Subsequence Automata with Default Transitions

Size: px
Start display at page:

Download "Subsequence Automata with Default Transitions"

Transcription

1 Susequene Automt with Defult Trnsitions Philip Bille, Inge Li Gørtz, n Freerik Rye Skjoljensen Tehnil University of Denmrk {phi,inge,fskj}@tu.k Astrt. Let S e string of length n with hrters from n lphet of size σ. The susequene utomton (often lle the irete yli susequene grph) is the miniml eterministi finite utomton epting ll susequenes of S. A strightforwr onstrution shows tht the size (numer of sttes n trnsitions) of the susequene utomton is O(nσ) n tht this oun is symptotilly optiml. In this pper, we onsier susequene utomt with efult trnsitions, tht is, speil trnsitions to e tken only if none of the regulr trnsitions mth the urrent hrter, n whih o not onsume the urrent hrter. We show tht with efult trnsitions, muh smller susequene utomt re possile, n provie full tre-off etween the size of the utomton n the ely, i.e., the mximum numer of efult trnsition followe efore onsuming hrter. Speifilly, given ny integer prmeter k, 1 < k σ, we present susequene utomton with filure trnsition of size O(nk log k σ) n ely O(log k σ). Hene, with k = 2 we otin n utomton of size O(n log σ) n ely O(log σ). On the other extreme, with k = σ, we otin n utomton of size O(nσ) n ely O(1), thus mthing the oun for the stnr susequene utomton onstrution. The key omponent of our result is novel hierrhil utomt onstrution of inepenent interest. 1 Introution Let S e string of length n with hrters from n lphet of size σ. A susequene of S is ny string otine y eleting zero or more hrters from S. The susequene utomton (often lle the irete yli susequene grph) is the miniml eterministi finite utomton epting ll susequenes of S. Bez-Ytes [1] initite the stuy of susequene utomt. He presente simple onstrution using O(nσ) size (size enotes the totl numer of sttes n trnsitions) n showe tht this oun is optiml in the sense tht there re susequene utomt of size t lest Ω(nσ). He lso onsiere vritions with enoe input strings n multiple strings. Susequently, severl reserhers hve further stuie susequene utomt (n its vrints) [2 9]. See lso the surveys y Troníček n others [10, 11]. The generl prolem of susequene inexing, not limite to utomt se solutions, is investigte y Bille et l. [12].

2 In this pper, we onsier susequene utomt in the ontext of efult trnsitions, tht is, speil trnsitions to e tken only if none of the regulr trnsitions mth the urrent hrter, n whih o not onsume the urrent hrter. Eh stte hs t most one efult trnsition n hene the utomton remins eterministi. The key point of efult trnsitions is to reue the size of stnr utomt t the ost of introuing ely, i.e., the mximum numer of efult trnsition followe efore onsuming hrter. For instne, given pttern string of length m the lssi Knuth-Morris-Prtt (KMP) [13] string mthing lgorithm my e viewe s n utomton with efult trnsitions (typilly referre to s filure trnsitions). This utomton hs size O(m), wheres the stnr utomton with no efult trnsition woul nee Θ(mσ) spe. The ely of the utomton in the KMP lgorithm is either O(m) or O(log m) epening on the version. Similrly, the Aho-Corsik string mthing lgorithm for multiple strings my lso e viewe s n utomton with efult trnsitions [14]. More reently, efult trnsitions hve lso een use extensively to signifintly reue sizes of eterministi utomt for regulr expression [15, 16]. The min ie is to effetively enle sttes with lrge overlpping ientil sets of outgoing trnsitions to shre outgoing trnsitions using efult trnsitions. Surprisingly, no non-trivil ouns for susequene utomt with efult trnsitions re known. Nively, we n immeitely otin n O(nσ) size solution with O(1) ely y using the stnr susequene utomton (without efult trnsitions). At the other extreme, we n uil n utomton with n + 1 sttes (eh orresponing to prefix of S) with stnr n efult trnsition from the stte orresponing to the ith prefix to the stte orresponing to the i + 1st prefix (the stnr trnsition is lele S[i + 1]). It is strightforwr to show tht this les to n O(n) size solution with O(n) ely. Our min result is sustntilly improve tre-off etween the size n ely of the susequene utomton: Theorem 1. Let S e string of n hrters from n lphet of size σ. For ny integer prmeter k, 1 < k σ, we n onstrut susequene utomton with efult trnsitions of size O(nk log k σ) n ely O(log k σ). Hene, with k = 2 we otin n utomton of size O(n log σ) n ely O(log σ). On the other extreme, with k = σ, we otin n utomton of size O(nσ) n ely O(1), thus mthing the oun for the stnr susequene utomton onstrution. To otin our result, we first introue the level utomton. Intuitively, this utomton uses the sme sttes s the stnr solution, ut hierrhilly orers them in tree-like struture n smples seletion of their originl trnsitions se on their position in the tree, n s efult trnsition to the next stte on higher level. We show how to o this effiiently leing to solution with O(n log n) size n O(log n) ely. To hieve our full treoff from Theorem 1 we show how to ugment the onstrution with itionl triks for smll lphets n generlize the level utomton with prmeter k,

