Lambda Calculus and Products e : t e1 : s! t e2 : s e : s t e1 : s e2 : t x s : s x s :e : s! t e1 e 2 : t () : unit 1 e : s 2 e : t (e1 ; e 2) : s t

Size: px
Start display at page:

Download "Lambda Calculus and Products e : t e1 : s! t e2 : s e : s t e1 : s e2 : t x s : s x s :e : s! t e1 e 2 : t () : unit 1 e : s 2 e : t (e1 ; e 2) : s t"

Transcription

1 Conservativity of Nested Relational Calculi with Internal Generic Functions Leonid Libkin and Limsoon Wong Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA , USA. flibkin, Abstract It is known that queries in nested relational calculus are independent of the depth of set nesting in the intermediate data and this remains true in the presence of aggregate functions. We prove that this continues to be true if the calculus is augmented with any internal generic family of functions. 1 Introduction Paredaens and Van Gucht [7] proved that the nested relational calculus is no more expressive than the traditional relational calculus when input and output are at relations. That is, every query on input and output whose depth of set nesting is at most 1 (or at relations) can be expressed without using any intermediate data whose depth of set nesting is greater than 1. This result was generalized by Wong [10] who showed that every query on input and output whose depth of set nesting is at most k can be expressed without using any intermediate data whose depth of set nesting is greater than k. Hence the expressive power of the nested relational calculus is independent of the depth of set nesting allowed in the intermediate data. This property is called the conservative extension property. Libkin and Wong [5] showed that when the nested relational calculus is endowed with aggregate functions, it retains the conservative extension property. The aim of this paper is to demonstrate that the nested relational calculus continues to retain the conservative extension property even in the presence of any family of functions that are generic and do not invent new values. The strategy is to combine the rewriting technique of Wong [10] and the eective lifting of linear orders of Libkin and Wong [5] to encode any such function into one whose height is minimum. To appear in Information Processing Letters. 1

2 Lambda Calculus and Products e : t e1 : s! t e2 : s e : s t e1 : s e2 : t x s : s x s :e : s! t e1 e 2 : t () : unit 1 e : s 2 e : t (e1 ; e 2) : s t Set Monad fg s : fsg e : s feg : fsg e1 : fsg e2 : fsg e1 [ e2 : fsg e1 : ftg e2 S : fsg fe1 j x s 2 e2g : ftg Booleans true : B false : B if e1 : B 2 : t 3 : t e1 then e 2 else e 3 : t 2 Nested relational calculus We use the nested relational calculus N RC as presented in Breazu-Tannen, Buneman, and Wong [3]. In this section, it is extended with natural numbers, simple arithmetics, and a summation operator. Types. A type in N RC is either a complex object type or is a function type s! t where s and t are complex object types. The complex object types are given by the grammar: s; t ::= b j B j unit j s t j fsg Objects of type B are the two boolean values true and false. The unique object of type unit is denoted by (). Objects of type s t are pairs whose rst (or left) components are objects of type s and second (or right) components are objects of type t. Objects of type fsg are nite sets of objects of type s. We also included some uninterpreted base types b. Expressions. Expressions of N RC are constructed using the rules in the gure. The language also contains some uninterpreted constants c of base type T ype(c) and uninterpreted functions p of function type T ype(p). The type superscripts are omitted in the rest of the paper because they can be inferred [6]. The semantics of N RC was described in [3]. We repeat the meaning of the set monad constructs here. fg is the empty set. feg is the singleton set containing e. e 1 [ e 2 is the union of sets e 1 and e 2. The construct S fe 1 j x 2 e 2 g denotes the set obtained by rst applying the function x:e 1 to elements of the set e 2 and then taking their union. The shorthand fo 1 ; : : : :o n g is used to denote fo 1 g [ : : : [ fo n g, provided o 1,..., o n are distinct objects. As it stands, N RC can merely express queries that are purely structural. It was shown in [3] that endowing N RC with equality test = s : s s! B at all types s elevates N RC to a fully edged nested relational language (in fact, equivalent to classical nested relational algebra of [8]). That is, operations such as nest, membership test 2 s : s fsg! B, subset test s : fsg fsg! B, set intersection, set 2

3 dierence, etc. are expressible in N RC(=) (we write the additional primitive in brackets to distinguish various extensions of the language). Practical database query languages frequently have to deal with queries such as \select maximum of column," \select count from column," etc. To handle this kind of queries, we add natural numbers (whose type is denoted by N) and the following constructs: e 1 : N 2 : N e 1 + e 2 : N e 1 : N 2 : N e 1 e 2 : N e 1 : N 2 : N e 1 : e 2 : N e 1 : N 2 : fsg P fe1 j x s 2 e 2 g : N where +,, and : are respectively addition, multiplication, and modied subtraction on natural numbers. (That is, if n m, then n : m = 0f n > m, then n : m = n m.) The summation construct P fe 1 j x s 2 e 2 g denotes the number obtained by rst applying the function x:e 1 to every item in the set e 2 and then adding the results up. That is, P fe 1 j x 2 Xg is f(o 1 ) + : : : + f(o n ) if f is the function denoted by x:e 1 and fo 1 ; : : : o n g is the set denoted by X. It should be stressed that in the construct P fe 1 j x 2 e 2 g, the fe 1 j x 2 e 2 g part is not an expression; hence this construct is stronger than just adding P : fng! N. The extended language N RC(N; +; ; : ; P ; =), where the additional base types and primitives are explicitly listed between brackets, is capable of expressing many aggregate functions found in commercial databases. Here are two examples: \count the number of records in R" is count(r), P f1j x 2 Rg and \total up the rst column of R" is total(r), P f 1 x j x 2 Rg. Now we formally dene the conservative extension property. The set height ht(s) of a type s is dened by induction on the structure of type: ht(unit) = ht(b) = 0, ht(s t) = ht(s! t) = max(ht(s); ht(t)), and ht(fsg) = 1 + ht(s). Every expression of our language has a unique typing derivation. The set height of expression e is dened as ht(e) = maxfht(s) j s occurs in the type derivation of eg. Let L i;o;h denote the class of functions whose input has set height at most i, whose output has set height at most o, and which are denable in the language L using an expression whose set height is at most h max(i; o). L is said to have the conservative extension property with xed constant k if L i;o;h = L i;o;h+1 for all i, o, and h max(i; o; k). It is known from Wong [10] and Libkin and Wong [5] that Theorem 2.1 N RC(N; +; ; : ; P ; =) has the conservative extension property with xed constant 0. Moreover, N RC(N; +; ; : ; P ; =) endowed with any additional primitive p : s! t also has the conservative extension property with xed constant ht(s! t). 2 3 Lifting of linear orders In this section we present a technique to lift linear order from base types to all types. This technique plays the key role in the encoding used in the next section. The following lemma is a folklore (see Wechler [9]): 3

