nd edges. Eh edge hs either one endpoint: end(e) = fxg in whih se e is termed loop t vertex x, or two endpoints: end(e) = fx; yg in whih se e is terme

Size: px
Start display at page:

Download "nd edges. Eh edge hs either one endpoint: end(e) = fxg in whih se e is termed loop t vertex x, or two endpoints: end(e) = fx; yg in whih se e is terme"

Transcription

1 Theory of Regions Eri Bdouel nd Philippe Drondeu Iris, Cmpus de Beulieu, F Rennes Cedex, Frne E-mil : feri.bdouel,philippe.drondeug@iris.fr Astrt. The synthesis prolem for nets onsists in deiding whether given grph is isomorphi to the mrking grph of some net nd then onstruting it. This prolem hs een solved in the literture for vrious types of nets rnging from elementry nets to Petri nets. The generl priniple for the synthesis is to inspet regions of grphs representing extensions of ples of the likely generting nets. We follow in this survey the grdul development of the theory of regions from its foundtion y Ehrenfeuht nd Rozenerg, with prtiulr insistene on the strt mening of the theory, whih is generl produt deomposition of grphs into tomi omponents determined y prmeter lled type of nets, nd on the derivtion of eient lgorithms for net synthesis sed on liner lger. Tle of Contents: 1 Terminology of Grphs 2 Regionl Representtion of Prtil 2-Strutures 3 The Synthesis of Elementry Net Systems 4 Cutset Representtion of Finite Grphs 5 Flip-Flop Nets nd their Synthesis 6 Regions for Aritrry Types of Nets 7 Polynomil Time Algorithms for the Synthesis of Petri Nets 8 Regions in Step Trnsition Systems 9 Dul Adjuntions etween Trnsition Systems nd Nets 10 Some Applitions 1 Terminology of Grphs Sine the terminology on grph theory vries lot from one uthor to the other, we found it neessry to egin y dening the terminology used in this doument. 1.1 Grphs A grph G = (X; E) is olletion X of verties or nodes together with olletion E of edges. The grph is sid to e nite if it hs nitely mny verties

2 nd edges. Eh edge hs either one endpoint: end(e) = fxg in whih se e is termed loop t vertex x, or two endpoints: end(e) = fx; yg in whih se e is termed link etween verties x nd y. A grph is simple if it is loop-free: eh edge is link, nd hs no multiple edge: end(e 1 ) = end(e 2 ) ) e 1 = e 2. Therefore n edge of simple grph my e identied with the pir of its endpoints. The inidene mtrix of grph G is mtrix A with elements 0 nd 1, where eh row orresponds to vertex, eh olumn orresponds to n edge, nd A(x; e) is 1 if nd only if x is n endpoint of e. A hin of length n 1 with endpoints fx 1 ; x n+1 g is nite sequene (x 1 ; e 1 ; x 2 ; : : : ; x n ; e n ; x n+1 ) of verties nd edges suh tht end(e i ) = fx i ; x i+1 g for ll 1 i n. We sy tht the hin onnets its endpoints. For onveniene, we onsider tht every vertex is onneted to itself y n empty hin. The onneted omponent of vertex is the set of verties onneted to this vertex y some hin; the grph is onneted if it hs only one onneted omponent. A non empty hin is sid to e simple if ll edges re distint, hin is sid to e elementry if ll the verties ut possily the endpoints re pirwise distint. A yle is simple hin whose endpoints oinide: x 1 = x n+1. A tree is grph with no yle or lterntively grph in whih ny two verties re onneted y unique hin. G 0 = (X 0 ; E 0 ) is sugrph of G = (X; E) if X 0 X, E 0 E, nd the mppings tht send n edge e 2 E 0 to its endpoints in G 0 nd in G oinide. G 0 spns G if X 0 = X; spnning tree of G is sugrph whih is tree spnning G. 1.2 Direted Grphs An orienttion of n edge e is n ordered pir of verties (x; y) suh tht end(e) = fx; yg, thus loop t x hs only one possile orienttion: (x; x), while link etween x nd y hs two possile orienttions: (x; y) nd (y; x). We let e : (x; y) denote the ssignement of the orienttion (x; y) to the edge e; the verties x 0 (e) nd y 1 (e) re respetively lled the soure nd trget of edge e. An oriented edge is sometimes lled n r. A direted grph is grph whose edges re given n orienttion. A direted grph is simple if it is loop-free nd hs no multiple r in the sense tht two edges with the sme endpoints re neessrily given opposite orienttions: (e 1 : (x; y) ^ e 2 : (x; y)) ) e 1 = e 2. Therefore n edge of simple direted grph my e identied with the ordered pir of its endpoints, nd in tht se we write e = (x; y) 0 (e) = x 1 (e) = y. Notie tht the underlying grph of simple oriented grph my not e simple s we n nd two edges with the sme endpoints ut with opposite orienttions. A sugrph of direted grph G is sugrph of the underlying grph with the orienttions of edges inherited from G. The notions of hin, yle, tree, spnning sugrph nd spnning tree do not depend on the orienttion of edges; therefore hin (yle, tree,...) of direted grph is hin (yle, tree,...) of the underlying grph. The spei notions tht tke the orienttion into ount re the following. A pth of length n 1 from x 1 to x n+1 is nite sequene (x 1 ; e 1 ; x 2 ; : : : ; x n ; e n ; x n+1 ) of verties nd edges suh 0 (e i ) = x i 1 (e i ) = x i+1 for ll 1 i n. For onveniene, we onsider tht there exists n empty pth from ny vertex to itself. A non empty pth is sid to

3 e simple if ll edges re distint. A pth is sid to e elementry if ll the verties ut possily the endpoints re distint. A iruit is simple pth whose endpoints oinide: x 1 = x n+1. Thus pths nd iruits re respetively hins nd yles of the underlying grph whose edges hve omptile orienttions. The inidene mtrix of direted grph is the mtrix A : X E! f 1; 0; 1g ( 1 0 (e) = x given y A(x; e) = 1 1 (e) = x. 0 otherwise 2 Regionl Representtion of Prtil 2-Strutures The theory of regions ws founded y Ehrenfeuht nd Rozenerg in [22] with the im to otin set-theoreti representtion of direted grphs (X; E), enrihed with n equivlene on edges. The resulting strutures (X; E; ) re termed prtil 2-strutures. The representtion prolem for prtil 2-strutures onsists in tthing properties p to nodes x so tht the Kripke struture so otined my e strted without loss of informtion to the dt fx j x 2 Xg nd fe j e 2 Eg, where node is enoded y the set x = fp j x j= pg of properties it stises nd n edge y the pir e = (x n y ; y n x ) where x nd y re the respetive soure nd trget of e. The min diulty is to reonstrut the equivlene reltion, nd this nnot e done unless the onsidered properties re ltered uniformly when pssing long every edge in eh equivlene lss. These spei properties, seen s sets of nodes when identied with their extensions fx j x j= pg, re lled regions in [22]. The presenttion of regions in prtil 2-strutures given elow is diretly inspired from [22], where the proofs of the results my e found. The lgorithmi spets of elementry net synthesis will e exmined in the next setion. 2.1 Prtil 2-Strutures nd their Regions Denition 2.1 A prtil 2-struture is triple G = (X; E; ) where X is nite non empty set of nodes, E E 2 (X) = f(x 1 ; x 2 ) 2 X Xj x 1 6= x 2 g is set of 2-edges over X, nd is n equivlene reltion on E. When E = E 2 (X) is the whole set of 2-edges over X, G is lled 2-struture. Prtil 2-strutures my e viewed s equivlene lsses of lelled simple direted grphs, where two grphs re equivlent if their lelling funtions hve the sme kernel. Of prtiulr interest re the prtil set 2-strutures dened s follows. Denition 2.2 A prtil set 2-struture of nite set B is prtil 2-struture G = (X; E; ) where X P(B) nd is the kernel of the funtion ((M; M 0 )) = (M nm 0 ; M 0 nm) for M; M 0 2 X. Let S2S(B) denote the (full) set 2-struture of B; i.e. when X = P(B) nd E = E 2 (X).

4 Thus in prtiulr, ny prtil set 2-struture G of B is sustruture of S2S(B). In nottion, G S2S(B) where (X 1 ; E 1 ; 1 ) (X 2 ; E 2 ; 2 ) if X 1 X 2, E 1 E 2 nd 1 is the restrition of 2 on E 1 E 1. The representtion prolem for prtil 2-strutures my e stted s follows. Whih prtil 2-strutures re isomorphi to sustrutures of S2S(B) for some nite set B (of tokens)? The est wy to grsp this prolem is to exmine the extents R of representtion tokens 2 B in the struture S2S(B) itself, let R = fm 2 P(B)j 2 Mg. So, 2 M if nd only if M 2 R. The following my e oserved. 1. For every pir of equivlent 2-edges (M 1 ; M 0 1) nd (M 2 ; M 0 2), nd for every 2 B, 2 M 1 n M 0 1 entils 2 M 2 n M 0 2 nd symmetrilly 2 M 0 1 n M 1 entils 2 M 0 2 n M 2. This n lso e expressed s follows: { (M 1 2 R ^ M R ) ) (M 2 2 R ^ M R ); { (M 1 62 R ^ M R ) ) (M 2 62 R ^ M R ). Thus, ll the 2-edges in n equivlene lss re inident to R outwrds, or they re inident to R inwrds, or they re not inident to R. 2. 8M 1 ; M 2 2 P(B) M 1 6= M 2 ) (9 2 B M 1 2 R, M 2 62 R ). 3. For every pir of inequivlent 2-edges (M 1 ; M 0 1) nd (M 2 ; M 0 2), there exists some token 2 B suh tht one 2-edge is inident to R nd the other is not, or one 2-edge is inident to R inwrds nd the other is inident to R outwrds. These properties re lso vlid for sustrutures (X; E; ) of S2S(B), where R is the set fm 2 Xj 2 Mg. Denition 2.3 A region in prtil 2-struture G = (X; E; ) is suset of nodes R X suh tht for every pir of equivlent 2-edges (x 1 ; x 0 1) nd (x 2 ; x 0 2) in E: (x 1 2 R ^ x R) ) (x 2 2 R ^ x R), nd (x 1 62 R ^ x R) ) (x 2 62 R ^ x R). Let R G denote the set of (non trivil) regions of G, nd for x 2 X, let R G (x) = fr 2 R G j x 2 Rg. It is worth noting tht the omplement X n R of region R is region. In prtiulr X nd ; re regions (the trivil regions). Now the non trivil regions my serve s representtion tokens for sttes, tht is nodes, nd t the sme time for events, tht is lsses of equivlent 2-edges. One otins in this wy regionl versions of prtil 2-strutures dened s follows. Denition 2.4 Given prtil 2-struture G = (X; E; ), the regionl version of G is the prtil set 2-struture regv(g) = (X 0 ; E 0 ; ) with omponents X 0 = fr G (x)j x 2 Xg nd E 0 = f(r G (x); R G (x 0 ))j (x; x 0 ) 2 Eg. In this onstrution, illustrted in Fig. 1, node x is mpped to the set R G (x) of the regions whih inlude x. It ppers from Fig. 1, where equivlent edges er ommon lel, tht the mp regv is not n equivlene of prtil 2- strutures. The following theorem sttes when regv mps prtil 2-struture isomorphilly to prtil set 2-struture (the regionl representtion of the ltter).

