Universal Levenshtein Automata. Building and Properties

Μέγεθος: px
Εμφάνιση ξεκινά από τη σελίδα:

Download "Universal Levenshtein Automata. Building and Properties"

Transcript

1 Sofa Uversty St. Klmet Ohrdsk Faculty of Mathematcs ad Iformatcs Departmet of Mathematcal Logc ad Applcatos Uversal Leveshte Automata. Buldg ad Propertes A thess submtted for the degree of Master of Computer Scece by Petar Nkolaev Mtak supervsor: Dr. Stoya Mhov Sofa, 2005

2 Cotets 1 Itroducto. 2 2 Leveshte dstaces. Propertes. 3 3 Nodetermstc fte Leveshte automata for fxed word. 8 4 Determstc fte Leveshte automata for fxed word Uversal Leveshte automata Buldg of A,ɛ, A,t ad A,ms Summarzed pseudo code Detaled pseudo code Complexty Some fal results Mmalty of A,ɛ, A,t ad A,ms Some propertes of A,ɛ. 72 1

3 1 Itroducto. Oe possble measure for the proxmty of two strgs s the so-called Leveshte dstace (kow also as edt dstace), based o prmtve edt operatos. Prmtve edt operatos are replacemet of oe symbol wth aother (substtuto), deleto of a symbol, serto of a symbol ad others. The dstace betwee two strgs w ad v s defed as the mmal umber of the prmtve edt operatos that trasform w to v. Ths master thess gves a detaled formal revew of the so-called uversal Leveshte automato. The put word for ths automato s a sequece of bt vectors (w, v) whch s computed by gve two words w ad v. The automato recogzes (w, v) ff the dstace betwee w ad v s ot greater tha. The greatest advatage of the uversal Leveshte automata A,χ s obtaed whe we have to extract from a dctoary all words v that are close eough to a gve word w. If the dctoary s repeseted as a fte determstc automato D we ca traverse parallelly the two automata A,χ ad D to fd all these words. Descrpto of ths algorthm ad ts modfed verso called forward-backward method, whch s extremely fast practce, ca be foud [MSFASLD]. Short revew of the cotets Secto 2 - defto of three dfferet Leveshte dstaces based o the umber of edt operatos. Secto 3 - defto of the odetermstc Leveshte automato A ND,χ (w) cossts of all strgs x such that the dstace betwee w ad x s ot greater tha. (w) ad proof that the laguage of A ND,χ Secto 4 - defto of the determstc Leveshte automato A D,χ proof that the laguages of A ND,χ Leveshte automato A,χ (w) ad (w) ad A D,χ (w) are equal. The uversal s defed secto 5. Secto 6 - the algorthm s mmal. Secto 8 - some prop- for ts buldg. Secto 7 - proof that A,χ ertes of A,ɛ. Remarks The am of ths master thess s to revew the determstc Leveshte automata ad the uversal Leveshte automata preseted by ther authors Mhov ad Schulz [SMFSCLA] ad [MSFASLD]. The ma efforts ths master thess are cocetrated o the strct proofs ad the detals. Ths paper s a draft trasato of the orgal text wth addtoal commets ad more fgures. The orgal ca be foud at [ORIG]. The term Leveshte dstaces s used the text for d ɛ L, dt L ad dms L, although for the words w 1 = abcd, w 2 = abdc ad w 3 = bdac the tragle equalty s ot satsfed for d t L. dt L (abcd, abdc) = 1, dt L (abdc, bdac) = 2, but (abcd, bdac) = 4. d t L 2

4 2 Leveshte dstaces. Propertes. Let Σ be a fte set of letters. Defto 1 d ɛ L : Σ Σ N Let v, w, v, w Σ ad a, b Σ. 1) v = ɛ or w = ɛ d ɛ def L (v, w) = max( v, w ) 2) v 1 ad w 1 Let v = av ad w = bw. d ɛ def L (v, w) = m( f(a = b, d ɛ L (v, w ), ), 1 + d ɛ L (v, bw ), 1 + d ɛ L (av, w ), 1 + d ɛ L (v, w ) ) Notatos Here ad what follows the value of the expresso f(codto, V alueif CodtoIsT rue, V alueif CodtoIsF alse) s V alueif CodtoIsT rue f Codto s satsfed ad V alueif CodtoIsF alse otherwse. x deotes the legth of x. The fucto d ɛ L s called Leveshte dstace. dɛ L (v, w) s called Leveshte dstace betwee the words v ad w. The Leveshte dstace betwee the words v ad w s the mmal umber of prmtve edt operatos that trasform v to w. The prmtve edt operatos are deleto of a letter, serto of a letter ad substtuto of oe letter wth aother. Defto 2 : Σ N Σ Let k N, x 1, x 2,..., x k Σ ad t N. { x 1 x 2...x k t def ɛ f t k = x t+1 x t+2...x k otherwse Treatg the trasposto of two letters also as a prmtve edt operato we receve the followg defto of Leveshte dstace exteded wth trasposto: Defto 2 d t L : Σ Σ N Let v, w, v, w Σ ad a, b, a 1, b 1 Σ. 1) v = ɛ or w = ɛ d t def L (v, w) = max( v, w ) 2) v 1 ad w 1 Let v = av ad w = bw. 3

5 d t def L (v, w) = m( f(a = b, d t L (v, w ), ), 1 + d t L (v, bw ), 1 + d t L (av, w ), 1 + d t L (v, w ), f(a 1 < v & b 1 < w & a = b 1 & a 1 = b, 1 + d t L (v 2, w 2), ) ) Notatos We use c < d to deote that c s a prefx of d f c ad d are words. The fucto d t L s called Leveshte dstace exteded wth trasposto. Whe mergg of two letters to oe ad splttg of oe letter to two other letters are cosdered as prmtve edt operatos we use the followg defto of Leveshte dstace exteded wth merge ad splt: Defto 3 d ms L : Σ Σ N Let v, w, v, w Σ ad a, b Σ. 1) v = ɛ or w = ɛ d ms def L (v, w) = max( v, w ) 2) v 1 ad w 1 Let v = av ad w = bw. d ms L def (v, w) = m( f(a = b, d ms L (v, w ), ), 1 + d ms L (v, bw ), 1 + d ms L (av, w ), 1 + d ms L (v, w ), f( w 2, 1 + d ms L (v, w 2), ), f( v 2, 1 + d ms L (v 2, w ), ) ) The fucto d ms L splt. d ms L s called Leveshte dstace exteded wth merge ad Notatos We use χ as a metasymbol. For example d χ L deotes dɛ L, dt L or f χ {ɛ, t, ms}. Proposto 1 Let χ {ɛ, t, ms} ad v, w Σ. The d χ L (v, w) = 0 v = w. Proof ) Let v = w = x. Usg ducto o x we prove that d χ L (x, x) = 0. 1) x = 0 d χ L (x, x) = dχ L (ɛ, ɛ) = 0 2) Iducto hypothess: d χ L (x, x) = 0 Let a Σ. We prove that d χ L (ax, ax) = 0: d χ L (ax, ax) = m( f(a = a, dχ L (x, x), ),... ) = 4

6 m( f(a = a, 0, ),... ) = 0 ) Wth ducto o v we prove that d χ L (v, w) = 0 v = w. 1) v = ɛ. Let d χ L (v, w) = 0. dχ L (v, w) = max( v, w ) = 0. Hece w = ɛ. 2) Iducto hypothess: w Σ (d χ L (v, w) = 0 v = w) Let a Σ ad w Σ. We have to prove that d χ L (av, w) = 0 av = w. Let d χ L (av, w) = 0. From the defto of dχ L t follows that w 1. Let b Σ, w Σ ad w = bw. From the defto of d χ L t follows that a = b ad d χ L (v, w ) = 0. The ducto hypothess mples that v = w. Therefore av = w. Proposto 2 Let χ {ɛ, t, ms} ad v, w Σ. The d χ L (v, w) = dχ L (w, v). The proof of the Proposto 2 s straghtforward. Remark As we kow Proposto 1 ad Proposto 2, t remas to prove the tragle equalty for d χ L ( dχ L (v, w) dχ L (v, x) + dχ L (x, w) ) to show that d χ L s dstace. But ths property s used owhere ths paper. That s why we do t prove t. Defto 4 Let χ {ɛ, t, ms}. L χ Lev : N Σ P (Σ ) L χ def Lev (, w) = {v d χ L (v, w) } We ca fd the deftos of L ɛ Lev, Lt Lev ad Lms Lev [SMFSCLA]. Proposto 3 Let χ {ɛ, t, ms}. Let a Σ ad v, w Σ. The d χ L (v, w) = k dχ L (av, w) k + 1. Proof Let d χ L (v, w) = k. 1) w = ɛ d χ L (av, w) = dχ L (av, ɛ) = k + 1 2) w 1 Form the defto of d χ L t follows that dχ L (av, w) 1 + dχ L (v, w) = k + 1. Proposto 4 Let χ {ɛ, t, ms}. Let a, w 1 Σ ad v, w Σ. The d χ L (v, w) = k dχ L (av, w 1w) k + 1. Proof Let d χ L (v, w) = k. From the defto of dχ L t follows that dχ L (av, w 1w) 1 + d ms L (v, w) = k + 1. Proposto 5 Let χ {ɛ, t, ms}. Let w 1 Σ ad v, w Σ. The d χ L (v, w) = k dχ L (v, w 1w) k + 1. Proof Proposto 5 follows drectly from Proposto 3 ad Proposto 2. 5

