Deadlocking States in Context-free Process Algebra

Size: px
Start display at page:

Download "Deadlocking States in Context-free Process Algebra"

Transcription

1 Dedlocking Sttes in Context-free Process Algebr JiříSrb Fculty of Informtics MU, Botnická 68, Brno, Czech Republic Abstrct. Recently the clss of BPA (or context-free) processes hs been intensively studied nd bisimilrity nd regulrity ppered to be decidble (see [CHS95, BCS95, BCS96]). We extend these processes with dedlocking stte into BPA δ systems. Bosscher hs proved tht bisimilrity nd regulrity remin decidble [Bos97]. We generlise his pproch introducing strict nd nonstrict version of bisimilrity. We show tht the BPA δ clss is more expressive w.r.t. (both strict nd nonstrict) bisimilrity but it remins lnguge equivlent to BPA. Finlly we give chrcteriztion of those BPA δ processes which cn be equivlently (up to bisimilrity) described within the pure BPA syntx. 1 Introduction This pper dels with BPA processes (Bsic Process Algebr) extended with dedlocks. BPA represents the clss of processes introduced by Bergstr nd Klop (see [BK85]), which corresponds to the trnsition systems ssocited with Greibch norml form (GNF) context-free grmmrs in which only left-most derivtions re permitted. For detiled description of the reltion between lnguge nd process theory we refer to [HM96]. We ine the clss BPA δ of BPA processes extended with dedlocks nd introduce two lterntive initions strict nd nonstrict of bisimilrity within this clss. The inition of BPA δ systems is bsed on specil vrible δ (we cll it dedlock). In the usul presenttion every vrible used in BPA system is supposed to be ined but for the dedlock vrible we llow no inition. This cuses tht if the system reches stte where the first vrible is δ, the system sticks t this stte nd no more ctions cn be performed. Bosscher hs proved in [Bos97] tht decidbility of bisimilrity nd regulrity in BPA systems extends to the BPA δ systems. The trick used for this extention is bsed on the ide tht δ cn be simulted by n unnormed vrible. The min topic this rticle dels with is the issue of the lnguge equivlence nd of describing BPA δ in bisimilr BPA syntx. We show in Section 3 tht extending the BPA systems with dedlocks does not yield ny lnguge extension. The uthor is supported by the Grnt Agency of the Czech Republic, grnt No. 201/97/0456

2 On the other hnd the clss of BPA δ systems is lrger with regrd to bisimilrity. An interesting question explored in this pper (Section 4) is concerned with deciding whether there exists n lterntive description of BPA δ system in bisimilr BPA syntx. We show tht it is decidble for the strict bisimilrity nd we find nice semntic chrcteriztion of the sitution in the nonstrict cse. Moreover we show tht the corresponding BPA syntx cn be effectively constructed. Severl proofs in this pper re just sketched nd their full version cn be obtined in [Srb98]. 2 Bsic initions When deling with processes we need some structure to describe their opertionl semntics. As the most suitble structure trnsition systems re widely used. We introduce the lbelled trnsition system in the extended version with the set of finl sttes s cn be found e.g. in [Mol96]. Definition 1. (lbelled trnsition system) A lbelled trnsition system is tuple (S, Act,,α 0,F) where S is set of sttes; Act is set of ctions (or lbels); S Act S is trnsition reltion, written α β, for (α,, β) ; α 0 S is the root (or strt stte) of the trnsition system; F S is the set of finl sttes which re terminl: forechα F there is no Act nd β S such tht α β. As usul we extend the trnsition reltion to the elements of Act.Welsowrite α β insted of α w β if w Act is irrelevnt. Definition 2. (lnguge genertion) Let (S, Act,,α 0,F) be lbelled trnsition system nd suppose tht α S. The lnguge generted by the stte α is L(α) = {w Act α F :α w α }. We sy tht two sttes α nd β re lnguge equivlent, written α = L β,iffl(α)=l(β). Two lbelled trnsition systems re lnguge equivlent iff their roots re lnguge equivlent. Definition 3. (bisimilrity) Let (S, Act,,α 0,F) be lbelled trnsition system. A binry reltion R S S is bisimultion iff whenever (α, β) R then for ech Act: if α α then β S : β β (α,β ) R if β β then α S : α α (α,β ) R α F β F Sttes α, β S re bisimilr (α β), iff (α, β) R for some bisimultion R. 2.1 BPA nd BPA δ systems Assume tht Vr nd Act re finite sets of vribles nd ctions such tht Vr Act =. We ine the clss E BPA of BPA expressions s the union of ɛ (empty process) nd set E BPA, + which is ined by the following bstrct syntx: E ::= X E 1.E 2 E 1 + E 2

3 Here rnges over Act nd X rnges over Vr. We stte E BPA = {ɛ} E +. BPA We cll the BPA expressions s processes nd lter on we ssume fixed sets Vr nd Act if no confusion is cused. As usul, we restrict our ttention to gurded expressions: BPA expression is gurded iff every vrible occurrence is within the scope of n tomic ction. Definition 4. (BPA system) A BPA system is qudruple (Vr, Act,,X 1 ) where Vr nd Act re finite sets of distinct vribles (Vr = {X 1,...,X n }) resp. ctions; X 1 Vr is the leding vrible; is finite set of recursive equtions = {X i = E i i =1,...,n} where ech E i E + BPA is gurded BPA expression with vribles drwn from the set Vr nd ctions from Act. Speking bout vribles nd ctions used in the system (Vr, Act,,X 1 ) we use the nottion Vr( ) ndact( ) nd for shorter referring to the BPA system we often identify the system (Vr, Act,,X 1 )with. Assume tht we hve BPA system (Vr, Act,,X 1 ). This system determines lbelled trnsition system (S, Act,,X 1,{ɛ}) whose sttes re BPA expressions built over Vr nd Act, Act is the set of lbels, the trnsition reltion is the lest reltion stisfying the following SOS rules, X 1 is the root nd ɛ is the only finl stte. ɛ E E E.F E.F if E ɛ E ɛ E.F F E E E + F E F F E + F F E E X E if X = E We now ine the clss BPA δ of BPA systems with dedlock. The inition is very similr to the inition of BPA systems except for new distinct vrible δ. There is no opertionl rule for δ in the BPA δ systems. Definition 5. (BPA δ system) A BPA δ system is qudruple (Vr, Act,,X 1 ) where Vr = {X 1,...,X n,δ} (δ is specil vrible clled dedlock), Act is finite set of ctions nd is finite set of recursive equtions = {X i = E i i =1,...,n} where ech E i E + is gurded BPA expression with vribles BPA drwn from the set Vr nd ctions from Act. It is obvious tht ny BPA system is trivilly BPA δ system. BPA δ lbelled (strict or nonstrict) trnsition system is ined s in the cse of BPA systems. If F = {ɛ} is the only finl stte we cll the lbelled trnsition system strict nd if the finl sttes re F = {ɛ, δ} {δ.e E E BPA} + we cll it nonstrict. This mens tht the reltion of bisimultion differs for both these pproches. Similrly, we cll the bisimultion strict resp. nonstrict (nd write s resp. )ccording to the type of the lbelled trnsition system we tke into ccount. These n two notions of bisimilrity imply tht δ n s ɛ but δ ɛ. An esy consequence of decidbility of bisimilrity in BPA δ [Bos97] is tht both s nd n re decidble. Following lemm results from the inition of s nd. n