5 1 2 g 3 4 R 0 = f1; 2; 3; 4g R 0 = ; R 1 = f1; 2g R 1 = f3; 4g R 2 = f1; 3g R 2 = f2; 4g (fr 1 g; fr 1 g) fr 0 ; R 1 ; R 2 g fr 0 ; R 1 ; R 2 g (fr 2 g; fr 2 g) regv(g) (fr 2 g; fr 2 g) fr 0 ; R 1 ; R 2 g fr 0 ; R 1 ; R 2 g (fr 1 g; fr 1 g) Fig. 1. prtil 2-struture nd its regionl version Theorem 2.5 A prtil 2-struture G = (X; E; ) is isomorphi to sustruture of some set 2-struture if nd only if G = regv(g) (with R G () s the isomorphism) if nd only if the following two xioms of seprtion re stised: 1. sttes seprtion: 8x 1 ; x 2 2 X x 1 6= x 2 ) 9R 2 R G (x 1 2 R, x 2 62 R). 2. events seprtion: for ll (x 1 ; x 0 1); (x 2 ; x 0 2) 2 E with (x 1 ; x 0 1) 6 (x 2 ; x 0 2) there exists some region R 2 R G suh tht either (x 1 ; x 0 1) is inident to R outwrds nd (x 2 ; x 0 2) is not or (x 2 ; x 0 2) is inident to R outwrds nd (x 1 ; x 0 1) is not. There my exist nodes x 1, x 2, x 3 nd x 4 suh tht (x 1 ; x 2 ) 2 E, (x 1 ; x 2) = (x 3; x 4), nd (x 3 ; x 4 ) 62 E. Therefore regv(g) is not hrterized y the sets fx j x 2 Xg nd fe j e 2 Eg. In order to redue the mismth, one should impose the dditionl xiom: 8(x 1 ; x 2 ) 2 E 8x 3 ; x 4 2 X (R G (x 1 ); R G (x 2 )) = (R G (x 3 ); R G (x 4 )) ) (x 3 ; x 4 ) 2 E. Further on this wy, one n even impose one or two stronger xioms: forwrd losure: 8(x 1 ; x 2 ) 2 E 8x 3 2 X (R G (x 1 ) n R G (x 2 ) R G (x 3 ) ^ R G (x 3 )\R G (x 2 )nr G (x 1 ) = ;) ) 9x 4 2 X (x 3 ; x 4 ) 2 E ^ (R G (x 1 ); R G (x 2 )) = (R G (x 3 ); R G (x 4 )). kwrd losure: 8(x 1 ; x 2 ) 2 E 8x 4 2 X (R G (x 2 ) n R G (x 1 ) R G (x 4 ) ^ R G (x 4 )\R G (x 1 )nr G (x 2 ) = ;) ) 9x 3 2 X (x 3 ; x 4 ) 2 E ^ (R G (x 1 ); R G (x 2 )) = (R G (x 3 ); R G (x 4 )). Prtil 2-strutures my e onsidered too generl from prtil point of view, nd one my prefer fousing on rehle prtil 2-strutures, suh tht ll nodes n e rehed y pths with ommon origin. A fmilir exmple of rehle prtil set 2-strutures is the lss of sequentil se grphs of elementry net systems. Denition 2.6 An elementry net is direted iprtite grph N = (P; E; F ) suh tht dom(f ) [ rn(f ) = P [ E. Elements of P, respetively E, re lled onditions (or ples), resp. events. Let x 2 y nd y 2 x e lterntive nottions of (x; y) 2 F. A se (or mrking) of N is suset of onditions M 2 P(P ). An event e hs onession in se M (noted M[e>) if nd only if ( e; e ) = (M; M 0 ) for some se M 0 (thus uniquely dened). The event e

6 my then re t M, resulting in the step M[e>M 0. Thus, M[e> if nd only if e M ^ M \ e = ;, nd then M[e>M 0 where M 0 = (M n e) [ e. A net is pure if 8x 2 P [ E x \ x = ;; it is simple if 8x; y 2 P [ E (x = y ^ x = y) ) x = y. The elementry nets onsidered from now on re ssumed to e pure nd simple. Denition 2.7 An elementry net system is struture N = (P; E; F; M 0 ) where N = (P; E; F ) is the underlying net nd M 0 (in P(P )) is the initil se. The sequentil se grph of N is the prtil set 2-struture sg(n ) = (X 0 ; E 0 ; ) where X 0 P(P ) is the smllest set of ses rehle from M 0 y sequenes of steps M[e>M 0 nd E 0 is the set of orresponding pirs (M; M 0 ). Lemm 2.8 A prtil set 2-struture G = (X; E; ) is the sequentil se grph of n elementry net system if nd only if it is rehle nd the following property is stised: 8(x 1 ; x 2 ) 2 E 8x 3 2 X (x 1 n x 2 x 3 ^ x 3 \ x 2 n x 1 = ;) ) 9x 4 2 X ((x 3 ; x 4 ) 2 E ^ (x 1 ; x 2 ) = (x 3 ; x 4 )). From Theo. 2.5 nd Lem. 2.8, one otins the following. Corollry 2.9 A prtil 2-struture G = (X; E; ) is isomorphi to the sequentil se grph of n elementry net system if nd only if it is rehle nd stises the xioms of sttes seprtion, events seprtion, nd forwrd losure. The elementry net system in the ove orollry is essentilly the set of the ordered symmetri dierenes (R G (x); R G (y)) for 2-edges (x; y) 2 E. The representtion prolem for prtil 2-strutures set t the eginning of the setion hs in ft een given the solution x = R G (x). The ples of the net re the regions r 2 R(G), the events re the equivlene lsses of edges, nd the ow reltion is suh tht: F ([e] ; r), r 2 R G (y) n R G (x) for some (x; y) 2 E; nd F (r; [e] ), r 2 R G (x) n R G (y) for some (x; y) 2 E. The initil se of the net system is dened s R G (x 0 ) for some x 0 2 X suh tht every node of G is rehle from x Elementry Automt The seond prt of the setion pves the wy for the lgorithmi nlysis of the region sed orrespondene etween rehle grphs nd elementry net systems. With this ojetive in mind, we rest the results otined so fr into the frmework of trnsition systems, nd illustrte the modied orrespondene on omplete exmple. Denition 2.10 A (lelled) trnsition system is triple A = (S; E; T ) with set of sttes S, set of events E, nd set of trnsitions T S E S. Let s e! s 0 e n equivlent nottion for (s; e; s 0 ) 2 T. An event e is enled t stte s (noted s e!) if s e! s 0 for some s 0. An event e is o-enled t s 0 (noted e! s 0 ) if s e! s 0 for some s. An utomton is struture A = (S; E; T; s 0 ) onsisting of n underlying trnsition system A = (S; E; T ) nd n initil stte s 0 2 S.

7 A prtil 2-struture G = (X; E; ) my e identied with the trnsition system (X; E= ; T ) where x [e]! x 0 if nd only if (x; x 0 ) e. This trnsition system is loopfree: s! e s 0 ) s 6= s 0, hs no multiple r: s e 1! s 0 ^ s e 2! s 0 ) e 1 = e 2, nd it is redued: 8e 2 E 9s; s 0 2 S s! e s 0. The sequentil se grphs of the redued net systems dened herefter fll in this sulss of trnsition systems. Denition 2.11 An elementry net system N = (P; E; F; M 0 ) is redued if every event e 2 E hs onession t some se M rehle from M 0, nd for every two distint onditions p; p 0 2 P there exists some se M rehle from M 0 suh tht p 2 M, p 0 62 M. The dul of redued elementry net system N is the utomton N = (S; E; T; M 0 ) where S is the set of ses rehle from M 0 y sequenes of steps M[e>M 0 nd T is the set of the orresponding trnsitions (M; e; M 0 ). Thus N is essentilly the imge of sg(n ) through the mp whih sends the equivlene lss of 2-edges f(m; M 0 )j (M; M 0 ) = ( e; e )g to the event e. Sine N is simple nd redued, this mp is one to one nd onto. By onstrution, N is rehle from M 0, deterministi: M e! M 0 ^ M e! M 00 ) M 0 = M 00, nd o-deterministi: M 0 e! M ^ M 00 e! M ) M 0 = M 00. The denition of regions my e rried to utomt in the following form. Denition 2.12 A region in n utomton A = (S; E; T; s 0 ), or in the underlying trnsition system (S; E; T ), is suset of sttes R S suh tht e e s1 2 R ^ s 8e 2 E 8s 1; s 2; s 3; s 4 2 S s 1! s2 ^ s 3! s4 ) 2 62 R ) s 3 2 R ^ s 4 62 R s 1 62 R ^ s 2 2 R ) s 3 62 R ^ s 4 2 R Let R A denote the set of (non trivil) regions of A, nd for s 2 S let R A (s) = fr 2 R A j s 2 Rg. Thus, R is region if nd only if the lel e of trnsition sues to determine whether the trnsition is inident to R inwrds (R is then termed n output region for e, noted e R), or it is inident to R outwrds (R is then termed n input region for e, noted R e), or it is not inident to R (it is internl to R or externl to R). In prtiulr, if A is rehle nd redued, the non trivil regions of A my e represented s mps R : E! f 1; 0; 1g suh tht R (e) = 1 if e R, R (e) = 1 if R e, nd R (e) = 0 otherwise; the hrteristi funtion of R, let R : S! f0; 1g where R (s) = 1, s 2 R, is then the unique mp suh tht s e! s 0 ) R (e) = R (s 0 ) R (s). It is esily seen tht for every ondition p of net system N, the set of the rehle ses M tht ontin p is region of N. This region, denoted y p nd lled the extension of p, is suh tht e p, e 2 p nd p e, e 2 p. Reversing the proess whih leds from net systems to sequentil se grphs, let us rest the denition of regionl versions in terms of nets nd net systems. Denition 2.13 Given n utomton A = (S; E; T; s 0 ), the dul of A is the (redued) elementry net system A = (R A ; (E= ) n f"g; F; s 0) where: is the equivlene on E indued y regions, let e 1 e 2, (8R 2 R A e 1 R, e 2 R ^ R e 1, R e 2 );

8 " is the equivlene lss of the events whih re inputless nd outputless i.e. whih re internl or externl to ll regions, if suh events exist; F is the ow reltion suh tht F ([e] ; R), e R nd F (R; [e] ), R e; nd s 0 = fr 2 R A j s 0 2 Rg. The net system A is lso lled the sturted net version of A (for resons explined in the sequel). The ounterprt of Cor. 2.9 for utomt is the following. Theorem 2.14 An utomton A = (S; E; T; s 0 ) is isomorphi to the dul N of n elementry net system if nd only if A = A if nd only if A is simple (it hs neither loop nor multiple r), redued, rehle nd it stises the following properties of seprtion: ssp (Sttes Seprtion Property): 8s; s 0 2 S s 6= s 0 ) 9R 2 R A (s 2 R, s 0 62 R) esp (Events Seprtion Property): 8e; e 0 2 E e 6= e 0 ) 9R 2 R A (R e ^ not(r e 0 )) _ (e R ^ not(e 0 R)) essp (Events-Sttes Seprtion Property): 8e 2 E 8s 2 S not(s e!) ) 9R 2 R A (R e ^ s 62 R) _ (e R ^ s 2 R) An utomton stisfying these onditions is termed n elementry utomton. Oserve tht every event in n elementry utomton hs input regions nd output regions (from ssp), hene the mp sending e to [e] is ijetion etween E nd (E= ) n f"g (from esp). The isomorphism from A to A (the sequentil se grph of the sturted net version of A) mps e to [e] nd s to s = fr 2 R A j s 2 Rg. This isomorphism pplies in prtiulr to sequentil se grphs, whene N = N for every elementry net system. However, N = (P; E; F; M 0 ) is generlly not isomorphi to its doule dul N. In ft, every ondition p of N indues orresponding region p of N whih inludes the rehle ses in whih ondition p holds, nd N is isomorphi to the full sunet system of N with set of events E= (= E) nd set of ples fp j p 2 P g. Thus, whenever N 0 = N, N 0 is isomorphi to sunet system of N whih is for tht reson termed the sturted version of N. Now, for n elementry utomton A, A = A entils tht A = A, hene A is lwys sturted net system. The im of the next setion is to optimize the synthesis proess y looking t dmissile sunets N of A suh tht A = N. Before tkling the synthesis prolem, we proeed to simplifying the presenttion of elementry utomt, nd retrieve the usul presenttion given in [11, 19, 34]. Proposition 2.15 Let utomton A e simple, redued nd rehle, then A is elementry if nd only if the seprtion properties ssp nd essp re stised.