7 Proposto 6 Let χ {ɛ, t, ms}. Let w 1 Σ ad v, w Σ. The d χ L (v, w) = k dχ L (w 1v, w 1 w) k. d ms L Proof Let d χ L (v, w) = k. From the defto of dχ L t follows that dχ L (w 1v, w 1 w) (v, w) = k. Proposto 7 > 0. The Let χ {ɛ, t, ms}. Let w Σ, w = w 1 w 2...w p, p 1 ad L χ Lev (, w) Σ.Lχ Lev ( 1, w) Σ.L χ Lev ( 1, w 2w 3...w p ) L χ Lev ( 1, w 2w 3...w p ) w 1.L χ Lev (, w 2w 3...w p ). Proof From Propertes 3, 4, 5 ad 6 t follows respectvely that L χ Lev (, w) Σ.Lχ Lev ( 1, w), L χ Lev (, w) Σ.Lχ Lev ( 1, w 2w 3...w p ), L χ Lev (, w) Lχ Lev ( 1, w 2w 3...w p ) ad L χ Lev (, w) w 1.L χ Lev (, w 2w 3...w p ). Therefore L χ Lev (, w) Σ.Lχ Lev ( 1, w) Σ.L χ Lev ( 1, w 2w 3...w p ) L χ Lev ( 1, w 2w 3...w p ) w 1.L χ Lev (, w 2w 3...w p ). We show how to exted A = Σ.L χ Lev ( 1, w) Σ.L χ Lev ( 1, w 2w 3...w p ) L χ Lev ( 1, w 2w 3...w p ) w 1.L χ Lev (, w 2w 3...w p ) to L χ Lev (, w). Frst we defe Rχ as a exteso of A ad afterwards we prove that R χ = L χ Lev. Defto 5 Let χ {ɛ, t, ms}. R χ : N + Σ + P (Σ ) Let w Σ, w = w 1 w 2...w p, p 1 ad 1. 1) χ = ɛ R ɛ (, w) def = Σ.L ɛ Lev ( 1, w) Σ.L ɛ Lev ( 1, w 2w 3...w p ) L ɛ Lev ( 1, w 2w 3...w p ) w 1.L ɛ Lev (, w 2w 3...w p ) 6

8 2) χ = t 3) χ = ms R t (, w) def = Σ.L t Lev ( 1, w) Σ.L t Lev ( 1, w 2w 3...w p ) L t Lev ( 1, w 2w 3...w p ) w 1.L t Lev (, w 2w 3...w p ) f( w 2, w 2 w 1.L t Lev ( 1, w 3...w p ), φ). R ms (, w) def = Σ.L ms Lev ( 1, w) Σ.L ms Lev ( 1, w 2w 3...w p ) L ms Lev ( 1, w 2w 3...w p ) w 1.L ms Lev (, w 2w 3...w p ) Σ.Σ.L ms Lev ( 1, w 2w 3...w p ) f( w 2, Σ.L ms Lev ( 1, w 2), φ). Proposto 8 Let w Σ, w = w 1 w 2...w p, p 1 ad 1. The L χ Lev (, w) = Rχ (, w). Proof ) 1) χ = ɛ From Proposto 7 t follows that L ɛ Lev (, w) Rɛ (, w). 2) χ = t We have to prove that ( t ) w 2 L t Lev (, w) w 2w 1.L t Lev ( 1, w 3...w p ). Let w 2 ad v L t Lev ( 1, w 3...w p ). Hece d t L (v, w 3...w p ) 1. From the defto of d t L t follows that dt L (w 2w 1 v, w 1 w 2 w 3...w p ) 1 + d t L (v, w 3...w p ). From ( t ) ad Proposto 7 t drectly follows that L t Lev (, w) R t (, w). 3) χ = ms We have to prove that ( ms 1 ) L ms Lev (, w) Σ.Σ.Lms Lev ( 1, w 2...w p ) ad ( ms 2 ) w 2 L ms Lev (, w) Σ.Lms Lev ( 1, w 3...w p ). 3.1) Frst we prove ( ms 1 ) Let v L ms Lev ( 1, w 2...w p ) ad a, b Σ. Hece d ms L (v, w 2...w p ) 1. From the defto of d ms L t follows that dms L (abv, w 1w 2...w p ) 1+d ms L (v, w 2...w p ). 3.2) We prove ( ms 2 ) Let w 2, v L ms Lev ( 1, w 3...w p ) ad a Σ. Hece d ms L (v, w 3...w p ) 1. From the defto of d ms L t follows that dms L (av, w 1w 2 w 3...w p ) 1 + d ms L (v, w 3...w p ). From ( ms 1 ), ( ms 2 ) ad Proposto 7 t drectly follows that L ms Lev (, w) R ms (, w). 7

9 Therefore L χ Lev (, w) Rχ (, w). ) Let v L χ Lev (, w) ad dχ L (v, w) = k. 1) v = ɛ d χ L (v, w) = w = k d χ L (v, w 2...w p ) = w 2...w p = k 1 1 Therefore v L χ Lev ( 1, w 2...w p ). 2) v 1 Let v = t ad v = v 1 v 2...v t. Hece d χ L (v, w) = m( f(v 1 = w 1, d χ L (v 2...v t, w 2...w p ), ), 1 + d χ L (v 2...v t, w), 1 + d χ L (v, w 2...w p ), 1 + d χ L (v 2...v t, w 2...w p ),...) = k 2.1) v 1 = w 1 & d χ L (v 2...v t, w 2...w p ) = k I ths case v w 1.L χ Lev (, w 2...w p ). 2.2) d χ L (v 2...v t, w) = k 1 1 I ths case v Σ.L χ Lev ( 1, w). 2.3) d χ L (v, w 2...w p ) = k 1 1 I ths case v L χ Lev ( 1, w 2...w p ). 2.4) d χ L (v 2...v t, w 2...w p ) = k 1 1 I ths case v Σ.L χ Lev ( 1, w 2...w p ). Therefore L ɛ Lev (, w) Rɛ (, w). 2.5) χ = t ad w 2 & v 2 & v 1 = w 2 & v 2 = w 1 & d t L (v 2, w 2) = k 1 1 I ths case v Σ.Σ.L t Lev ( 1, w 2...w p ). Therefore L t Lev (, w) Rt (, w). 2.6) χ = ms ad w 2 & d ms L (v 2...v t, w 3...w p ) = k 1 1 I ths case v Σ.L ms Lev ( 1, w 2). 2.7) χ = ms ad v 2 & d ms L (v 3...v t, w 2...w p ) = k 1 1 I ths case v Σ.Σ.L ms Lev ( 1, w 2...w p ). Therefore L ms Lev (, w) Rms (, w). So L χ Lev (, w) Rχ (, w). 3 Nodetermstc fte Leveshte automata for fxed word. Notatos We deote the tuples <<, 0 >, e >, <<, 1 >, e > ad <<, 2 >, e > wth #e, #e t ad #e s correspodgly. Defto 6 Let χ {ɛ, t, ms}. Let w Σ ad N. We defe the odetermstc fte Leveshte automato A ND,χ (w). A ND,χ (w) def = < Σ, Q ND,χ 8, I ND,χ, F ND,χ, δ ND,χ >

10 Notatos Suppose that γ : A B s partal fucto. We use the expresso!γ(π) order to deote that γ(π) s defed ad!γ(π) - to deote that γ(π) s ot defed. The specal expresso < π 1, a, π 2 > δ ND,χ used for the trasto partal fucto δ ND,χ : Q ND,ɛ Σ {ɛ} P (Q ND,ɛ ) meas that!δ ND,χ (π1, a) & π 2 δ ND,χ (π1, a). Let w = p ad w = w 1 w 2...w p. 1) χ = ɛ Q ND,ɛ def ND,ɛ def = { #e 0 p & 0 e } I = {0 #0 } F ND,ɛ def = {p #e 0 e } Let a Σ {ɛ} ad q 1, q 2 Q ND,ɛ 2) χ = t Q ND,t def ND,t def I = Q ND,ɛ = {0 #0 } def. < q 1, a, q 2 > δ ND,ɛ def q 1 = #e & q 1 = #e+1 & a Σ or q 1 = #e & q 1 = + 1 #e+1 or q 1 = #e & q 1 = + 1 #e & a = w +1 { #e t 0 p 2 & 1 e } F ND,t = F ND,ɛ Let a Σ {ɛ} ad q 1, q 2 Q ND,t 3) χ = ms Q ND,ms def ND,ms def I = Q ND,ɛ = {0 #0 } def. def < q 1, a, q 2 > δ ND,t < q 1, a, q 2 > δ ND,ɛ or q 1 = #e & q 2 = #e+1 t & a = w +2 or q 1 = #e t & q 2 = + 2 #e & a = w +1 { #e s 0 p 1 & 1 e } F ND,ms = F ND,ɛ Let a Σ {ɛ} ad q 1, q 2 Q ND,ms. def < q 1, a, q 2 > δ ND,ms < q 1, a, q 2 > δ ND,ɛ or q 1 = #e & q 2 = + 2 #e+1 & a Σ or q 1 = #e & q 2 = + 1 #e s & a Σ or q 1 = #e s & q 2 = + 1 #e & a Σ The exteded trasto fucto δ ND,χ for A ND,χ s defed as usual. Frst we defe the ɛ-closure Cl ɛ : Q ND,ɛ P (Q ND,ɛ ): Cl ɛ (q) def = {q} {π k 0 η 1, η 2,..., η k (< q, ɛ, η 1 >, < η 1, ɛ, η 2 >,..., < η k, ɛ, π > δ ND,χ )} 9

11 We defe ɛ-closure for set of states ( Cl ɛ : P (Q ND,ɛ ) P (Q ND,ɛ ) ) the followg way: Cl ɛ (A) def = Cl ɛ (π) π A Let v Σ ad a Σ. We defe recursvely the partal fucto : Q ND,ɛ Σ P (Q ND,ɛ ): δ ND,χ δ ND,χ def (q, ɛ) δ ND,χ def (q, va) = = Cl ɛ (q)! f!δ ND,χ (q, v)! f!δ ND,χ (q, v) & π δ ND,χ Cl ɛ ( π δ ND,χ (q,v) δ ND,χ (π, a)) otherwse (q,v) δ ND,χ (π, a) = φ I what follows we use the expresso < π 1, v, π 2 > δ ND,χ to deote that!δ ND,χ (π1, v) & π 2 δ ND,χ (π1, v). Remark these otatos the word w wll be clear from the cotext. Q ND,χ Descrpto of A ND,ɛ, F ND,χ ad δ ND,χ deped o the word w. Whe we use ca be foud [MSFASLD]. Fg. 1 A ND,ɛ 2 (w 1 w 2...w 5 ) (Σ ɛ deotes Σ {ɛ}.) The trastos the automato A ND,χ (w) correspod wth the defto of R χ : A ND,ɛ the trastos < #e, a, #e+1 > (a Σ) correspod to 10