4 Lemm 1. s n Let X Vr. We ine the norm of X s X =min{length(w) E:X w E },ifsuchwexists; or X = otherwise. We cll the vrible X normed iff X <. A process is normed iff its leding vrible is normed. Definition 6. A BPA (resp. BPA δ )system is sid to be in Greibch Norml Form (GNF) iff ll its ining equtions re of the form X = m j=1 jα j where m>0, j Act( ) nd α j Vr( ). If length(α j ) <kfor ech j then is sid to be in k GNF. Following theorem justifies the usge of 3 GNF. Theorem 1. Let be BPA δ system. We cn effectively find BPA δ system in 3 GNF such tht s resp. n. Proof. The proof is bsed on the proof of 3 GNF for BPA systems (see e.g. [Hüt91]), which hd to be modified to cpture the behviour of dedlocks. In fct we hd to use some dditionl trnsformtions exploiting (from left to right) the rules δ + E E nd δ.e δ. 3 Expressibility of BPA δ systems In this section we justify the importnce of introducing dedlocking stte into the BPA systems. We show tht dedlocks enlrge the descriptive power of BPA systems w.r.t. both strict nd nonstrict bisimilrity. On the other hnd introducing dedlocks does not llow to generte more lnguges. Theorem 2. There exists BPA δ system such tht no BPA system is strictly bisimilr to it. Proof. No BPA system cn be strictly bisimilr to the system {X = δ} since δ is rechble in this system nd there is no mtch for δ in ny BPA system. system such tht no BPA system is non- Theorem 3. There exists BPA δ strictly bisimilr to it. Proof. We ine BPA δ system nd show tht there is no BPA system such tht n. Consider = {X = XX +b+cδ} nd suppose tht there is BPA system in 3 GNF, = {Y i = E i i =1,...,n}, such tht n. Then there re infinitely mny sttes rechble from the leding vrible X of the system. They re of the form X n for n 1 nd for ech such stte there must be rechble stte E from such tht X n n E. ThestteX n still hs norm 1 wheres norm 1 for BPA processes implies tht it must be single vrible. Thus is nonstrictly bisimilr to system with finitely mny rechble sttes, which is contrdiction is system where infinitely mny nonstrictly nonbisimilr sttes re rechble.

5 In wht follows we show tht the clsses of BPA nd BPA δ systems re equivlent w.r.t. lnguge genertion. We will consider just the nonstrict cse (F = {ɛ, δ} {δ.e E E BPA}) + since it is obvious tht the strict cse cnnot bring ny lnguge extention. Definition 7. We ine clsses of lnguges generted by BPA resp. BPA δ systems s following: L(BPA) = {L( ) is BPA system} nd L(BPA δ ) = {L( δ ) δ is BPA δ system}. Theorem 4. It holds tht L(BPA) =L(BPA δ ). Proof. We show tht for BPA δ system δ there exists BPA system such tht L( δ )=L( ). The other direction is obvious. Our proof will be constructive. For ech vrible X δ we ine couple of new vribles X ɛ,x δ. The first one will simulte the lnguge behviour of X when reching the stte ɛ, the second one will simulte ending in the suffix of the form δα. We use the nottion α Y mening tht α is summnd in the ining eqution of the vrible Y. W.l.o.g. let δ be BPA δ system in 3 GNF. The vribles of the system will be Vr( ) = X Vr( δ ) {δ}{x ɛ,x δ } {X1 ɛδ} where Xɛ, X δ re distinct fresh vribles nd X1 ɛδ is the leding vrible, supposing tht X 1 ws the leding vrible of δ. Next we relize tht the summnds of the ining eqution for X Vr( δ ) {δ}re exctly of one of the following form (becuse of 3 GNF): () AB (b) bc (c) c (d) ddδ (e) eδ (1) where, b, c, d, e Act( δ )nda, B, C, D Vr( δ ) such tht A, B, C, D δ. Notice tht we cn suppose tht there is no summnd of the form δa becuse it cn be replced with δ. We now ine the vribles from. ForechX Vr( δ ) {δ}nd for the summnds of the vribles X ɛ nd X δ will hold: if AB X then A ɛ B ɛ X ɛ nd A ɛ B δ + A δ X δ if bc X then bc ɛ X ɛ nd bc δ X δ if c X then c X ɛ if ddδ X then dd ɛ + dd δ X δ if eδ X then e X δ if X ɛ 1 = E nd X1 δ = F then X1 ɛδ = E + F If it is the cse tht there is vrible Y Vr( ) such tht Y does not hve ny summnd we ine Y = Y. (This vrible cnnot generte ny nonempty lnguge becuse it is unnormed). Finlly we stte X1 ɛδ to be the leding vrible of the system. Exmple 1. Let us hve BPA δ system δ = {X = XX + b + cδ + by, Y = b}. The corresponding lnguge equivlent BPA system looks s following: = {X ɛ = X ɛ X ɛ + b + by ɛ,x δ = X ɛ X δ + X δ + c + by δ,y ɛ = b, Y δ =.Y δ ɛδ, X = X ɛ X ɛ + b + by ɛ + X ɛ X δ + X δ + c + by δ }.

6 It is not difficult to see tht the newly ined system is in 3 GNF nd we show tht L( δ )=L( ). For this we need one lemm using following nottion. Definition 8. Let be BPA (resp. BPA δ )systemin3 GNF, n 1 nd Y Vr( ). We ine L ɛ n(y ) nd L δ n(y ) s following: L ɛ n(y ) = {w Act( ) Y w ɛ length(w) n} L δ n (Y ) = {w Act( ) α Vr( ) : Y w δα length(w) n}. Lemm 2. For ll n 1 nd X Vr( δ ) {δ} holds tht L ɛ n(x) =L ɛ n(x ɛ ) nd L δ n (X) =Lɛ n (Xδ ). Proof. The proof is led by induction on n, following the subcses from (1). To finish the proof of our theorem let us ine for n 1thesetL n (Y) = {w L(Y ) length(w) n}. Notice tht becuse of the Lemm 2 we get L n (X 1 )=L ɛ n(x 1 ) L δ n(x 1 )=L ɛ n(x1) L ɛ ɛ n(x1)=l δ n (X1 ɛδ ) for ll n 1. Now it is cler tht L(X 1 )=L(X1 ɛδ) since if w L(X 1)then n:w L n (X 1 ) nd so w L n (X1 ɛδ ) which implies tht w L(Xɛδ 1 ). The other direction is similr. We hve shown tht L( δ )=L( ) nd our proof is complete. 4 Describing BPA δ in BPA syntx We hve shown tht w.r.t. bisimilrity the clss of BPA δ systems is strictly lrger thn tht of BPA. This chllenges the question whether given BPA δ system cn be equivlently described in BPA syntx. Theorem 5. Let (Vr, Act,,X 1 ) be BPA δ system. It is decidble whether there exists BPA system such tht s. Moreover if the nswer is positive, the system cn be effectively constructed. s Proof. The proof is stndrd nd is bsed on the fct tht δ ɛ. Suppose w.l.o.g. tht the system is in 3 GNF. The nottion α E mens gin tht α is summnd in the expression E. We will construct the sets M 0,M 1,... of vribles from which the dedlock is rechble s following: M 0 = {δ} nd for i 0thesetsM i+1 re ined s = M i {X Vr Act, Y Vr, Z M i :(X = E),.Z M i+1 E.Z.Y E (.Y.Z E nd Y < )}. We remind tht the norm of vrible cn be effectively computed. Let us denote the fixed point of this construction s M. Wecnseethtforech X Vr: X δ.α for some α Vr iff X M. IfX 1 Mthen cnnot be expressed by BPA syntx since the dedlocking stte is rechble from X 1. If X 1 M we cn nturlly trnsform into BPA system. The sitution for the nonstrict cse will be nicely chrcterised by the Corollry 1. In wht follows, the set of vribles from which dedlocking stte is