4 Lemma 3.1 Given a partially ordered set ha, dene an ordering - on its nite powerset P n (A) as follows: X - Y i max((x Y )[(Y X)) Y, or, equivalently, if 8x 2 X Y 9y 2 Y X : x y. Then - is a partial order. Moreover, if is linear, then so is -. 2 This way of lifting linear order can be easily expressed in N RC(N; +; ; : ; P ; =). Therefore, we have Theorem 3.2 Suppose a linear order b is given for each base type b. Then a linear order t is computable by N RC(N; +; ; : ; P ; =) for each type t. Proof. Dene t by induction on types. For a base type b is given. For pairs it is dened lexicographically: x ts y, if 1 x s 1 y then (if 1 x = s 1 y then 2 x t 2 y else true) else false. For set types we use lemma 3.1: X fsg Y, if X v [ s Y then (if Y v[ s X then X [ s Y else true) else false. Here X v [ s Y i 8x 2 X9y 2 Y : x s y and X [ s Y i X Y v[ s Y X. These can be implemented as follows. X v [ s Y, 0 =B fif (fif x s y then 1 else 0 j y 2 Y g) = N 0 then 1 else 0 j x 2 Xg and X [ s Y, (fif x 2s Y then 0 else (if (fif y 2 s X then 0 else (if x s y then 1 else 0) j y Y g) = N 0 then 1 else 0) j x 2 Xg) = N 0. 2 We denote N RC(N; +; ; : P ; ; =) endowed with linear orders at base types by N RC(N; +; ; : P ; ; =; ). Notice that if B and N are the only base types, then this does not add expressive power since the orderings on booleans and naturals are denable: false B true and n N m, (n : m = N 0). Corollary 3.3 A rank assignment is a function sort : fsg! fs Ng such that sortfa 1 ; : : : ; a n g = f(a 1 ; 1); : : : ; (a n ; n)g where a 1 s : : : s a n. Rank assignment is expressible in N RC(N; +; ; : ; P ; =; ). Proof. Dene sort(x), S f(r; fif c s r then 1 else 0 j c 2 xg) j r 2 xg. 2 Finally, we have the following Corollary 3.4 If all orderings on the base types are well-founded, then so are the lifted orderings. Furthermore, if constant min b : b and function succ b : b! b (meaning minimal element and successor) are given for each base type b, they can be dened for any type. Proof. The rst statement follows from the fact that X - Y implies X v [ Y where X v [ Y i 8x 2 X9y 2 Y : x y, and v [ is known to be well-founded if is [2]. Minimal elements are denable as follows: min ts = (min t ; min s ) and min ftg = fg. It is clear how to dene succ for pairs. To dene succ for set types, consider a set X : ftg. Let a be the minimal element of type t which is not in X, and let X 0 = fx 2 X j x t ag and X 1 = fx 2 X j a t xg. We claim X 1 [ a = succ ftg (X). Clearly, X ftg X 1 [ a. Let X ftg Y. Let a 2 Y. Assume x 2 X 1 [ a Y = X 1 Y. Then x 2 X Y and there exists y 2 Y X such that x t y. Since y 6= a, we obtain y 2 Y (X 1 [ a) and X 1 [ a ftg Y. If a 62 Y and x 2 X 1 [ a Y, then Y (X 1 [ a) = Y X 1. Therefore, if x 2 X 1 Y, there exists y 2 Y X 1 such that x t y. Let y m = max(y X 1 ). If y m t a, then Y X 1 X 0 and Y X which contradicts our assumption. Then, since a 62 Y, a t y m and therefore X 1 [ a ftg Y. This proves our claim. Therefore, succ ftg (X) is fmin t g [ X if min t 62 X and X 1 [ a if min t 2 X where a = succ t (x 0 ) and x 0 = minfx 2 X j succ t (x) 62 Xg and X 1 is dened as above. The expressibility of succ ftg follows immediately. 2 4

5 4 The main result We rst dene the notion of internal and generic family of functions. Then we show that the conservative extension property of N RC(N; +; ; : ; P ; =) endowed with well-founded linear orders can be preserved in the presence of any such family of functions. Introduce type variables i and consider nonground complex object types ; ::= j b j B j N j unit j j fg If 1,..., n occur in, then [s 1 = 1 ; : : : ; s n = n ] stands for the type obtained by replacing every occurrence of i in by s i. A complex object type s is an instance of a nonground complex object type if there are complex object types s 1,..., s n such that s = [s 1 = 1 ; : : : ; s n = n ] where 1,..., n are all the type variables in. The minimal height mht() of type is dened as the depth of nesting of set brackets in. That is, mht() is equivalent to ht(s) where s is obtained from by replacing all occurrences of type variables in by unit. Let p 1;:::; n :! stand for a family of functions p s 1;:::;s n : s! t where s = [s 1 = 1 ; : : : ; s n = n ] and t = [s 1 = 1 ; : : : ; s n = n ]. (Note that for each s 1,..., s n, there is exactly one p s 1;:::;s n in the family p 1;:::; n.) The minimal height mht(p) of p 1;:::; n :! is dened as max(mht(); mht()). Let s = [s 1 = 1 ; : : : ; s n = n ; t=]. Let dom s;t ;(o) be the set of subobjects of type t in the object o : s occurring at positions corresponding the the type variable. Formally, dene dom s;t ; : s! ftg as follows: dom s;t b; (x) = fg; doms;t ;(x) = fxg; dom s;t 0 ;(x) = fg, where and 0 are distinct type variables; dom uv;t ;(x; y) = dom u;t ;(x) [ dom v;t ;(y); and dom fsg;t (X) = S fdom s;t fg; ;(x) j x 2 Xg. Denition 4.1 The family of functions p 1;:::; n :! is internal (see [4]) in i if for all complex object types s = [s 1 = 1 ; : : : ; s n = n ], t = [s 1 = 1 ; : : : ; s n = n ], and complex object o : s, it is the case that dom t;s i (p s 1;:::;s n (o)) dom s;s i (o). 2 In other words, p 1;:::; n :! is internal in i if it does not invent new values in positions corresponding to the type variable i. Let s = [s 1 = 1 ; : : : ; s n = n ; t=], r = [s 1 = 1 ; : : : ; s n = n ; t 0 =], and : t! t 0. Let modulate s;t;t0 be the object O 0 : r obtained by replacing every subobject o : t in O : s occurring in positions corresponding to type variable by (o) : t 0. Formally, dene modulate s;t;t0 : s! r as follows: modulate s;t;t0 b (x) = x; modulates;t;t0 (x) = (x); modulates;t;t0 0 (x) = x, where and 0 are distinct type variables; modulate uv;t;t0 (x) j x 2 Xg. fmodulate s;t;t0 (x; y) = (modulateu;t;t0 (x); (modulatev;t;t0 (O) (y)); modulatefsg;t;t0 fg (X) = Denition 4.2 The family of functions p 1;:::; n :! is generic in i if for all complex object types s = [s 1 = 1 ; : : : ; s n = n ], t = [s 1 = 1 ; : : : ; s n = n ], complex object o : s, set R : frg, and : s i! r such that is a bijection from dom s;s i (o) to R and 1 : r! s i is its inverse when restricted to 5