3 1 < k σ, where lrge k reues the height of the tree ut inreses the numer of trnsitions. 2 Preliminries A eterministi finite utomton (DFA) is tuple A = (Q, Σ, δ, q 0, F ) where Q is set of noes lle sttes, δ is set of lele irete eges etween sttes lle trnsitions where eh lel is hrter from the lphet Σ, q 0 Q is the initil stte n F Q is set of epting sttes. No outgoing trnsitions from the sme stte hve the sme lel. The size of A is the sum of the numer of sttes n trnsitions. We n think of A s n ege-lele irete grph. Given string P n pth p in A we sy tht p n P mth if the ontention of the lels on the trnsitions in p is P. We sy tht A epts string P if there is pth in A, from q 0 to ny stte in F, tht mthes P. Otherwise A rejets P. A eterministi finite utomton with efult trnsitions is eterministi finite utomton AD where eh stte n hve single unlele efult trnsition. Given string P n pth p in AD we efine mth etween P n p s efore, with the exeption tht for ny efult trnsition in p the orresponing hrter in P nnot mth ny stnr trnsition out of the strt stte of. Definition of epte n rejete strings re s efore. The ely of AD is the mximum length of ny pth tht only uses efult trnsitions. A susequene of S is string P, otine y removing zero or more hrters from S. A susequene utomton onstrute from S, enote SA, is eterministi finite utomton tht epts string P iff P is susequene of S. The SA is often lle the irete yli susequene grph or DASG. The SA hs n + 1 sttes, ll epting, tht we ientify with the integers {0, 1,..., n}. For eh stte s, 0 s n, we hve the following trnsitions: For eh unique hrter α in S[s + 1, n], there is trnsition lele α to the smllest stte s > s suh tht S[s ] = α. The SA hs size O(nσ) sine every stte n hve t most σ trnsitions. An exmple of n SA is given in Fig. 1. A susequene utomton with efult trnsitions onstrute from S, enote SAD, is eterministi finite utomton with efult trnsitions tht epts string P iff P is susequene of S. The next setion explores ifferent onfigurtions of trnsitions n efult trnsitions in SADs. 3 New Tre-Offs for Susequene Automt. We now present new tre-off for susequene utomt, with efult trnsitions. We will grully refine our onstrution until we otin n utomton tht gives the result presente in Theorem 1. In eh onstrution we hve n + 1

4 Fig. 1. An exmple of n SA onstrute from the string. sttes tht we ientify with the integers {0, 1,..., n}. Eh of these sttes represents prefix of the string S n re ll epting sttes. We first present the level utomton tht gives the first non-trivil tre-off tht exploits efult trnsitions. The generl ie is to onstrut hierrhy of sttes, suh tht every pth tht only uses efult trnsitions is gurntee to go through sttes where the outegree inreses t lest exponentilly. The level utomton is SAD of size O(n log n) n ely O(log n). By rguing tht ny pth going through stte with outegree σ will o so y tking regulr trnsition, we re le to improve oth the size n ely of the level utomton. This results in the lphet wre level utomton whih is SAD of size O(n log σ) n ely O(log σ). Finlly we present generlize onstrution tht gives tre-off etween size n ely y letting prmeter k, 1 < k σ, e the se of the exponentil inrese in outegree on pths with only efult trnsitions. This SAD hs size O(nk log k σ) n ely O(log k σ). With k = 2 we get n utomton of size O(n log σ) n ely O(log σ). In the other extreme, for k = σ we get n utomton of size O(nσ) n ely O(1). 3.1 Level Automton The level utomton is SAD with n+1 sttes tht we ientify with the integers {0, 1,..., n}. All sttes re epting. For eh stte i > 0, we ssoite level, level(i), given y: level(i) = mx({x i mo 2 x = 0}) Hene, level(i) is the exponent of the lrgest power of two tht ivies i. We o not ssoite ny level with stte 0. For stte s, we efine s to e the smllest stte suh tht s > s n level(s) level(s) + 1. The onfigurtion of the trnsitions in the level utomton re s follows: From stte 0 we hve efult trnsition to stte 1 n regulr trnsition to stte 1 with lel S[1]. For every other stte s, 1 s n, we hve the following trnsitions. A filure trnsition to stte s. If no suh stte exist, the stte s oes not hve filure trnsition.

5 For eh unique hrter α in S[s+1, min(s, n)], there is trnsition lele α to the smllest stte s > s suh tht S[s ] = α. An exmple of the level utomton onstrute from the string n lphet {,,, } is given in Fig. 2. The she rrows enote efult trnsitions n the vertil position of the sttes enotes their level. Fig. 2. The level utomton onstrute from the string. We first show tht the level utomton is SAD for S, i.e., the level utomton epts string iff. the string is susequene of S. To o so suppose tht P is string of length m epte y the level utomton n let s 1, s 2,..., s m e the sequene of sttes visite with regulr trnsitions on the pth tht epts P. From the efinition of the trnsition funtion, we know tht if trnsition with lel α les to stte s, then S[s ] = α. This mens tht S[s 1 ]S[s 2 ]... S[s m ] spells out susequene of S if the sequene s 1, s 2,..., s m is stritly monotonilly inresing. From the efinition of the trnsitions, stte s only hve trnsitions to sttes s if s > s. Hene, the sequene is stritly monotonilly inresing. For the other iretion, let P e susequene of S. Let S[1, s m ] e the miniml prefix of S tht ontins P s susequene n let s 1, s 2,..., s m e stritly monotonilly inresing sequene of sttes, suh tht S[s 1 ]S[s 2 ]... S[s m ] = P. Assume for ontrition tht P is not epte y the level utomton. Let P p e the lrgest prefix of P tht is epte y the level utomton suh tht the sequene of sttes visite with regulr trnsition on the mthing pth is prefix of s 1, s 2,..., s m. Let s i e the lst stte of tht prefix n let α = S[s i+1 ]. Consier the pth of sttes s i, 1,..., j otine y ontinuously following efult trnsitions from s i. When the efult trnsition of s i les to 1 we know tht s i hs trnsition with lel α, if α ours in S[s i +1, 1 ]. Applying this rgument to the rest of the sttes on the pth, we know tht one of the sttes s i, 1,..., j hs trnsition lele α, if α ours in S[s i + 1, min( j, n)], where min( j, n) = n euse j hs no efult trnsition. Sine S[s i+1 ]S[s i+2 ]... S[s m ] is susequene of S[s i + 1, n] one of the sttes s i, 1,..., j hs trnsition to s i+1 with lel α. This ontrits how we initil selete P p.