9 Proof: Let A = (S; E; T; s 0 ), nd ssume for ontrdition e 6= e 0 nd 8R 2 R A (R e, R e 0 ) ^ (e R, e 0 R). We show tht s e! s 0 entils s e0! s 0 ontrditing the ssumption tht A is simple. Assume s! e s 0 nd not s!, e0 then y essp: 9R 2 R A (R e 0 ^ s 62 R) _ (e 0 R ^ s 2 R) nd the ontrdition of s! e s 0 follows from the denition of regions. Let s 00 2 S suh tht s! e0 s 00, then R A (s 00 ) = R A (s) n e 0 [ e 0 = R A (s) n e [ e = R A (s 0 ) nd s 0 = s 00 follows from esp. For omplete proofs of the results whih hve een stted in this susetion, the reder is referred to [19] where prtil 2-strutures re y-pssed. As n illustrtion, let us onsider the elementry net system nd the se grph given in Fig. 2. In Fig. 3 re displyed some of the non trivil regions of x 3 x2 z x 1 y 1 ' y 2 ' ' y 3 s 0 = fx 1; z; y 1g s 1 = fx 2; y 1g s 2 = fx 1; y 2g s 3 = fx 3; z; y 1g s 4 = fx 1; z; y 3g s 5 = fx 3; y 2g s 6 = fx 2; y 3g s 7 = fx 3; z; y 3g ' s 1 s 2 ' ' s 6 s 0 s 5 ' ' s 4 s 3 ' s 7 ' Fig. 2. n elementry net system nd its se grph this utomton. The missing items n e otined y symmetry. Eh drwing ' ' ' ' ' X ' 1 ' ' ' ' X 3 ' ' :X 1 ' ' :X 3 ' ' ' X 2 ' ' ' Z ' ' ' ' ' :X 2 ' ' ' ' :Z ' Fig. 3. some regions of the se grph of the elementry net system of Fig. 2 nd their ssoited tomi net systems represents region R onsisting of lk sttes. The ow reltions for the region R nd for its omplement :R = S n R re lso represented pitorilly; nlly one token indites whih of these omplementry regions ontins the initil stte.

10 We end up with the elementry net system of Fig. 4, whih is the originl net of Fig. 2 enrihed with dditionl ples (indited y dshed lines) ut with unhnged ehviour. The originl net system is emedded into its sturted :X 1 :Y 1 X 1 Z Y 1 ' ' X 3 X 2 Y 2 Y 3 :Z :X 3 :Y 3 ' :X 2 :Y 2 Fig. 4. the emedding of the elementry net system of Fig. 2 into its doule dul version y the mp tht sends ple x to its extension in the stte grph i.e. the set of mrkings fm 2 Sj x 2 Mg. 3 The Synthesis of Elementry Net Systems All utomt onsidered in this setion re ssumed to e pre-elementry, i.e. simple, rehle nd redued. The synthesis prolem of elementry net systems [19] is s follows: Given nite utomton A = (S; E; T; s 0 ), deide whether A = N for some elementry net system N with the sme set of events E, nd if so, onstrut N. Sine the set R A of ll the regions of A is nite, we lredy know from Prop tht this prolem n e deided in exponentil time y simultneously exploring R A, for heking stisftion of the seprtion properties esp nd essp, nd onstruting N = A. The im of this setion is to improve on this rute fore solution. We review rst Desel nd Reisig's study of dmissile sets of regions nd their tehniques for eliminting redundnt regions. Next we ount for Bernrdinello's results on the synthesis of stte mhine deomposle net systems, sed on the ruil remrk tht the miniml regions of n utomton form n dmissile set, nd for susequent work y Cortdell et l. on the reliztion of utomt y elementry nets up to some quotient of utomt. We nlly report the results otined on the omplexity of the synthesis prolem in [25, 3].

11 3.1 Admissile sets of regions In n elementry net system N = (P; E; F; M 0 ), eh ondition p 2 P determines n tomi sunet system of N, let N p = (fpg; E; F p ; M 0;p ) where F p is the restrition of F nd M 0;p (p) = M 0 (p). If we do not re out the isolted events P in N p, these tomi sunet systems re elementry nd N is just their sum p2p N p, where nets re glued together on events e 2 E. This deomposition my e used to isolte the ontriution of eh ondition p 2 P to the glol struture of the sequentil se grph N. This utomton my e seen s deterministi reognizer of nite sequenes, in whih every stte (i.e. se) is epting. An utomton of this type is hrterized up to isomorphism y the lnguge L it epts plus the equivlene on L whih identies these sequenes tht led to ommon (epting) stte. Now in the se of N, L nd re the intersetions for p rnging over P of the respetive lnguges nd equivlenes hrteristi of Np : L = \ p2p L p nd = \ p2p p. Thus the role of eh ondition p is twofold: on the one hnd, p uts o sequenes u e suh tht u 2 L ut u e 62 L p, nd on the other hnd p seprtes pirs of words u; v 2 L suh tht u 6 p v. Returning to the synthesis prolem, let us now lrify the reltionship etween utomt nd tomi net systems. Let A = (S; E; T; s 0 ) e nite deterministi utomton, with lnguge L nd equivlene, nd let N p = (fpg; E; F p ; M 0;p ) e n tomi net system, induing dul utomton Np with lnguge L p nd equivlene p. The utomton Np hs two sttes, ; nd fpg, one of whih is M 0;p, nd it hs trnsitions ;! e fpg if F p (e; p), fpg! e ; if F p (p; e), nd otherwise ;! e ; nd fpg! e fpg. Suppose L L p nd p. Let R p e the u u suset of sttes s 2 S suh tht s 0! s in A nd M0;p! fpg in N p for some sequene of events u 2 E. Then R p is region of A, s 0 2 R p, M 0;p = fpg, nd for every e 2 E: R p e, F p (p; e) nd e R p, F p (e; p). Conversely, for ny region R p of A, the elementry net system N p dened y the ove reltions indues dul utomton Np suh tht L L p nd p. Moreover, R p seprtes two distint sttes s 0 nd s 00 u v suh tht s 0! s0 nd s 0! s 00 in A if u nd only if u 6 p v, nd R p seprtes stte s suh tht s 0! s from n event e suh tht not(s!) e if nd only if u e 62 L p. Therefore, given net system N = (P; E; F; M 0 ) = P p2p N p, the dul utomton N is isomorphi to the utomton A if nd only if L = \ p2p L p nd = \ p2p p, if nd only if for ll p 2 P, N p is n tomi net system dened from some orresponding region R p in A nd the following properties re stised: ssp': 8u; v 2 L u 6 v ) 9p 2 P u 6 p v, essp': 8u 2 L 8e 2 E u e 62 L ) 9p 2 P u e 62 L p, if nd only if the fmily of regions fr p j p 2 P g is dmissile ording to the following denition. Denition 3.1 Given n utomton A = (S; E; T; s 0 ), suset of regions fr p j p 2 P g R A is dmissile if nd only if it inludes witnesses for the stisftion of every instne of the following seprtion prolems where e 2 E nd s; s 0 ; s 00 2 S re suh tht s 0 6= s 00 nd not(s e!):

12 ssp(s 0 ; s 00 ) : 9p 2 P s 0 2 R p, s R p, essp(s; e) : 9p 2 P (R p e ^ s 62 R p ) _ (e R p ^ s 2 R p ). It is esily seen tht prolem ssp(s 0 ; s 00 ) nnot e solved positively in nondeterministi utomton A where s e! s 0 nd s e! s 00 for s 0 6= s 00. One redisovers in this wy si result estlished in [19]. Theorem 3.2 An utomton A = (S; E; T; s 0 ) is isomorphi to N for N = (P; E; F; M 0 ) if nd only if for every p 2 P, the tomi sunet system N p of N my e dened from some orresponding region R p of A, nd the set of regions fr p j p 2 P g is dmissile. In view of Def. 3.1 nd Theo. 3.2, the synthesis prolem for A = (S; E; T; s 0 ) my e solved y onsidering t most jsj (jsj + jej) regions of A. Nevertheless, this does not indite how to selet these regions from R A. The purpose is to onstrut suset of regions R R A s smll s possile suh tht R is dmissile if nd only if the whole set of regions R A is dmissile. Some struturl rules re proposed in [19] for the stepwise elimintion of redundnt regions, strting from R A. Denition 3.3 Let R R A e set of regions. A region R 2 R is redundnt in R if the following ssertions re equivlent: (i) R is dmissile (ii) R n frg is dmissile. Proposition 3.4 Let A = (S; E; T; s 0 ) nd R 2 R R A. In eh of the following ses R is redundnt in R. 1. S n R 2 R, 2. 9R 1 ; R 2 ; R 3 ; R 4 2 R R = R 1 \ R 2 ^ S n R = R 3 \ R 4, 3. 9R 1 ; R 2 ; R 3 ; R 4 2 R R = R 1 [ R 2 ^ S n R = R 3 [ R 4, 4. 9R 1 ; R 2 2 R R = R 1 \ R 2 ^ 8s 2 R 8e 2 E 8s 0 2 S n R s! e s 0 ) s 0 62 R 1 [ R 2. One redued set of regions R hs een otined from R A, one n hek diretly from Def. 3.1 whether it is dmissile, proving tht A is elementry, nd then extrt from R miniml suset fr p j p 2 P g suh tht A = ( P p2p N p). It is worth noting tht there exists in generl no lest dmissile set of regions. This ft is illustrted in Fig. 5 y the so-lled \four sesons" exmple reprodued from [19]. The \four sesons" utomton my e relized y two miniml sunet systems of the dul sturted net system: one hs four onditions nd is onttfree while the other one hs three onditions ut is not ontt-free. Denition 3.5 An elementry net system N = (P; E; F; M 0 ) is ontt-free if e M ) M \ e = ; for every event e nd for every rehle se M. Thus, the sulss of elementry net systems whih re ontt-free nd redued oinides with the sulss of the redued nd one-sfe Petri nets. Now, every sturted net system N = (P; E; F; M 0 ) is ontt-free: every ondition p 2 P indues two omplementry regions R p nd R p in N, nd sine N = N there

13 f1g f2g d 1 2 f4g f3g d 4 3 d f1; 3g d f2; 3g f3; 4g Fig. 5. the four sesons exmple: the utomton (on the left), the sturted net system (on the middle) nd two elementry net systems orresponding to miniml sets of regions (on the right) should exist some ondition p 2 P suh tht R p = R p. Therefore, every elementry utomton my e relized y one-sfe Petri net. The following dpttion of Theo. 3.2, sed on the use of omplementry regions, is estlished in [19] Proposition 3.6 An utomton A = (S; E; T; s 0 ) is isomorphi to N for ontt-free net system N = (P; E; F; M 0 ) = P p2p N p if nd only if every tomi sunet system N p of N my e dened from orresponding region R p 2 R A nd the following properties of seprtion re stised: ssp : 8s; s 0 2 S s 6= s 0 ) 9p 2 P s 2 R p, s 0 62 R p essp ] : 8e 2 E 8s 2 S not(s!) e ) 9p 2 P R p e ^ s 62 R p. 3.2 Miniml Regions Among the dmissile sets of regions of n elementry utomton, the set of miniml regions plys distinguished role euse it leds nturlly, s shown in [11], to stte mhine deomposle (nd hene ontt-free) net system relizing the utomton. Denition 3.7 An elementry net system N = (P; E; F; M 0 ) is stte mhine if its initil se is singleton nd every event hs one preondition nd one postondition. A stte mhine omponent of N = (P; E; F; M 0 ) is stte mhine N 0 = (P 0 ; E 0 ; F 0 ; M 0 0) suh tht P 0 P, E 0 = fe 2 Ej( e [ e ) \ P 0 6= ;g, F 0 = F \(E 0 P 0 [P 0 E 0 ), nd M 0 0 = M 0 \P 0. A stte mhine deomposition of N = (P; E; F; M 0 ) is fmily of stte mhines, let N i = (P i ; E i ; F i ; M 0;i ), suh tht P = [ i P i, E = [ i E i, F = [ i F i, nd M 0 = [ i M 0;i. A stte mhine is nothing else thn rehle utomton, s n e seen from Fig. 6 where the elementry net system given in Fig. 2 is deomposed into three stte mhine omponents. The respetive stte mhine omponents model sequentil proesses whih re synhronized on their ommon events. In this exmple, the synhroniztion prevents the leftmost nd rightmost proesses from