12 Σ.L ɛ Lev ( 1, w). The trastos < #e, a, + 1 #e+1 > (a Σ) - to Σ.L ɛ Lev ( 1, w 2 w 3...w p ). The trastos < #e, ɛ, #e+1 > - to L ɛ Lev ( 1, w 2w 3...w p ). Ad the trastos < #e, w, + 1 #e > - to w 1.L ɛ Lev (, w 2w 3...w p ). Later we prove that L(A ND,χ (w)) = L ɛ Lev (, w). Fg. 2 A ND,t 2 (w 1 w 2...w 5 ) Fg. 3 A ND,ms 2 (w 1 w 2...w 5 ) 11

13 Proposto 9 Let χ {ɛ, t, ms}. Let N ad w Σ. Let #e Q ND,χ. The L( #e ) = L χ Lev ( e, w +1...w p ). ( L(π) = {w π F ND,χ (< π, w, π > δ ND,χ )} ) The propertes L( #e ) = L ɛ Lev ( e, w +1...w p ), L( #e ) = L t Lev ( e, w +1...w p ) ad L( #e ) = L ms Lev ( e, w +1...w p ) are formulated [SMFSCLA]. Proof Iducto o. 1) = p L(p #e ) = {x x Σ & x e} = L χ Lev ( e, ɛ) 2) 0 p 1 Iducto hypothess: (IH 1 ) j 1 e(l( + j #e ) = L χ Lev ( e, w +j+1...w p )) We prove wth ducto o e that L( #e ) = L χ Lev ( e, w +1...w p ). 2.1) e = L( # ) = w +1.L( + 1 # ) = IH1 w +1.L χ Lev (0, w +2...w p ) = w +1...w p = L χ Lev (0, w +1...w p ) = L χ Lev ( e, w +1...w p ) 2.2) 0 e 1 Iducto hypothess: (IH 2 ) L( #e+1 ) = L χ Lev ( e 1, w +1...w p ) 2.2.1) χ = ɛ L( #e ) = Σ.L( #e+1 ) Σ.L(+1 #e+1 ) L(+1 #e+1 ) w +1.L(+1 #e ) = IH1,2 Σ.L ɛ Lev ( e 1, w +1...w p ) Σ.L ɛ Lev ( e 1, w +2...w p ) L ɛ Lev ( e 1, w +2...w p ) w +1.L ɛ Lev ( e, w +2...w p ) = R ɛ ( e, w +1...w p ) = Proposto 8 L ɛ Lev ( e, w +1...w p ) 2.2.2) χ = t L( #e ) = Σ.L( #e+1 ) Σ.L( + 1 #e+1 ) L( + 1 #e+1 ) w +1.L( + 1 #e ) f( p 2, w +2 w +1.L( + 1 #e+1 ), φ) = IH1,2 Σ.L t Lev ( e 1, w +1...w p ) Σ.L t Lev ( e 1, w +2...w p ) L t Lev ( e 1, w +2...w p ) w +1.L t Lev ( e, w +2...w p ) f( w +1...w p 2, w +2 w +1.L t Lev ( e 1, w +3...w p ), φ) = R t ( e, w +1...w p ) = Proposto 8 L t Lev ( e, w +1...w p ) 2.2.3) χ = ms 12

14 L( #e ) = Σ.L( #e+1 ) Σ.L( + 1 #e+1 ) L( + 1 #e+1 ) w +1.L( + 1 #e ) Σ.Σ.L( + 1 #e+1 ) f( p 2, Σ.L( + 2 #e+1 ), φ) = IH1,2 Σ.L ms Lev ( e 1, w +1...w p ) Σ.L ms Lev ( e 1, w +2...w p ) L ms Lev ( e 1, w +2...w p ) w +1.L ms Lev ( e, w +2...w p ) Σ.Σ.L ms Lev ( e 1, w +2...w p ) f( w +1...w p 2, Σ.L ms Lev ( e 1, w +3...w p ), φ) = R ms ( e, w +1...w p ) = Proposto 8 L ms Lev ( e, w +1...w p ) Corollary Let χ {ɛ, t, ms}, w Σ ad N. Proposto 9 mples that (w)) = L(0 #0 ) = L χ Lev (, w). L(A ND,χ 4 Determstc fte Leveshte automata for fxed word. I ths secto we show a specal way for determzato of A ND,χ (w). As a result we receve the determstc automato A D,χ (w). Defto 7 Let χ {ɛ, t, ms}. ND,ɛ def Q ND,t def Q ND,ms def Q = { #e, e Z} = Q ND,ɛ { #e t, e Z} = Q ND,ɛ { #e s, e Z} Let N. We defe δe D,χ : Q ND,χ {0, 1} P (Q ND,χ ). Let b {0, 1}, k N ad b = b 1 b 2...b k. 1) χ = ɛ δe D,ɛ ( #e, b) def = { + 1 #e } f 1 < b { #e+1, + 1 #e+1 } f b = 0 k & b ɛ & e < { #e+1, + 1 #e+1, + j #e+j 1 } f 0 < b & j = µz[b z = 1] { #e+1 } f b = ɛ & e < φ otherwse µz[a] deotes the least z such that A s satsfed. 2) χ = t 2.1) 13

15 δe D,t ( #e, b) def = 2.2) 3) χ = ms 3.1) δe D,ms ( #e, b) def = A ND,χ 3.2) δ D,ms e ( #e s The fucto δ D,χ e { + 1 #e } f 1 < b { #e+1, + 1 #e+1, + 2 #e+1, #e+1 t } f 01 < b { #e+1, + 1 #e+1, + j #e+j 1 } f 00 < b & j = µz[b z = 1] { #e+1, + 1 #e+1 } f b = 0 k & b ɛ & e < { #e+1 } f b = ɛ & e < φ otherwse δe D,t ( #e t {, b) def { + 2 = #e } f 1 < b φ otherwse { + 1 #e } f 1 < b { #e+1, #e+1 s, + 1 #e+1, + 2 #e+1 } f 00 < b 01 < b { #e+1, #e+1 s, + 1 #e+1 } f 0 = b & e < { #e+1 } f ɛ = b & e < φ otherwse, b) def = { + 1 #e } s called fucto of the elemetary trastos. Defto 8 Let χ {ɛ, t, ms}. Let w Σ, N ad (w) =< Σ, Q ND,χ, I ND,χ, F ND,χ, δ ND,χ >. We defe w [ ] : Q ND,χ Σ. Let w = w 1 w 2...w p ad π Q ND,χ. 1) π = #e Q ND,χ w [ #e ] = w +1 w +2...w +k where k = m( e + 1, p ) 2) π = #e t w [ #e t ] = w [ #e ] 3) π = #e s Q ND,t Q ND,ms w [ #e s ] = w [ #e ] The word w [π] s called relevat subword of w for π ([SMFSCLA]). Defto 9 β : Σ Σ {0, 1} β(x, w 1 w 2...w p ) = b 1 b 2...b p where b = 1 x = w. β(x, w 1 w 2...w p ) s called characterstc vector of x wth respect to the word w 1 w 2...w p. Defto 10 Let χ {ɛ, t, ms}. Let w Σ, N ad (w) =< Σ, Q ND,χ, I ND,χ, F ND,χ, δ ND,χ >. A ND,χ 14

16 We defe δe D,χ : Q ND,χ Σ P (Q ND,χ ). δe D,χ (π, x) def = δe D,χ (π, β(x, w [π] )) The deftos of δe D,ɛ, δe D,t ad δe D,ms are gve [SMFSCLA]. Defto 11 Let χ {ɛ, t, ms}. We defe < χ s Q ND,ɛ Q ND,χ. 1) χ = ɛ #e < ɛ #f def s j f > e & j f e 2) χ = t #e < t #f def s j #e < ɛ s j #f #e < t s j #f t 3) χ = ms #e < ms s def f > e & j + 1 f e #f def j #e < ms s j s #f #e < ɛ s j #f def #e < ɛ s j #f The relato < χ s s defed such way that π 1 < χ s π 2 L(π 1 ) L(π 2 ). That s why, whe we determze A ND,χ, for each state A of the receved determstc automato t wll be true that (*) q 1, q 2 A(q 1 χ s q 2 ). As we take to accout that q δe D,χ (q, x) < q, x, q > δ ND,χ ad q1 χ s q 2 & < q 2, x, q 2 > δ ND,χ q 1 δe D,χ (q 1, x)(q 1 χ s q 2) ad also (*), we ca defe the trasto fucto δ D,χ for the determstc automato: δ D,χ (A, x) = q A δd,χ e (q, x) where B removes from B each π for whch there s such q B that q < χ s π. Remark π 1 < χ s π 2 correspods to π 1 subsumes π 2 from [SMFSCLA]. We do t defe whe #e t < t s π or #e s < ms s π,.e. our defto of < χ s mples that #e t t s π ad #e s ms s π for each #e t s ad π. We do t defe whe #e t < t s π or #e s < ms s π because every good defto of #e t < χ s π or #e s < χ s π wll satsfy the property #e t < t s π + 1 #e < χ s π or #e s < ms s π #e < χ s π correspodgly. If we keep md that #e t δe D,t (A, x) + 1 #e δe D,t (A, x), #e s δe D,t (A, x) #e δe D,t (A, x) ad look at the defto of, we shall see easly that each good defto of #e t < t s π or #e s < ms s π leads to the same automata A D,χ ad A,χ as our defto. The set {π π Q ND,ɛ 2 & 3 #0 ɛ s π} whe A ND,ɛ 2 (w 1 w 2...w 5 ) =< Σ, Q ND,ɛ 2, I ND,ɛ, F ND,ɛ 2, δ ND,ɛ 2 > s depcted o fg

17 Fg. 4 A ND,ɛ 2 (w 1 w 2...w 5 ) =< Σ, Q ND,ɛ 2, I ND,ɛ, F ND,ɛ 2, δ ND,ɛ 2 >, The elemets of {π π Q ND,ɛ 2 & 3 #0 ɛ s π} are bold. We ca easly prove the followg proposto. Proposto 10 Let χ {ɛ, t, ms}. The χ s a partal order. Defto 12 Let χ {ɛ, t, ms}. Let w Σ ad N ad (w) =< Σ, Q ND,χ, I ND,χ, F ND,χ, δ ND,χ >. : P (P (Q ND,χ )) P (Q ND,χ ) def A = {π π A & π A(π < χ s π)} A ND,χ s defed [SMFSCLA]. Proposto 11 Let χ {ɛ, t, ms}. Let w Σ, w = p ad N. The (w)) = L(< Σ, Q ND,χ, I ND,χ, F ND,χ, δ ND,χ >) where F ND,χ = { #e p e}. L(A ND,χ Proof Let #e be such that p e. It follows from the defto of that < #e, ɛ, + 1 #e+1 > δ ND,χ, < + 1 #e+1, ɛ, + 2 #e+2 > δ ND,χ,... < p 1 #e+p 1, ɛ, p #e+p > δ ND,χ. Hece < #e, ɛ, p #e+p > δ ND,χ. Therefore L(A ND,χ (w)) L(< Σ, Q ND,χ δ ND,χ Obvously F ND,χ Hece L(A ND,χ F ND,χ. (w)) L(< Σ, Q ND,χ, I ND,χ, F ND,χ, δ ND,χ >)., I ND,χ, F ND,χ, δ ND,χ >). 16