6 For ll s > 0, we wnt to show the following property of s n level(s): s s = 2 level(s) (1) By efinition 2 level(s) ivies s. Hene, the next integer, lrger thn s, tht 2 level(s) ivies must e s + 2 level(s). But sine 2 level(s) ws the lrgest power of two tht ivies s, we know tht the eomposition of s into sum of unique powers of two must ontin 2 level(s) n lso tht this power is the smllest. Hene, when we eompose s + 2 level(s) into unique powers of two we know tht the smllest power of two in this eomposition is t lest 2 level(s)+1. The efinition of s implies tht s = s + 2 level(s) whih is extly wht we wnte to show. Assume tht the ely of the level utomton is ue to pth p. We wnt to oun the length of p. The stte s on p is the only stte on p tht n hve trnsitions to the sttes s + 1, s + 2,..., s. Sine s s = 2 level(s) n level(s) > level(s), the length of p is oune y O(log n) euse stte s t level O(log n) n hve trnsitions to s s = 2 O(log n) = O(n) sttes. At eh level l we hve O(n/2 l+1 ) sttes, euse every 2 l th stte is ivie y 2 l, ut 2 l is only the lrgest ivisor in every seon of these ses. Beuse s s = 2 level(s) eh stte t level l hs t most 2 l outgoing trnsitions. Therefore, eh level ontriute with size t most n/2 l+1 2 l = O(n). Sine the ely is O(log n) we hve t most O(log n) levels n the totl size therefore eomes O(n log n). In summry, we hve shown the following result. Lemm 1. Let S e string of n hrters. We n onstrut susequene utomton with efult trnsitions of size O(n log n) n ely O(log n). 3.2 Alphet wre level utomton We introue the Alphet wre level utomton. When the level utomton rehes stte s where s s σ, then s n hve up to σ outgoing trnsitions without violting the spe nlysis ove. The level utomton only hs trnsition for eh unique hrter in S[s + 1, min(s, n)]. Hene, for ll sttes s in the lphet wre level utomton where s s σ, we let s hve trnsition for eh symol α in Σ, to the smllest stte s > s suh tht S[s ] = α. No epting pth woul ever tke efult trnsition from stte with σ outgoing trnsitions. Hene, sttes with σ outgoing trnsitions o not nee efult trnsitions. We hnge the level funtion to reflet this. For eh stte 1 i n we hve tht: level(i) = min( log 2 σ, mx({x i mo 2 x = 0})) (2) The onfigurtion of the trnsitions in the lphet wre level utomton is s follows: From stte 0 we hve efult trnsition to stte 1 n regulr trnsition to stte 1 with lel S[1]. For every other stte s, 1 s n, we hve the following trnsitions.

7 A filure trnsition to stte s. If no suh stte exist, the stte s oes not hve filure trnsition. If s s < σ then for eh unique hrter α in S[s + 1, min(s, n)], there is trnsition lele α to the smllest stte s > s suh tht S[s ] = α. If s s σ then for eh unique hrter α in S[s+1, n], there is trnsition lele α to the smllest stte s > s suh tht S[s ] = α. An exmple of the lphet wre level utomton onstrute from the string n lphet {,,, } is given in Fig. 3. The level utomton in Fig. 2 is onstrute from the sme string n the sme lphet. For omprison, stte 4 in Fig. 3 now hs outegree σ n hs trnsitions to the first sueeing ourrene of ny unique hrter n stte 8 hs een onstrine to level log 2 σ. Fig. 3. The lphet level utomton onstrute from the string. It is esy to show tht the lphet wre utomton is SAD y slightly moifying the rguments tht le to Lemm 1. Now, it is not neessrily true tht min( j, n) = n for ll pths s i, 1,..., j. So ssume tht min( j, n) < n. But euse j hs no efult trnsition, we know inste tht j hs σ outgoing trnsitions where one of them hs lel α, if α ours in S[ j + 1, n]. From this we n onlue tht one of the sttes s i, 1,..., j hs trnsition to s i+1 with lel α. The ely is now oune y O(log σ) euse no stte is ssigne level higher thn log 2 σ. The size is oune y O(n log σ) euse eh level hs size O(n) n we hve t most O(log σ) levels. In summry, we hve shown the following result. Lemm 2. Let S e string of n hrters. We n onstrut SAD of S with size O(n log σ) n ely O(log σ). 3.3 Full tre off We n generlize the onstrution ove y introuing prmeter k, 1 < k σ, whih is the se of the exponentil inrese in outegree of sttes on every pth