6 dom s;s i (o), it is the case that s modulate s;s i;r ; p s 1;:::;s n - t 6 modulate t0 ;r;s i ; 1 s 0 p s0 1 ;:::;s0 n the above diagram, where s 0 j = s j for j 6= i and s 0 i = r, commutes. 2 - t 0 A family p 1;:::; n :! is called internal generic if it is internal and generic in all type variables. We remark here that our notion of genericity is slightly dierent from that in [4]. In particular, generic does not imply internal, in contrast to [4]. Now we demonstrate that adding a internal and generic family p 1;:::; n to N RC(N; +; ; : P ; ; =; ) does not destroy its conservative extension property. We assume that b : b b! B is a well-founded linear order for every base type b. Consider N RC(N; +; ; : P F ; ; =; ; ) obtained by adding the following construct to N RC(N; +; ; : P ; ; =; ): e 1 : s e 2 : ftg F fe1 j x t 2 e 2 g : s where F fe 1 j x t 2 e 2 g is the greatest element in the set fe 1 j x t 2 e 2 g (it is min s when the set is empty). Note that F fe 1 j x 2 e 2 g is already denable in N RC(N; +; ; : P ; ; =; ) if e 1 : fsg, and can be treated as a syntactic sugar. It is clear that both dom s;t ; and modulate s;t;t0 are denable in N RC(N; +; ; : P F ; ; =; ; ) whenever is. Proposition 4.3 Let p 1;:::; n :! be a family of functions that is internal generic. Then N RC(N; +; ; : P F ; ; =; ; ) endowed with the family of primitives p 1;:::; n has precisely the expressive power of N RC(N; +; ; : P F ; ; =; ; ) endowed with just the primitive p N;:::;N. Proof. For each s = [s 1 = 1 ; : : : ; s n = n ] and o : fs i g, dene (o), x: F fif x = 1 y then 2 y else 0 j y 2 sort(o)g and 1 (o), x: F fif x = 2 y then 1 y else min s j y 2 sort(o)g, where sort : fs i g! fs i Ng is as dened in corollary 3.3. (o) and 1 (o) are functions of type s i! N and N! s i respectively. Clearly, (o) when restricted to o is a bijection whose inverse is 1 (o). Let u i = [N= 1 ; : : : ; N= i1 ; s i = i ; : : : s n = n ] and v i = [N= 1 ; : : : ; N= i1 ; s i = i ; : : : s n = n ]. Note that s = u 1 and t = v 1. Dene i (o), modulate u i;s i ;N ; (dom s;s i (o)) and 6

7 1 i (o), modulate v i+1;n;s i. ; 1 (dom s;s i (o)) Then the following diagram commutes by induction on n and by the assumption that the family p 1;:::; n is internal and generic. o : u 1 1(o) - : u : u n n(o) - : un+1 p s 1;:::;s n : v 1 p N;s 2;:::;s n 1 (o) : v : vn 1 p N;:::N;s n p N;:::;N 1 n (o) : v n+1 Hence p s 1;:::;s n = x: (x) n (x) p N;:::;N n (x) 1 (x). The right hand side is clearly expressible in N P F RC(N; +; ; ; ; ; =; ; ; p N;:::;N ). 2 Next we sketch the proof of the conservativity of N RC(N; +; ; : ; P ; =; ; F ). Proposition 4.4 N RC(N; +; ; : ; P ; =; ; F ) has the conservative extension property with xed constant 0. Moreover, when endowed with any additional primitive p, it retains the conservative extension property with xed constant ht(p). Proof sketch. The proof of theorem 2.1 is based on rewriting expressions of the language to normal forms in which the e 2 in subexpressions of the form S fe 1 j x 2 e 2 g are variables. Such rewrite system was exhibited in Wong [10]. In Libkin and Wong [5] additional rules for P were given which guarantee that e 2 in any subexpression P fe 1 j x 2 e 2 g is a variable. The rewrite system was shown to be strongly normalizing and conservativity was derived by analyzing the normal forms. Now we add the following rewrite rules for F, assuming that the use of the construct F fe 1 j x 2 e 2 g is restricted to the situation when the type of e 1 is not a set type (when e 1 : fsg, it is treated as a shorthand.) F fe j x 2 fgg ; min F fe 1 j x 2 fe 2 gg ; e 1 [e 2 =x] F fe j x 2 e 1 [ e 2 g f F fe j x 2 e1 g F fe j x 2 e 2 g then F fe j x 2 e 2 g else F fe j x 2 e 1 g F fe 1 j x 2 S fe 2 j y 2 e 3 gg ; F f F fe 1 j x 2 e 2 g j y 2 e 3 g F fe j x 2 if e 1 then e 2 else e 3 g f e 1 then F fe j x 2 e 2 g else F fe j x 2 e 3 g F i fe1 j x 2 e 2 g ; S fif e 2 g, when e 1 : fsg. P fif e1 e 1 [y=x] then 1 else 0 j y 2 e 2 g = 1 then i e 1 else fg j x 2 F i fe1 j x 2 e 2 g ; F P fif fif e1 e 1 [y=x] then 1 else 0 j y 2 e 2 g = 1 then i e 1 else fg j x 2 e 2 g, when e 1 is not of set type. 7

8 The extended collection of rewrite rules forms a weakly normalizing rewrite system and conservativity can be derived by induction on the induced normal forms along the lines of Wong [10]. 2 Putting together the two previous propositions, the desired theorem follows straightforwardly. Theorem 4.5 N RC(N; +; ; : P F ; ; =; ; ) endowed with a internal generic family p 1;:::; n :! has the conservative extension property with xed constant mht(p). 2 5 Some corollaries As remarked earlier, F fe 1 j x 2 e 2 g is already denable in N RC(N; +; ; : ; P ; =; ) if e 1 : fsg. Therefore, if every type variable occurs in the scope of some set brackets in and, then the assumption of well-foundedness on b used in proposition 4.3 is not required and the proposition holds for N RC(N; +; ; : ; P ; =; ). Thus, we have Corollary 5.1 N RC(N; +; ; : ; P ; =; ) endowed with a internal generic family p 1;:::; n :!, where each type variable is within the scope of some set brackets, has the conservative extension property at all input and output heights with xed constant mht(p). 2 In particular, any polymorphic function denable in the algebra of Abiteboul and Beeri [1], which is equivalent to N RC(=; powerset), gives rise to a internal generic family of functions for all possible instantiations of type variables. (Observe that this is not true for N RC(N; +; ; : ; P ; =; ) because succ N is denable and therefore can be lifted to any type by corollary 3.4. This also indicates that the notion of internal and generic family is more general than polymorphic functions.) Now we conclude immediately that the following languages possess the conservative extension property (see also [5]): N RC(N; +; ; : ; P ; =; ; t) with xed constant 1, and N RC(N; +; ; : ; P ; =; ; powrst) with xed constant 2. where tc s : fs sg! fs sg is the transitive closure of binary relations. Since the Abiteboul and Beeri algebra has the power of a xpoint logic, a great deal of polymorphic functions can be added to N RC(N; +; ; : ; P ; =; ) without destroying its conservative extension property (but may be increasing the xed constant). Acknowledgements. We thank Peter Buneman, Rick Hull and Dan Suciu for helpful discussions. We are also grateful to the anonymous referees for the useful comments. Leonid Libkin was supported in part by NSF Grant IRI and AT&T Doctoral Fellowship and Limsoon Wong was supported in part by ARO Grant DAAL03-89-C-0031-PRIME. References [1] S. Abiteboul and C. Beeri. On the power of languages for the manipulation of complex objects. In Proc. International Workshop on Theory and Applications of Nested Relations and Complex Objects, Darmstadt,