7 rechble will be of gret importnce. Hence we ine the set Vr δ of such vribles: Vr δ = {X Vr X δ or E E + : X BPA δ.e} {δ}nd we stte Vr ɛ = Vr {δ} Vr δ. The sets Vr δ nd Vr ɛ cn be effectively constructed s we hve demonstrted in the proof of the Theorem 5. In wht follows let the vribles U, V, X, Y, Z rnge over Vr δ nd A, B, C over Vr ɛ. Theorem 6. Let (Vr, Act,,X 1 ) be BPA δ system in 3 GNF. Suppose tht there re only finitely mny pirwise nonstrictly nonbisimilr Yα Vr δ.vr such tht X 1 Yα. Then there exists BPA system (Vr, Act,,X 1 ) such tht n. Proof. Let us suppose tht X 1 Vr ɛ. Then the system cn be trivilly trnsformed into bisimilr BPA system. Thus ssume tht X 1 Vr δ. We my suppose w.l.o.g. tht ech summnd of every ining eqution in does not contin n unnormed vrible (resp. δ) followed by nother vrible. We ine functions f α for ech α Vr. These functions tke n expression from E + in 3 GNF nd trnsform it into nother expression. Our gol is following. BPA We wnt to chieve f α (E) n Eα nd there should be no dedlock in f α (E). For ech α Vr let us lso ine function r α which returns the set of the new vribles dded by the function f α. Let us ssume tht X, Y, U Vr δ, A, B, C Vr ɛ with C =, β Vr ɛ such tht β < nd γ Vr. n n n n f α ( i α i )= f α ( i α i ) r α ( i α i )= r α ( i α i ) i=1 i=1 f α (XY ) = X Yα r α (XY ) = {X Yα } f α (Xδ) = X ɛ r α (Xδ) = {X ɛ } f α (δ) = r α (δ) = f α (X) = X α r α (X) = {X α } f α (AB) = ABβU γ r α (AB) = {U γ } if α = βuγ = ABβC = if α = βcγ = ABα = otherwise f α () = βu γ r α () = {U γ } if α = βuγ = βc = if α = βcγ = α = otherwise f α (Aδ) = A r α (Aδ) = f α (A) = AβU γ r α (A) = {U γ } if α = βuγ = AβC = if α = βcγ = Aα = otherwise f α (XA) = X Aα r α (XA) = {X Aα } f α (AX) = AX α r α (AX) = {X α } Let us now construct the nonstrictly bisimilr BPA system where Vr = Vr ɛ Added; Act = Act; = ɛ Γ ; X 1 = X1 ɛ.thesetsadded nd Γ re outputs of the following lgorithm nd ɛ contins exctly the ining equtions for vribles from Vr ɛ. i=1 i=1

8 Algorithm 1 1 Solve:={X1 ɛ} 2 Added:={X1} ɛ 3 Γ := 4 while Solve do 5 Let us fix X α Solve with (X = E) 6 Γ := Γ {X α = f α (E)} 7 Add:={Y β r α (E) Z ω n Added : Yβ Zω} 8 while Y β,z ω Add : Y β Z ω Yβ Zω n do 9 Add:= Add {Y β } 10 endwhile 11 Solve:= (Solve {X α }) Add 12 Added:= Added Add 13 for Y β r α (E) Add do 14 replce ll occurences of Y β in Γ with Z ω 15 where Z ω Added : Yβ Zω n 16 endfor 17 endwhile In the following lemms we demonstrte tht the lgorithm is correct nd yields BPA system such tht n. Lemm 3. For the loop 4 17 of Alg.1 holds the following invrint: Y β,z ω Added : Y β Z ω n Yβ Zω. Proof. An esy observtion. Lemm 4. Whenever during the execution of Alg.1 we hve Y α Added then Y Vr δ. Proof. All vribles in Added hd to be produced by the function r α (see line 7 nd 12). It is esily seen tht {Y Y β r α (E)} Vr δ for ny α Vr nd E E + such tht E is in 3 GNF. BPA Lemm 5. Whenever during the execution of Alg.1 we hve Y β Added then X 1 Yβ. Proof. By induction on the number of repetitions of the loop Bsic step: The only vrible in the set Added before the execution of the loop 4 17 strted is X1 ɛ. However X 1ɛ = X 1 nd so X 1 X 1 ɛ. Induction step: Suppose tht t line 12 we hve dded new vrible Y β into Added. So t line 7 we hd to hve Y β r α (E) forsomex α Solve nd (X = E). The induction hypothesis sys tht X 1 Xα (X α hd to be dded in some previous repetition of the min loop). It must hold tht γy β f α (E) whereγ Vr ɛ nd γ <. From the construction of f α we cn lso see tht Xα Yβ.ThuswegetX 1 Xα Yβ.

9 Lemm 6. Alg.1 cnnot loop forever (under the ssumption of the Theorem 6). Proof. Suppose tht the lgorithm loops forever which mens tht the set Solve is never empty. But in every loop we remove exctly one element from the set Solve (line 11). This implies tht the set Added will grow rbitrrily becuse the set Add is infinitely often unempty (otherwise the lgorithm would stop). The contrdiction is immedite from the Lemms 3, 4 nd 5. The following lemm is crucil for the proof of our theorem. Lemm 7. After the execution of Alg.1 we hve V α n Vαfor ll V α Added. Proof. We use the strtified bisimultion reltions [Mil89] k. By induction on k we show tht V α k Vα for ll k 0. This implies tht V α n Vα. This strightforwrd but lso long nd technicl proof cn be found in [Srb98]. Lemm 8. The system n is BPA system nd moreover X 1 X ɛ 1. Proof. Immeditely from the Lemm 7. We hve constructed BPA system such tht n. Theorem 7. Let (Vr, Act,,X 1 ) be BPA δ system. Suppose tht there re infinitely mny pirwise nonstrictly nonbisimilr Yα Vr δ.vr such tht X 1 Yα. Then there is no BPA system such tht n. Proof. The proof is bsed on the fct tht α n β implies α = β. Let us ssume w.l.o.g. tht there exists in 3 GNF such tht n. We show tht this is not possible. Since there re infinitely mny rechble sttes Y 1 α 1,Y 2 α 2,... of which re pirwise nonstrictly nonbisimilr there must be corresponding sttes β 1,β 2,... of the system such tht Y i α i n βi for i =1,2,... Let us now ine constnt N mx s N mx = mx{ Y i δ i =1,2,...} where Y δ =min{length(w) Y w δ or E E + BPA : Y w δ.e}. Notice tht the inition of N mx is correct since for ll i Y i δ < (becuse Y i Vr δ )nd there re only finitely mny different Y i s. Clerly Y i α i N mx for ll i. This implies tht the norm of β i is lso less or equl N mx for ll i. However, is BPA system nd ll vribles in re gurded. This mens tht there re only finitely mny different sttes of such tht their norm is less or equl N mx. Hence there must be two sttes β k nd β l n n with k l such tht β k = β l. This implies tht β k βl. Then lso Y k α k Yl α l, which is contrdiction. Suppose tht we hve BPA δ system nd tht there re infinitely mny nonbisimilr sttes from which, fter some short sequence of ctions, dedlocking stte is rechble. Then the corresponding (nonstrictly bisimilr) BPA system does not exists. This condition ppers to be both necessry nd sufficient s is illustrted by the following corollry. Corollry 1. Let (Vr, Act,,X 1 ) be BPA δ system. There re only finitely mny pirwise nonstrictly nonbisimilr Yα Vr δ.vr such tht X 1 Yα if nd only if there exists BPA system (Vr, Act,,X 1) such tht n. Proof. An immedite consequence of the Theorems 6 nd 7.