14 x 1 z y 1 ' ' ' x 3 x 2 x 2 y 2 y 2 y 3 ' ' Fig. 6. three stte mhine omponents of the net system of Fig. 2 entering simultneously the ritil setion gured y the mutully exlusive onditions x 2 nd y 2. Eh stte mhine omponent N i of net system N = P i N i my e seen s sequentil oserver of N, projeting ses of N on oservle onditions p 2 P i. By denition of stte mhine omponents, eh se of N projets to one nd extly one ondition p 2 P i, hene eh se of N elongs to extly one region R p of N suh tht p 2 P i. Proposition 3.8 Every stte mhine omponent N i = (P i ; E i ; F i ; M 0;i ) of n elementry net system N = P i N i determines regionl prtition fr p j p 2 P i g of the sequentil se grph N. Conversely, every regionl prtition fr p j p 2 P g of N determines stte mhine omponent of the sturted net system N. Returning to the exmple, the regionl prtitions of N (Fig. 3) whih determine the three stte mhine omponents shown in Fig. 6 re respetively fx 1 ; X 2 ; X 3 g, fx 2 ; Z; Y 2 g, nd fy 1 ; Y 2 ; Y 3 g where: Z = fs 0; s 3; s 4; s 7g X 1 = fs 0; s 2; s 4g X 2 = fs 1; s 6g X 3 = fs 3; s 5; s 7g Y 1 = fs 0; s 1; s 3g Y 2 = fs 2; s 5g = fs 4; s 6; s 7g Y 3 It my e oserved tht ll these regions re miniml w.r.t. set inlusion in R N. The prtiulr interest of miniml regions for the net system reliztion of elementry utomt is shown y the following proposition nd orollries. Proposition 3.9 Given n utomton A = (S; E; T; s 0 ), the following properties re stised y the set R A of regions of A: 1. If R 1 nd R 2 re disjoint regions then R 1 [ R 2 is region with (R 1 [ R 2 ) = ( R 1 [ R 2 ) n (( R 1 \ R 2 ) [ ( R 2 \ R 1 )) (R 1 [ R 2 ) = (R 1 [ R 2 ) n (( R 1 \ R 2 ) [ ( R 2 \ R 1 )):

15 2. If R nd R 0 re regions nd R 0 R then R n R 0 is region. If moreover R 0 is miniml then e (R n R 0 ) for every event e 2 R 0 whih is not inident to R (i.e. suh tht e 62 R [ R ). 3. If R is region nd s 2 R, then s 2 R 0 for some miniml region R 0 R. 4. If R is region nd e n event suh tht R e, then R 0 e for some miniml region R 0 R; symmetrilly if e is n event suh tht e R, then e R 0 for some miniml region R 0 R. 5. Every region is disjoint union of miniml regions. Corollry 3.10 A pre-elementry utomton is elementry if nd only if its set of miniml regions is dmissile. It my e further oserved tht the set of miniml regions of pre-elementry utomton A is dmissile w.r.t. the seprtion properties ssp nd essp if nd only if it is dmissile w.r.t. the seprtion properties ssp nd essp ]. In ft, let fr 1 ; : : : ; R n g e ny prtition of the set of sttes of A into miniml regions, then eh instne of the prolem essp(s; e) solved y region R i suh tht e R i nd s 2 R i n lso e solved y region R j suh tht R j e nd s 62 R j. Sine the set of ll prtitions of the set of sttes of A into miniml regions indues stte mhine deomposition of the net system P p N p dened from the set of ll miniml regions R p of A, one dedues lso the following. Corollry 3.11 Every elementry utomton my e relized y stte mhine deomposle (nd hene ontt-free) elementry net system. An lgorithm sed on miniml regions hs een proposed in [14] for vrint prolem of reliztion of utomt y net systems whih my e stted s follows. Given pre-elementry utomton A, deide whether exists nd onstrut (miniml) elementry net system N suh tht N = A 0 for some quotient A 0 of A. We rell tht A 0 = (S 0 ; E; T 0 ; s 0 e 0) is quotient of A = (S; E; T; s 0 ) if s 1! s2 in A if nd only if (s 1)! e (s 2) in A 0 for some surjetive mp : S! S 0 suh tht s 0 0 = (s 0 ). This prolem is similr to the originl synthesis prolem, up to the ft tht the sttes seprtion property ssp is ignored. Now the events-sttes seprtion property essp ] is vlid in A if nd only if for every event e the set of sttes fs 2 Sj s!g e oinides with the intersetion of the miniml regions R suh tht R e. The lgorithm strts from the sets fs 2 Sj s!g e nd inreses them into miniml regions, whih re generted until the vlidity of essp ] n e deided upon. The net N is then onstruted from miniml set of miniml regions dmissile with respet to essp ]. A vrint form of this lgorithm hs een integrted to softwre tool for the synthesis of synhronous iruits [15]. It should e noted tht the prolem of relizing utomt y nets up to quotient diers signintly from the prolem of relizing utomt y nets up to ehviourl equivlene (equlity of the epted lnguges). In order to mke the dierene visile, let us fous on nite nd deterministi utomt.

16 In this ontext, ehviourl equivlene oinides with isimilrity. Given - nite deterministi utomton A, with lnguge L nd hrteristi equivlene on L, the prolem of relizing A up to ehviourl equivlene onsists in onstruting n elementry net system N suh tht N reognizes L. For the prolem of relizing A up to quotient, it is set s further requirement tht ny two equivlent sequenes in L led to the sme se when they re red from the initil se of N. In orther words, it is sked tht N. The reson why this onstrint mkes notle dierene is tht the elementry utomt re not losed under quotient. This ounterft is illustrted in Fig. 7: the utomton shown on the middle is isomorphi to the se grph of the net displyed on the left, ut its minimized version shown on the right is not elementry (ny region R suh tht R must inlude stte 3, hene the prolem essp(3; ) nnot e solved) d 2 3 d d 4 5 d d 6 5 Fig. 7. elementry utomt re not losed under quotient 3.3 Complexity Results Hirishi proved in [25] tht the seprtion prolems ssp(s; s 0 ) nd essp ] (s; e) re NP-omplete in the respetive dt (A; s; s 0 ) nd (A; s; e). Sine regions in A re losed under omplementtion, the prolem essp(s; e) is lso NP-omplete. It does not follow therefrom tht the synthesis prolem for elementry net systems is NP-omplete; however this is the se. The synthesis prolem is oviously in NP sine the totl numer of instnes of seprtion prolems in n utomton A is qudrti in the size of A, nd it n e heked in polynomil time whether non-deterministilly hosen suset of sttes is region solving xed seprtion prolem. Now polynomil redution of 3-SAT to the synthesis prolem of elementry net systems ws estlished in [3], showing NP-hrdness sine 3- SAT is NP-omplete (see e.g. [23]). Rell tht 3-SAT is the prolem whether, given nite set of oolen luses over V, with three litterls per luse, there exists some truth ssignment for V vlidting eh luse. Eh lusl system of this form is ssoited in [3] with n utomton suh tht the lusl system is stisle if nd only if the utomton is elementry if nd only if the seprtion property essp ] is vlid. Therefore, the synthesis prolem for elementry net systems is NP-omplete, nd so is the prolem of relizing utomt y nets

17 up to quotient. The prolems of relizing utomt y nets up to ehviourl equivlene, or up to n unfolding (given A nd N suh tht A is isomorphi to quotient of N ) hve unknown omplexity. 4 Cutset Representtion of Finite Grphs We hve seen tht the region sed synthesis of elementry net systems from initilized prtil 2-strutures (X; E; ; x 0 ) is NP-omplete prolem. Nevertheless, this prolem is trivil when the lelling equivlene is disrete: in tht se, the prtil 2-struture is essentilly stte mhine with set of ples X; even etter, this stte mhine is equivlent to net system with jxj 1 ples, whose se grph is prtil set 2-struture isomorphi to the given prtil 2-struture. There exists lrge vriety of set-theoreti representtions for n unlelled grph (X; E), ll of whih using t most jxj 1 tokens. These representtions, sed on uts nd utsets, my e omputed y liner lgeri methods whih re quite stndrd in pplied grph theory. The purpose of this setion is to review these methods, nd therey shed light on regions in two respets. First, we exmine the lose reltionship etween regions nd uts (this nlogy ws rst pointed out to us y T. Murt). Seond, we indite the ostles to using liner lgeri methods for the region sed representtion of lelled grphs. On ount of this nlysis, vrint denition of regions is proposed in the next setion. 4.1 Cuts nd Cutsets Let G = (X; E) e nite, onneted nd simple direted grph with set of nodes X = fx 1 ; : : : ; x n g nd set of 2-edges E = fe 1 ; : : : ; e m g. So, G is free of loops multiple rs, lthough 2-edge e = (x l ; x k ) my hve n inverse e 1 = (x k ; x l ) in E. A utset of G is miniml set of 2-edges whose removl inreses the numer of onneted omponents y one. A ut of G is utset or n edge disjoint union of utsets. Sine G is onneted, every ut or utset C E determines two omplementry susets of nodes p nd X n p, oth non empty, suh tht for every 2-edge e = (x k ; x l ), e 2 C if nd only if x k 2 p, x l 62 p. Conversely, every non trivil suset p X determines ut etween p nd X n p, whih is utset when oth p nd X n p re onneted. An orienttion of the ut C results from the hoie of one of the two omplementry susets of nodes determined y the ut, let p. An oriented ut C my e oded y vetor C 2 IR m suh tht for every 2-edge e i = (x k ; x l ), C(i) = 1 if x k 62 p nd x l 2 p, C(i) = 1 if x k 2 p nd x l 62 p, nd C(i) = 0 if x k 2 p, x l 2 p. Let X = fx 1 ; : : : ; x n g nd E = fe 1 ; : : : ; e m g. We will ddress the prolem of onstruting vriety of sets of properties fp 1 ; : : : ; p n 1 g where p i X suh tht the prtil 2-struture (fx j x 2 Xg; fe j e 2 Eg; ) where x = fp i j x 2 p i g, (x k ; x l ) = (x k ; x l ), nd (x k ; x l ) = (x k n x l ; x l n x k ) is isomorphi to G viewed s prtil 2-struture: G = (X; E; id E ). Eh fmily of tokens fp 1 ; : : : ; p n 1 g

18 will determine orresponding set of (oriented) uts fc 1 ; : : : C n 1 g whih re linerly independent s vetors C i 2 IR m. The interesting ft here is tht one n esily onstrut liner ses of uts, given s sets of fundmentl utsets of G with respet to ritrry spnning trees. Rell tht spnning tree is set of edges U E, free of yles nd onneting X. The fundmentl utsets w.r.t. U re the uts whih inlude extly one rnh of U. Eh rnh of U determines two onneted omponents of U (nd thus of G), with set of nodes p nd X n p, suh tht every other rnh of U is internl either to p or to X n p. The fundmentl utsets w.r.t. U my e omputed y lssil methods of liner lger. These methods re relled elow, following the nottions of [16]. 4.2 Computing Cutsets The grph G = (X; E) is hrterized up to isomorphism y its inidene mtrix. We rell tht this mtrix A = [ i;j ] is n n m mtrix with entries in f 1; 0; 1g, with i;j = 0 if edge e j is not inident to node x i, i;j = 1 if x i is the soure of e j, nd i;j = 1 if x i is the trget of e j. Sine every olumn ontins extly two non zero entries (1 nd 1) every row n e omputed from the other rows, nd the mtrix A hs the sme rnk s the mtrix A 1 otined y ersing its lst row. Let A = A1 A 2 where A 1 is n (n 1) m mtrix nd A 2 is n 1 m mtrix. Atully A 1 nd A hve rnk n 1. Assume w.l.o.g. tht the (n 1) rnhes of the spnning tree U re the edges e 0 j = e j+(m n+1) for j 2 f1; : : : ; n 1g. Then A 1 = A 11 A 12 where A12 is the (n 1) (n 1) mtrix orresponding to the edges of the tree (the rnhes) nd A 11 is the (n 1) (m n + 1) mtrix orresponding to the other edges (the hords). The fundmentl utset C i of G determined y the edge e 0 i of the spnning tree is given y the i th row of the fundmentl utset mtrix Q f = A 1 12 A 1. This (n 1) m mtrix hs the form Q f 11 I n 1 where Ik is the identity mtrix of rnk k. The i th row of Q f ssoited with the fundmentl utset C i is n m vetor with entries in f 1; 0; 1g. Let p i nd X n p i e the two onneted omponents of G seprted y C i, suh tht e 0 i hs its soure in X n p i nd its trget in p i. Then for every j 2 f1; : : : ; mg, C i (j) = 0 if e j is not in C i, C i (j) = 1 if e j is oriented from X n p i to p i nd C i (j) = 1 if e j is oriented from p i to X n p i. A omplete exmple is shown in Fig. 8. It is worth noting tht the mtrix A 1 12 n e omputed diretly from G without inverting mtrix A 12, for it oinides with the pth mtrix P = [p i;j ] dened s follows. For eh j 2 f1; : : : ; n 1g, let j e the unique hin (in the tree U) onneting x j nd the referene node x n ; then for 1 i; j n 1, let p i;j = 0 if e 0 i does not elong to j, p i;j = 1 if e 0 i elongs to j nd is oriented towrds the referene node x n, nd p i;j = 1 if e 0 i elongs to j nd is oriented towrds node x j.