9 [2] G. Birkho. Lattice Theory. American Mathematical Society, 3rd edition, [3] V. Breazu-Tannen, P. Buneman, and L. Wong. Naturally embedded query languages. In J. Biskup and R. Hull, editors, LNCS 646: Proc. International Conference on Database Theory, Berlin, Germany, October, 1992, pages 140{154. Springer-Verlag, October [4] R. Hull. Relative information capacity of simple relational database schemata. SIAM Journal of Computing, 15(3):865{886, [5] L. Libkin and L. Wong. Aggregate functions, conservative extension, and linear orders. In Proceedings of 4th International Workshop on Database Programming Languages, Springer Verlag, 1993, pages 279{292. [6] A. Ohori, P. Buneman, and V. Breazu-Tannen. Database programming in Machiavelli: A polymorphic language with static type inference. In J. Cliord, et al., editors, Proc. ACM-SIGMOD International Conference on Management of Data, pages 46{57, Portland, Oregon, June [7] J. Paredaens and D. Van Gucht. Converting nested relational algebra expressions into at algebra expressions. ACM Transaction on Database Systems, 17(1):65{93, [8] S. J. Thomas and P. C. Fischer. Nested relational structures. In P. C. Kanellakis, editor, Advances in Computing Research: The Theory of Databases, pages 269{307. JAI Press, [9] W. Wechler. Universal Algebra for Computer Scientists, Springer-Verlag, Berlin, [10] L. Wong. Normal forms and conservative properties for query languages over collection types. In Proceedings of 12th ACM Symposium on Principles of Database Systems, pages 26{36, Washington, D. C., May

3. Mathematical Induction

3. Mathematical Induction 3. MATHEMATICAL INDUCTION 83 3. Mathematical Induction 3.1. First Principle of Mathematical Induction. Let P (n) be a predicate with domain of discourse (over) the natural numbers N = {0, 1,,...}. If (1)

More information

Full and Complete Binary Trees

Full and Complete Binary Trees Full and Complete Binary Trees Binary Tree Theorems 1 Here are two important types of binary trees. Note that the definitions, while similar, are logically independent. Definition: a binary tree T is full

More information

Invertible elements in associates and semigroups. 1

Invertible elements in associates and semigroups. 1 Quasigroups and Related Systems 5 (1998), 53 68 Invertible elements in associates and semigroups. 1 Fedir Sokhatsky Abstract Some invertibility criteria of an element in associates, in particular in n-ary

More information

A Formal Basis for Extending SQL to Object-Oriented Databases Jan Van den Bussche University of Antwerp (UIA) Abstract A formal basis for extending SQL in a natural way to complex object databases is laid.

More information

Chapter 3. Cartesian Products and Relations. 3.1 Cartesian Products

Chapter 3. Cartesian Products and Relations. 3.1 Cartesian Products Chapter 3 Cartesian Products and Relations The material in this chapter is the first real encounter with abstraction. Relations are very general thing they are a special type of subset. After introducing

More information

Some other convex-valued selection theorems 147 (2) Every lower semicontinuous mapping F : X! IR such that for every x 2 X, F (x) is either convex and

Some other convex-valued selection theorems 147 (2) Every lower semicontinuous mapping F : X! IR such that for every x 2 X, F (x) is either convex and PART B: RESULTS x1. CHARACTERIZATION OF NORMALITY- TYPE PROPERTIES 1. Some other convex-valued selection theorems In this section all multivalued mappings are assumed to have convex values in some Banach

More information

Mathematical Induction

Mathematical Induction Mathematical Induction (Handout March 8, 01) The Principle of Mathematical Induction provides a means to prove infinitely many statements all at once The principle is logical rather than strictly mathematical,

More information

Parametric Domain-theoretic models of Linear Abadi & Plotkin Logic

Parametric Domain-theoretic models of Linear Abadi & Plotkin Logic Parametric Domain-theoretic models of Linear Abadi & Plotkin Logic Lars Birkedal Rasmus Ejlers Møgelberg Rasmus Lerchedahl Petersen IT University Technical Report Series TR-00-7 ISSN 600 600 February 00

More information

1 if 1 x 0 1 if 0 x 1

1 if 1 x 0 1 if 0 x 1 Chapter 3 Continuity In this chapter we begin by defining the fundamental notion of continuity for real valued functions of a single real variable. When trying to decide whether a given function is or

More information

No: 10 04. Bilkent University. Monotonic Extension. Farhad Husseinov. Discussion Papers. Department of Economics

No: 10 04. Bilkent University. Monotonic Extension. Farhad Husseinov. Discussion Papers. Department of Economics No: 10 04 Bilkent University Monotonic Extension Farhad Husseinov Discussion Papers Department of Economics The Discussion Papers of the Department of Economics are intended to make the initial results

More information

Math 223 Abstract Algebra Lecture Notes

Math 223 Abstract Algebra Lecture Notes Math 223 Abstract Algebra Lecture Notes Steven Tschantz Spring 2001 (Apr. 23 version) Preamble These notes are intended to supplement the lectures and make up for the lack of a textbook for the course

More information

Max-Min Representation of Piecewise Linear Functions

Max-Min Representation of Piecewise Linear Functions Beiträge zur Algebra und Geometrie Contributions to Algebra and Geometry Volume 43 (2002), No. 1, 297-302. Max-Min Representation of Piecewise Linear Functions Sergei Ovchinnikov Mathematics Department,

More information

Language. Johann Eder. Universitat Klagenfurt. Institut fur Informatik. Universiatsstr. 65. A-9020 Klagenfurt / AUSTRIA

Language. Johann Eder. Universitat Klagenfurt. Institut fur Informatik. Universiatsstr. 65. A-9020 Klagenfurt / AUSTRIA PLOP: A Polymorphic Logic Database Programming Language Johann Eder Universitat Klagenfurt Institut fur Informatik Universiatsstr. 65 A-9020 Klagenfurt / AUSTRIA February 12, 1993 Extended Abstract The

More information

Mathematics for Computer Science/Software Engineering. Notes for the course MSM1F3 Dr. R. A. Wilson

Mathematics for Computer Science/Software Engineering. Notes for the course MSM1F3 Dr. R. A. Wilson Mathematics for Computer Science/Software Engineering Notes for the course MSM1F3 Dr. R. A. Wilson October 1996 Chapter 1 Logic Lecture no. 1. We introduce the concept of a proposition, which is a statement

More information

CHAPTER 3. Methods of Proofs. 1. Logical Arguments and Formal Proofs

CHAPTER 3. Methods of Proofs. 1. Logical Arguments and Formal Proofs CHAPTER 3 Methods of Proofs 1. Logical Arguments and Formal Proofs 1.1. Basic Terminology. An axiom is a statement that is given to be true. A rule of inference is a logical rule that is used to deduce

More information

Regular Expressions with Nested Levels of Back Referencing Form a Hierarchy

Regular Expressions with Nested Levels of Back Referencing Form a Hierarchy Regular Expressions with Nested Levels of Back Referencing Form a Hierarchy Kim S. Larsen Odense University Abstract For many years, regular expressions with back referencing have been used in a variety

More information

Linear Algebra. A vector space (over R) is an ordered quadruple. such that V is a set; 0 V ; and the following eight axioms hold:

Linear Algebra. A vector space (over R) is an ordered quadruple. such that V is a set; 0 V ; and the following eight axioms hold: Linear Algebra A vector space (over R) is an ordered quadruple (V, 0, α, µ) such that V is a set; 0 V ; and the following eight axioms hold: α : V V V and µ : R V V ; (i) α(α(u, v), w) = α(u, α(v, w)),

More information

This asserts two sets are equal iff they have the same elements, that is, a set is determined by its elements.