10 5 Conclusion remrks In this pper we hve focused on the clss of BPA processes extended with dedlocks. We hve shown tht for lnguge equivlence the extention is no cquisition. On the other hnd the BPA δ clss is lrger with regrd to the reltion of bisimultion. We introduce two notions of bisimilrity to cpture the different understnding of dedlock behviour. If we do not distinguish between ɛ nd δ, we spek bout nonstrict bisimilrity nd if we do, we cll the pproprite bisimultion s strict. We hve solved the question whether, given BPA δ system, there is n equivlent description (with regrd to bisimilrity) of in terms of BPA syntx. The solution for the strict bisimilrity is strightforwrd. However, the nswer to the problem deling with the nonstrict bisimilrity exploited nice semntic chrcteriztion of the subclss of BPA δ processes bisimilrly describble in BPA syntx: BPA δ system cn be trnsformed into BPA system (preserving nonstrict bisimilrity) if nd only if finitely mny nonbisimilr sttes strting with some in δ-ending vrible re rechble. There is still n open problem whether this semntic chrcteriztion is syntcticlly checkble. Acknowledgements: First of ll, I would like to thnk Ivn Černá forherhelp nd encourgement throughout the work. I m very grteful for her dvise nd vluble discussions. My wrm thnks go lso to Mojmír Křetínský nd Antonín Kučer for their constnt support nd comments. References [BCS95] O. Burkrt, D. Cucl, nd B. Steffen. An elementry decision procedure for rbitrry context-free processes. In Proceedings of MFCS 95, volume 969 of LNCS, pges Springer-Verlg, [BCS96] O. Burkrt, D. Cucl, nd B. Steffen. Bisimultion collpse nd the process txonomy. In Proceedings of CONCUR 96, volume 1119 of LNCS, pges Springer-Verlg, [BK85] J.A. Bergstr nd J.W. Klop. Algebr of communicting processes with bstrction. Theoreticl Computer Science, 37:77 121, [Bos97] D. Bosscher. Grmmrs Modulo Bisimultion. PhD thesis, CWI, University [CHS95] of Amsterdm, S. Christensen, H. Hüttel, nd C. Stirling. Bisimultion equivlence is decidble for ll context-free processes. Informtion nd Computtion, 121: , [HM96] Y. Hirshfeld nd F. Moller. Decidbility results in utomt nd process theory. In Logics for Concurrency: Automt vs Structure, volume 1043 of LNCS, pges F. Moller nd G. Birtwistle, [Hüt91] H. Hüttel. Decidbility, Behviourl Equivlences nd Infinite Trnsition Grphs. PhD thesis, The University of Edinburgh, [Mil89] R. Milner. Communiction nd Concurrency. Prentice-Hll, [Mol96] [Srb98] F. Moller. Infinite results. In Proceedings of CONCUR 96, volume 1119 of LNCS, pges Springer-Verlg, Jiří Srb. Compring the clsses BPA nd BPA with dedlocks. Technicl Report FIMU-RS-98-05, Fculty of Informtics, Msryk University, 1998.

Semantic Reachability. Richard Mayr. Institut fur Informatik. Technische Universitat Munchen. Arcisstr. 21, D Munchen, Germany E. N. T. C. S.

Semantic Reachability. Richard Mayr. Institut fur Informatik. Technische Universitat Munchen. Arcisstr. 21, D Munchen, Germany E. N. T. C. S. URL: http://www.elsevier.nl/locte/entcs/volume6.html?? pges Semntic Rechbility Richrd Myr Institut fur Informtik Technische Universitt Munchen Arcisstr. 21, D-80290 Munchen, Germny e-mil: myrri@informtik.tu-muenchen.de

More information

Strong Bisimulation. Overview. References. Actions Labeled transition system Transition semantics Simulation Bisimulation

Strong Bisimulation. Overview. References. Actions Labeled transition system Transition semantics Simulation Bisimulation Strong Bisimultion Overview Actions Lbeled trnsition system Trnsition semntics Simultion Bisimultion References Robin Milner, Communiction nd Concurrency Robin Milner, Communicting nd Mobil Systems 32

More information

Semantic reachability for simple process algebras. Richard Mayr. Abstract

Semantic reachability for simple process algebras. Richard Mayr. Abstract Semntic rechbility for simple process lgebrs Richrd Myr Abstrct This pper is n pproch to combine the rechbility problem with semntic notions like bisimultion equivlence. It dels with questions of the following

More information

Coalgebra, Lecture 15: Equations for Deterministic Automata

Coalgebra, Lecture 15: Equations for Deterministic Automata Colger, Lecture 15: Equtions for Deterministic Automt Julin Slmnc (nd Jurrin Rot) Decemer 19, 2016 In this lecture, we will study the concept of equtions for deterministic utomt. The notes re self contined

More information

Advanced Calculus: MATH 410 Notes on Integrals and Integrability Professor David Levermore 17 October 2004

Advanced Calculus: MATH 410 Notes on Integrals and Integrability Professor David Levermore 17 October 2004 Advnced Clculus: MATH 410 Notes on Integrls nd Integrbility Professor Dvid Levermore 17 October 2004 1. Definite Integrls In this section we revisit the definite integrl tht you were introduced to when

More information

CS 275 Automata and Formal Language Theory

CS 275 Automata and Formal Language Theory CS 275 Automt nd Forml Lnguge Theory Course Notes Prt II: The Recognition Problem (II) Chpter II.6.: Push Down Automt Remrk: This mteril is no longer tught nd not directly exm relevnt Anton Setzer (Bsed

More information

AUTOMATA AND LANGUAGES. Definition 1.5: Finite Automaton

AUTOMATA AND LANGUAGES. Definition 1.5: Finite Automaton 25. Finite Automt AUTOMATA AND LANGUAGES A system of computtion tht only hs finite numer of possile sttes cn e modeled using finite utomton A finite utomton is often illustrted s stte digrm d d d. d q

More information

Finite Automata Theory and Formal Languages TMV027/DIT321 LP4 2018

Finite Automata Theory and Formal Languages TMV027/DIT321 LP4 2018 Finite Automt Theory nd Forml Lnguges TMV027/DIT321 LP4 2018 Lecture 10 An Bove April 23rd 2018 Recp: Regulr Lnguges We cn convert between FA nd RE; Hence both FA nd RE ccept/generte regulr lnguges; More

More information

CS 275 Automata and Formal Language Theory

CS 275 Automata and Formal Language Theory CS 275 Automt nd Forml Lnguge Theory Course Notes Prt II: The Recognition Problem (II) Chpter II.5.: Properties of Context Free Grmmrs (14) Anton Setzer (Bsed on book drft by J. V. Tucker nd K. Stephenson)

More information

Lecture 09: Myhill-Nerode Theorem

Lecture 09: Myhill-Nerode Theorem CS 373: Theory of Computtion Mdhusudn Prthsrthy Lecture 09: Myhill-Nerode Theorem 16 Ferury 2010 In this lecture, we will see tht every lnguge hs unique miniml DFA We will see this fct from two perspectives

More information

Theory of Computation Regular Languages. (NTU EE) Regular Languages Fall / 38

Theory of Computation Regular Languages. (NTU EE) Regular Languages Fall / 38 Theory of Computtion Regulr Lnguges (NTU EE) Regulr Lnguges Fll 2017 1 / 38 Schemtic of Finite Automt control 0 0 1 0 1 1 1 0 Figure: Schemtic of Finite Automt A finite utomton hs finite set of control

More information

Handout: Natural deduction for first order logic

Handout: Natural deduction for first order logic MATH 457 Introduction to Mthemticl Logic Spring 2016 Dr Json Rute Hndout: Nturl deduction for first order logic We will extend our nturl deduction rules for sententil logic to first order logic These notes

More information

The Regulated and Riemann Integrals

The Regulated and Riemann Integrals Chpter 1 The Regulted nd Riemnn Integrls 1.1 Introduction We will consider severl different pproches to defining the definite integrl f(x) dx of function f(x). These definitions will ll ssign the sme vlue

More information

Theory of Computation Regular Languages

Theory of Computation Regular Languages Theory of Computtion Regulr Lnguges Bow-Yw Wng Acdemi Sinic Spring 2012 Bow-Yw Wng (Acdemi Sinic) Regulr Lnguges Spring 2012 1 / 38 Schemtic of Finite Automt control 0 0 1 0 1 1 1 0 Figure: Schemtic of

More information

More on automata. Michael George. March 24 April 7, 2014

More on automata. Michael George. March 24 April 7, 2014 More on utomt Michel George Mrch 24 April 7, 2014 1 Automt constructions Now tht we hve forml model of mchine, it is useful to mke some generl constructions. 1.1 DFA Union / Product construction Suppose

More information

Talen en Automaten Test 1, Mon 7 th Dec, h45 17h30

Talen en Automaten Test 1, Mon 7 th Dec, h45 17h30 Tlen en Automten Test 1, Mon 7 th Dec, 2015 15h45 17h30 This test consists of four exercises over 5 pges. Explin your pproch, nd write your nswer to ech exercise on seprte pge. You cn score mximum of 100

More information

Homework Solution - Set 5 Due: Friday 10/03/08

Homework Solution - Set 5 Due: Friday 10/03/08 CE 96 Introduction to the Theory of Computtion ll 2008 Homework olution - et 5 Due: ridy 10/0/08 1. Textook, Pge 86, Exercise 1.21. () 1 2 Add new strt stte nd finl stte. Mke originl finl stte non-finl.