8 tht only uses efult trnsitions. Now, when we follow efult trnsition from s to s, the numer of outgoing trnsitions inrese with ftor k inste of ftor 2. This gives tre-off etween size n ely in the SAD etermine y k. Inresing k gives shorter ely of the SAD ut inreses the size n vie vers. Eh stte, exept stte 0, is still ssoite with level, ut we nee to generlize the level funtion to ount for the prmeter k. For every k n 1 i n we hve tht: level(i, k) = min( log k σ, mx({x i mo k x = 0})) (3) Now, the level funtion gives the lrgest power of k tht ivies i. The onfigurtion of the trnsitions in the generlize lphet wre level utomton is ientil to the onfigurtion of the lphet wre utomton ut is restte here for ompleteness. From stte 0 we hve efult trnsition to stte 1 n regulr trnsition to stte 1 with lel S[1]. For every other stte s, 1 s n, we hve the following trnsitions: A filure trnsition to stte s. If no suh stte exist, the stte s oes not hve filure trnsition. If s s < σ then for eh unique hrter α in S[s + 1, min(s, n)], there is trnsition lele α to the smllest stte s > s suh tht S[s ] = α. If s s σ then for eh unique hrter α in S[s+1, n], there is trnsition lele α to the smllest stte s > s suh tht S[s ] = α. We n show tht the generlize lphet wre utomton is SAD y the sme rguments tht le to 2. With the new efinition of the level funtion we hve tht s s = k level(s,k)+1 (s mo k level(s,k)+1 ) (4) for ll s > 0. The moulo term gives the ifferene etween s n the lrgest j < s suh tht k level(s,k)+1 ivies j. We re intereste in the ifferene etween s n the smllest i > s suh tht k level(s,k)+1 ivies i. But sine the ifferene etween j n i is k level(s,k)+1, we just sutrt the moulo term from k level(s,k)+1 to get the ifferene etween s n i. This expression gives the numer of outgoing trnsitions from stte s. The ely is oune y O(log k σ) euse we inrese the numer of outgoing trnsitions with ftor k eh time we follow efult trnsition n no stte is ssigne level higher thn log σ. Eh stte hs t most s s = k level(s,k)+1 (s mo k level(s,k)+1 ) k level(s,k)+1 outgoing trnsitions. At level l we hve O(n(k 1)/(k l+1 )) sttes eh with O(k l+1 ) outgoing trnsitions suh tht eh level hs size O(nk). The size of the utomton eomes O(nk log k σ) euse we hve O(log k σ) levels. In summry, we hve shown Theorem 1.

9 Referenes 1. Bez-Ytes, R.A.: Serhing susequenes. Theoret. Comput. Si. 78(2) (1991) Troníček, Z., Shinohr, A.: The size of susequene utomton. Theoret. Comput. Si. 341(1) (2005) Crohemore, M., Melihr, B., Troníček, Z.: Direte yli susequene grph: Overview. J. Dis. Algorithms 1(3-4) (2003) Crohemore, M., Tronıek, Z.: Direte yli susequene grph for multiple texts. Tehnil Repport, Institut Gspr-Monge (1999) Troníček, Z.: Episoe mthing*. In: Pro. 12th. CPM. (2001) Hoshino, H., Shinohr, A., Tke, M., Arikw, S.: Online onstrution of susequene utomt for multiple texts. In: Pro. 7th SPIRE. (2000) Frhn, E., Ferous, J., Moos, T., Rhmn, M.S.: Finite utomt se lgorithms for the generlize onstrine longest ommon susequene prolems. In: Pro. 17th SPIRE. (2010) Bnni, H., Ineng, S., Shinohr, A., Tke, M.: Inferring strings from grphs n rrys. In: Pro. 28th MFCS. (2003) Tronìček, Z.: Opertions on DASG. In: Pro. 4th WIA. (1999) Troníček, Z.: Serhing susequenes. Ph. D. Thesis, Deprtment of Computer Siene n Engineering, FEE CTU in Prgue (2001) 11. Troniĉek, Z.: Common susequene utomton. In: Pro. 8th CIAA. (2003) Bille, P., Frh-Colton, M.: Fst n ompt regulr expression mthing. Theoret. Comput. Si. 409 (2008) Knuth, D.E., Jmes H. Morris, J., Prtt, V.R.: Fst pttern mthing in strings. SIAM J. Comput. 6(2) (1977) Aho, A.V., Corsik, M.J.: Effiient string mthing: n i to iliogrphi serh. Commun. ACM 18(6) (1975) Kumr, S., Dhrmpurikr, S., Yu, F., Crowley, P., Turner, J.: Algorithms to elerte multiple regulr expressions mthing for eep pket inspetion. In: Pro. 12th SIGCOMM. (2006) Hyes, C.L., Luo, Y.: Dpio: high spee eep pket inspetion engine using ompt finite utomt. In: Pro. 3r ANCS. (2007)

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

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

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

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

Lecture 6: Coding theory

Lecture 6: Coding theory Leture 6: Coing theory Biology 429 Crl Bergstrom Ferury 4, 2008 Soures: This leture loosely follows Cover n Thoms Chpter 5 n Yeung Chpter 3. As usul, some of the text n equtions re tken iretly from those

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

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

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

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

Compression of Palindromes and Regularity.