This asserts two sets are equal iff they have the same elements, that is, a set is determined by its elements. 3. Axioms of Set theory Before presenting the axioms of set theory, we first make a few basic comments about the relevant first order logic. We will give a somewhat more detailed discussion later, but

More information

Variations of Batteric V, Part 1

Variations of Batteric V, Part 1 CBV Semantics (Draft) Navit Fedida, John Havlicek, Nissan Levi, and Hillel Miller Motorola, Inc. 7700 W. Parmer Lane Austin, TX 78729 13 January 2002 Abstract A precise denition is given of the semantics

More information

Mathematical Induction. Lecture 10-11

Mathematical Induction. Lecture 10-11 Mathematical Induction Lecture 10-11 Menu Mathematical Induction Strong Induction Recursive Definitions Structural Induction Climbing an Infinite Ladder Suppose we have an infinite ladder: 1. We can reach

More information

The Structure of Galois Algebras

The Structure of Galois Algebras The Structure of Galois Algebras George Szeto Department of Mathematics, Bradley University Peoria, Illinois 61625 { U.S.A. Email: szeto@hilltop.bradley.edu and Lianyong Xue Department of Mathematics,

More information

Cartesian Products and Relations

Cartesian Products and Relations Cartesian Products and Relations Definition (Cartesian product) If A and B are sets, the Cartesian product of A and B is the set A B = {(a, b) :(a A) and (b B)}. The following points are worth special

More information

Biinterpretability up to double jump in the degrees

Biinterpretability up to double jump in the degrees Biinterpretability up to double jump in the degrees below 0 0 Richard A. Shore Department of Mathematics Cornell University Ithaca NY 14853 July 29, 2013 Abstract We prove that, for every z 0 0 with z

More information

CS 598CSC: Combinatorial Optimization Lecture date: 2/4/2010

CS 598CSC: Combinatorial Optimization Lecture date: 2/4/2010 CS 598CSC: Combinatorial Optimization Lecture date: /4/010 Instructor: Chandra Chekuri Scribe: David Morrison Gomory-Hu Trees (The work in this section closely follows [3]) Let G = (V, E) be an undirected

More information

So let us begin our quest to find the holy grail of real analysis.

So let us begin our quest to find the holy grail of real analysis. 1 Section 5.2 The Complete Ordered Field: Purpose of Section We present an axiomatic description of the real numbers as a complete ordered field. The axioms which describe the arithmetic of the real numbers

More information

Notes on Complexity Theory Last updated: August, 2011. Lecture 1

Notes on Complexity Theory Last updated: August, 2011. Lecture 1 Notes on Complexity Theory Last updated: August, 2011 Jonathan Katz Lecture 1 1 Turing Machines I assume that most students have encountered Turing machines before. (Students who have not may want to look

More information

Portable Bushy Processing Trees for Join Queries

Portable Bushy Processing Trees for Join Queries Reihe Informatik 11 / 1996 Constructing Optimal Bushy Processing Trees for Join Queries is NP-hard Wolfgang Scheufele Guido Moerkotte 1 Constructing Optimal Bushy Processing Trees for Join Queries is NP-hard

More information

Mathematical Induction

Mathematical Induction Mathematical Induction In logic, we often want to prove that every member of an infinite set has some feature. E.g., we would like to show: N 1 : is a number 1 : has the feature Φ ( x)(n 1 x! 1 x) How

More information

facultad de informática universidad politécnica de madrid

facultad de informática universidad politécnica de madrid facultad de informática universidad politécnica de madrid On the Confluence of CHR Analytical Semantics Rémy Haemmerlé Universidad olitécnica de Madrid & IMDEA Software Institute, Spain TR Number CLI2/2014.0

More information

THE BANACH CONTRACTION PRINCIPLE. Contents

THE BANACH CONTRACTION PRINCIPLE. Contents THE BANACH CONTRACTION PRINCIPLE ALEX PONIECKI Abstract. This paper will study contractions of metric spaces. To do this, we will mainly use tools from topology. We will give some examples of contractions,

More information

LEARNING OBJECTIVES FOR THIS CHAPTER

LEARNING OBJECTIVES FOR THIS CHAPTER CHAPTER 2 American mathematician Paul Halmos (1916 2006), who in 1942 published the first modern linear algebra book. The title of Halmos s book was the same as the title of this chapter. Finite-Dimensional

More information

I. GROUPS: BASIC DEFINITIONS AND EXAMPLES

I. GROUPS: BASIC DEFINITIONS AND EXAMPLES I GROUPS: BASIC DEFINITIONS AND EXAMPLES Definition 1: An operation on a set G is a function : G G G Definition 2: A group is a set G which is equipped with an operation and a special element e G, called

More information

Row Ideals and Fibers of Morphisms

Row Ideals and Fibers of Morphisms Michigan Math. J. 57 (2008) Row Ideals and Fibers of Morphisms David Eisenbud & Bernd Ulrich Affectionately dedicated to Mel Hochster, who has been an inspiration to us for many years, on the occasion

More information

Finite dimensional topological vector spaces

Finite dimensional topological vector spaces Chapter 3 Finite dimensional topological vector spaces 3.1 Finite dimensional Hausdorff t.v.s. Let X be a vector space over the field K of real or complex numbers. We know from linear algebra that the

More information

INCIDENCE-BETWEENNESS GEOMETRY

INCIDENCE-BETWEENNESS GEOMETRY INCIDENCE-BETWEENNESS GEOMETRY MATH 410, CSUSM. SPRING 2008. PROFESSOR AITKEN This document covers the geometry that can be developed with just the axioms related to incidence and betweenness. The full

More information

Logic, Algebra and Truth Degrees 2008. Siena. A characterization of rst order rational Pavelka's logic

Logic, Algebra and Truth Degrees 2008. Siena. A characterization of rst order rational Pavelka's logic Logic, Algebra and Truth Degrees 2008 September 8-11, 2008 Siena A characterization of rst order rational Pavelka's logic Xavier Caicedo Universidad de los Andes, Bogota Under appropriate formulations,

More information

ON THE DEGREE OF MAXIMALITY OF DEFINITIONALLY COMPLETE LOGICS

ON THE DEGREE OF MAXIMALITY OF DEFINITIONALLY COMPLETE LOGICS Bulletin of the Section of Logic Volume 15/2 (1986), pp. 72 79 reedition 2005 [original edition, pp. 72 84] Ryszard Ladniak ON THE DEGREE OF MAXIMALITY OF DEFINITIONALLY COMPLETE LOGICS The following definition

More information

DIFFERENTIABILITY OF COMPLEX FUNCTIONS. Contents

DIFFERENTIABILITY OF COMPLEX FUNCTIONS. Contents DIFFERENTIABILITY OF COMPLEX FUNCTIONS Contents 1. Limit definition of a derivative 1 2. Holomorphic functions, the Cauchy-Riemann equations 3 3. Differentiability of real functions 5 4. A sufficient condition

More information

PYTHAGOREAN TRIPLES KEITH CONRAD

PYTHAGOREAN TRIPLES KEITH CONRAD PYTHAGOREAN TRIPLES KEITH CONRAD 1. Introduction A Pythagorean triple is a triple of positive integers (a, b, c) where a + b = c. Examples include (3, 4, 5), (5, 1, 13), and (8, 15, 17). Below is an ancient

More information

POLYNOMIAL RINGS AND UNIQUE FACTORIZATION DOMAINS

POLYNOMIAL RINGS AND UNIQUE FACTORIZATION DOMAINS POLYNOMIAL RINGS AND UNIQUE FACTORIZATION DOMAINS RUSS WOODROOFE 1. Unique Factorization Domains Throughout the following, we think of R as sitting inside R[x] as the constant polynomials (of degree 0).

