER ANTIORDERS. Daniel A. Romano

THE SECOND ISOMORPHISM THEOREM ON ORDERED SET UNDER ANTIORDERS. Daniel A. Romano 235 Kragujevac J. Math. 30 (2007) 235 242. THE SECOND ISOMORPHISM THEOREM ON ORDERED SET UNDER ANTIORDERS Daniel A. Romano Department of Mathematics and Informatics, Banja Luka University, Mladena Stojanovića

Διαβάστε περισσότερα

ω ω ω ω ω ω+2 ω ω+2 + ω ω ω ω+2 + ω ω+1 ω ω+2 2 ω ω ω ω ω ω ω ω+1 ω ω2 ω ω2 + ω ω ω2 + ω ω ω ω2 + ω ω+1 ω ω2 + ω ω+1 + ω ω ω ω2 + ω

ω ω ω ω ω ω+2 ω ω+2 + ω ω ω ω+2 + ω ω+1 ω ω+2 2 ω ω ω ω ω ω ω ω+1 ω ω2 ω ω2 + ω ω ω2 + ω ω ω ω2 + ω ω+1 ω ω2 + ω ω+1 + ω ω ω ω2 + ω 0 1 2 3 4 5 6 ω ω + 1 ω + 2 ω + 3 ω + 4 ω2 ω2 + 1 ω2 + 2 ω2 + 3 ω3 ω3 + 1 ω3 + 2 ω4 ω4 + 1 ω5 ω 2 ω 2 + 1 ω 2 + 2 ω 2 + ω ω 2 + ω + 1 ω 2 + ω2 ω 2 2 ω 2 2 + 1 ω 2 2 + ω ω 2 3 ω 3 ω 3 + 1 ω 3 + ω ω 3 +

Διαβάστε περισσότερα

Ψηφιακή Επεξεργασία Φωνής

Ψηφιακή Επεξεργασία Φωνής ΕΛΛΗΝΙΚΗ ΔΗΜΟΚΡΑΤΙΑ ΠΑΝΕΠΙΣΤΗΜΙΟ ΚΡΗΤΗΣ Ψηφιακή Επεξεργασία Φωνής Ενότητα 1η: Ψηφιακή Επεξεργασία Σήματος Στυλιανού Ιωάννης Τμήμα Επιστήμης Υπολογιστών CS578- Speech Signal Processing Lecture 1: Discrete-Time

Διαβάστε περισσότερα

6.3 Forecasting ARMA processes

6.3 Forecasting ARMA processes 122 CHAPTER 6. ARMA MODELS 6.3 Forecasting ARMA processes The purpose of forecasting is to predict future values of a TS based on the data collected to the present. In this section we will discuss a linear

Διαβάστε περισσότερα

Test Data Management in Practice

Test Data Management in Practice Problems, Concepts, and the Swisscom Test Data Organizer Do you have issues with your legal and compliance department because test environments contain sensitive data outsourcing partners must not see?

Διαβάστε περισσότερα

The Probabilistic Method - Probabilistic Techniques. Lecture 7: The Janson Inequality

The Probabilistic Method - Probabilistic Techniques. Lecture 7: The Janson Inequality The Probabilistic Method - Probabilistic Techniques Lecture 7: The Janson Inequality Sotiris Nikoletseas Associate Professor Computer Engineering and Informatics Department 2014-2015 Sotiris Nikoletseas,

Διαβάστε περισσότερα

Durbin-Levinson recursive method

Durbin-Levinson recursive method Durbin-Levinson recursive method A recursive method for computing ϕ n is useful because it avoids inverting large matrices; when new data are acquired, one can update predictions, instead of starting again

Διαβάστε περισσότερα

Derivation of Optical-Bloch Equations

Derivation of Optical-Bloch Equations Appendix C Derivation of Optical-Bloch Equations In this appendix the optical-bloch equations that give the populations and coherences for an idealized three-level Λ system, Fig. 3. on page 47, will be