Compression of Palindromes and Regularity. Compression of Plinromes n Regulrity. Kyoko Shikishim-Tsuji Center for Lierl Arts Eution n Reserh Tenri University 1 Introution In [1], property of likstrem t t view of tse is isusse n it is shown tht

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

Data Structures LECTURE 10. Huffman coding. Example. Coding: problem definition

Data Structures LECTURE 10. Huffman coding. Example. Coding: problem definition Dt Strutures, Spring 24 L. Joskowiz Dt Strutures LEURE Humn oing Motivtion Uniquel eipherle oes Prei oes Humn oe onstrution Etensions n pplitions hpter 6.3 pp 385 392 in tetook Motivtion Suppose we wnt

More information

On-Line Construction of Compact Directed Acyclic Word Graphs

On-Line Construction of Compact Directed Acyclic Word Graphs On-Line Constrution of Compt Direte Ayli Wor Grphs Shunsuke neng, Hiroms Hoshino, Ayumi Shinohr, Msyuki Tke,SetsuoArikw, Ginrlo Muri 2, n Giulio Pvesi 2 Dept. of nformtis, Kyushu University, Jpn {s-ine,hoshino,yumi,tke,rikw}@i.kyushu-u..jp

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

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

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

Data Structures and Algorithm. Xiaoqing Zheng

Data Structures and Algorithm. Xiaoqing Zheng Dt Strutures nd Algorithm Xioqing Zheng zhengxq@fudn.edu.n String mthing prolem Pttern P ours with shift s in text T (or, equivlently, tht pttern P ours eginning t position s + in text T) if T[s +... s

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

Laboratory for Foundations of Computer Science. An Unfolding Approach. University of Edinburgh. Model Checking. Javier Esparza

Laboratory for Foundations of Computer Science. An Unfolding Approach. University of Edinburgh. Model Checking. Javier Esparza An Unfoling Approh to Moel Cheking Jvier Esprz Lbortory for Fountions of Computer Siene University of Einburgh Conurrent progrms Progrm: tuple P T 1 T n of finite lbelle trnsition systems T i A i S i i

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

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

A Disambiguation Algorithm for Finite Automata and Functional Transducers

A Disambiguation Algorithm for Finite Automata and Functional Transducers A Dismigution Algorithm for Finite Automt n Funtionl Trnsuers Mehryr Mohri Cournt Institute of Mthemtil Sienes n Google Reserh 51 Merer Street, New York, NY 1001, USA Astrt. We present new ismigution lgorithm

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

The size of subsequence automaton

The size of subsequence automaton Theoreticl Computer Science 4 (005) 79 84 www.elsevier.com/locte/tcs Note The size of susequence utomton Zdeněk Troníček,, Ayumi Shinohr,c Deprtment of Computer Science nd Engineering, FEE CTU in Prgue,

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

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

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

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

Global alignment. Genome Rearrangements Finding preserved genes. Lecture 18

Global alignment. Genome Rearrangements Finding preserved genes. Lecture 18 Computt onl Biology Leture 18 Genome Rerrngements Finding preserved genes We hve seen before how to rerrnge genome to obtin nother one bsed on: Reversls Knowledge of preserved bloks (or genes) Now we re

More information

CS261: A Second Course in Algorithms Lecture #5: Minimum-Cost Bipartite Matching

CS261: A Second Course in Algorithms Lecture #5: Minimum-Cost Bipartite Matching CS261: A Seon Course in Algorithms Leture #5: Minimum-Cost Biprtite Mthing Tim Roughgren Jnury 19, 2016 1 Preliminries Figure 1: Exmple of iprtite grph. The eges {, } n {, } onstitute mthing. Lst leture

More information

Common intervals of genomes. Mathieu Raffinot CNRS LIAFA

Common intervals of genomes. Mathieu Raffinot CNRS LIAFA Common intervls of genomes Mthieu Rffinot CNRS LIF Context: omprtive genomis. set of genomes prtilly/totlly nnotte Informtive group of genes or omins? Ex: COG tse Mny iffiulties! iology Wht re two similr

More information

General Suffix Automaton Construction Algorithm and Space Bounds

General Suffix Automaton Construction Algorithm and Space Bounds Generl Suffix Automton Constrution Algorithm nd Spe Bounds Mehryr Mohri,, Pedro Moreno, Eugene Weinstein, Cournt Institute of Mthemtil Sienes 251 Merer Street, New York, NY 10012. Google Reserh 76 Ninth

More information

Computational Biology Lecture 18: Genome rearrangements, finding maximal matches Saad Mneimneh

Computational Biology Lecture 18: Genome rearrangements, finding maximal matches Saad Mneimneh Computtionl Biology Leture 8: Genome rerrngements, finding miml mthes Sd Mneimneh We hve seen how to rerrnge genome to otin nother one sed on reversls nd the knowledge of the preserved loks or genes. Now

More information

On a Class of Planar Graphs with Straight-Line Grid Drawings on Linear Area

On a Class of Planar Graphs with Straight-Line Grid Drawings on Linear Area Journl of Grph Algorithms n Applitions http://jg.info/ vol. 13, no. 2, pp. 153 177 (2009) On Clss of Plnr Grphs with Stright-Line Gri Drwings on Liner Are M. Rezul Krim 1,2 M. Siur Rhmn 1 1 Deprtment of

More information

CHAPTER 1 Regular Languages. Contents

CHAPTER 1 Regular Languages. Contents Finite Automt (FA or DFA) CHAPTE 1 egulr Lnguges Contents definitions, exmples, designing, regulr opertions Non-deterministic Finite Automt (NFA) definitions, euivlence of NFAs nd DFAs, closure under regulr

More information

Automata and Regular Languages

Automata and Regular Languages Chpter 9 Automt n Regulr Lnguges 9. Introution This hpter looks t mthemtil moels of omputtion n lnguges tht esrie them. The moel-lnguge reltionship hs multiple levels. We shll explore the simplest level,

More information

Lecture 2: Cayley Graphs

Lecture 2: Cayley Graphs Mth 137B Professor: Pri Brtlett Leture 2: Cyley Grphs Week 3 UCSB 2014 (Relevnt soure mteril: Setion VIII.1 of Bollos s Moern Grph Theory; 3.7 of Gosil n Royle s Algeri Grph Theory; vrious ppers I ve re