More information

Quotient Rings and Field Extensions

Quotient Rings and Field Extensions Chapter 5 Quotient Rings and Field Extensions In this chapter we describe a method for producing field extension of a given field. If F is a field, then a field extension is a field K that contains F.

More information

Formal Languages and Automata Theory - Regular Expressions and Finite Automata -

Formal Languages and Automata Theory - Regular Expressions and Finite Automata - Formal Languages and Automata Theory - Regular Expressions and Finite Automata - Samarjit Chakraborty Computer Engineering and Networks Laboratory Swiss Federal Institute of Technology (ETH) Zürich March

More information

DEGREES OF CATEGORICITY AND THE HYPERARITHMETIC HIERARCHY

DEGREES OF CATEGORICITY AND THE HYPERARITHMETIC HIERARCHY DEGREES OF CATEGORICITY AND THE HYPERARITHMETIC HIERARCHY BARBARA F. CSIMA, JOHANNA N. Y. FRANKLIN, AND RICHARD A. SHORE Abstract. We study arithmetic and hyperarithmetic degrees of categoricity. We extend

More information

MA651 Topology. Lecture 6. Separation Axioms.

MA651 Topology. Lecture 6. Separation Axioms. MA651 Topology. Lecture 6. Separation Axioms. This text is based on the following books: Fundamental concepts of topology by Peter O Neil Elements of Mathematics: General Topology by Nicolas Bourbaki Counterexamples

More information

6.3 Conditional Probability and Independence

6.3 Conditional Probability and Independence 222 CHAPTER 6. PROBABILITY 6.3 Conditional Probability and Independence Conditional Probability Two cubical dice each have a triangle painted on one side, a circle painted on two sides and a square painted

More information

Question Answering and the Nature of Intercomplete Databases

Question Answering and the Nature of Intercomplete Databases Certain Answers as Objects and Knowledge Leonid Libkin School of Informatics, University of Edinburgh Abstract The standard way of answering queries over incomplete databases is to compute certain answers,

More information

Elements of probability theory

Elements of probability theory 2 Elements of probability theory Probability theory provides mathematical models for random phenomena, that is, phenomena which under repeated observations yield di erent outcomes that cannot be predicted

More information

(a) Write each of p and q as a polynomial in x with coefficients in Z[y, z]. deg(p) = 7 deg(q) = 9

(a) Write each of p and q as a polynomial in x with coefficients in Z[y, z]. deg(p) = 7 deg(q) = 9 Homework #01, due 1/20/10 = 9.1.2, 9.1.4, 9.1.6, 9.1.8, 9.2.3 Additional problems for study: 9.1.1, 9.1.3, 9.1.5, 9.1.13, 9.2.1, 9.2.2, 9.2.4, 9.2.5, 9.2.6, 9.3.2, 9.3.3 9.1.1 (This problem was not assigned

More information

On strong fairness in UNITY

On strong fairness in UNITY On strong fairness in UNITY H.P.Gumm, D.Zhukov Fachbereich Mathematik und Informatik Philipps Universität Marburg {gumm,shukov}@mathematik.uni-marburg.de Abstract. In [6] Tsay and Bagrodia present a correct

More information

INTRODUCTORY SET THEORY

INTRODUCTORY SET THEORY M.Sc. program in mathematics INTRODUCTORY SET THEORY Katalin Károlyi Department of Applied Analysis, Eötvös Loránd University H-1088 Budapest, Múzeum krt. 6-8. CONTENTS 1. SETS Set, equal sets, subset,

More information

How To Understand The Theory Of Computer Science

How To Understand The Theory Of Computer Science Theory of Computation Lecture Notes Abhijat Vichare August 2005 Contents 1 Introduction 2 What is Computation? 3 The λ Calculus 3.1 Conversions: 3.2 The calculus in use 3.3 Few Important Theorems 3.4 Worked

More information

Follow links for Class Use and other Permissions. For more information send email to: permissions@pupress.princeton.edu

Follow links for Class Use and other Permissions. For more information send email to: permissions@pupress.princeton.edu COPYRIGHT NOTICE: Ariel Rubinstein: Lecture Notes in Microeconomic Theory is published by Princeton University Press and copyrighted, c 2006, by Princeton University Press. All rights reserved. No part

More information

CODING TRUE ARITHMETIC IN THE MEDVEDEV AND MUCHNIK DEGREES

CODING TRUE ARITHMETIC IN THE MEDVEDEV AND MUCHNIK DEGREES CODING TRUE ARITHMETIC IN THE MEDVEDEV AND MUCHNIK DEGREES PAUL SHAFER Abstract. We prove that the first-order theory of the Medvedev degrees, the first-order theory of the Muchnik degrees, and the third-order

More information

Semantics of UML class diagrams

Semantics of UML class diagrams 1 Otto-von-Guericke Universität Magdeburg, Germany April 26, 2016 Sets Definition (Set) A set is a collection of objects. The basic relation is membership: x A (x is a member of A) The following operations

More information

5. Linear algebra I: dimension

5. Linear algebra I: dimension 5. Linear algebra I: dimension 5.1 Some simple results 5.2 Bases and dimension 5.3 Homomorphisms and dimension 1. Some simple results Several observations should be made. Once stated explicitly, the proofs

More information

x 2 f 2 e 1 e 4 e 3 e 2 q f 4 x 4 f 3 x 3

x 2 f 2 e 1 e 4 e 3 e 2 q f 4 x 4 f 3 x 3 Karl-Franzens-Universitat Graz & Technische Universitat Graz SPEZIALFORSCHUNGSBEREICH F 003 OPTIMIERUNG und KONTROLLE Projektbereich DISKRETE OPTIMIERUNG O. Aichholzer F. Aurenhammer R. Hainz New Results

More information

Discrete Mathematics

Discrete Mathematics Discrete Mathematics Chih-Wei Yi Dept. of Computer Science National Chiao Tung University March 16, 2009 2.1 Sets 2.1 Sets 2.1 Sets Basic Notations for Sets For sets, we ll use variables S, T, U,. We can

More information

Relational Databases

Relational Databases Relational Databases Jan Chomicki University at Buffalo Jan Chomicki () Relational databases 1 / 18 Relational data model Domain domain: predefined set of atomic values: integers, strings,... every attribute