18 Therefore L(A ND,χ (w)) = L(< Σ, Q ND,χ I what follows we presume that A ND,χ, I ND,χ, F ND,χ, δ ND,χ >). (w) =< Σ, Q ND,χ, I ND,χ, F ND,χ, δ ND,χ >. Fg. 5 A ND,t 2 (w 1 w 2...w 5 ) =< Σ, Q ND,t 2, I ND,t, F ND,t 2, δ ND,t 2 > Defto 13 Let χ {ɛ, t, ms}. Let w Σ ad N ad (w) =< Σ, Q ND,χ, I ND,χ, F ND,χ, δ ND,χ >. Let M Q ND,χ ad π. M s called state wth base posto π ff π M(π χ s π ) & π 1, π 2 A ND,χ Q ND,ɛ M(π 1 χ s π 2 ). Defto 14 Let χ {ɛ, t, ms}. Let w Σ ad N. We defe the determstc fte automato A D,χ (w). A D,χ (w) def = < Σ, Q D,χ Let w = p ad w = w 1 w 2...w p. ρ : [0, p] P (P (Q ND,χ )) ρ() def = {M M s state wth base posto #0 } D,χ def I def F D,χ δ D,χ = {0 #0 } Q D,χ def = ( 0 p, I D,χ, F D,χ, δ D,χ > ρ())\{φ} = {M M Q D,χ & π M(π F ND,χ )} : Q D,χ Σ Q D,χ 17

19 { δ D,χ (M, x) def = π M δd,χ e (π, x) f π M δd,χ e (π, x) φ! otherwse The fte automata A D,ɛ SCLA]. Correctess of the defto 1) We prove that (w), A D,t (w) ad A D,ms (w) are defed [SMF- M ρ() & 0 p 1 & x Σ π M(δ D,χ e (π, x) ρ( + 1)) Let M ρ(), 0 p 1, x Σ ad π M. We prove that δe D,χ (π, x) ρ( + 1): 1.1) χ = ɛ Let π = j #f. Hece #0 ɛ s j #f ad j f ) δe D,ɛ (π, x) = {j + 1 #f } j +1 (+1) f. Hece +1 #0 ɛ s j +1 #f. Therefore δ D,ɛ (π, x) ρ(+1) ) δe D,ɛ (π, x) = {j #f+1, j + 1 #f+1, j + z #f+z 1 } for some z such that z > 1 Obvously π 1, π 2 δe D,ɛ (π, x)(π 1 ɛ s π 2 ). It follows from j f that j ( + 1) f + 1, j + 1 ( + 1) f + 1 ad j + z ( + 1) f + z 1. Therefore + 1 #0 ɛ s j #f+1, + 1 #0 ɛ s j + 1 #f+1 ad + 1 #0 ɛ s j + z #f+z 1. Hece δe D,ɛ (π, x) ρ( + 1) ) δe D,ɛ φ δ D,t (π, x) = {j #f+1, j+1 #f+1 } or δ D,ɛ e Obvously δe D,ɛ (π, x) ρ( + 1). 1.2) χ = t 1.2.1) π = j #f I ths case #0 t s j #f ad j f ) δe D,t (π, x) = {j + 1 #f } j + 1 ( + 1) f. Hece + 1 #0 t s j + 1 #f ad δ D,t ) δ D,t e (π, x) = {j #f+1 } or δ D,ɛ e (π, x) = e (π, x) ρ( + 1). (π, x) = {j #f+1, j + 1 #f+1, j + 2 #f+1, j #f+1 t } I ths case π δe D,t (π, x)(π = k #l (t) l = f + 1). Therefore π 1, π 2 e (π, x)(π 1 t s π 2 ). It follows from j f that j ( + 1) f + 1, j + 1 ( + 1) f + 1, j + 2 ( + 1) f + 1 ad j + 1 f + 1. Hece + 1 #0 t s j #f+1, + 1 #0 t s j + 1 #f+1, + 1 #0 t s j + 2 #f+1 ad + 1 #0 t s j #f+1 t. Therefore δe D,t (π, x) ρ( + 1) ) δe D,t (π, x) = {j #f+1, j + 1 #f+1, j + z #f+z 1 } for some z such that z > 2 Obvously π 1, π 2 δe D,t (π, x)(π 1 t s π 2 ). It follows from j f that j + z ( + 1) f + z 1. Therefore δe D,t (π, x) ρ( + 1) ) δe D,t (π, x) = {j #f+1, j + 1 #f+1 } or δe D,t (π, x) = φ Obvously δe D,t (π, x) ρ( + 1) ) π = j #f t 18

20 I ths case #0 t s j #f t ad j + 1 f ) δe D,t (π, x) = {j + 2 #f } j + 2 ( + 1) f. Hece + 1 #0 t s j + 2 #f ad δe D,t (π, x) ρ( + 1) ) δe D,t (π, x) = φ Obvosly φ ρ( + 1). 1.3) χ = ms 1.3.1) π = j #f I ths case #0 ms s j #f ad j f ) δe D,ms (π, x) = {j + 1 #f } j + 1 ( + 1) f. Hece + 1 #0 ms s j + 1 #f ad δe D,ms (π, x) ρ( + 1) ) δe D,ms (π, x) = {j #f+1, j s #f+1, j + 1 #f+1, j + 2 #f+1 } I ths case π δe D,ms (π, x)(π = k #l (s) l = f + 1). Therefore π 1, π 2 δe D,ms (π, x)(π 1 ms s π 2 ). It follows from j f that j ( + 1) f + 1, j + 1 ( + 1) f + 1 ad j + 2 ( + 1) f + 1. Hece + 1 #0 ms s j #f #0 ms s ρ( + 1) ) δe D,ms (π, x) = φ or δ D,ms e j + 1 #f+1 ad + 1 #0 ms s (π, x) = {j #f+1, j #f+1 s Obvously δe D,ms (π, x) ρ( + 1) ) π = j s #f. I ths case #0 ms s j s #f ( + 1) f. Hece + 1 #0 ms s 2) We prove that j + 2 #f+1. Therefore δ D,ms e (s), (π, x), j + 1 #f+1 } or δe D,ms (π, x) = {j #f+1 } ad j f. δe D,ms j + 1 #f ad δ D,ms (π, x) = {j + 1 #f }. j + 1 e (π, x) ρ( + 1). M s state wth base posto p #e & 0 e 1 & x Σ π M(δe D,χ (π, x) s state wth base posto p #e+1 ). Let M be state wth base posto p #e, 0 e 1 ad x Σ. Let π M. We have to prove that δe D,χ (π, x) s state wth base posto p #e ) χ = ɛ Let π = j #f. Hece p #e ɛ s j #f, j p f e ad f e ) δe D,ɛ (π, x) = {j + 1 #f } It follows from j p f e that j + 1 p f (e + 1). Therefore p #e+1 ɛ s j + 1 #f ad δe D,ɛ (π, x) s state wth base posto p #e ) δe D,ɛ (π, x) = {j #f+1, j + 1 #f+1, j + z #f+z 1 } for some z such that z > 1 Obvously π 1, π 2 δe D,ɛ (π, x)(π 1 ɛ s π 2 ). It follows from j p f e that j p f +1 (e+1), j+1 p f +1 (e+1) ad j +z p f +z 1 (e+1). Hece p #e+1 ɛ s j #f+1, p #e+1 ɛ s j + 1 #f+1 ad p #e+1 ɛ s j + z #f+z 1. Therefore δe D,ɛ (π, x) s state wth base posto p #e ) δe D,ɛ (π, x) = {j #f+1, j+1 #f+1 } or δe D,ɛ (π, x) = {j #f+1 } or δe D,ɛ (π, x) = φ Obvously δe D,ɛ (π, x) s state wth base posto p #e ) χ = t 2.2.1) π = j #f I ths case p #e t s j #f, j p f e ad f e ) δ D,t e (π, x) = {j + 1 #f } 19

21 It follows from j p f e that j + 1 p f (e + 1). Therefore p #e+1 t s j + 1 #f ad δe D,t (π, x) s state wth base posto p #e ) δe D,t (π, x) = {j #f+1, j + 1 #f+1, j + 2 #f+1, j #f+1 t } I ths case π δe D,t (π, x)(π = k #l (t) l = f + 1). Therefore π 1, π 2 δ D,t e (π, x)(π 1 t s π 2 ). It follows from j p f e that j p f + 1 (e + 1), j+1 p f +1 (e+1), j +2 p f +1 (e+1) ad j+1 p f +1 (e+1). Hece p #e+1 t s j #f+1, p #e+1 t s j + 1 #f+1, p #e+1 t s j + 2 #f+1 ad p #e+1 t s j #f+1 t. Therefore δe D,t (π, x) s state wth base posto p #e ) δe D,t (π, x) = {j #f+1, j + 1 #f+1, j + z #f+z 1 } for some z such that z > 2 Obvously π 1, π 2 δe D,t (π, x)(π 1 t s π 2 ). It follows from j p f e that j + z p f + z 1 (e + 1). Therefore δe D,t (π, x) s state wth base posto p #e ) δe D,t (π, x) = {j #f+1, j + 1 #f+1 } or δe D,t (π, x) = φ Obvously δe D,t (π, x) s state wth base posto p #e ) π = j #f t. Therefore p #e t s j #f t, j + 1 p f e ad f e ) δe D,t (π, x) = {j + 2 #f } It follows from j + 1 p f e that j + 2 p f (e + 1). Therefore p #e+1 t s j + 2 #f ad δe D,t (π, x) s state wth base posto p #e ) δe D,t (π, x) = φ Obvously φ s state wth base posto p #e ) χ = ms 2.3.1) π = j #f I ths case p #e ms s j #f ad j p f e ad f e ) δe D,ms (π, x) = {j + 1 #f } It follows from j p f e that j + 1 p f (e + 1). Therefore p #e+1 ms s j + 1 #f ad δe D,ms (π, x) s state wth base posto p #e ) δe D,ms (π, x) = {j #f+1, j s #f+1, j + 1 #f+1, j + 2 #f+1 } I ths case π δe D,ms (π, x)(π = k #l (s) l = f + 1). Therefore π 1, π 2 δe D,ms (π, x)(π 1 ms s π 2 ). It follows from j p f e that j p f +1 (e+1), j+1 p f +1 (e+1) ad j+2 p f +1 (e+1). Hece p #e+1 ms s j #f+1 (s), p #e+1 ms s j + 1 #f+1 ad p #e+1 ms s j + 2 #f+1. Therefore δe D,ms (π, x) s state wth base posto p #e ) δe D,ms (π, x) = {j #f+1, j s #f+1, j + 1 #f+1 } or δe D,ms (π, x) = {j #f+1 } or δe D,ms (π, x) = φ Obvously δe D,ms (π, x) s state wth base posto p #e ) π = j s #f I ths case p #e ms s j s #f, j p f e, f e ad δe D,ms (π, x) = {j +1 #f }. It follows from j p f e that j + 1 p f (e + 1). Therefore p #e+1 ms s j + 1 #f ad δe D,ms (π, x) s state wth base posto p #e+1. 3) We prove that A {M M s state wth base posto #e } 20