19 4.3 Cutset Representtion of Grphs The nodes of G my e oded injetively y f0; 1g vetors ording to their memership to the properties p j determined y the uts C j, resulting in n n (n 1) mtrix S = [s i;j ], lled the stte mtrix, suh tht s i;j = 1 if x i 2 p j, nd s i;j = 0 if x i 62 p j. Let S = [X 1 X n] t, where the X i re olumn vetors. The set fxi tj i ng of rows of S, representing nodes x i, together with the set fc i j i < ng of rows of Q f, representing fundmentl utsets, provide representtion of G. These dt re lso suient for retrieving the spnning tree. Atully, there is extly one wy to ssemle the row vetors C i into mtrix of the form Q f = Q f 11 I n 1 ; nd n ordered pir of vetors (Xk ; X l ) represents n edge e j = (x k ; x l ) if nd only if X l X k = Q f (; j). 4.4 Vrint Representtions A vrint representtion of G is given y the pir of mtries P nd Q f11. As mtter of ft, the redued inidene mtrix A 1 = A 11 A 12 my e omputed y A 12 = P 1 nd A 11 = P 1 Q f11. The pth mtrix P n in turn e reonstruted from X n nd the redued stte mtrix S = [X 1; : : : ; X n 1] t. Atully, for every j < n, X n = X j +P j where P j is the j th olumn of P (oding the hin j onneting x j nd x n ), hene the pth mtrix P nd the redued stte mtrix (n 1) times z } { S re onneted y the identity S t = [X n; : : : ; X n] - P. In prtiulr, S = P t if ll edges e 0 j of U re oriented wy from the referene node x n. 4.5 Fundmentl Cyles It hs some importne for the sequel to note tht the informtion provided y the fundmentl mtrix Q f is extly the sme s the informtion provided y the fundmentl yle mtrix 1 B f, dened s follows from the spnning tree U. Eh hord (i.e. edge in E n U) determines yle in G, onsisting of this edge nd the unique hin in U tht onnets its endpoints. This yle my e represented y n m vetor B i with entries in f 1; 0; 1g s follows: B i (j) = 0 if e j is not ontined in the yle, else B i (j) = 1 or 1 depending on whether the orienttion of e j grees with, or is opposite to the orienttion of e i within this yle. The fundmentl yle mtrix B f is the (m n + 1) m mtrix dened y B f (i; j) = B i (j). This mtrix is of the form B f = I m n+1 B f 12, where B f12 = Q t f11 (in prtiulr, rnh elongs to the fundmentl yle dened y hord if nd only if the hord elongs to the fundmentl utset dened y the rnh). Therefore, B f Q t f = 0, nd the vetor spes V B nd V Q respetively generted over IR y the fundmentl yles (rows of B f ) nd y the fundmentl utsets (rows of Q f ) re orthogonl. These two vetor spes, whih do not depend on the hoie of the spnning tree, re indeed orthogonl omplements of IR m. 1 lled fundmentl iruit mtrix in [16]

20 e s 5 1 s 2 e 2 e1 e 4 s 4 s 3 e 3 Inidene Mtrix: A = e 1 e 2 e 3 e 4 e 5 s s s s = 2 4 A11 A12 A n = 4 verties m = 5 edges r = n 1 = 3 rnk C 5 Pth Mtrix: s 1 s 2 s 3 P = A 1 12 = e e e e s 5 1 s 2 C 4 Fundmentl Cutset Mtrix: e1 e 2 s 4 e 3 s 3 e 4 Q f = e 1 e 2 e 3 e 4 e 5 C C C = Q f11 I r C 3 Stte Mtrix: Q f = P A 1 S = C 3 C 4 C 5 s s s s e s 5 1 s 2 1 e 2 e1 e 4 2 s 4 s 3 e 3 Fundmentl Cyle Mtrix: B f = e 1 e 2 e 3 e 4 e = I m r Bf12 B f 12 = Q t f 11 Fig. 8. fundmentl utsets nd yles

21 Every non null vetor in V B with entries 1, 0, nd 1 is sum of fundmentl yles nd/or inverses of fundmentl yles, hene it is either yle or n edge disjoint union of yles in vetor form. Similrly, every non null vetor C in V Q with entries 1, 0, nd 1 denes ut fe j j C(j) 6= 0g, ut C my dier y the sign of its omponents from the vetor whih represents this ut (nd lso from the opposite of this vetor). For ounterexmple, let C = ( 1; 1) where e 1 nd e 2 hve the sme trget nd distint soures. 4.6 Bk to set 2-Strutures We sw tht G my e represented y set of f0; 1g vetors expressing the set of properties of its nodes (x j 2 p i, X j (i) = 1), plus the set of the fundmentl utsets whih dene these properties (the utset C i dening p i is given y the i th row of Q f ). A node x j is then identied with the set of tokens x j = fij X j(i) = 1g; similrly, n edge e j = (x k ; x l ) is identied with the ordered pir e j = (x k ; x l ). We show tht the resulting prtil 2-struture G = (fx j x 2 Xg; fe j e 2 Eg; ) is tully isomorphi to the given grph G = (X; E; id E ). It is esily seen tht the ove representtion is injetive on nodes, sine two dierent nodes of the spnning tree re lwys seprted y fundmentl utset. In order to prove tht G = G, it sues therefore to show tht (e j ) = (e l ) entils e j = e l. We estlish stronger property, nmely: Lemm 4.1 Let e j = (x k ; x l ) e n edge of G, then for every pir of nodes x p nd x q, (x k ; x l ) = (x p ; x q) entils tht x p = x k nd x l = x q. Proof: Assuming the premises, let e hin onneting x p nd x q in the spnning tree U, represented y vetor 2 f 1; 0; 1g m y \orienting" the hin from x p to x q. Suppose (j) = 1, thus the edge e j is oriented wy from x q nd towrds x p in tht hin. Let p j e the property dened y the fundmentl utset whih inludes e j, then neessrily x p ; x l 2 p j nd x q ; x k 62 p j, hene x k n x l 6= x p n x q, ontrditing our ssumptions. Therefore, if we let 1 j denote the vetor with 1 t position j nd 0 elsewhere, the vetor 1 j hs ll entries in f 1; 0; 1g. Sine Q f mesures vritions of properties long, the ssumption (x k ; x l ) = (x p; x q) reds s Q f = Q f 1 j. Thus the vetor 1 j lies in V B, nd it is either yle or disjoint union of yles in vetor form. Sine there is no yle in U, it follows tht 1 j is yle, hene x p = x k nd x q = x l s ws to show. Now, ny set fp 0 1 ; : : : ; p0 n 1g of non trivil susets of X determines orresponding 2-struture G = (fx j x 2 Xg; fe j e 2 Eg; ), dened s ove y setting Xj = fij x j 2 p 0 i g nd (x k; x l ) = (x k ; x l ). For 1 i n 1, let C0 i denote the ut seprting the omplementry susets X np 0 i nd p0 i. We will show tht G = G whenever the orresponding vetors C1; 0 : : : ; Cn 1 0 re linerly independent. This is for instne the se when p 0 i = fx ig. Bewre of the ft tht G my e isomorphi to G even though C1; 0 : : : ; Cn 1 0 re not linerly independent. For n illustrtion, let p 0 1 = fx 2 ; x 3 g, p 0 2 = fx 2 ; x 4 g nd p 0 3 = fx 1 ; x 3 g in

22 G = (X; E) where X = fx 1 ; x 2 ; x 3 ; x 4 g nd E = fe 1 ; e 2 ; e 3 g with e i = (x 1 ; x i+1 ), then G = G ut C2 0 +C3 0 = 0. Notie tht in this representtion of G the vetors e 1, e 2, nd e 3 re not linerly independent: e e 2 + e 3 = 0 even though there is no yle in G. Assuming tht C1; 0 : : : ; Cn 1 0 re linerly independent, let us prove tht G = (X; E; id E ) nd G = (fx j x 2 Xg; fe j e 2 Eg; ) re isomorphi prtil 2-strutures. Let x k 6= x l nd ssume for ontrdition x k = x l. Let e the hin in U onneting the verties x k nd x l. By onstrution of the uts Ci, 0 Ci 0 = 0 for every i n 1. Sine C0 1 ; : : : ; C0 n 1 re linerly independent, they spn the vetor spe V Q nd is yle, thus x k = x l. It remins to show tht (e j ) = (e i ) entils e j = e i. Lemm 4.2 Let e j = (x k ; x l ) e n edge of G, then for every pir of nodes x p nd x q, (x k ; x l ) = (x p; x q) entils tht x p = x k nd x l = x q. Proof: Let e hin onneting x p nd x q in the spnning tree, represented y vetor 2 f 1; 0; 1g m y \orienting" the hin from x p to x q. Suppose (j) = 1, thus the edge e j is in nd it is oriented wy from x q nd towrds x p in tht hin. From the ssumption (x k ; x l ) = (x p ; x q) nd y onstrution of the uts C 0 i, it follows tht C 0 i = 1 j C 0 i for ll i n 1, where 1 j denotes the vetor with 1 t position j nd 0 elsewhere. Thus ( 1 j ) C 0 i = 0 for ll i, nd sine C 0 1 ; : : : ; C0 n 1 form sis of the vetor spe V Q, it follows tht ( 1 j ) C k = 0 for ll k n 1 nd in prtiulr for h = j (m n + 1). Now the rst m n + 1 entries of the vetor 1 j re zeros nd the lst n 1 entries of C h re zeros ut C h (j) whih is 1. Therefore, (j) = 1 nd we hve rehed ontrdition. Thus the vetor 1 j hs ll entries in f 1; 0; 1g. Sine ( 1 j ) C 0 i = 0 for ll i, the vetor 1 j lies in V B, nd it is either yle or disjoint union of yles in vetor form. Sine there is no yle in U, it follows tht 1 j is yle, hene x p = x k nd x q = x l s ws to show. We now give n exmple (see Fig. 9) showing tht the omputtion of uts nd utsets nnot led diretly to net representtion of G = (X; E). Let fp 1 ; p 2 g e 3 e 1 x 1 x 1 x 2 x 3 e 1 e 2 p p 2 p 1 fp 1 g e p x 2 e x 3 3 e 1 e 2 e 2 e 2 e 1 ; fp 2 g Fig. 9. elementry net system ssoited with sis of uts X = fx 1 ; x 2 ; x 3 g nd E = fe 1 ; e 2 ; e 3 g with e 1 = (x 1 ; x 2 ), e 2 = (x 1 ; x 3 ) nd e 3 = (x 2 ; x 3 ). A sis of uts for G is given y the vetors C 1 = (0; 1; 1)

Nondeterministic Automata vs Deterministic Automata

Nondeterministic Automata vs Deterministic Automata Nondeterministi Automt vs Deterministi Automt We lerned tht NFA is onvenient model for showing the reltionships mong regulr grmmrs, FA, nd regulr expressions, nd designing them. However, we know tht n

More information

Technische Universität München Winter term 2009/10 I7 Prof. J. Esparza / J. Křetínský / M. Luttenberger 11. Februar Solution

Technische Universität München Winter term 2009/10 I7 Prof. J. Esparza / J. Křetínský / M. Luttenberger 11. Februar Solution Tehnishe Universität Münhen Winter term 29/ I7 Prof. J. Esprz / J. Křetínský / M. Luttenerger. Ferur 2 Solution Automt nd Forml Lnguges Homework 2 Due 5..29. Exerise 2. Let A e the following finite utomton:

More information

CS311 Computational Structures Regular Languages and Regular Grammars. Lecture 6

CS311 Computational Structures Regular Languages and Regular Grammars. Lecture 6 CS311 Computtionl Strutures Regulr Lnguges nd Regulr Grmmrs Leture 6 1 Wht we know so fr: RLs re losed under produt, union nd * Every RL n e written s RE, nd every RE represents RL Every RL n e reognized

More information

Solutions for HW9. Bipartite: put the red vertices in V 1 and the black in V 2. Not bipartite!