More information

Degrees that are not degrees of categoricity

Degrees that are not degrees of categoricity Degrees that are not degrees of categoricity Bernard A. Anderson Department of Mathematics and Physical Sciences Gordon State College banderson@gordonstate.edu www.gordonstate.edu/faculty/banderson Barbara

More information

Representing Reversible Cellular Automata with Reversible Block Cellular Automata

Representing Reversible Cellular Automata with Reversible Block Cellular Automata Discrete Mathematics and Theoretical Computer Science Proceedings AA (DM-CCG), 2001, 145 154 Representing Reversible Cellular Automata with Reversible Block Cellular Automata Jérôme Durand-Lose Laboratoire

More information

Properties of Stabilizing Computations

Properties of Stabilizing Computations Theory and Applications of Mathematics & Computer Science 5 (1) (2015) 71 93 Properties of Stabilizing Computations Mark Burgin a a University of California, Los Angeles 405 Hilgard Ave. Los Angeles, CA

More information

A CONSTRUCTION OF THE UNIVERSAL COVER AS A FIBER BUNDLE

A CONSTRUCTION OF THE UNIVERSAL COVER AS A FIBER BUNDLE A CONSTRUCTION OF THE UNIVERSAL COVER AS A FIBER BUNDLE DANIEL A. RAMRAS In these notes we present a construction of the universal cover of a path connected, locally path connected, and semi-locally simply

More information

The Mean Value Theorem

The Mean Value Theorem The Mean Value Theorem THEOREM (The Extreme Value Theorem): If f is continuous on a closed interval [a, b], then f attains an absolute maximum value f(c) and an absolute minimum value f(d) at some numbers

More information

CONTENTS 1. Peter Kahn. Spring 2007

CONTENTS 1. Peter Kahn. Spring 2007 CONTENTS 1 MATH 304: CONSTRUCTING THE REAL NUMBERS Peter Kahn Spring 2007 Contents 2 The Integers 1 2.1 The basic construction.......................... 1 2.2 Adding integers..............................

More information

Class notes Program Analysis course given by Prof. Mooly Sagiv Computer Science Department, Tel Aviv University second lecture 8/3/2007

Class notes Program Analysis course given by Prof. Mooly Sagiv Computer Science Department, Tel Aviv University second lecture 8/3/2007 Constant Propagation Class notes Program Analysis course given by Prof. Mooly Sagiv Computer Science Department, Tel Aviv University second lecture 8/3/2007 Osnat Minz and Mati Shomrat Introduction This

More information

Sample Induction Proofs

Sample Induction Proofs Math 3 Worksheet: Induction Proofs III, Sample Proofs A.J. Hildebrand Sample Induction Proofs Below are model solutions to some of the practice problems on the induction worksheets. The solutions given

More information

GROUPS ACTING ON A SET

GROUPS ACTING ON A SET GROUPS ACTING ON A SET MATH 435 SPRING 2012 NOTES FROM FEBRUARY 27TH, 2012 1. Left group actions Definition 1.1. Suppose that G is a group and S is a set. A left (group) action of G on S is a rule for

More information

On computer algebra-aided stability analysis of dierence schemes generated by means of Gr obner bases

On computer algebra-aided stability analysis of dierence schemes generated by means of Gr obner bases On computer algebra-aided stability analysis of dierence schemes generated by means of Gr obner bases Vladimir Gerdt 1 Yuri Blinkov 2 1 Laboratory of Information Technologies Joint Institute for Nuclear

More information

Group Theory. Contents

Group Theory. Contents Group Theory Contents Chapter 1: Review... 2 Chapter 2: Permutation Groups and Group Actions... 3 Orbits and Transitivity... 6 Specific Actions The Right regular and coset actions... 8 The Conjugation

More information

Gröbner Bases and their Applications

Gröbner Bases and their Applications Gröbner Bases and their Applications Kaitlyn Moran July 30, 2008 1 Introduction We know from the Hilbert Basis Theorem that any ideal in a polynomial ring over a field is finitely generated [3]. However,

More information

GRAPH THEORY LECTURE 4: TREES

GRAPH THEORY LECTURE 4: TREES GRAPH THEORY LECTURE 4: TREES Abstract. 3.1 presents some standard characterizations and properties of trees. 3.2 presents several different types of trees. 3.7 develops a counting method based on a bijection

More information

Tree sums and maximal connected I-spaces

Tree sums and maximal connected I-spaces Tree sums and maximal connected I-spaces Adam Bartoš drekin@gmail.com Faculty of Mathematics and Physics Charles University in Prague Twelfth Symposium on General Topology Prague, July 2016 Maximal and

More information

Generating models of a matched formula with a polynomial delay

Generating models of a matched formula with a polynomial delay Generating models of a matched formula with a polynomial delay Petr Savicky Institute of Computer Science, Academy of Sciences of Czech Republic, Pod Vodárenskou Věží 2, 182 07 Praha 8, Czech Republic

More information

SMALL SKEW FIELDS CÉDRIC MILLIET

SMALL SKEW FIELDS CÉDRIC MILLIET SMALL SKEW FIELDS CÉDRIC MILLIET Abstract A division ring of positive characteristic with countably many pure types is a field Wedderburn showed in 1905 that finite fields are commutative As for infinite

More information

MATH10040 Chapter 2: Prime and relatively prime numbers

MATH10040 Chapter 2: Prime and relatively prime numbers MATH10040 Chapter 2: Prime and relatively prime numbers Recall the basic definition: 1. Prime numbers Definition 1.1. Recall that a positive integer is said to be prime if it has precisely two positive

More information

CS 3719 (Theory of Computation and Algorithms) Lecture 4

CS 3719 (Theory of Computation and Algorithms) Lecture 4 CS 3719 (Theory of Computation and Algorithms) Lecture 4 Antonina Kolokolova January 18, 2012 1 Undecidable languages 1.1 Church-Turing thesis Let s recap how it all started. In 1990, Hilbert stated a

More information

Degrees of Truth: the formal logic of classical and quantum probabilities as well as fuzzy sets.

Degrees of Truth: the formal logic of classical and quantum probabilities as well as fuzzy sets. Degrees of Truth: the formal logic of classical and quantum probabilities as well as fuzzy sets. Logic is the study of reasoning. A language of propositions is fundamental to this study as well as true

More information

MATH10212 Linear Algebra. Systems of Linear Equations. Definition. An n-dimensional vector is a row or a column of n numbers (or letters): a 1.