More information

CSC 473 Automata, Grammars & Languages 11/9/10

CSC 473 Automata, Grammars & Languages 11/9/10 CSC 473 utomt, Grmmrs & Lnguges 11/9/10 utomt, Grmmrs nd Lnguges Discourse 06 Decidbility nd Undecidbility Decidble Problems for Regulr Lnguges Theorem 4.1: (embership/cceptnce Prob. for DFs) = {, w is

More information

Bisimulation. R.J. van Glabbeek

Bisimulation. R.J. van Glabbeek Bisimultion R.J. vn Glbbeek NICTA, Sydney, Austrli. School of Computer Science nd Engineering, The University of New South Wles, Sydney, Austrli. Computer Science Deprtment, Stnford University, CA 94305-9045,

More information

CS 275 Automata and Formal Language Theory

CS 275 Automata and Formal Language Theory CS 275 utomt nd Forml Lnguge Theory Course Notes Prt II: The Recognition Prolem (II) Chpter II.5.: Properties of Context Free Grmmrs (14) nton Setzer (Bsed on ook drft y J. V. Tucker nd K. Stephenson)

More information

1.4 Nonregular Languages

1.4 Nonregular Languages 74 1.4 Nonregulr Lnguges The number of forml lnguges over ny lphbet (= decision/recognition problems) is uncountble On the other hnd, the number of regulr expressions (= strings) is countble Hence, ll

More information

Lecture 1. Functional series. Pointwise and uniform convergence.

Lecture 1. Functional series. Pointwise and uniform convergence. 1 Introduction. Lecture 1. Functionl series. Pointwise nd uniform convergence. In this course we study mongst other things Fourier series. The Fourier series for periodic function f(x) with period 2π is

More information

20 MATHEMATICS POLYNOMIALS

20 MATHEMATICS POLYNOMIALS 0 MATHEMATICS POLYNOMIALS.1 Introduction In Clss IX, you hve studied polynomils in one vrible nd their degrees. Recll tht if p(x) is polynomil in x, the highest power of x in p(x) is clled the degree of

More information

MAA 4212 Improper Integrals

MAA 4212 Improper Integrals Notes by Dvid Groisser, Copyright c 1995; revised 2002, 2009, 2014 MAA 4212 Improper Integrls The Riemnn integrl, while perfectly well-defined, is too restrictive for mny purposes; there re functions which

More information

arxiv: v1 [math.ra] 1 Nov 2014

arxiv: v1 [math.ra] 1 Nov 2014 CLASSIFICATION OF COMPLEX CYCLIC LEIBNIZ ALGEBRAS DANIEL SCOFIELD AND S MCKAY SULLIVAN rxiv:14110170v1 [mthra] 1 Nov 2014 Abstrct Since Leibniz lgebrs were introduced by Lody s generliztion of Lie lgebrs,

More information

1 Nondeterministic Finite Automata

1 Nondeterministic Finite Automata 1 Nondeterministic Finite Automt Suppose in life, whenever you hd choice, you could try oth possiilities nd live your life. At the end, you would go ck nd choose the one tht worked out the est. Then you

More information

7.2 The Definite Integral

7.2 The Definite Integral 7.2 The Definite Integrl the definite integrl In the previous section, it ws found tht if function f is continuous nd nonnegtive, then the re under the grph of f on [, b] is given by F (b) F (), where

More information

Automata and Languages

Automata and Languages Automt nd Lnguges Prof. Mohmed Hmd Softwre Engineering Lb. The University of Aizu Jpn Grmmr Regulr Grmmr Context-free Grmmr Context-sensitive Grmmr Regulr Lnguges Context Free Lnguges Context Sensitive

More information

Finite Automata. Informatics 2A: Lecture 3. John Longley. 22 September School of Informatics University of Edinburgh

Finite Automata. Informatics 2A: Lecture 3. John Longley. 22 September School of Informatics University of Edinburgh Lnguges nd Automt Finite Automt Informtics 2A: Lecture 3 John Longley School of Informtics University of Edinburgh jrl@inf.ed.c.uk 22 September 2017 1 / 30 Lnguges nd Automt 1 Lnguges nd Automt Wht is

More information

Decidability of Weak Bisimilarity for a Subset of Basic Parallel Processes

Decidability of Weak Bisimilarity for a Subset of Basic Parallel Processes Decidbility of We Bisimilrity for Subset of Bsic Prllel Processes Colin Stirling Division of Informtics University of Edinburgh emil: cps@dcs.ed.c.u 1 Introduction In the pst decde there hs been vriety

More information

1. For each of the following theorems, give a two or three sentence sketch of how the proof goes or why it is not true.

1. For each of the following theorems, give a two or three sentence sketch of how the proof goes or why it is not true. York University CSE 2 Unit 3. DFA Clsses Converting etween DFA, NFA, Regulr Expressions, nd Extended Regulr Expressions Instructor: Jeff Edmonds Don t chet y looking t these nswers premturely.. For ech

More information

Improper Integrals, and Differential Equations

Improper Integrals, and Differential Equations Improper Integrls, nd Differentil Equtions October 22, 204 5.3 Improper Integrls Previously, we discussed how integrls correspond to res. More specificlly, we sid tht for function f(x), the region creted

More information

Parse trees, ambiguity, and Chomsky normal form

Parse trees, ambiguity, and Chomsky normal form Prse trees, miguity, nd Chomsky norml form In this lecture we will discuss few importnt notions connected with contextfree grmmrs, including prse trees, miguity, nd specil form for context-free grmmrs

More information

Advanced Calculus: MATH 410 Uniform Convergence of Functions Professor David Levermore 11 December 2015

Advanced Calculus: MATH 410 Uniform Convergence of Functions Professor David Levermore 11 December 2015 Advnced Clculus: MATH 410 Uniform Convergence of Functions Professor Dvid Levermore 11 December 2015 12. Sequences of Functions We now explore two notions of wht it mens for sequence of functions {f n

More information

Riemann is the Mann! (But Lebesgue may besgue to differ.)

Riemann is the Mann! (But Lebesgue may besgue to differ.) Riemnn is the Mnn! (But Lebesgue my besgue to differ.) Leo Livshits My 2, 2008 1 For finite intervls in R We hve seen in clss tht every continuous function f : [, b] R hs the property tht for every ɛ >

More information

Summer School Verification Technology, Systems & Applications

Summer School Verification Technology, Systems & Applications VTSA 2011 Summer School Verifiction Technology, Systems & Applictions 4th edition since 2008: Liège (Belgium), Sep. 19 23, 2011 free prticiption, limited number of prticipnts ppliction dedline: July 22,

More information

Chapter 14. Matrix Representations of Linear Transformations

Chapter 14. Matrix Representations of Linear Transformations Chpter 4 Mtrix Representtions of Liner Trnsformtions When considering the Het Stte Evolution, we found tht we could describe this process using multipliction by mtrix. This ws nice becuse computers cn

More information

THE EXISTENCE-UNIQUENESS THEOREM FOR FIRST-ORDER DIFFERENTIAL EQUATIONS.