22 A s state wth base posto #e. Let A {M M s state wth base posto #e }. It follows from the defto of that A A. Therefore π A( #e χ s π). It follows from the defto of that π 1, π 2 A(π 1 χ s π 2 ). Therefore A s state wth base posto #e. 1), 2) ad 3) mply that δ D,χ The propertes for correctess of δ D,ɛ SCLA]. s well defed., δ D,t ad δ D,ms ca be foud [SMF- Proposto 12 Let χ {ɛ, t, ms}. Let w Σ, w = p, N ad A ND,χ (w) =< Σ, Q ND,χ, I ND,χ, F ND,χ, δ ND,χ >. The #e F ND,χ & π χ s #e π F ND,χ. Proof Let #e F ND,χ ad π = j #f χ s #e. Hece j e f ad p e. Therefore p j f (e f ( j)). Therefore p j f ad π F ND,χ. Proposto 13 Let χ {ɛ, t, ms}. Let w Σ, w = p, N ad (w) =< Σ, Q ND,χ, I ND,χ, F ND,χ, δ ND,χ >. Let x Σ, s N, ξ 0 = j #f (s) A ND,χ ad ξ 1, ξ 2...ξ s, η 2 Q ND,χ j < p & < ξ 0, ɛ, ξ 1 > δ ND,χ < ξ s, x, η 2 > δ ND,χ j + 1 #f χ s η 2.. The & < ξ 1, ɛ, ξ 2 > δ ND,χ &... & < ξ s 1, ɛ, ξ s > δ ND,χ & < j #f t Remark Proposto 13 does ot hold for ξ 0 = j #f t, x, j + 2 #f > δ ND,t ad j + 1 #f t s j + 2 #f. because we may have Proof Let j < p, < j #f δ ND,χ (s), ɛ, ξ 1 > δ ND,χ ad < ξ s, x, η 2 > δ ND,χ. 1.1) χ = ɛ ξ 0 = j #f It follows from the defto of δ ND,ɛ s #f+1+s, j s #f+s }. Therefore j + 1 #f ɛ s η ) χ = t ξ 0 = j #f It follows from the defto of δ ND,t, < ξ 1, ɛ, ξ 2 > δ ND,χ,..., < ξ s 1, ɛ, ξ s > that η 2 {j + s #f+1+s, j that η 2 {j + s #f+1+s, j s #f+1+s, j s #f+s, j + s #f+1+s t }. Therefore j + 1 #f t s η 2 1.3) χ = ms 1.3.1) ξ 0 = j #f It follows from the defto of δ ND,ms s #f+1+s, j s #f+s, j + s #f+1+s s 1.3.2) ξ 0 = j #f s that η 2 {j + s #f+1+s, j + 1 +, j s #f+s }. Therefore j + 1 #f ms s η 2. 21

23 I ths case we have that s = 0 ad η 2 = j + 1 #f. Therefore j + 1 #f ms s η 2. Proposto 14 Let χ {ɛ, t, ms}. Let w Σ, N ad (w) =< Σ, Q ND,χ, I ND,χ, F ND,χ, δ ND,χ A ND,χ N, ξ 0 = η 2 ad ξ 1, ξ 2...ξ s, η 2 Q ND,χ η 1 χ s η 2 & < ξ 0, ɛ, ξ 1 > δ ND,χ < ξ s, x, η 2 > δ ND,χ η 1 δ D,χ e (η 1, x)(η 1 χ s η 2).. The & < ξ 1, ɛ, ξ 2 > δ ND,χ >. Let η 1, η 2 Q ND,χ, x Σ, s &... & < ξ s 1, ɛ, ξ s > δ ND,χ & Fg. 6 Remark Usg Proposto 14 we ca easly prove that η 1 χ s η 2 L(η 1 ) L(η 2 ). Proof Let w = p ad η 1 = #e. 1) χ = ɛ Let η 2 = j #f. 1.1) δe D,ɛ ( #e, x) = { + 1 #e } 1.1.1) j < p We have from Proposto 13 that j + 1 #f ɛ s η 2. It follows from #e ɛ s j #f that + 1 #e ɛ s j + 1 #f. Therefore + 1 #e ɛ s η ) j = p 22

24 We have from the defto of δ ND,ɛ that f <, η 2 = p #f ad η 2 = p #f+1. Therefore p f e ad f e. Hece p ( + 1) (f + 1) e. Therefore + 1 #e ɛ s p #f+1 = η ) δe D,ɛ ( #e, x) = { #e+1, + 1 #e+1, + z #e+z 1 } ad 0 < β(x, w [ #e ]) ad z = µz [β(x, w [ #e ]) z = 1] 1.2.1) j < p We have from Proposto 13 that j + 1 #f ɛ s η 2. It follows from #e ɛ s j #f that + 1 #e ɛ s j + 1 #f. Therefore + 1 #e ɛ s η 2. 0 < β(x, w [ #e ]) mples that + 1 #e η 2. Therefore + 1 #e < ɛ s η 2. Obvously γ Q ND,ɛ ( + 1 #e < ɛ s γ ɛ s η 2 & π( + 1 #e < ɛ s π < ɛ s γ) γ { #e+1, + 1 #e+1, + 2 #e+1 }) ) γ Q ND,ɛ ( + 1 #e < ɛ s γ ɛ s η 2 & π( + 1 #e < ɛ s π < ɛ s γ) & γ { #e+1, + 1 #e+1 }). { #e+1, + 1 #e+1 } δe D,ɛ ( #e, x). Therefore there s γ δe D,ɛ ( #e, x) such that γ ɛ s η ) γ Q ND,ɛ ( + 1 < ɛ s γ ɛ s η 2 & π( + 1 #e < ɛ s π < ɛ s γ) γ { #e+1, + 1 #e+1 }). Therefore γ Q ND,ɛ ( + 1 < ɛ s γ ɛ s η 2 & π( + 1 #e < ɛ s π < ɛ s γ) γ = + 2 #e+1 ). Therefore η 2 = + m e+m 1 for some m such that m > 1 ad β(x, w [ #e ]) m = 1. z = µz [β(x, w [ #e ]) z = 1] mples that z m. Therefore + z #e+z 1 ɛ s η ) j = p We have from the defto of δ ND,ɛ that f <, η 2 = p #f ad η 2 = p #f+1. It follows from #e ɛ s p #f that #e+1 ɛ s p #f ) δe D,ɛ ( #e, x) = { #e+1, + 1 #e+1 } ad β(x, w [ #e ]) = 0 k for some k such that k > 0. We prove that η 1 δe D,ɛ (η 1, x)(η 1 ɛ s η 2) a way aalogous to 1.2). 1.4) δe D,ɛ ( #e, x) = { #e+1 } ad = p ad e < ) η 2 = p #f ad f < Obvously #e+1 ɛ s p #f+1 = η ) η 1 < ɛ s η 2 ad η 2 = j #f ad j < p We have from Proposto 13 that j +1 #f ɛ s η 2. It follows from p #e < ɛ s j #f that j p f e ad f > e. Therefore j + 1 p f (e + 1) ad p #e+1 ɛ s j + 1 #f ɛ s η ) δe D,ɛ ( #e, x) = φ Obvously e = ad η 1 = η 2 ad ( = p or 0 = β(x, w [ #e ]) ). Therefore η 2(< ξ 0, ɛ, ξ 1 > δ ND,ɛ & < ξ 1, ɛ, ξ 2 > δ ND,ɛ &... & < ξ s 1, ɛ, ξ s > δ ND,ɛ & < ξ s, x, η 2 >). Cotradcto. (Ths case s mpossble.) 2) χ = t 2.1) η 2 = j #f 2.1.1) δe D,t ( #e, x) = { + 1 #e } ) j < p We have from Proposto 13 that j + 1 #f t s η 2. It follows from #e t s j #f that + 1 #e t s j + 1 #f. Therefore + 1 #e t s η ) j = p 23