Solutions for HW9. Bipartite: put the red vertices in V 1 and the black in V 2. Not bipartite! Solutions for HW9 Exerise 28. () Drw C 6, W 6 K 6, n K 5,3. C 6 : W 6 : K 6 : K 5,3 : () Whih of the following re iprtite? Justify your nswer. Biprtite: put the re verties in V 1 n the lk in V 2. Biprtite:

More information

CS 573 Automata Theory and Formal Languages

CS 573 Automata Theory and Formal Languages Non-determinism Automt Theory nd Forml Lnguges Professor Leslie Lnder Leture # 3 Septemer 6, 2 To hieve our gol, we need the onept of Non-deterministi Finite Automton with -moves (NFA) An NFA is tuple

More information

Discrete Structures, Test 2 Monday, March 28, 2016 SOLUTIONS, VERSION α

Discrete Structures, Test 2 Monday, March 28, 2016 SOLUTIONS, VERSION α Disrete Strutures, Test 2 Mondy, Mrh 28, 2016 SOLUTIONS, VERSION α α 1. (18 pts) Short nswer. Put your nswer in the ox. No prtil redit. () Consider the reltion R on {,,, d with mtrix digrph of R.. Drw

More information

NON-DETERMINISTIC FSA

NON-DETERMINISTIC FSA Tw o types of non-determinism: NON-DETERMINISTIC FS () Multiple strt-sttes; strt-sttes S Q. The lnguge L(M) ={x:x tkes M from some strt-stte to some finl-stte nd ll of x is proessed}. The string x = is

More information

The University of Nottingham SCHOOL OF COMPUTER SCIENCE A LEVEL 2 MODULE, SPRING SEMESTER MACHINES AND THEIR LANGUAGES ANSWERS

The University of Nottingham SCHOOL OF COMPUTER SCIENCE A LEVEL 2 MODULE, SPRING SEMESTER MACHINES AND THEIR LANGUAGES ANSWERS The University of ottinghm SCHOOL OF COMPUTR SCIC A LVL 2 MODUL, SPRIG SMSTR 2015 2016 MACHIS AD THIR LAGUAGS ASWRS Time llowed TWO hours Cndidtes my omplete the front over of their nswer ook nd sign their

More information

Hybrid Systems Modeling, Analysis and Control

Hybrid Systems Modeling, Analysis and Control Hyrid Systems Modeling, Anlysis nd Control Rdu Grosu Vienn University of Tehnology Leture 5 Finite Automt s Liner Systems Oservility, Rehility nd More Miniml DFA re Not Miniml NFA (Arnold, Diky nd Nivt

More information

= state, a = reading and q j

= state, a = reading and q j 4 Finite Automt CHAPTER 2 Finite Automt (FA) (i) Derterministi Finite Automt (DFA) A DFA, M Q, q,, F, Where, Q = set of sttes (finite) q Q = the strt/initil stte = input lphet (finite) (use only those

More information

1 PYTHAGORAS THEOREM 1. Given a right angled triangle, the square of the hypotenuse is equal to the sum of the squares of the other two sides.

1 PYTHAGORAS THEOREM 1. Given a right angled triangle, the square of the hypotenuse is equal to the sum of the squares of the other two sides. 1 PYTHAGORAS THEOREM 1 1 Pythgors Theorem In this setion we will present geometri proof of the fmous theorem of Pythgors. Given right ngled tringle, the squre of the hypotenuse is equl to the sum of the

More information

CS 491G Combinatorial Optimization Lecture Notes

CS 491G Combinatorial Optimization Lecture Notes CS 491G Comintoril Optimiztion Leture Notes Dvi Owen July 30, August 1 1 Mthings Figure 1: two possile mthings in simple grph. Definition 1 Given grph G = V, E, mthing is olletion of eges M suh tht e i,

More information

Finite State Automata and Determinisation

Finite State Automata and Determinisation Finite Stte Automt nd Deterministion Tim Dworn Jnury, 2016 Lnguges fs nf re df Deterministion 2 Outline 1 Lnguges 2 Finite Stte Automt (fs) 3 Non-deterministi Finite Stte Automt (nf) 4 Regulr Expressions

More information

Discrete Structures Lecture 11

Discrete Structures Lecture 11 Introdution Good morning. In this setion we study funtions. A funtion is mpping from one set to nother set or, perhps, from one set to itself. We study the properties of funtions. A mpping my not e funtion.

More information

Lecture Notes No. 10

Lecture Notes No. 10 2.6 System Identifition, Estimtion, nd Lerning Leture otes o. Mrh 3, 26 6 Model Struture of Liner ime Invrint Systems 6. Model Struture In representing dynmil system, the first step is to find n pproprite

More information

Chapter 4 State-Space Planning

Chapter 4 State-Space Planning Leture slides for Automted Plnning: Theory nd Prtie Chpter 4 Stte-Spe Plnning Dn S. Nu CMSC 722, AI Plnning University of Mrylnd, Spring 2008 1 Motivtion Nerly ll plnning proedures re serh proedures Different

More information

Bisimulation, Games & Hennessy Milner logic

Bisimulation, Games & Hennessy Milner logic Bisimultion, Gmes & Hennessy Milner logi Leture 1 of Modelli Mtemtii dei Proessi Conorrenti Pweł Soboiński Univeristy of Southmpton, UK Bisimultion, Gmes & Hennessy Milner logi p.1/32 Clssil lnguge theory

More information

LIP. Laboratoire de l Informatique du Parallélisme. Ecole Normale Supérieure de Lyon

LIP. Laboratoire de l Informatique du Parallélisme. Ecole Normale Supérieure de Lyon LIP Lortoire de l Informtique du Prllélisme Eole Normle Supérieure de Lyon Institut IMAG Unité de reherhe ssoiée u CNRS n 1398 One-wy Cellulr Automt on Cyley Grphs Zsuzsnn Rok Mrs 1993 Reserh Report N

More information

Descriptional Complexity of Non-Unary Self-Verifying Symmetric Difference Automata

Descriptional Complexity of Non-Unary Self-Verifying Symmetric Difference Automata Desriptionl Complexity of Non-Unry Self-Verifying Symmetri Differene Automt Lurette Mris 1,2 nd Lynette vn Zijl 1 1 Deprtment of Computer Siene, Stellenosh University, South Afri 2 Merk Institute, CSIR,

More information

where the box contains a finite number of gates from the given collection. Examples of gates that are commonly used are the following: a b

where the box contains a finite number of gates from the given collection. Examples of gates that are commonly used are the following: a b CS 294-2 9/11/04 Quntum Ciruit Model, Solovy-Kitev Theorem, BQP Fll 2004 Leture 4 1 Quntum Ciruit Model 1.1 Clssil Ciruits - Universl Gte Sets A lssil iruit implements multi-output oolen funtion f : {0,1}

More information

Figure 1. The left-handed and right-handed trefoils

Figure 1. The left-handed and right-handed trefoils The Knot Group A knot is n emedding of the irle into R 3 (or S 3 ), k : S 1 R 3. We shll ssume our knots re tme, mening the emedding n e extended to solid torus, K : S 1 D 2 R 3. The imge is lled tuulr

More information

Pre-Lie algebras, rooted trees and related algebraic structures

Pre-Lie algebras, rooted trees and related algebraic structures Pre-Lie lgers, rooted trees nd relted lgeri strutures Mrh 23, 2004 Definition 1 A pre-lie lger is vetor spe W with mp : W W W suh tht (x y) z x (y z) = (x z) y x (z y). (1) Exmple 2 All ssoitive lgers

More information

Counting Paths Between Vertices. Isomorphism of Graphs. Isomorphism of Graphs. Isomorphism of Graphs. Isomorphism of Graphs. Isomorphism of Graphs

Counting Paths Between Vertices. Isomorphism of Graphs. Isomorphism of Graphs. Isomorphism of Graphs. Isomorphism of Graphs. Isomorphism of Graphs Isomorphism of Grphs Definition The simple grphs G 1 = (V 1, E 1 ) n G = (V, E ) re isomorphi if there is ijetion (n oneto-one n onto funtion) f from V 1 to V with the property tht n re jent in G 1 if

More information

Petri Nets. Rebecca Albrecht. Seminar: Automata Theory Chair of Software Engeneering

Petri Nets. Rebecca Albrecht. Seminar: Automata Theory Chair of Software Engeneering Petri Nets Ree Alreht Seminr: Automt Theory Chir of Softwre Engeneering Overview 1. Motivtion: Why not just using finite utomt for everything? Wht re Petri Nets nd when do we use them? 2. Introdution:

More information

s the set of onsequenes. Skeptil onsequenes re more roust in the sense tht they hold in ll possile relities desried y defult theory. All its desirle p

s the set of onsequenes. Skeptil onsequenes re more roust in the sense tht they hold in ll possile relities desried y defult theory. All its desirle p Skeptil Rtionl Extensions Artur Mikitiuk nd Miros lw Truszzynski University of Kentuky, Deprtment of Computer Siene, Lexington, KY 40506{0046, frtur mirekg@s.engr.uky.edu Astrt. In this pper we propose

More information

Nondeterministic Finite Automata

Nondeterministic Finite Automata Nondeterministi Finite utomt The Power of Guessing Tuesdy, Otoer 4, 2 Reding: Sipser.2 (first prt); Stoughton 3.3 3.5 S235 Lnguges nd utomt eprtment of omputer Siene Wellesley ollege Finite utomton (F)

More information

Graph Theory. Simple Graph G = (V, E). V={a,b,c,d,e,f,g,h,k} E={(a,b),(a,g),( a,h),(a,k),(b,c),(b,k),...,(h,k)}

Graph Theory. Simple Graph G = (V, E). V={a,b,c,d,e,f,g,h,k} E={(a,b),(a,g),( a,h),(a,k),(b,c),(b,k),...,(h,k)} Grph Theory Simple Grph G = (V, E). V ={verties}, E={eges}. h k g f e V={,,,,e,f,g,h,k} E={(,),(,g),(,h),(,k),(,),(,k),...,(h,k)} E =16. 1 Grph or Multi-Grph We llow loops n multiple eges. G = (V, E.ψ)

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

Logic Synthesis and Verification

Logic Synthesis and Verification Logi Synthesis nd Verifition SOPs nd Inompletely Speified Funtions Jie-Hong Rolnd Jing 江介宏 Deprtment of Eletril Engineering Ntionl Tiwn University Fll 2010 Reding: Logi Synthesis in Nutshell Setion 2 most

More information

6.5 Improper integrals

6.5 Improper integrals Eerpt from "Clulus" 3 AoPS In. www.rtofprolemsolving.om 6.5. IMPROPER INTEGRALS 6.5 Improper integrls As we ve seen, we use the definite integrl R f to ompute the re of the region under the grph of y =

More information

MAT 403 NOTES 4. f + f =

MAT 403 NOTES 4. f + f = MAT 403 NOTES 4 1. Fundmentl Theorem o Clulus We will proo more generl version o the FTC thn the textook. But just like the textook, we strt with the ollowing proposition. Let R[, ] e the set o Riemnn

More information

AP Calculus BC Chapter 8: Integration Techniques, L Hopital s Rule and Improper Integrals

AP Calculus BC Chapter 8: Integration Techniques, L Hopital s Rule and Improper Integrals AP Clulus BC Chpter 8: Integrtion Tehniques, L Hopitl s Rule nd Improper Integrls 8. Bsi Integrtion Rules In this setion we will review vrious integrtion strtegies. Strtegies: I. Seprte the integrnd into