Διαβάστε περισσότερα

derivation of the Laplacian from rectangular to spherical coordinates

derivation of the Laplacian from rectangular to spherical coordinates derivation of the Laplacian from rectangular to spherical coordinates swapnizzle 03-03- :5:43 We begin by recognizing the familiar conversion from rectangular to spherical coordinates (note that φ is used

Διαβάστε περισσότερα

The Properties of Fuzzy Relations

The Properties of Fuzzy Relations International Mathematical Forum, 5, 2010, no. 8, 373-381 The Properties of Fuzzy Relations Yong Chan Kim Department of Mathematics, Gangneung-Wonju National University Gangneung, Gangwondo 210-702, Korea

Διαβάστε περισσότερα

6.1. Dirac Equation. Hamiltonian. Dirac Eq.

6.1. Dirac Equation. Hamiltonian. Dirac Eq. 6.1. Dirac Equation Ref: M.Kaku, Quantum Field Theory, Oxford Univ Press (1993) η μν = η μν = diag(1, -1, -1, -1) p 0 = p 0 p = p i = -p i p μ p μ = p 0 p 0 + p i p i = E c 2 - p 2 = (m c) 2 H = c p 2

Διαβάστε περισσότερα

b. Use the parametrization from (a) to compute the area of S a as S a ds. Be sure to substitute for ds!

b. Use the parametrization from (a) to compute the area of S a as S a ds. Be sure to substitute for ds! MTH U341 urface Integrals, tokes theorem, the divergence theorem To be turned in Wed., Dec. 1. 1. Let be the sphere of radius a, x 2 + y 2 + z 2 a 2. a. Use spherical coordinates (with ρ a) to parametrize.

Διαβάστε περισσότερα

Lecture 34 Bootstrap confidence intervals

Lecture 34 Bootstrap confidence intervals Lecture 34 Bootstrap confidence intervals Confidence Intervals θ: an unknown parameter of interest We want to find limits θ and θ such that Gt = P nˆθ θ t If G 1 1 α is known, then P θ θ = P θ θ = 1 α

Διαβάστε περισσότερα

w o = R 1 p. (1) R = p =. = 1

w o = R 1 p. (1) R = p =. = 1 Πανεπιστήµιο Κρήτης - Τµήµα Επιστήµης Υπολογιστών ΗΥ-570: Στατιστική Επεξεργασία Σήµατος 205 ιδάσκων : Α. Μουχτάρης Τριτη Σειρά Ασκήσεων Λύσεις Ασκηση 3. 5.2 (a) From the Wiener-Hopf equation we have:

Διαβάστε περισσότερα

Srednicki Chapter 55

Srednicki Chapter 55 Srednicki Chapter 55 QFT Problems & Solutions A. George August 3, 03 Srednicki 55.. Use equations 55.3-55.0 and A i, A j ] = Π i, Π j ] = 0 (at equal times) to verify equations 55.-55.3. This is our third

Διαβάστε περισσότερα

Trigonometric Formula Sheet

Trigonometric Formula Sheet Trigonometric Formula Sheet Definition of the Trig Functions Right Triangle Definition Assume that: 0 < θ < or 0 < θ < 90 Unit Circle Definition Assume θ can be any angle. y x, y hypotenuse opposite θ

Διαβάστε περισσότερα

Approximation of distance between locations on earth given by latitude and longitude

Approximation of distance between locations on earth given by latitude and longitude Approximation of distance between locations on earth given by latitude and longitude Jan Behrens 2012-12-31 In this paper we shall provide a method to approximate distances between two points on earth

Διαβάστε περισσότερα

MATHEMATICS. 1. If A and B are square matrices of order 3 such that A = -1, B =3, then 3AB = 1) -9 2) -27 3) -81 4) 81

MATHEMATICS. 1. If A and B are square matrices of order 3 such that A = -1, B =3, then 3AB = 1) -9 2) -27 3) -81 4) 81 1. If A and B are square matrices of order 3 such that A = -1, B =3, then 3AB = 1) -9 2) -27 3) -81 4) 81 We know that KA = A If A is n th Order 3AB =3 3 A. B = 27 1 3 = 81 3 2. If A= 2 1 0 0 2 1 then

Διαβάστε περισσότερα