25 We have from the defto of δ ND,t that f <, η 2 = p #f ad η 2 = p #f+1. Therefore p f e ad f e. Hece p ( + 1) (f + 1) e. Therefore + 1 #e t s p #f+1 = η ) δ D,ms e ( #e, x) = { #e+1, +1 #e+1, +2 #e+1, #e+1 t } ad 01 < β(x, w [ #e ]) ) j < p We have from the Proposto 13 that j + 1 #f t s η 2. It follows from #e t s j #f that + 1 #e t s j + 1 #f. Ad from 01 < β(x, w [ #e ]) - that η #f. Therefore + 1 #e < t s η 2. Let η 1 Q ND,t be such that + 1 < t s η 1 t s η 2 ad π( + 1 #e < t s π < t s η 1) (obvously such η 1 exsts). It follows from the defto of < t s that η 1 { #e+1, + 1 #e+1, + 2 #e+1, #e+1 t that η 1 { 2 #e+1 t, 1 #e+1 cotradcto) ) j = p We have from the defto of δ ND,t } (f we suppose t } the s = 0, η 2 { 2 #e, 1 #e }, η 1 t s η 2, that f <, η 2 = p #f ad η 2 = p #f+1. It follows from #e t s p #f that #e+1 t s p #f ) δe D,t ( #e, x) = { #e+1, + 1 #e+1, + z #e+z 1 } ad 00 < β(x, w [ #e ]) ad z = µz [β(x, w [ #e ]) z = 1] The proof that η 1 δe D,t (η 1, x)(η 1 t s η 2) s aalogous to the proof 1.2) ) δe D,t ( #e, x) = { #e+1, + 1 #e+1 } The proof that η 1 δe D,t (η 1, x)(η 1 t s η 2) s aalogous to the proof 1.2) ) δe D,t ( #e, x) = { #e+1 } ad = p ad e < I ths case the proof s aalogous to the oe 1.4) ) δe D,ms ( #e, x) = φ Lke 1.5) we prove that ths case s mpossble. 2.2) η 2 = j #f t We have from the defto of δ ND,t β(x, w [ j #f t ]). It follows from #e < t s j #f 2.2.1) δe D,t ( #e, x) = { + 1 #e } that s = 0, η 2 = j + 2 #f ad 1 < t that j + 1 f e ad f > e. We have from j + 1 f e that j + 2 ( + 1) f e. Therefore + 1 #e t s j + 2 #f ) δe D,t ( #e, x) = { #e+1, + 1 #e+1, + 2 #e+1, #e+1 t } It follows from j + 1 f e that j + 2 f (e + 1) (j + 1 < ) or j +2 (+1) f (e+1) ( = j +1) or j +2 (+2) f (e+1) ( < j +1). Therefore #e+1 t s j + 2 #f or + 1 #e+1 t s j + 2 #f or + 2 #e+1 t s j + 2 #f ) δe D,t ( #e, x) = { #e+1, + 1 #e+1, + z #e+z 1 } ad 00 < β(x, w [ #e ]) ad z = µz [β(x, w [ #e ]) z = 1] It follows from j + 1 f e that j + 2 ( + 1) f e. Hece + 1 #e < t s j + 2 #f. The proof that η 1 δe D,t (η 1, x)(η 1 t s η 2) s aalogous to 1.2) ) δe D,t ( #e, x) = { #e+1, + 1 #e+1 } ad β(x, w [ #e ]) = 0 k for some k such that k > 0 Lke 2.2.2) we prove that #e+1 t s j + 2 #f or + 1 #e+1 t s j + 2 #f. (It follows from β(x, w [ #e ]) = 0 k ad 1 < β(x, w [ j #f t ]) that j + 1 ) ) δe D,t ( #e, x) = { #e+1 } ad = p < 24

26 j #f (s) We have from j + 1 f e that j + 2 f (e + 1) 2.2.6) δe D,t ( #e, x) = φ Obvously ths case s mpossble. 3) χ = ms Let η 2 = j #f (s). 3.1) δe D,ms ( #e, x) = { + 1 #e } 3.1.1) j < p We have from Proposto 13 that j + 1 #f ms s that + 1#e ms s j + 1 #f. Therefore + 1 #e ms 3.1.2) j = p η 2. It follows from #e ms s s η 2. We have from the defto of δ ND,ms that f <, η 2 = p #f ad η 2 = p #f+1. Therefore p f e ad f e. Hece p ( + 1) (f + 1) e. Therefore + 1 #e ms s p #f+1 = η ) δe D,ms ( #e, x) = { #e+1, #e+1 s, +1 #e+1, +2 #e+1 } ad (00 < β(x, w [ #e ]) or 01 < β(x, w [ #e ])) 3.2.1) j < p j #f (s) We have from Proposto 13 that j + 1 #f ms s that + 1#e ms η 2. It follows from #e ms s s j + 1 #f. Ad from 00 < β(x, w [ #e ]) or 01 < β(x, w [ #e ]) - that η #f. Therefore + 1 #e < ms s that + 1 < ms s η 1 ms s η 2. Let η 1 Q ND,ms be such π < ms s η 1) (obvously such that η 1 { #e+1, #e+1 s, +, + 1 #e+1 s } the s = 0, η 2, cotradcto). η 2 ad π( + 1 #e < ms s η 1 exsts). It follows from the defto of < ms s 1 #e+1, + 2 #e+1 }. (f we suppose that η 1 { 1 #e+1 s η 2 { 1 #e, + 1 #e }, η 1 ms s 3.2.2) j = p We have from the defto of δ ND,ms that f <, η 2 = p #f ad η 2 = p #f+1. It follows from #e ms s p #f that #e+1 ms s p #f ) δe D,ms ( #e, x) = { #e+1, #e+1 s, + 1 #e+1 } ad 0 < β(x, w [ #e ]) The proof that η 1 δe D,ms (η 1, x)(η 1 ms s η 2) s aalogous to the proof 3.2). 3.4) δe D,ms ( #e, x) = { #e+1 } ad = p ad e <. Lke 1.4) we prove that η 1 δe D,ms (η 1, x)(η 1 ms s η 2). 3.5) δe D,ms ( #e, x) = φ Obvously ths case s mpossble. Proposto 15 Let χ {ɛ, t, ms}. Let w Σ, N, A ND,χ (w) =< Σ, Q ND,χ, I ND,χ, F ND,χ, δ ND,χ > ad A D,χ (w) =< Σ, Q D,χ, I D,χ, F D,χ, δ D,χ >. The L(A ND,χ (w)) L(A D,χ (w)). Proof Let x 1...x k L(A ND,χ (w)) 1) x 1...x k = ɛ. Let π 0, π 1,..., π r Q ND,χ, r N be such states that π 0 = 0 #0, < π 0, ɛ, π 1 > δ ND,χ, < π 1, ɛ, π 2 > δ ND,χ,..., < π r 1, ɛ, π r > δ ND,χ ad π r F ND,χ (t follows from ɛ L(A ND,χ (w)) that such states exst). The defto of δ ND,χ mples that π = # for 0 r. Therefore r #r F ND,χ. Obvously 25

27 0 #0 χ s r #r. We have from Proposto 12 that 0 #0 F ND,χ. Therefore {0 #0 } = I D,χ F D,χ ad ɛ = x 1...x k L(A D,χ (w)). 2) x 1...x k ɛ. Let π 0, π 1,..., π r Q ND,χ, r N be such states that π 0 = 0 #0, < π 0, p 1, π 1 > δ ND,χ, < π 1, p 2, π 2 > δ ND,χ,..., < π r 1, p r, π r > δ ND,χ, p Σ {ɛ} for 0 r, π r F ND,χ ad p 1 p 2...p r = x 1 x 2...x k (t follows from x 1 x 2...x k L(A ND,χ (w)) that such states exst). Let r be such that r r ad p r = x k ad p r+1 = p r+2 =... = p r = ɛ. Obvously π r χ s π r. It follows from Proposto 12 that π r F ND,χ. Let M 0 = {0 #0 } ad M +1 = δ D,χ (M, x +1 ) for = 0, 1,..., k 1. We have to prove that M k F D,χ. Let j 1 < j 2 <... < j k be such that p j1 p j2...p jk = x 1 x 2...x k ad p j Σ for 1 k. Usg ducto o we prove that π M (π χ s π j ) f 1 k. 2.1) = 1 M 1 = δ D,χ ({0 #0 }, x 1 ) = δe D,χ (0 #0, x 1 ) Let η 1 = η 2 = 0 #0 ad s = j 1 1. Therefore < 0 #0, ɛ, π 1 > δ ND,χ < π 1, ɛ, π 2 > δ ND,χ,.,..., < π s 1, ɛ, π s > δ ND,χ ad < π s, x 1, π j1 > δ ND,χ It follows from Proposto 14 that π M 1 (π χ s π j1 ). 2.2) Iducto hypothess: π M (π χ s π j ). We have to prove that π M +1 (π χ s π j+1 ). Let η 1 M ad η 1 χ s π j. Let η 2 = π j. Obvously we ca fd such s N that < η 2, ɛ, π j +1 >, < π j +1, ɛ, π j +2 >,...,. Let π δe D,χ (t follows from Proposto 14 that such π exsts). < π j+s 1, ɛ, π j+s > < π j+s, x +1, π j+1 > δ ND,χ such that π χ s π j+1 π q M δ D,χ M +1 = e (q, x +1 ) q M δ D,χ e (q, x +1 ) (η 1, x +1 ) be Therefore π M +1 (π χ s π ). Therefore π M +1 (π χ s π j+1 ). We proved that π M (π χ s π j ) f 1 k. So π M k (π χ s π jk ). π jk = π r F ND,χ. Hece π M k (π F ND,χ ). Therefore M k F D,χ ad x 1 x 2...x k L(A D,χ (w)). Proposto 16 Let χ {ɛ, t, ms}. Let w Σ, N, (w) =< Σ, Q ND,χ, I ND,χ, F ND,χ, δ ND,χ A ND,χ q δ D,χ e (π, x). The s N η 0 η 1...η s Q ND,χ δ ND,χ δ ND,χ ). & < η 1, ɛ, η 2 > δ ND,χ > ad π Q ND,χ, x Σ ad (η 0 = π & < η 0, ɛ, η 1 > &... & < η s 1, ɛ, η s > δ ND,χ & < η s, x, q > 26