More information

@#? Text Search ] { "!" Nondeterministic Finite Automata. Transformation NFA to DFA and Simulation of NFA. Text Search Using Automata

@#? Text Search ] { ! Nondeterministic Finite Automata. Transformation NFA to DFA and Simulation of NFA. Text Search Using Automata g Text Serh @#? ~ Mrko Berezovský Rdek Mřík PAL 0 Nondeterministi Finite Automt n Trnsformtion NFA to DFA nd Simultion of NFA f Text Serh Using Automt A B R Power of Nondeterministi Approh u j Regulr Expression

More information

Lecture 8: Abstract Algebra

Lecture 8: Abstract Algebra Mth 94 Professor: Pri Brtlett Leture 8: Astrt Alger Week 8 UCSB 2015 This is the eighth week of the Mthemtis Sujet Test GRE prep ourse; here, we run very rough-n-tumle review of strt lger! As lwys, this

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

CSCI 340: Computational Models. Kleene s Theorem. Department of Computer Science

CSCI 340: Computational Models. Kleene s Theorem. Department of Computer Science CSCI 340: Computtionl Models Kleene s Theorem Chpter 7 Deprtment of Computer Science Unifiction In 1954, Kleene presented (nd proved) theorem which (in our version) sttes tht if lnguge cn e defined y ny

More information

Surds and Indices. Surds and Indices. Curriculum Ready ACMNA: 233,

Surds and Indices. Surds and Indices. Curriculum Ready ACMNA: 233, Surs n Inies Surs n Inies Curriulum Rey ACMNA:, 6 www.mthletis.om Surs SURDS & & Inies INDICES Inies n surs re very losely relte. A numer uner (squre root sign) is lle sur if the squre root n t e simplifie.

More information

Now we must transform the original model so we can use the new parameters. = S max. Recruits

Now we must transform the original model so we can use the new parameters. = S max. Recruits MODEL FOR VARIABLE RECRUITMENT (ontinue) Alterntive Prmeteriztions of the pwner-reruit Moels We n write ny moel in numerous ifferent ut equivlent forms. Uner ertin irumstnes it is onvenient to work with

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

CSC2542 State-Space Planning

CSC2542 State-Space Planning CSC2542 Stte-Spe Plnning Sheil MIlrith Deprtment of Computer Siene University of Toronto Fll 2010 1 Aknowlegements Some the slies use in this ourse re moifitions of Dn Nu s leture slies for the textook

More information

Section 2.3. Matrix Inverses

Section 2.3. Matrix Inverses Mtri lger Mtri nverses Setion.. Mtri nverses hree si opertions on mtries, ition, multiplition, n sutrtion, re nlogues for mtries of the sme opertions for numers. n this setion we introue the mtri nlogue

More information

Numbers and indices. 1.1 Fractions. GCSE C Example 1. Handy hint. Key point

Numbers and indices. 1.1 Fractions. GCSE C Example 1. Handy hint. Key point GCSE C Emple 7 Work out 9 Give your nswer in its simplest form Numers n inies Reiprote mens invert or turn upsie own The reiprol of is 9 9 Mke sure you only invert the frtion you re iviing y 7 You multiply

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

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

INTRODUCTION TO AUTOMATA THEORY

INTRODUCTION TO AUTOMATA THEORY Chpter 3 INTRODUCTION TO AUTOMATA THEORY In this hpter we stuy the most si strt moel of omputtion. This moel els with mhines tht hve finite memory pity. Setion 3. els with mhines tht operte eterministilly

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

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

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

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

CMSC 330: Organization of Programming Languages

CMSC 330: Organization of Programming Languages CMSC 330: Orgniztion of Progrmming Lnguges Finite Automt 2 CMSC 330 1 Types of Finite Automt Deterministic Finite Automt (DFA) Exctly one sequence of steps for ech string All exmples so fr Nondeterministic

More information

Particle Physics. Michaelmas Term 2011 Prof Mark Thomson. Handout 3 : Interaction by Particle Exchange and QED. Recap

Particle Physics. Michaelmas Term 2011 Prof Mark Thomson. Handout 3 : Interaction by Particle Exchange and QED. Recap Prtile Physis Mihelms Term 2011 Prof Mrk Thomson g X g X g g Hnout 3 : Intertion y Prtile Exhnge n QED Prof. M.A. Thomson Mihelms 2011 101 Rep Working towrs proper lultion of ey n sttering proesses lnitilly

More information

Outline Data Structures and Algorithms. Data compression. Data compression. Lossy vs. Lossless. Data Compression

Outline Data Structures and Algorithms. Data compression. Data compression. Lossy vs. Lossless. Data Compression 5-2 Dt Strutures n Algorithms Dt Compression n Huffmn s Algorithm th Fe 2003 Rjshekr Rey Outline Dt ompression Lossy n lossless Exmples Forml view Coes Definition Fixe length vs. vrile length Huffmn s

More information

GNFA GNFA GNFA GNFA GNFA

GNFA GNFA GNFA GNFA GNFA DFA RE NFA DFA -NFA REX GNFA Definition GNFA A generlize noneterministic finite utomton (GNFA) is grph whose eges re lele y regulr expressions, with unique strt stte with in-egree, n unique finl stte with

More information

CSE 332. Sorting. Data Abstractions. CSE 332: Data Abstractions. QuickSort Cutoff 1. Where We Are 2. Bounding The MAXIMUM Problem 4