THE EXISTENCE-UNIQUENESS THEOREM FOR FIRST-ORDER DIFFERENTIAL EQUATIONS. THE EXISTENCE-UNIQUENESS THEOREM FOR FIRST-ORDER DIFFERENTIAL EQUATIONS RADON ROSBOROUGH https://intuitiveexplntionscom/picrd-lindelof-theorem/ This document is proof of the existence-uniqueness theorem

More information

p-adic Egyptian Fractions

p-adic Egyptian Fractions p-adic Egyptin Frctions Contents 1 Introduction 1 2 Trditionl Egyptin Frctions nd Greedy Algorithm 2 3 Set-up 3 4 p-greedy Algorithm 5 5 p-egyptin Trditionl 10 6 Conclusion 1 Introduction An Egyptin frction

More information

Minimal DFA. minimal DFA for L starting from any other

Minimal DFA. minimal DFA for L starting from any other Miniml DFA Among the mny DFAs ccepting the sme regulr lnguge L, there is exctly one (up to renming of sttes) which hs the smllest possile numer of sttes. Moreover, it is possile to otin tht miniml DFA

More information

Kleene Theorems for Free Choice Nets Labelled with Distributed Alphabets

Kleene Theorems for Free Choice Nets Labelled with Distributed Alphabets Kleene Theorems for Free Choice Nets Lbelled with Distributed Alphbets Rmchndr Phwde Indin Institute of Technology Dhrwd, Dhrwd 580011, Indi Emil: prb@iitdh.c.in Abstrct. We provided [15] expressions for

More information

Anatomy of a Deterministic Finite Automaton. Deterministic Finite Automata. A machine so simple that you can understand it in less than one minute

Anatomy of a Deterministic Finite Automaton. Deterministic Finite Automata. A machine so simple that you can understand it in less than one minute Victor Admchik Dnny Sletor Gret Theoreticl Ides In Computer Science CS 5-25 Spring 2 Lecture 2 Mr 3, 2 Crnegie Mellon University Deterministic Finite Automt Finite Automt A mchine so simple tht you cn

More information

Farey Fractions. Rickard Fernström. U.U.D.M. Project Report 2017:24. Department of Mathematics Uppsala University

Farey Fractions. Rickard Fernström. U.U.D.M. Project Report 2017:24. Department of Mathematics Uppsala University U.U.D.M. Project Report 07:4 Frey Frctions Rickrd Fernström Exmensrete i mtemtik, 5 hp Hledre: Andres Strömergsson Exmintor: Jörgen Östensson Juni 07 Deprtment of Mthemtics Uppsl University Frey Frctions

More information

Bernoulli Numbers Jeff Morton

Bernoulli Numbers Jeff Morton Bernoulli Numbers Jeff Morton. We re interested in the opertor e t k d k t k, which is to sy k tk. Applying this to some function f E to get e t f d k k tk d k f f + d k k tk dk f, we note tht since f

More information

5. (±±) Λ = fw j w is string of even lengthg [ 00 = f11,00g 7. (11 [ 00)± Λ = fw j w egins with either 11 or 00g 8. (0 [ ffl)1 Λ = 01 Λ [ 1 Λ 9.

5. (±±) Λ = fw j w is string of even lengthg [ 00 = f11,00g 7. (11 [ 00)± Λ = fw j w egins with either 11 or 00g 8. (0 [ ffl)1 Λ = 01 Λ [ 1 Λ 9. Regulr Expressions, Pumping Lemm, Right Liner Grmmrs Ling 106 Mrch 25, 2002 1 Regulr Expressions A regulr expression descries or genertes lnguge: it is kind of shorthnd for listing the memers of lnguge.

More information

Properties of Integrals, Indefinite Integrals. Goals: Definition of the Definite Integral Integral Calculations using Antiderivatives

Properties of Integrals, Indefinite Integrals. Goals: Definition of the Definite Integral Integral Calculations using Antiderivatives Block #6: Properties of Integrls, Indefinite Integrls Gols: Definition of the Definite Integrl Integrl Clcultions using Antiderivtives Properties of Integrls The Indefinite Integrl 1 Riemnn Sums - 1 Riemnn

More information

Lecture Note 9: Orthogonal Reduction

Lecture Note 9: Orthogonal Reduction MATH : Computtionl Methods of Liner Algebr 1 The Row Echelon Form Lecture Note 9: Orthogonl Reduction Our trget is to solve the norml eution: Xinyi Zeng Deprtment of Mthemticl Sciences, UTEP A t Ax = A

More information

CS 330 Formal Methods and Models

CS 330 Formal Methods and Models CS 330 Forml Methods nd Models Dn Richrds, George Mson University, Spring 2017 Quiz Solutions Quiz 1, Propositionl Logic Dte: Ferury 2 1. Prove ((( p q) q) p) is tutology () (3pts) y truth tle. p q p q

More information

Finite Automata. Informatics 2A: Lecture 3. Mary Cryan. 21 September School of Informatics University of Edinburgh

Finite Automata. Informatics 2A: Lecture 3. Mary Cryan. 21 September School of Informatics University of Edinburgh Finite Automt Informtics 2A: Lecture 3 Mry Cryn School of Informtics University of Edinburgh mcryn@inf.ed.c.uk 21 September 2018 1 / 30 Lnguges nd Automt Wht is lnguge? Finite utomt: recp Some forml definitions

More information

Assignment 1 Automata, Languages, and Computability. 1 Finite State Automata and Regular Languages

Assignment 1 Automata, Languages, and Computability. 1 Finite State Automata and Regular Languages Deprtment of Computer Science, Austrlin Ntionl University COMP2600 Forml Methods for Softwre Engineering Semester 2, 206 Assignment Automt, Lnguges, nd Computility Smple Solutions Finite Stte Automt nd

More information

Theoretical foundations of Gaussian quadrature

Theoretical foundations of Gaussian quadrature Theoreticl foundtions of Gussin qudrture 1 Inner product vector spce Definition 1. A vector spce (or liner spce) is set V = {u, v, w,...} in which the following two opertions re defined: (A) Addition of

More information

Grammar. Languages. Content 5/10/16. Automata and Languages. Regular Languages. Regular Languages

Grammar. Languages. Content 5/10/16. Automata and Languages. Regular Languages. Regular Languages 5//6 Grmmr Automt nd Lnguges Regulr Grmmr Context-free Grmmr Context-sensitive Grmmr Prof. Mohmed Hmd Softwre Engineering L. The University of Aizu Jpn Regulr Lnguges Context Free Lnguges Context Sensitive

More information

Non-Deterministic Finite Automata. Fall 2018 Costas Busch - RPI 1

Non-Deterministic Finite Automata. Fall 2018 Costas Busch - RPI 1 Non-Deterministic Finite Automt Fll 2018 Costs Busch - RPI 1 Nondeterministic Finite Automton (NFA) Alphbet ={} q q2 1 q 0 q 3 Fll 2018 Costs Busch - RPI 2 Nondeterministic Finite Automton (NFA) Alphbet

More information

CS5371 Theory of Computation. Lecture 20: Complexity V (Polynomial-Time Reducibility)

CS5371 Theory of Computation. Lecture 20: Complexity V (Polynomial-Time Reducibility) CS5371 Theory of Computtion Lecture 20: Complexity V (Polynomil-Time Reducibility) Objectives Polynomil Time Reducibility Prove Cook-Levin Theorem Polynomil Time Reducibility Previously, we lernt tht if

More information

CMPSCI 250: Introduction to Computation. Lecture #31: What DFA s Can and Can t Do David Mix Barrington 9 April 2014

CMPSCI 250: Introduction to Computation. Lecture #31: What DFA s Can and Can t Do David Mix Barrington 9 April 2014 CMPSCI 250: Introduction to Computtion Lecture #31: Wht DFA s Cn nd Cn t Do Dvid Mix Brrington 9 April 2014 Wht DFA s Cn nd Cn t Do Deterministic Finite Automt Forml Definition of DFA s Exmples of DFA

More information

W. We shall do so one by one, starting with I 1, and we shall do it greedily, trying

W. We shall do so one by one, starting with I 1, and we shall do it greedily, trying Vitli covers 1 Definition. A Vitli cover of set E R is set V of closed intervls with positive length so tht, for every δ > 0 nd every x E, there is some I V with λ(i ) < δ nd x I. 2 Lemm (Vitli covering)

More information

Expressiveness modulo Bisimilarity of Regular Expressions with Parallel Composition (Extended Abstract)

Expressiveness modulo Bisimilarity of Regular Expressions with Parallel Composition (Extended Abstract) Expressiveness modulo Bisimilrity of Regulr Expressions with Prllel Composition (Extended Abstrct) Jos C. M. Beten Eindhoven University of Technology, The Netherlnds j.c.m.beten@tue.nl Tim Muller University

More information

Lecture 3. In this lecture, we will discuss algorithms for solving systems of linear equations.