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

Necessary and sucient conditions for some two. Abstract. Further we show that the necessary conditions for the existence of an OD(44 s 1 s 2 )

Necessary and sucient conditions for some two. Abstract. Further we show that the necessary conditions for the existence of an OD(44 s 1 s 2 ) Neessry n suient onitions for some two vrile orthogonl esigns in orer 44 C. Koukouvinos, M. Mitrouli y, n Jennifer Seerry z Deite to Professor Anne Penfol Street Astrt We give new lgorithm whih llows us

More information

#A42 INTEGERS 11 (2011) ON THE CONDITIONED BINOMIAL COEFFICIENTS

#A42 INTEGERS 11 (2011) ON THE CONDITIONED BINOMIAL COEFFICIENTS #A42 INTEGERS 11 (2011 ON THE CONDITIONED BINOMIAL COEFFICIENTS Liqun To Shool of Mthemtil Sienes, Luoyng Norml University, Luoyng, Chin lqto@lynuedun Reeived: 12/24/10, Revised: 5/11/11, Aepted: 5/16/11,

More information

Arrow s Impossibility Theorem

Arrow s Impossibility Theorem Rep Voting Prdoxes Properties Arrow s Theorem Arrow s Impossiility Theorem Leture 12 Arrow s Impossiility Theorem Leture 12, Slide 1 Rep Voting Prdoxes Properties Arrow s Theorem Leture Overview 1 Rep

More information

18.06 Problem Set 4 Due Wednesday, Oct. 11, 2006 at 4:00 p.m. in 2-106

18.06 Problem Set 4 Due Wednesday, Oct. 11, 2006 at 4:00 p.m. in 2-106 8. Problem Set Due Wenesy, Ot., t : p.m. in - Problem Mony / Consier the eight vetors 5, 5, 5,..., () List ll of the one-element, linerly epenent sets forme from these. (b) Wht re the two-element, linerly

More information

A CLASS OF GENERAL SUPERTREE METHODS FOR NESTED TAXA

A CLASS OF GENERAL SUPERTREE METHODS FOR NESTED TAXA A CLASS OF GENERAL SUPERTREE METHODS FOR NESTED TAXA PHILIP DANIEL AND CHARLES SEMPLE Astrt. Amlgmting smller evolutionry trees into single prent tree is n importnt tsk in evolutionry iology. Trditionlly,

More information

A Lower Bound for the Length of a Partial Transversal in a Latin Square, Revised Version

A Lower Bound for the Length of a Partial Transversal in a Latin Square, Revised Version A Lower Bound for the Length of Prtil Trnsversl in Ltin Squre, Revised Version Pooy Htmi nd Peter W. Shor Deprtment of Mthemtil Sienes, Shrif University of Tehnology, P.O.Bo 11365-9415, Tehrn, Irn Deprtment

More information

Arrow s Impossibility Theorem

Arrow s Impossibility Theorem Rep Fun Gme Properties Arrow s Theorem Arrow s Impossiility Theorem Leture 12 Arrow s Impossiility Theorem Leture 12, Slide 1 Rep Fun Gme Properties Arrow s Theorem Leture Overview 1 Rep 2 Fun Gme 3 Properties

More information

Part 4. Integration (with Proofs)

Part 4. Integration (with Proofs) Prt 4. Integrtion (with Proofs) 4.1 Definition Definition A prtition P of [, b] is finite set of points {x 0, x 1,..., x n } with = x 0 < x 1

More information

Project 6: Minigoals Towards Simplifying and Rewriting Expressions

Project 6: Minigoals Towards Simplifying and Rewriting Expressions MAT 51 Wldis Projet 6: Minigols Towrds Simplifying nd Rewriting Expressions The distriutive property nd like terms You hve proly lerned in previous lsses out dding like terms ut one prolem with the wy

More information

Mid-Term Examination - Spring 2014 Mathematical Programming with Applications to Economics Total Score: 45; Time: 3 hours

Mid-Term Examination - Spring 2014 Mathematical Programming with Applications to Economics Total Score: 45; Time: 3 hours Mi-Term Exmintion - Spring 0 Mthemtil Progrmming with Applitions to Eonomis Totl Sore: 5; Time: hours. Let G = (N, E) e irete grph. Define the inegree of vertex i N s the numer of eges tht re oming into

More information

Compiler Design. Spring Lexical Analysis. Sample Exercises and Solutions. Prof. Pedro C. Diniz

Compiler Design. Spring Lexical Analysis. Sample Exercises and Solutions. Prof. Pedro C. Diniz University of Southern Cliforni Computer Siene Deprtment Compiler Design Spring 7 Lexil Anlysis Smple Exerises nd Solutions Prof. Pedro C. Diniz USC / Informtion Sienes Institute 47 Admirlty Wy, Suite

More information

System Validation (IN4387) November 2, 2012, 14:00-17:00

System Validation (IN4387) November 2, 2012, 14:00-17:00 System Vlidtion (IN4387) Novemer 2, 2012, 14:00-17:00 Importnt Notes. The exmintion omprises 5 question in 4 pges. Give omplete explntion nd do not onfine yourself to giving the finl nswer. Good luk! Exerise

More information

y1 y2 DEMUX a b x1 x2 x3 x4 NETWORK s1 s2 z1 z2

y1 y2 DEMUX a b x1 x2 x3 x4 NETWORK s1 s2 z1 z2 BOOLEAN METHODS Giovnni De Miheli Stnford University Boolen methods Exploit Boolen properties. { Don't re onditions. Minimiztion of the lol funtions. Slower lgorithms, etter qulity results. Externl don't

More information

Engr354: Digital Logic Circuits

Engr354: Digital Logic Circuits Engr354: Digitl Logi Ciruits Chpter 4: Logi Optimiztion Curtis Nelson Logi Optimiztion In hpter 4 you will lern out: Synthesis of logi funtions; Anlysis of logi iruits; Tehniques for deriving minimum-ost

More information

Introduction to Olympiad Inequalities

Introduction to Olympiad Inequalities Introdution to Olympid Inequlities Edutionl Studies Progrm HSSP Msshusetts Institute of Tehnology Snj Simonovikj Spring 207 Contents Wrm up nd Am-Gm inequlity 2. Elementry inequlities......................

More information

CIT 596 Theory of Computation 1. Graphs and Digraphs

CIT 596 Theory of Computation 1. Graphs and Digraphs CIT 596 Theory of Computtion 1 A grph G = (V (G), E(G)) onsists of two finite sets: V (G), the vertex set of the grph, often enote y just V, whih is nonempty set of elements lle verties, n E(G), the ege

More information

T b a(f) [f ] +. P b a(f) = Conclude that if f is in AC then it is the difference of two monotone absolutely continuous functions.

T b a(f) [f ] +. P b a(f) = Conclude that if f is in AC then it is the difference of two monotone absolutely continuous functions. Rel Vribles, Fll 2014 Problem set 5 Solution suggestions Exerise 1. Let f be bsolutely ontinuous on [, b] Show tht nd T b (f) P b (f) f (x) dx [f ] +. Conlude tht if f is in AC then it is the differene

More information

Chapter 3. Vector Spaces. 3.1 Images and Image Arithmetic

Chapter 3. Vector Spaces. 3.1 Images and Image Arithmetic Chpter 3 Vetor Spes In Chpter 2, we sw tht the set of imges possessed numer of onvenient properties. It turns out tht ny set tht possesses similr onvenient properties n e nlyzed in similr wy. In liner

More information

Unit 4. Combinational Circuits

Unit 4. Combinational Circuits Unit 4. Comintionl Ciruits Digitl Eletroni Ciruits (Ciruitos Eletrónios Digitles) E.T.S.I. Informáti Universidd de Sevill 5/10/2012 Jorge Jun 2010, 2011, 2012 You re free to opy, distriute

More information

Linear choosability of graphs

Linear choosability of graphs Liner hoosility of grphs Louis Esperet, Mikel Montssier, André Rspud To ite this version: Louis Esperet, Mikel Montssier, André Rspud. Liner hoosility of grphs. Stefn Felsner. 2005 Europen Conferene on

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

Section 4.4. Green s Theorem

Section 4.4. Green s Theorem The Clulus of Funtions of Severl Vriles Setion 4.4 Green s Theorem Green s theorem is n exmple from fmily of theorems whih onnet line integrls (nd their higher-dimensionl nlogues) with the definite integrls

More information

The Word Problem in Quandles

The Word Problem in Quandles The Word Prolem in Qundles Benjmin Fish Advisor: Ren Levitt April 5, 2013 1 1 Introdution A word over n lger A is finite sequene of elements of A, prentheses, nd opertions of A defined reursively: Given

More information

Math 32B Discussion Session Week 8 Notes February 28 and March 2, f(b) f(a) = f (t)dt (1)

Math 32B Discussion Session Week 8 Notes February 28 and March 2, f(b) f(a) = f (t)dt (1) Green s Theorem Mth 3B isussion Session Week 8 Notes Februry 8 nd Mrh, 7 Very shortly fter you lerned how to integrte single-vrible funtions, you lerned the Fundmentl Theorem of lulus the wy most integrtion

More information

Regular languages refresher

Regular languages refresher Regulr lnguges refresher 1 Regulr lnguges refresher Forml lnguges Alphet = finite set of letters Word = sequene of letter Lnguge = set of words Regulr lnguges defined equivlently y Regulr expressions Finite-stte

More information

Graph States EPIT Mehdi Mhalla (Calgary, Canada) Simon Perdrix (Grenoble, France)

Graph States EPIT Mehdi Mhalla (Calgary, Canada) Simon Perdrix (Grenoble, France) Grph Sttes EPIT 2005 Mehdi Mhll (Clgry, Cnd) Simon Perdrix (Grenole, Frne) simon.perdrix@img.fr Grph Stte: Introdution A grph-sed representtion of the entnglement of some (lrge) quntum stte. Verties: quits

More information

April 8, 2017 Math 9. Geometry. Solving vector problems. Problem. Prove that if vectors and satisfy, then.

April 8, 2017 Math 9. Geometry. Solving vector problems. Problem. Prove that if vectors and satisfy, then. pril 8, 2017 Mth 9 Geometry Solving vetor prolems Prolem Prove tht if vetors nd stisfy, then Solution 1 onsider the vetor ddition prllelogrm shown in the Figure Sine its digonls hve equl length,, the prllelogrm

More information

INTEGRATION. 1 Integrals of Complex Valued functions of a REAL variable

INTEGRATION. 1 Integrals of Complex Valued functions of a REAL variable INTEGRATION NOTE: These notes re supposed to supplement Chpter 4 of the online textbook. 1 Integrls of Complex Vlued funtions of REAL vrible If I is n intervl in R (for exmple I = [, b] or I = (, b)) nd

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

A negative answer to a question of Wilke on varieties of!-languages

A negative answer to a question of Wilke on varieties of!-languages A negtive nswer to question of Wilke on vrieties of!-lnguges Jen-Eric Pin () Astrct. In recent pper, Wilke sked whether the oolen comintions of!-lnguges of the form! L, for L in given +-vriety of lnguges,

More information

Free groups, Lecture 2, part 1

Free groups, Lecture 2, part 1 Free groups, Lecture 2, prt 1 Olg Khrlmpovich NYC, Sep. 2 1 / 22 Theorem Every sugroup H F of free group F is free. Given finite numer of genertors of H we cn compute its sis. 2 / 22 Schreir s grph The

More information

Topologie en Meetkunde 2011 Lecturers: Marius Crainic and Ivan Struchiner

Topologie en Meetkunde 2011 Lecturers: Marius Crainic and Ivan Struchiner Topologie en Meetkunde 21 Leturers: Mrius Crini nd Ivn Struhiner CHAPTER 1 The Clssifition Prolem for Compt Surfes 1. Introdution In this Chpter we will introdue nd strt deling with the lssifition prolem

More information

Section 1.3 Triangles

Section 1.3 Triangles Se 1.3 Tringles 21 Setion 1.3 Tringles LELING TRINGLE The line segments tht form tringle re lled the sides of the tringle. Eh pir of sides forms n ngle, lled n interior ngle, nd eh tringle hs three interior

More information

Transition systems (motivation)