28 Fg. 7 Proof Proposto 16 follows drectly from the deftos of δ D,χ e ad δ ND,χ. Proposto 17 Let χ {ɛ, t, ms}. Let w Σ, N, A ND,χ (w) =< Σ, Q ND,χ, I ND,χ, F ND,χ, δ ND,χ > ad A D,χ (w) =< Σ, Q D,χ, I D,χ, F D,χ, δ D,χ >. The L(A ND,χ (w)) L(A D,χ (w)). Proof Let x 1 x 2...x k L(A D,χ (w)). Let M 0 = {0 #0 }, M +1 = δ D,χ (M, x +1 ) for 0 k 1 ad M k F D,χ. We prove wth ducto o that π M (< 0 #0, x 1 x 2...x, π > δ ND,χ ) f 0 k. 1) = 0 < 0 #0, ɛ, 0 #0 > δ ND,χ 2) Iducto hypothess: π M (< 0 #0, x 1 x 2...x, π > δ ND,χ We have to prove that π M +1 (< 0 #0, x 1 x 2...x +1, π > δ ND,χ M +1 = (q, x +1 ) δe D,χ q M q M δ D,χ e (q, x +1 ) Let π M +1. Therefore q M (π δe D,χ (q, x +1 )). Let q be such that q M ad π δe D,χ (q, x +1 ). Therefore < 0 #0, x 1 x 2...x, q > δ ND,χ It follows from Proposto 16 that < q, x +1, π 0 #0, x 1 x 2...x +1, π > δ ND,χ. > δ ND,χ We proved that π M (< 0 #0, x 1 x 2...x, π > δ ND,χ π be such that π M k F ND,χ (sce M k F D,χ < 0 #0, x 1 x 2...x k, π > δ ND,χ ad x1 x 2...x k L(A D,χ Corollary Let χ {ɛ, t, ms}. Let w Σ, N, A ND,χ (w) =< Σ, Q ND,χ, I ND,χ, F ND,χ, δ ND,χ > ad A D,χ It follows from Proposto 17 ad Proposto 15 that L χ Lev L(A D,χ (w)). ) )... Therefore < ) f 0 k. Let such π exsts). Therefore (w)). (w) =< Σ, Q D,χ, I D,χ, F D,χ, δ D,χ >. (, w) = L(AND,χ (w)) = 27

29 I [SMFSCLA] we ca also fd proof that L χ Lev (, w) = L(AD,χ (w)). Proposto 18 Let χ {ɛ, t, ms}. Let N ad b {0, 1}. The 1) δe D,χ ( + t #e, b) = {j + t #f (t s) j#f (t s) δd,χ e ( #e, b)} 2) δe D,χ ( + t #e t, b) = {j + t #f (t) j#f (t) δd,χ e ( #e t, b)} 3) δe D,χ ( + t #e s, b) = {j + t #f (s) j#f (s) δd,χ e ( #e s, b)} Proof Proposto 18 follows drectly from the defto of δ D,χ e. 5 Uversal Leveshte automata. We show that for each N we ca buld fte determstc automato A,χ such way that: 1) whe χ = ɛ every ofal state of A,χ s fte set that cossts of elemets of the type I + #e ad every fal state s fte set that cossts of elemets of the type M +j #f. (Whe χ = t there are the states also elemets of the type I t + #e ad M t + j #f, whe χ = ms - of the type I s + #e ad M s + j #f ); 2) each symbol from the put alphabet for A,χ s bary vector,.e. word from the laguage {0, 1} ; 3) for every two words v 1 v 2...v k ad w from Σ we ca buld b = b 1 b 2...b k such that b {0, 1} ad b L(A,χ ) v L(A D,χ (w)),.e. b L(A,χ ) v L χ Lev (, w) (v s called symbol correspodg to the word b ). Let q0, q1,..., qk be the states of A,χ that we vst traversg A,χ wth b ad let q0 D, q1 D,..., qk D be the states of AD,χ (w) that we vst traversg A D,χ (w) wth v. We buld A,χ such way that we receve qj D whe we replace the parameters I qj wth j (f qj s ofal) or the parameters M q j wth k (f q j s fal). Ad also: qj s fal oly f qd j s fal. Notatos We use expressos of the type F (I) #e, F (I t ) #e, F (I s ) #e, F (M) #e, F (M t ) #e ad F (M s ) #e to deote correspodgly tuples of the type << λi.f (I), 0 >, e >, << λi.f (I), 1 >, e >, << λi.f (I), 2 >, e >, << λm.f (M), 3 >, e >, << λm.f (M), 4 >, e > ad << λm.f (M), 5 >, e >. A,χ. Defto 15 Let χ {ɛ, t, ms}. Let N. We defe the fte automato Σ A,χ def = < Σ, Q,χ def = {x x {0, 1} + & x 2 + 2}, I,χ, F,χ, δ,χ > We defe Is χ : 1) χ = ɛ def = {I + t #k t k & t & 0 k } I ɛ s 28

30 Fg. 8 I ɛ s, = 2 2) χ = t def = Is ɛ {It + t #k t k & t 2 & 1 k } I t s Fg. 9 I t s, = 2 3) χ = ms I ms s def = I ɛ s {Is + t #k t k & t 2 & 1 k } 29

31 Fg. 10 I ms s, = 2 We defe Ms χ : 1) χ = ɛ def = {M + t #k k t & 2 t 0 & 0 k } M ɛ s Fg. 11 M ɛ s, = 2 2) χ = t 30

32 Ms t def = Ms ɛ {Mt + t #k k t & 2 t 2 & 1 k } Fg. 12 M t s, = 2 3) χ = ms M ms s def = M ɛ s {Ms + t #k k t & 2 t 1 & 1 k } Fg. 13 M ms s, = 2 We defe < χ s (I χ s M χ s ) (I χ s M χ s ): 31

33 1) χ = ɛ I + #e < ɛ #f def s I + j #e < ɛ s j #f M + #e < ɛ #f def s M + j #e < ɛ s j #f 2) χ = t I + #e < t #f def s I + j #e < t s j #f I + #e < t #f def s I t + j #e < t s j #f t M + #e < t #f def s M + j #e < t s j #f M + #e < t #f def s M t + j #e < t s j #f t 3) χ = ms #f def I + j #e < ms I + #e < ms s I + #e < ms s M + #e < ms s M + #e < ms s #f def I s + j #f def M + j #f def M s + j #e < ms s j #f s j s #f #e < ms #e < ms s j #f s j s #f We are ready to defe I χ states ad M χ states. I χ def states M χ states = {Q Q Is χ & q 1, q 2 Q(q 1 χ s q 2 )}\{φ} def = {Q Q Ms χ & q 1, q 2 Q(q 1 χ s q 2 ) & q Q(q χ s M # ) & [, 0] q Q(M + #0 χ s q)} Q,χ def,χ def I F,χ = I χ states = {I #0 } def = M χ states M χ states {0, 1}. r (S, x) represets the char- correspodg to S ad the We defe r : (Is χ M χ s ) Σ acterstc vetor determed by the state of A ND,χ symbol correspodg to x. 1) S = I + #e or S = I t + #e or S = I s + #e r (S, x 1 x 2...x k ) def = where h = m( e + 1, k ) x ++1 x x ++h f h > 0 ɛ f h = 0! f h < 0 32

34 Fg. 14 r 5 (I + 1 #3, x 1 x 2...x 12 ) = x 7 x 8 x 9 2) S = M + #e or S = M t + #e or S = M s + #e r (S, x 1 x 2...x k ) def = where h = m( e + 1, ) x k++1 x k++2...x k++h f h > 0 ɛ f h = 0! f h < 0 Fg. 15 r 5 (M 4 #3, x 1 x 2...x 7 ) = x 4 x 5 x 6 33

35 P ɛ def = {I + #e, e Z} {M + #e, e Z} = P ɛ {I t + #e, e Z} {M t + #e, e Z} ms def P = P ɛ {I s + #e, e Z} {M s + #e, e Z} P t def We defe m : P χ N P χ. Whe from some ofal state wth word b 1 b 2...b k Σ we have to reach fal state, we use the fucto m to covert the elemets of the type I + #e to elemets of the type M + #e. Ad also, whe from some fal state we have to reach ofal state, we covert wth the fucto m the elemets of the type M + #e to elemets of the type I + #e. 1) χ = ɛ 2) χ = t 3) χ = ms m (S, k) def = m (S, k) def = m (S, k) def = { M k #e f S = I + #e We defe m : P (P χ ) N P (P χ ): m (A, x) def = {m (a, x) a A} I k #e f S = M + #e M k #e f S = I + #e I k #e f S = M + #e M t k #e f S = I t + #e I t k #e f S = M t + #e M k #e f S = I + #e I k #e f S = M + #e M s k #e f S = I s + #e I s k #e f S = M s + #e We defe also f : (Is χ M χ s ) N {true, false}. Let A be some ofal state. We wat to fd the state A reached from A wth the symbol b = b 1 b 2...b k. Frst usg A ad b we fd ew set B that cossts of elemets of the type I + #e. B s a caddate to be A. But f f (S, k) = true for some elemet S from B, B s ot good caddate ad A = m (B, k). If for each elemet S from B we have that f (S, k) = false B s good caddate ad A = B. I A ND,χ (w) a state #e s fal f e <= ( w ).e. f the state s o the rght of the dagoal y = x ( w ). For ofal state A,χ (w) the dagoal correspodg to y = x ( w ) s I + k 2 + t #t (0 t ), f k < (k s the legth of the put symbol b). I A D,χ (w) there s o ofte state that has a elemet o the rght of the dagoal y = x ( w ). Aalogously A,χ the trasto fucto δ,χ s ot defed for ofal state ad symbol b 1 b 2...b k f the ofal state has a elemet of the type I + #e o the rght of the dagoal I + k 2 + t #t. (The caddate B has such a elemet S oly f f (S, k) = true.) The oly oe excepto s {I #0 } - the tal 34

36 state of A,χ. Ths state s ofal but A D,χ (w) ts correspodg state {0 #0 } may be fal. For each fal state of A,χ the dagoal correspodg to y = x ( w ) cossts of the elemets M + t #t (0 t ) ad the trasto fucto δ,χ s ot defed for a fal state whose elemets of the type M + #e are o the rght of the dagoal M + t #t. (S s o the rght of the dagoal M + t #t oly f f (S, k) = true.) 1) S = I + #e or S = I t + #e or S = I s + #e f (S, k) def = { true f k & e k f alse otherwse 2) S = M + #e or S = M t + #e or S = M s + #e f (S, k) def = { true f e > + f alse otherwse We defe I χ : P (Q ND,χ ) P (P χ ): 1) χ = ɛ I ɛ (A) def = {I + 1 #e #e A} 2) χ = t I t (A) def = {I + 1 #e #e A} {I t + 1 #e #e t A} 3) χ = ms I ms (A) def = {I + 1 #e #e A} {I s + 1 #e #e s A} We defe M χ : P (Q ND,χ ) P (P χ ): 1) χ = ɛ M ɛ (A) def = {M + #e #e A} 2) χ = t M t (A) def = {M + #e #e A} {M t + #e #e t A} 3) χ = ms M ms (A) def = {M + #e #e A} {M s + #e #e s A} We defe rm : I χ states M χ states Is ɛ M ɛ s : rm(a) def = { I + #e f A I χ states & e = µz[z = e & I + #e A] M + #e f A M χ states & e = µz[z = e & M + #e A] The elemet rm(a) s called rght-most elemet of A. To kow whether we have to covert wth the fucto m the caddate B that we receve from some state ad put character b 1 b 2...b k, t s suffce to check the value f (rm(b), k) because for each ofal state A ad each k t s true that f (rm(a), k) = false S A(S = I + #e for some ad e f (S, k) = false) ad for each fal state C t s true that f (rm(c), k) = true S C(S = M + #e for some ad e f (S, k) = true),.e. t s o matter whether the type of the elemets of the caddate B s I + #e or t s M + #e - f f (rm(b), k) = true 35