Lecture 3. In this lecture, we will discuss algorithms for solving systems of linear equations. Lecture 3 3 Solving liner equtions In this lecture we will discuss lgorithms for solving systems of liner equtions Multiplictive identity Let us restrict ourselves to considering squre mtrices since one

More information

1.3 Regular Expressions

1.3 Regular Expressions 56 1.3 Regulr xpressions These hve n importnt role in describing ptterns in serching for strings in mny pplictions (e.g. wk, grep, Perl,...) All regulr expressions of lphbet re 1.Ønd re regulr expressions,

More information

CSCI FOUNDATIONS OF COMPUTER SCIENCE

CSCI FOUNDATIONS OF COMPUTER SCIENCE 1 CSCI- 2200 FOUNDATIONS OF COMPUTER SCIENCE Spring 2015 My 7, 2015 2 Announcements Homework 9 is due now. Some finl exm review problems will be posted on the web site tody. These re prcqce problems not

More information

Formal languages, automata, and theory of computation

Formal languages, automata, and theory of computation Mälrdlen University TEN1 DVA337 2015 School of Innovtion, Design nd Engineering Forml lnguges, utomt, nd theory of computtion Thursdy, Novemer 5, 14:10-18:30 Techer: Dniel Hedin, phone 021-107052 The exm

More information

arxiv:math/ v2 [math.ho] 16 Dec 2003

arxiv:math/ v2 [math.ho] 16 Dec 2003 rxiv:mth/0312293v2 [mth.ho] 16 Dec 2003 Clssicl Lebesgue Integrtion Theorems for the Riemnn Integrl Josh Isrlowitz 244 Ridge Rd. Rutherford, NJ 07070 jbi2@njit.edu Februry 1, 2008 Abstrct In this pper,

More information

Riemann Sums and Riemann Integrals

Riemann Sums and Riemann Integrals Riemnn Sums nd Riemnn Integrls Jmes K. Peterson Deprtment of Biologicl Sciences nd Deprtment of Mthemticl Sciences Clemson University August 26, 2013 Outline 1 Riemnn Sums 2 Riemnn Integrls 3 Properties

More information

Physics 116C Solution of inhomogeneous ordinary differential equations using Green s functions

Physics 116C Solution of inhomogeneous ordinary differential equations using Green s functions Physics 6C Solution of inhomogeneous ordinry differentil equtions using Green s functions Peter Young November 5, 29 Homogeneous Equtions We hve studied, especilly in long HW problem, second order liner

More information

Lecture 6 Regular Grammars

Lecture 6 Regular Grammars Lecture 6 Regulr Grmmrs COT 4420 Theory of Computtion Section 3.3 Grmmr A grmmr G is defined s qudruple G = (V, T, S, P) V is finite set of vribles T is finite set of terminl symbols S V is specil vrible

More information

Tutorial Automata and formal Languages

Tutorial Automata and formal Languages Tutoril Automt nd forml Lnguges Notes for to the tutoril in the summer term 2017 Sestin Küpper, Christine Mik 8. August 2017 1 Introduction: Nottions nd sic Definitions At the eginning of the tutoril we

More information

Riemann Sums and Riemann Integrals

Riemann Sums and Riemann Integrals Riemnn Sums nd Riemnn Integrls Jmes K. Peterson Deprtment of Biologicl Sciences nd Deprtment of Mthemticl Sciences Clemson University August 26, 203 Outline Riemnn Sums Riemnn Integrls Properties Abstrct

More information

Unit #9 : Definite Integral Properties; Fundamental Theorem of Calculus

Unit #9 : Definite Integral Properties; Fundamental Theorem of Calculus Unit #9 : Definite Integrl Properties; Fundmentl Theorem of Clculus Gols: Identify properties of definite integrls Define odd nd even functions, nd reltionship to integrl vlues Introduce the Fundmentl

More information

MATH 101A: ALGEBRA I PART B: RINGS AND MODULES 35

MATH 101A: ALGEBRA I PART B: RINGS AND MODULES 35 MATH 101A: ALGEBRA I PART B: RINGS AND MODULES 35 9. Modules over PID This week we re proving the fundmentl theorem for finitely generted modules over PID, nmely tht they re ll direct sums of cyclic modules.

More information

For convenience, we rewrite m2 s m2 = m m m ; where m is repeted m times. Since xyz = m m m nd jxyj»m, we hve tht the string y is substring of the fir

For convenience, we rewrite m2 s m2 = m m m ; where m is repeted m times. Since xyz = m m m nd jxyj»m, we hve tht the string y is substring of the fir CSCI 2400 Models of Computtion, Section 3 Solutions to Homework 4 Problem 1. ll the solutions below refer to the Pumping Lemm of Theorem 4.8, pge 119. () L = f n b l k : k n + lg Let's ssume for contrdiction

More information

Lecture 9: LTL and Büchi Automata

Lecture 9: LTL and Büchi Automata Lecture 9: LTL nd Büchi Automt 1 LTL Property Ptterns Quite often the requirements of system follow some simple ptterns. Sometimes we wnt to specify tht property should only hold in certin context, clled

More information

Review of Riemann Integral

Review of Riemann Integral 1 Review of Riemnn Integrl In this chpter we review the definition of Riemnn integrl of bounded function f : [, b] R, nd point out its limittions so s to be convinced of the necessity of more generl integrl.

More information

Petri Nets and Regular Processes

Petri Nets and Regular Processes Uppsl Computing Science Reserch Report No. 162 Mrch 22, 1999 ISSN 1100 0686 Petri Nets nd Regulr Processes Petr Jnčr y Deprtment of Computer Science, Technicl University of Ostrv 17. listopdu 15, CZ-708

More information

Integral points on the rational curve

Integral points on the rational curve Integrl points on the rtionl curve y x bx c x ;, b, c integers. Konstntine Zeltor Mthemtics University of Wisconsin - Mrinette 750 W. Byshore Street Mrinette, WI 5443-453 Also: Konstntine Zeltor P.O. Box

More information

Basic Process Algebra with Iteration: Completeness of its Equational Axioms

Basic Process Algebra with Iteration: Completeness of its Equational Axioms Bsic Process Algebr with Itertion: Completeness of its Equtionl Axioms Wn Fokkink CWI Kruisln 413, 1098 SJ Amsterdm, The Netherlnds Hns Zntem Utrecht University Pduln 14, 3508 TB Utrecht, The Netherlnds

More information

440-2 Geometry/Topology: Differentiable Manifolds Northwestern University Solutions of Practice Problems for Final Exam

440-2 Geometry/Topology: Differentiable Manifolds Northwestern University Solutions of Practice Problems for Final Exam 440-2 Geometry/Topology: Differentible Mnifolds Northwestern University Solutions of Prctice Problems for Finl Exm 1) Using the cnonicl covering of RP n by {U α } 0 α n, where U α = {[x 0 : : x n ] RP

More information

Regular expressions, Finite Automata, transition graphs are all the same!!