CSE 332. Sorting. Data Abstractions. CSE 332: Data Abstractions. QuickSort Cutoff 1. Where We Are 2. Bounding The MAXIMUM Problem 4 Am Blnk Leture 13 Winter 2016 CSE 332 CSE 332: Dt Astrtions Sorting Dt Astrtions QuikSort Cutoff 1 Where We Are 2 For smll n, the reursion is wste. The onstnts on quik/merge sort re higher thn the ones

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

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

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

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

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

COMPUTING THE QUARTET DISTANCE BETWEEN EVOLUTIONARY TREES OF BOUNDED DEGREE

COMPUTING THE QUARTET DISTANCE BETWEEN EVOLUTIONARY TREES OF BOUNDED DEGREE COMPUTING THE QUARTET DISTANCE BETWEEN EVOLUTIONARY TREES OF BOUNDED DEGREE M. STISSING, C. N. S. PEDERSEN, T. MAILUND AND G. S. BRODAL Bioinformtis Reserh Center, n Dept. of Computer Siene, University

More information

NFA DFA Example 3 CMSC 330: Organization of Programming Languages. Equivalence of DFAs and NFAs. Equivalence of DFAs and NFAs (cont.

NFA DFA Example 3 CMSC 330: Organization of Programming Languages. Equivalence of DFAs and NFAs. Equivalence of DFAs and NFAs (cont. NFA DFA Exmple 3 CMSC 330: Orgniztion of Progrmming Lnguges NFA {B,D,E {A,E {C,D {E Finite Automt, con't. R = { {A,E, {B,D,E, {C,D, {E 2 Equivlence of DFAs nd NFAs Any string from {A to either {D or {CD

More information

Logic, Set Theory and Computability [M. Coppenbarger]

Logic, Set Theory and Computability [M. Coppenbarger] 14 Orer (Hnout) Definition 7-11: A reltion is qusi-orering (or preorer) if it is reflexive n trnsitive. A quisi-orering tht is symmetri is n equivlene reltion. A qusi-orering tht is nti-symmetri is n orer

More information

Types of Finite Automata. CMSC 330: Organization of Programming Languages. Comparing DFAs and NFAs. NFA for (a b)*abb.

Types of Finite Automata. CMSC 330: Organization of Programming Languages. Comparing DFAs and NFAs. NFA for (a b)*abb. CMSC 330: Orgniztion of Progrmming Lnguges Finite Automt 2 Types of Finite Automt Deterministic Finite Automt () Exctly one sequence of steps for ech string All exmples so fr Nondeterministic Finite Automt

More information

Types of Finite Automata. CMSC 330: Organization of Programming Languages. Comparing DFAs and NFAs. Comparing DFAs and NFAs (cont.) Finite Automata 2

Types of Finite Automata. CMSC 330: Organization of Programming Languages. Comparing DFAs and NFAs. Comparing DFAs and NFAs (cont.) Finite Automata 2 CMSC 330: Orgniztion of Progrmming Lnguges Finite Automt 2 Types of Finite Automt Deterministic Finite Automt () Exctly one sequence of steps for ech string All exmples so fr Nondeterministic Finite Automt

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

Worked out examples Finite Automata

Worked out examples Finite Automata Worked out exmples Finite Automt Exmple Design Finite Stte Automton which reds inry string nd ccepts only those tht end with. Since we re in the topic of Non Deterministic Finite Automt (NFA), we will

More information

Monochromatic Plane Matchings in Bicolored Point Set

Monochromatic Plane Matchings in Bicolored Point Set CCCG 2017, Ottw, Ontrio, July 26 28, 2017 Monohromti Plne Mthings in Biolore Point Set A. Krim Au-Affsh Sujoy Bhore Pz Crmi Astrt Motivte y networks interply, we stuy the prolem of omputing monohromti

More information

Fast index for approximate string matching

Fast index for approximate string matching Fst index for pproximte string mthing Dekel Tsur Astrt We present n index tht stores text of length n suh tht given pttern of length m, ll the sustrings of the text tht re within Hmming distne (or edit

More information

On the Spectra of Bipartite Directed Subgraphs of K 4

On the Spectra of Bipartite Directed Subgraphs of K 4 On the Spetr of Biprtite Direte Sugrphs of K 4 R. C. Bunge, 1 S. I. El-Znti, 1, H. J. Fry, 1 K. S. Kruss, 2 D. P. Roerts, 3 C. A. Sullivn, 4 A. A. Unsiker, 5 N. E. Witt 6 1 Illinois Stte University, Norml,

More information

Algorithm Design and Analysis

Algorithm Design and Analysis Algorithm Design nd Anlysis LECTURE 8 Mx. lteness ont d Optiml Ching Adm Smith 9/12/2008 A. Smith; sed on slides y E. Demine, C. Leiserson, S. Rskhodnikov, K. Wyne Sheduling to Minimizing Lteness Minimizing

More information

Algorithm Design and Analysis

Algorithm Design and Analysis Algorithm Design nd Anlysis LECTURE 5 Supplement Greedy Algorithms Cont d Minimizing lteness Ching (NOT overed in leture) Adm Smith 9/8/10 A. Smith; sed on slides y E. Demine, C. Leiserson, S. Rskhodnikov,

More information

Computing on rings by oblivious robots: a unified approach for different tasks

Computing on rings by oblivious robots: a unified approach for different tasks Computing on rings y olivious roots: unifie pproh for ifferent tsks Ginlorenzo D Angelo, Griele Di Stefno, Alfreo Nvrr, Niols Nisse, Krol Suhn To ite this version: Ginlorenzo D Angelo, Griele Di Stefno,

More information

Chapter 2 Finite Automata

Chapter 2 Finite Automata Chpter 2 Finite Automt 28 2.1 Introduction Finite utomt: first model of the notion of effective procedure. (They lso hve mny other pplictions). The concept of finite utomton cn e derived y exmining wht

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

Speech Recognition Lecture 2: Finite Automata and Finite-State Transducers

Speech Recognition Lecture 2: Finite Automata and Finite-State Transducers Speech Recognition Lecture 2: Finite Automt nd Finite-Stte Trnsducers Eugene Weinstein Google, NYU Cournt Institute eugenew@cs.nyu.edu Slide Credit: Mehryr Mohri Preliminries Finite lphet, empty string.