MATH10212 Linear Algebra. Systems of Linear Equations. Definition. An n-dimensional vector is a row or a column of n numbers (or letters): a 1. MATH10212 Linear Algebra Textbook: D. Poole, Linear Algebra: A Modern Introduction. Thompson, 2006. ISBN 0-534-40596-7. Systems of Linear Equations Definition. An n-dimensional vector is a row or a column

More information

CHAPTER 7 GENERAL PROOF SYSTEMS

CHAPTER 7 GENERAL PROOF SYSTEMS CHAPTER 7 GENERAL PROOF SYSTEMS 1 Introduction Proof systems are built to prove statements. They can be thought as an inference machine with special statements, called provable statements, or sometimes

More information

Reading 13 : Finite State Automata and Regular Expressions

Reading 13 : Finite State Automata and Regular Expressions CS/Math 24: Introduction to Discrete Mathematics Fall 25 Reading 3 : Finite State Automata and Regular Expressions Instructors: Beck Hasti, Gautam Prakriya In this reading we study a mathematical model

More information

INDISTINGUISHABILITY OF ABSOLUTELY CONTINUOUS AND SINGULAR DISTRIBUTIONS

INDISTINGUISHABILITY OF ABSOLUTELY CONTINUOUS AND SINGULAR DISTRIBUTIONS INDISTINGUISHABILITY OF ABSOLUTELY CONTINUOUS AND SINGULAR DISTRIBUTIONS STEVEN P. LALLEY AND ANDREW NOBEL Abstract. It is shown that there are no consistent decision rules for the hypothesis testing problem

More information

God created the integers and the rest is the work of man. (Leopold Kronecker, in an after-dinner speech at a conference, Berlin, 1886)

God created the integers and the rest is the work of man. (Leopold Kronecker, in an after-dinner speech at a conference, Berlin, 1886) Chapter 2 Numbers God created the integers and the rest is the work of man. (Leopold Kronecker, in an after-dinner speech at a conference, Berlin, 1886) God created the integers and the rest is the work

More information

Linear Algebra I. Ronald van Luijk, 2012

Linear Algebra I. Ronald van Luijk, 2012 Linear Algebra I Ronald van Luijk, 2012 With many parts from Linear Algebra I by Michael Stoll, 2007 Contents 1. Vector spaces 3 1.1. Examples 3 1.2. Fields 4 1.3. The field of complex numbers. 6 1.4.

More information

263-2200 Types and Programming Languages

263-2200 Types and Programming Languages 263-2200 Types and Programming Languages 1 / 49 Outline Types Evaluation Rules Typing Rules Properties of the Typing Relation The Inversion Lemma Prolog Implementation Reasoning Involving Types Progress

More information

Solving Linear Systems, Continued and The Inverse of a Matrix

Solving Linear Systems, Continued and The Inverse of a Matrix , Continued and The of a Matrix Calculus III Summer 2013, Session II Monday, July 15, 2013 Agenda 1. The rank of a matrix 2. The inverse of a square matrix Gaussian Gaussian solves a linear system by reducing

More information

Software Engineering

Software Engineering Software Engineering Lecture 04: The B Specification Method Peter Thiemann University of Freiburg, Germany SS 2013 Peter Thiemann (Univ. Freiburg) Software Engineering SWT 1 / 50 The B specification method

More information

Similarity and Diagonalization. Similar Matrices

Similarity and Diagonalization. Similar Matrices MATH022 Linear Algebra Brief lecture notes 48 Similarity and Diagonalization Similar Matrices Let A and B be n n matrices. We say that A is similar to B if there is an invertible n n matrix P such that

More information

Vector and Matrix Norms

Vector and Matrix Norms Chapter 1 Vector and Matrix Norms 11 Vector Spaces Let F be a field (such as the real numbers, R, or complex numbers, C) with elements called scalars A Vector Space, V, over the field F is a non-empty

More information

The Prime Numbers. Definition. A prime number is a positive integer with exactly two positive divisors.

The Prime Numbers. Definition. A prime number is a positive integer with exactly two positive divisors. The Prime Numbers Before starting our study of primes, we record the following important lemma. Recall that integers a, b are said to be relatively prime if gcd(a, b) = 1. Lemma (Euclid s Lemma). If gcd(a,

More information

Non-deterministic Semantics and the Undecidability of Boolean BI

Non-deterministic Semantics and the Undecidability of Boolean BI 1 Non-deterministic Semantics and the Undecidability of Boolean BI DOMINIQUE LARCHEY-WENDLING, LORIA CNRS, Nancy, France DIDIER GALMICHE, LORIA Université Henri Poincaré, Nancy, France We solve the open

More information

C H A P T E R Regular Expressions regular expression

C H A P T E R Regular Expressions regular expression 7 CHAPTER Regular Expressions Most programmers and other power-users of computer systems have used tools that match text patterns. You may have used a Web search engine with a pattern like travel cancun

More information

Arkansas Tech University MATH 4033: Elementary Modern Algebra Dr. Marcel B. Finan

Arkansas Tech University MATH 4033: Elementary Modern Algebra Dr. Marcel B. Finan Arkansas Tech University MATH 4033: Elementary Modern Algebra Dr. Marcel B. Finan 3 Binary Operations We are used to addition and multiplication of real numbers. These operations combine two real numbers

More information

Lecture 16 : Relations and Functions DRAFT

Lecture 16 : Relations and Functions DRAFT CS/Math 240: Introduction to Discrete Mathematics 3/29/2011 Lecture 16 : Relations and Functions Instructor: Dieter van Melkebeek Scribe: Dalibor Zelený DRAFT In Lecture 3, we described a correspondence

More information

COMBINATORIAL PROPERTIES OF THE HIGMAN-SIMS GRAPH. 1. Introduction

COMBINATORIAL PROPERTIES OF THE HIGMAN-SIMS GRAPH. 1. Introduction COMBINATORIAL PROPERTIES OF THE HIGMAN-SIMS GRAPH ZACHARY ABEL 1. Introduction In this survey we discuss properties of the Higman-Sims graph, which has 100 vertices, 1100 edges, and is 22 regular. In fact

More information

Degree Hypergroupoids Associated with Hypergraphs

Degree Hypergroupoids Associated with Hypergraphs Filomat 8:1 (014), 119 19 DOI 10.98/FIL1401119F Published by Faculty of Sciences and Mathematics, University of Niš, Serbia Available at: http://www.pmf.ni.ac.rs/filomat Degree Hypergroupoids Associated

More information

Lecture 17 : Equivalence and Order Relations DRAFT

Lecture 17 : Equivalence and Order Relations DRAFT CS/Math 240: Introduction to Discrete Mathematics 3/31/2011 Lecture 17 : Equivalence and Order Relations Instructor: Dieter van Melkebeek Scribe: Dalibor Zelený DRAFT Last lecture we introduced the notion

More information

E3: PROBABILITY AND STATISTICS lecture notes

E3: PROBABILITY AND STATISTICS lecture notes E3: PROBABILITY AND STATISTICS lecture notes 2 Contents 1 PROBABILITY THEORY 7 1.1 Experiments and random events............................ 7 1.2 Certain event. Impossible event............................

More information

Automata on Infinite Words and Trees

Automata on Infinite Words and Trees Automata on Infinite Words and Trees Course notes for the course Automata on Infinite Words and Trees given by Dr. Meghyn Bienvenu at Universität Bremen in the 2009-2010 winter semester Last modified:

More information

A simple algorithm with no simple verication

A simple algorithm with no simple verication A simple algorithm with no simple verication Laszlo Csirmaz Central European University Abstract The correctness of a simple sorting algorithm is resented, which algorithm is \evidently wrong" at the rst

More information

Chapter 4, Arithmetic in F [x] Polynomial arithmetic and the division algorithm.

Chapter 4, Arithmetic in F [x] Polynomial arithmetic and the division algorithm. Chapter 4, Arithmetic in F [x] Polynomial arithmetic and the division algorithm. We begin by defining the ring of polynomials with coefficients in a ring R. After some preliminary results, we specialize

More information