Transition systems (motivation) Trnsition systems (motivtion) Course Modelling of Conurrent Systems ( Modellierung neenläufiger Systeme ) Winter Semester 2009/0 University of Duisurg-Essen Brr König Tehing ssistnt: Christoph Blume In

More information

Matrices SCHOOL OF ENGINEERING & BUILT ENVIRONMENT. Mathematics (c) 1. Definition of a Matrix

Matrices SCHOOL OF ENGINEERING & BUILT ENVIRONMENT. Mathematics (c) 1. Definition of a Matrix tries Definition of tri mtri is regulr rry of numers enlosed inside rkets SCHOOL OF ENGINEERING & UIL ENVIRONEN Emple he following re ll mtries: ), ) 9, themtis ), d) tries Definition of tri Size of tri

More information

Exercise sheet 6: Solutions

Exercise sheet 6: Solutions Eerise sheet 6: Solutions Cvet emptor: These re merel etended hints, rther thn omplete solutions. 1. If grph G hs hromti numer k > 1, prove tht its verte set n e prtitioned into two nonempt sets V 1 nd

More information

Green s Theorem. (2x e y ) da. (2x e y ) dx dy. x 2 xe y. (1 e y ) dy. y=1. = y e y. y=0. = 2 e

Green s Theorem. (2x e y ) da. (2x e y ) dx dy. x 2 xe y. (1 e y ) dy. y=1. = y e y. y=0. = 2 e Green s Theorem. Let be the boundry of the unit squre, y, oriented ounterlokwise, nd let F be the vetor field F, y e y +, 2 y. Find F d r. Solution. Let s write P, y e y + nd Q, y 2 y, so tht F P, Q. Let

More information

Solutions to Assignment 1

Solutions to Assignment 1 MTHE 237 Fll 2015 Solutions to Assignment 1 Problem 1 Find the order of the differentil eqution: t d3 y dt 3 +t2 y = os(t. Is the differentil eqution liner? Is the eqution homogeneous? b Repet the bove

More information

arxiv: v1 [math.ca] 21 Aug 2018

arxiv: v1 [math.ca] 21 Aug 2018 rxiv:1808.07159v1 [mth.ca] 1 Aug 018 Clulus on Dul Rel Numbers Keqin Liu Deprtment of Mthemtis The University of British Columbi Vnouver, BC Cnd, V6T 1Z Augest, 018 Abstrt We present the bsi theory of

More information

Test Generation from Timed Input Output Automata

Test Generation from Timed Input Output Automata Chpter 8 Test Genertion from Timed Input Output Automt The purpose of this hpter is to introdue tehniques for the genertion of test dt from models of softwre sed on vrints of timed utomt. The tests generted

More information

TOPIC: LINEAR ALGEBRA MATRICES

TOPIC: LINEAR ALGEBRA MATRICES Interntionl Blurete LECTUE NOTES for FUTHE MATHEMATICS Dr TOPIC: LINEA ALGEBA MATICES. DEFINITION OF A MATIX MATIX OPEATIONS.. THE DETEMINANT deta THE INVESE A -... SYSTEMS OF LINEA EQUATIONS. 8. THE AUGMENTED

More information

Prefix-Free Regular-Expression Matching

Prefix-Free Regular-Expression Matching Prefix-Free Regulr-Expression Mthing Yo-Su Hn, Yjun Wng nd Derik Wood Deprtment of Computer Siene HKUST Prefix-Free Regulr-Expression Mthing p.1/15 Pttern Mthing Given pttern P nd text T, find ll sustrings

More information

Random subgroups of a free group

Random subgroups of a free group Rndom sugroups of free group Frédérique Bssino LIPN - Lortoire d Informtique de Pris Nord, Université Pris 13 - CNRS Joint work with Armndo Mrtino, Cyril Nicud, Enric Ventur et Pscl Weil LIX My, 2015 Introduction

More information

CS 2204 DIGITAL LOGIC & STATE MACHINE DESIGN SPRING 2014

CS 2204 DIGITAL LOGIC & STATE MACHINE DESIGN SPRING 2014 S 224 DIGITAL LOGI & STATE MAHINE DESIGN SPRING 214 DUE : Mrh 27, 214 HOMEWORK III READ : Relte portions of hpters VII n VIII ASSIGNMENT : There re three questions. Solve ll homework n exm prolems s shown

More information

Intermediate Math Circles Wednesday 17 October 2012 Geometry II: Side Lengths

Intermediate Math Circles Wednesday 17 October 2012 Geometry II: Side Lengths Intermedite Mth Cirles Wednesdy 17 Otoer 01 Geometry II: Side Lengths Lst week we disussed vrious ngle properties. As we progressed through the evening, we proved mny results. This week, we will look t

More information

Algorithms & Data Structures Homework 8 HS 18 Exercise Class (Room & TA): Submitted by: Peer Feedback by: Points:

Algorithms & Data Structures Homework 8 HS 18 Exercise Class (Room & TA): Submitted by: Peer Feedback by: Points: Eidgenössishe Tehnishe Hohshule Zürih Eole polytehnique fédérle de Zurih Politenio federle di Zurigo Federl Institute of Tehnology t Zurih Deprtement of Computer Siene. Novemer 0 Mrkus Püshel, Dvid Steurer

More information

Abstraction of Nondeterministic Automata Rong Su

Abstraction of Nondeterministic Automata Rong Su Astrtion of Nondeterministi Automt Rong Su My 6, 2010 TU/e Mehnil Engineering, Systems Engineering Group 1 Outline Motivtion Automton Astrtion Relevnt Properties Conlusions My 6, 2010 TU/e Mehnil Engineering,

More information

22: Union Find. CS 473u - Algorithms - Spring April 14, We want to maintain a collection of sets, under the operations of:

22: Union Find. CS 473u - Algorithms - Spring April 14, We want to maintain a collection of sets, under the operations of: 22: Union Fin CS 473u - Algorithms - Spring 2005 April 14, 2005 1 Union-Fin We wnt to mintin olletion of sets, uner the opertions of: 1. MkeSet(x) - rete set tht ontins the single element x. 2. Fin(x)

More information

CONTROLLABILITY and observability are the central

CONTROLLABILITY and observability are the central 1 Complexity of Infiml Oservle Superlnguges Tomáš Msopust Astrt The infiml prefix-losed, ontrollle nd oservle superlnguge plys n essentil role in the reltionship etween ontrollility, oservility nd o-oservility

More information

Linear Algebra Introduction

Linear Algebra Introduction Introdution Wht is Liner Alger out? Liner Alger is rnh of mthemtis whih emerged yers k nd ws one of the pioneer rnhes of mthemtis Though, initilly it strted with solving of the simple liner eqution x +

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

dx dt dy = G(t, x, y), dt where the functions are defined on I Ω, and are locally Lipschitz w.r.t. variable (x, y) Ω.

dx dt dy = G(t, x, y), dt where the functions are defined on I Ω, and are locally Lipschitz w.r.t. variable (x, y) Ω. Chpter 8 Stility theory We discuss properties of solutions of first order two dimensionl system, nd stility theory for specil clss of liner systems. We denote the independent vrile y t in plce of x, nd

More information

Speech Recognition Lecture 2: Finite Automata and Finite-State Transducers. Mehryar Mohri Courant Institute and Google Research

Speech Recognition Lecture 2: Finite Automata and Finite-State Transducers. Mehryar Mohri Courant Institute and Google Research Speech Recognition Lecture 2: Finite Automt nd Finite-Stte Trnsducers Mehryr Mohri Cournt Institute nd Google Reserch mohri@cims.nyu.com Preliminries Finite lphet Σ, empty string. Set of ll strings over

More information

Behavior Composition in the Presence of Failure

Behavior Composition in the Presence of Failure Behvior Composition in the Presene of Filure Sestin Srdin RMIT University, Melourne, Austrli Fio Ptrizi & Giuseppe De Giomo Spienz Univ. Rom, Itly KR 08, Sept. 2008, Sydney Austrli Introdution There re

More information

Unfoldings of Networks of Timed Automata

Unfoldings of Networks of Timed Automata Unfolings of Networks of Time Automt Frnk Cssez Thoms Chtin Clue Jr Ptrii Bouyer Serge H Pierre-Alin Reynier Rennes, Deemer 3, 2008 Unfolings [MMilln 93] First efine for Petri nets Then extene to other

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

2.4 Theoretical Foundations

2.4 Theoretical Foundations 2 Progrmming Lnguge Syntx 2.4 Theoretil Fountions As note in the min text, snners n prsers re se on the finite utomt n pushown utomt tht form the ottom two levels of the Chomsky lnguge hierrhy. At eh level

More information

QUADRATIC EQUATION. Contents

QUADRATIC EQUATION. Contents QUADRATIC EQUATION Contents Topi Pge No. Theory 0-04 Exerise - 05-09 Exerise - 09-3 Exerise - 3 4-5 Exerise - 4 6 Answer Key 7-8 Syllus Qudrti equtions with rel oeffiients, reltions etween roots nd oeffiients,

More information

Lecture 08: Feb. 08, 2019

Lecture 08: Feb. 08, 2019 4CS4-6:Theory of Computtion(Closure on Reg. Lngs., regex to NDFA, DFA to regex) Prof. K.R. Chowdhry Lecture 08: Fe. 08, 2019 : Professor of CS Disclimer: These notes hve not een sujected to the usul scrutiny

More information

Model Reduction of Finite State Machines by Contraction

Model Reduction of Finite State Machines by Contraction Model Reduction of Finite Stte Mchines y Contrction Alessndro Giu Dip. di Ingegneri Elettric ed Elettronic, Università di Cgliri, Pizz d Armi, 09123 Cgliri, Itly Phone: +39-070-675-5892 Fx: +39-070-675-5900

More information

Automatic Synthesis of New Behaviors from a Library of Available Behaviors

Automatic Synthesis of New Behaviors from a Library of Available Behaviors Automti Synthesis of New Behviors from Lirry of Aville Behviors Giuseppe De Giomo Università di Rom L Spienz, Rom, Itly degiomo@dis.unirom1.it Sestin Srdin RMIT University, Melourne, Austrli ssrdin@s.rmit.edu.u

More information

TIME AND STATE IN DISTRIBUTED SYSTEMS

TIME AND STATE IN DISTRIBUTED SYSTEMS Distriuted Systems Fö 5-1 Distriuted Systems Fö 5-2 TIME ND STTE IN DISTRIUTED SYSTEMS 1. Time in Distriuted Systems Time in Distriuted Systems euse eh mhine in distriuted system hs its own lok there is

More information

arxiv: v1 [math.gr] 11 Jan 2019

arxiv: v1 [math.gr] 11 Jan 2019 The Generlized Dehn Property does not imply liner isoperimetri inequlity Owen Bker nd Timothy Riley rxiv:1901.03767v1 [mth.gr] 11 Jn 2019 Jnury 15, 2019 Astrt The Dehn property for omplex is tht every

More information

Symmetrical Components 1

Symmetrical Components 1 Symmetril Components. Introdution These notes should e red together with Setion. of your text. When performing stedy-stte nlysis of high voltge trnsmission systems, we mke use of the per-phse equivlent

More information

THE PYTHAGOREAN THEOREM

THE PYTHAGOREAN THEOREM THE PYTHAGOREAN THEOREM The Pythgoren Theorem is one of the most well-known nd widely used theorems in mthemtis. We will first look t n informl investigtion of the Pythgoren Theorem, nd then pply this

More information

An algebraic characterization of strictly piecewise languages

An algebraic characterization of strictly piecewise languages An lgeri hrteriztion of stritly pieewise lnguges Jie Fu, Jeffrey Heinz, nd Herert G. Tnner University of Delwre jiefu,heinz,tnner@udel.edu Astrt. This pper provides n lgeri hrteriztion of the Stritly Pieewise

More information

CS241 Week 6 Tutorial Solutions

CS241 Week 6 Tutorial Solutions 241 Week 6 Tutoril olutions Lnguges: nning & ontext-free Grmmrs Winter 2018 1 nning Exerises 1. 0x0x0xd HEXINT 0x0 I x0xd 2. 0xend--- HEXINT 0xe I nd ER -- MINU - 3. 1234-120x INT 1234 INT -120 I x 4.

More information