More information

Java II Finite Automata I

Java II Finite Automata I Jv II Finite Automt I Bernd Kiefer Bernd.Kiefer@dfki.de Deutsches Forschungszentrum für künstliche Intelligenz Finite Automt I p.1/13 Processing Regulr Expressions We lredy lerned out Jv s regulr expression

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

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

Lecture 4: Graph Theory and the Four-Color Theorem

Lecture 4: Graph Theory and the Four-Color Theorem CCS Disrete II Professor: Pri Brtlett Leture 4: Grph Theory n the Four-Color Theorem Week 4 UCSB 2015 Through the rest of this lss, we re going to refer frequently to things lle grphs! If you hen t seen

More information

6. Suppose lim = constant> 0. Which of the following does not hold?

6. Suppose lim = constant> 0. Which of the following does not hold? CSE 0-00 Nme Test 00 points UTA Stuent ID # Multiple Choie Write your nswer to the LEFT of eh prolem 5 points eh The k lrgest numers in file of n numers n e foun using Θ(k) memory in Θ(n lg k) time using

More information

12.1 Nondeterminism Nondeterministic Finite Automata. a a b ε. CS125 Lecture 12 Fall 2016

12.1 Nondeterminism Nondeterministic Finite Automata. a a b ε. CS125 Lecture 12 Fall 2016 CS125 Lecture 12 Fll 2016 12.1 Nondeterminism The ide of nondeterministic computtions is to llow our lgorithms to mke guesses, nd only require tht they ccept when the guesses re correct. For exmple, simple

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 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

Maximum size of a minimum watching system and the graphs achieving the bound

Maximum size of a minimum watching system and the graphs achieving the bound Mximum size of minimum wthing system n the grphs hieving the oun Tille mximum un système e ontrôle minimum et les grphes tteignnt l orne Dvi Auger Irène Chron Olivier Hury Antoine Lostein 00D0 Mrs 00 Déprtement

More information

Designing finite automata II

Designing finite automata II Designing finite utomt II Prolem: Design DFA A such tht L(A) consists of ll strings of nd which re of length 3n, for n = 0, 1, 2, (1) Determine wht to rememer out the input string Assign stte to ech of

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

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

Technology Mapping Method for Low Power Consumption and High Performance in General-Synchronous Framework

Technology Mapping Method for Low Power Consumption and High Performance in General-Synchronous Framework R-17 SASIMI 015 Proeeings Tehnology Mpping Metho for Low Power Consumption n High Performne in Generl-Synhronous Frmework Junki Kwguhi Yukihie Kohir Shool of Computer Siene, the University of Aizu Aizu-Wkmtsu

More information

I 3 2 = I I 4 = 2A

I 3 2 = I I 4 = 2A ECE 210 Eletril Ciruit Anlysis University of llinois t Chigo 2.13 We re ske to use KCL to fin urrents 1 4. The key point in pplying KCL in this prolem is to strt with noe where only one of the urrents

More information

COMPUTING THE QUARTET DISTANCE BETWEEN EVOLUTIONARY TREES OF BOUNDED DEGREE

COMPUTING THE QUARTET DISTANCE BETWEEN EVOLUTIONARY TREES OF BOUNDED DEGREE COMPUTING THE QUARTET DISTANCE BETWEEN EVOLUTIONARY TREES OF BOUNDED DEGREE M. STISSING, C. N. S. PEDERSEN, T. MAILUND AND G. S. BRODAL Bioinformtis Reserh Center, n Dept. of Computer Siene, University

More information

Intermediate Math Circles Wednesday, November 14, 2018 Finite Automata II. Nickolas Rollick a b b. a b 4

Intermediate Math Circles Wednesday, November 14, 2018 Finite Automata II. Nickolas Rollick a b b. a b 4 Intermedite Mth Circles Wednesdy, Novemer 14, 2018 Finite Automt II Nickols Rollick nrollick@uwterloo.c Regulr Lnguges Lst time, we were introduced to the ide of DFA (deterministic finite utomton), one

More information

CS375: Logic and Theory of Computing

CS375: Logic and Theory of Computing CS375: Logic nd Theory of Computing Fuhu (Frnk) Cheng Deprtment of Computer Science University of Kentucky 1 Tle of Contents: Week 1: Preliminries (set lger, reltions, functions) (red Chpters 1-4) Weeks

More information

arxiv: v2 [math.co] 31 Oct 2016

arxiv: v2 [math.co] 31 Oct 2016 On exlue minors of onnetivity 2 for the lss of frme mtrois rxiv:1502.06896v2 [mth.co] 31 Ot 2016 Mtt DeVos Dryl Funk Irene Pivotto Astrt We investigte the set of exlue minors of onnetivity 2 for the lss

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

Solutions to Problem Set #1

Solutions to Problem Set #1 CSE 233 Spring, 2016 Solutions to Prolem Set #1 1. The movie tse onsists of the following two reltions movie: title, iretor, tor sheule: theter, title The first reltion provies titles, iretors, n tors

More information