Regular expressions, Finite Automata, transition graphs are all the same!! CSI 3104 /Winter 2011: Introduction to Forml Lnguges Chpter 7: Kleene s Theorem Chpter 7: Kleene s Theorem Regulr expressions, Finite Automt, trnsition grphs re ll the sme!! Dr. Neji Zgui CSI3104-W11 1

More information

1 The Lagrange interpolation formula

1 The Lagrange interpolation formula Notes on Qudrture 1 The Lgrnge interpoltion formul We briefly recll the Lgrnge interpoltion formul. The strting point is collection of N + 1 rel points (x 0, y 0 ), (x 1, y 1 ),..., (x N, y N ), with x

More information

Chapter Five: Nondeterministic Finite Automata. Formal Language, chapter 5, slide 1

Chapter Five: Nondeterministic Finite Automata. Formal Language, chapter 5, slide 1 Chpter Five: Nondeterministic Finite Automt Forml Lnguge, chpter 5, slide 1 1 A DFA hs exctly one trnsition from every stte on every symol in the lphet. By relxing this requirement we get relted ut more

More information

3 Regular expressions

3 Regular expressions 3 Regulr expressions Given n lphet Σ lnguge is set of words L Σ. So fr we were le to descrie lnguges either y using set theory (i.e. enumertion or comprehension) or y n utomton. In this section we shll

More information

Formal Languages and Automata

Formal Languages and Automata Moile Computing nd Softwre Engineering p. 1/5 Forml Lnguges nd Automt Chpter 2 Finite Automt Chun-Ming Liu cmliu@csie.ntut.edu.tw Deprtment of Computer Science nd Informtion Engineering Ntionl Tipei University

More information

Non Deterministic Automata. Linz: Nondeterministic Finite Accepters, page 51

Non Deterministic Automata. Linz: Nondeterministic Finite Accepters, page 51 Non Deterministic Automt Linz: Nondeterministic Finite Accepters, pge 51 1 Nondeterministic Finite Accepter (NFA) Alphbet ={} q 1 q2 q 0 q 3 2 Nondeterministic Finite Accepter (NFA) Alphbet ={} Two choices

More information

State Minimization for DFAs

State Minimization for DFAs Stte Minimiztion for DFAs Red K & S 2.7 Do Homework 10. Consider: Stte Minimiztion 4 5 Is this miniml mchine? Step (1): Get rid of unrechle sttes. Stte Minimiztion 6, Stte is unrechle. Step (2): Get rid

More information

New data structures to reduce data size and search time

New data structures to reduce data size and search time New dt structures to reduce dt size nd serch time Tsuneo Kuwbr Deprtment of Informtion Sciences, Fculty of Science, Kngw University, Hirtsuk-shi, Jpn FIT2018 1D-1, No2, pp1-4 Copyright (c)2018 by The Institute

More information

Revision Sheet. (a) Give a regular expression for each of the following languages:

Revision Sheet. (a) Give a regular expression for each of the following languages: Theoreticl Computer Science (Bridging Course) Dr. G. D. Tipldi F. Bonirdi Winter Semester 2014/2015 Revision Sheet University of Freiurg Deprtment of Computer Science Question 1 (Finite Automt, 8 + 6 points)

More information

UNIFORM CONVERGENCE. Contents 1. Uniform Convergence 1 2. Properties of uniform convergence 3

UNIFORM CONVERGENCE. Contents 1. Uniform Convergence 1 2. Properties of uniform convergence 3 UNIFORM CONVERGENCE Contents 1. Uniform Convergence 1 2. Properties of uniform convergence 3 Suppose f n : Ω R or f n : Ω C is sequence of rel or complex functions, nd f n f s n in some sense. Furthermore,

More information

First Midterm Examination

First Midterm Examination Çnky University Deprtment of Computer Engineering 203-204 Fll Semester First Midterm Exmintion ) Design DFA for ll strings over the lphet Σ = {,, c} in which there is no, no nd no cc. 2) Wht lnguge does

More information

Chapter 0. What is the Lebesgue integral about?

Chapter 0. What is the Lebesgue integral about? Chpter 0. Wht is the Lebesgue integrl bout? The pln is to hve tutoril sheet ech week, most often on Fridy, (to be done during the clss) where you will try to get used to the ides introduced in the previous

More information

Harvard University Computer Science 121 Midterm October 23, 2012

Harvard University Computer Science 121 Midterm October 23, 2012 Hrvrd University Computer Science 121 Midterm Octoer 23, 2012 This is closed-ook exmintion. You my use ny result from lecture, Sipser, prolem sets, or section, s long s you quote it clerly. The lphet is

More information

Exercises with (Some) Solutions

Exercises with (Some) Solutions Exercises with (Some) Solutions Techer: Luc Tesei Mster of Science in Computer Science - University of Cmerino Contents 1 Strong Bisimultion nd HML 2 2 Wek Bisimultion 31 3 Complete Lttices nd Fix Points

More information

Foundations for Timed Systems

Foundations for Timed Systems Foundtions for Timed Systems Ptrici Bouyer LSV CNRS UMR 8643 & ENS de Cchn 6, venue du Président Wilson 9423 Cchn Frnce emil: bouyer@lsv.ens-cchn.fr Introduction Explicit timing constrints re nturlly present

More information

Heat flux and total heat

Heat flux and total heat Het flux nd totl het John McCun Mrch 14, 2017 1 Introduction Yesterdy (if I remember correctly) Ms. Prsd sked me question bout the condition of insulted boundry for the 1D het eqution, nd (bsed on glnce

More information

1.9 C 2 inner variations

1.9 C 2 inner variations 46 CHAPTER 1. INDIRECT METHODS 1.9 C 2 inner vritions So fr, we hve restricted ttention to liner vritions. These re vritions of the form vx; ǫ = ux + ǫφx where φ is in some liner perturbtion clss P, for

More information

Calculus of Variations

Calculus of Variations Clculus of Vritions Com S 477/577 Notes) Yn-Bin Ji Dec 4, 2017 1 Introduction A functionl ssigns rel number to ech function or curve) in some clss. One might sy tht functionl is function of nother function

More information

Closure Properties of Regular Languages

Closure Properties of Regular Languages Closure Properties of Regulr Lnguges Regulr lnguges re closed under mny set opertions. Let L 1 nd L 2 e regulr lnguges. (1) L 1 L 2 (the union) is regulr. (2) L 1 L 2 (the conctention) is regulr. (3) L

More information

11.1 Finite Automata. CS125 Lecture 11 Fall Motivation: TMs without a tape: maybe we can at least fully understand such a simple model?

11.1 Finite Automata. CS125 Lecture 11 Fall Motivation: TMs without a tape: maybe we can at least fully understand such a simple model? CS125 Lecture 11 Fll 2016 11.1 Finite Automt Motivtion: TMs without tpe: mybe we cn t lest fully understnd such simple model? Algorithms (e.g. string mtching) Computing with very limited memory Forml verifiction

More information

CS 373, Spring Solutions to Mock midterm 1 (Based on first midterm in CS 273, Fall 2008.)

CS 373, Spring Solutions to Mock midterm 1 (Based on first midterm in CS 273, Fall 2008.) CS 373, Spring 29. Solutions to Mock midterm (sed on first midterm in CS 273, Fll 28.) Prolem : Short nswer (8 points) The nswers to these prolems should e short nd not complicted. () If n NF M ccepts

More information

Nondeterminism and Nodeterministic Automata

Nondeterminism and Nodeterministic Automata Nondeterminism nd Nodeterministic Automt 61 Nondeterminism nd Nondeterministic Automt The computtionl mchine models tht we lerned in the clss re deterministic in the sense tht the next move is uniquely

More information

Decomposition of terms in Lucas sequences

Decomposition of terms in Lucas sequences Journl of Logic & Anlysis 1:4 009 1 3 ISSN 1759-9008 1 Decomposition of terms in Lucs sequences ABDELMADJID BOUDAOUD Let P, Q be non-zero integers such tht D = P 4Q is different from zero. The sequences

More information