37 the B s ot good caddate ad f f (rm(b), k) = false the B s good. δ,χ e We defe the fucto of the elemetary trastos : (Is χ M χ s ) Σ I χ states M χ states {φ}: 1) Let S = I + #e or S = I t + #e or S = I s + #e. We defe δe,χ 1.1)!r (S, x)!δ,χ e (S, x) 1.2)!r (S, x) δe,χ (S, x) def = I χ (δ D,χ e ( #e, r (S, x))) f S = I + #e I χ (δ D,χ e ( #e t, r (S, x))) f S = I t + #e I χ (δ D,χ e ( #e s, r (S, x))) f S = I s + #e (S, x): 2) Let S = M + #e or S = M t + #e or S = M s + #e. We defe δe,χ (S, x): 2.1)!r (S, x)!δe,χ (S, x) 2.2)!r (S, x) δe,χ (S, x) def = M χ (δ D,χ e ( #e, r (S, x))) f S = M + #e M χ (δ D,χ e ( #e t, r (S, x))) f S = M t + #e M χ (δ D,χ e ( #e s, r (S, x))) f S = M s + #e We defe : P (P (I χ s )) P (P (M χ s )) P (I χ s ) P (M χ s ): A def = {π π A & π A(π < χ s π)} We dfe a : I χ states M χ states P (N). Ths fucto s used for checkg whether the legth of the word b 1 b 2...b k s sutable to defe the trasto fucto δ,χ 1) Let Q I χ states 1.1) Q = {I #0 }. If k a (Q) the!δ,χ (Q, b 1 b 2...b k ). a (Q) def = {k k 2 + 2} 1.2) Q {I #0 } Let rm(q) = I + #e a (Q) def = {k 2 + e + 1 k 2 + 2} 36

38 Fg. 16 = 5, a ({I 2 #2, I 1 #2, I + 1 #3 }) = {9, 10, 11, 12} The legth k of the put charcter x 1 x 2...x k must be such that all elemets of the type I + #e to be o the left of the dagoal I + k 2 + t #t. 2) Let Q M χ states a (Q) def = {k N π Q(f(k <, M # k, M + k #0 ) χ s π)}\{0} 37

39 Fg. 17 = 5, a ({M 4 #2, M 2 #3, M 1 #3 }) = {7, 8, 9} We are ready to defe the trasto fucto δ,χ Let Q Q,χ ad x Σ. 1) x a (Q)!δ,χ (Q, x) 2) x a (Q) 2.1) q Q δ,χ e (q, x) = φ!δ,χ (Q, x) 2.2) q Q δ,χ e (q, x) φ Let = q Q δ,χ e (q, x). : Q,χ Σ Q,χ. { δ,χ (Q, x) def f f (rm( ), x ) = false = m (, x ) f f (rm( ), x ) = true I what follows we suppose that I χ states = {A x Σ (δ,χ ({I #0 }, x) = A) & A Is χ } ad M χ states = {A x Σ (δ,χ ({I #0 }, x) = A) & A Ms χ },.e. we cosder that A,χ has o state that caot be reached from {I #0 }. Defto of A,ɛ s gve [MSFASLD]. The three automata A,ɛ 1, A,t 1 ad A,ms 1 are depcted resp. o fg. 18, fg. 19 ad fg. 20. I these fgures x ca be terpreted as 0 or 1 ad the expressos brackets are optoal. For stace from {I #1, I + 1 #1 } wth x10(x) we ca reach {I #1 }. Ths meas that from {I #1, I + 1 #1 } we ca reach {I #1 } wth 010, 110, 0100, 0101, 1100 ad

40 Fg. 18 A,ɛ 1 39

41 Fg. 19 A,t 1 40

42 Fg. 20 A,ms 1 Defto 16 Let N ad $ Σ. def def w +1 = w +2 =... def def = w 0 = $ 41

43 s : Σ N + (Σ {$}) s (w, ) def = where v = m( w, + + 1). h : Σ Σ + Σ h (w, x 1 x 2...x t ) def = { w w +1...w v f v! f v < { β(x1, s (w, 1))β(x 2, s (w, 2))...β(x t, s (w, t)) f t w +! f t > w + We gve a example how A,χ ca be used. Let w = abcabb ad x = dacab. We wat to kow whether x L χ Lev (3, w). We fd b = h 3(w, x) = b 1 b 2...b 5. b 1 = β(x 1, s 3 (w, 1)) = β(d, $$$abcab) = , b 2 = β(x 2, s 3 (w, 2)) = β(a, $$abcabb) = , b 3 = β(x 3, s 3 (w, 3)) = β(c, $abcabb) = , b 4 = β(x 4, s 3 (w, 4)) = β(a, abcabb) = ad b 5 = β(x 5, s 3 (w, 5)) = β(b, bcabb) = x L χ Lev (w, 3) b L(A,χ 3 ). Proposto 19 Let χ {ɛ, t, ms}. Let w Σ, x Σ +, N +,!h (w, x), b = h (w, x) ad b = x = t. Let q,χ 0 = {I { #0 } ad δ +1 =,χ (q,χ, b +1 ) f!q,χ &!δ,χ! otherwse q,χ (q,χ, b +1 ) for 0 t 1. Let w = p. Let s : [0, t] N ad Let A D,χ (w) =< Σ, Q D,χ s() def = { p f q,χ f q,χ F,χ F,χ, I D,χ, F D,χ, δ D,χ >. Let q D,χ 0 = {0 { #0 } ad δ +1 = D,χ (q D,χ, x +1 ) f!q D,χ &!δ D,χ! otherwse q D,χ (q D,χ, x +1 ) for 0 t 1. Let d : (Is χ M χ s ) N Q ND,χ ad 1) whe χ = ɛ d(i + #e, z) def = z + #e ad d(m + #e, z) def = z + #e 2) whe χ = t d(i + #e, z) def = z + #e, d(m + #e, z) def = z + #e, d(i t + #e, z) def = z + #e t ad d(m t + #e, z) def = z + #e t 42

Estimators when the Correlation Coefficient. is Negative

Estimators when the Correlation Coefficient. is Negative It J Cotemp Math Sceces, Vol 5, 00, o 3, 45-50 Estmators whe the Correlato Coeffcet s Negatve Sad Al Al-Hadhram College of Appled Sceces, Nzwa, Oma abur97@ahoocouk Abstract Rato estmators for the mea of

Διαβάστε περισσότερα

Ordinal Arithmetic: Addition, Multiplication, Exponentiation and Limit

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

Διαβάστε περισσότερα

1. For each of the following power series, find the interval of convergence and the radius of convergence:

1. For each of the following power series, find the interval of convergence and the radius of convergence: Math 6 Practice Problems Solutios Power Series ad Taylor Series 1. For each of the followig power series, fid the iterval of covergece ad the radius of covergece: (a ( 1 x Notice that = ( 1 +1 ( x +1.

Διαβάστε περισσότερα

C.S. 430 Assignment 6, Sample Solutions

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

Διαβάστε περισσότερα

Fractional Colorings and Zykov Products of graphs

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

Διαβάστε περισσότερα

2 Composition. Invertible Mappings

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

Διαβάστε περισσότερα

L.K.Gupta (Mathematic Classes) www.pioeermathematics.com MOBILE: 985577, 4677 + {JEE Mai 04} Sept 0 Name: Batch (Day) Phoe No. IT IS NOT ENOUGH TO HAVE A GOOD MIND, THE MAIN THING IS TO USE IT WELL Marks:

Διαβάστε περισσότερα

Multi-dimensional Central Limit Theorem

Multi-dimensional Central Limit Theorem Mult-dmensonal Central Lmt heorem Outlne () () () t as () + () + + () () () Consder a sequence of ndependent random proceses t, t, dentcal to some ( t). Assume t 0. Defne the sum process t t t t () t tme

Διαβάστε περισσότερα

Markov Processes and Applications

Markov Processes and Applications Markov rocesses ad Applcatos Dscrete-Tme Markov Chas Cotuous-Tme Markov Chas Applcatos Queug theory erformace aalyss ΠΜΣ524: Μοντελοποίηση και Ανάλυση Απόδοσης Δικτύων (Ι. Σταυρακάκης - ΕΚΠΑ) Dscrete-Tme

Διαβάστε περισσότερα

SUPERPOSITION, MEASUREMENT, NORMALIZATION, EXPECTATION VALUES. Reading: QM course packet Ch 5 up to 5.6

SUPERPOSITION, MEASUREMENT, NORMALIZATION, EXPECTATION VALUES. Reading: QM course packet Ch 5 up to 5.6 SUPERPOSITION, MEASUREMENT, NORMALIZATION, EXPECTATION VALUES Readig: QM course packet Ch 5 up to 5. 1 ϕ (x) = E = π m( a) =1,,3,4,5 for xa (x) = πx si L L * = πx L si L.5 ϕ' -.5 z 1 (x) = L si

Διαβάστε περισσότερα

Discrete Fourier Transform { } ( ) sin( ) Discrete Sine Transformation. n, n= 0,1,2,, when the function is odd, f (x) = f ( x) L L L N N.

Discrete Fourier Transform { } ( ) sin( ) Discrete Sine Transformation. n, n= 0,1,2,, when the function is odd, f (x) = f ( x) L L L N N. Dscrete Fourer Trasform Refereces:. umercal Aalyss of Spectral Methods: Theory ad Applcatos, Davd Gottleb ad S.A. Orszag, Soc. for Idust. App. Math. 977.. umercal smulato of compressble flows wth smple

Διαβάστε περισσότερα

Examples of Cost and Production Functions

Examples of Cost and Production Functions Dvso of the Humates ad Socal Sceces Examples of Cost ad Producto Fuctos KC Border October 200 v 20605::004 These otes sho ho you ca use the frst order codtos for cost mmzato to actually solve for cost

Διαβάστε περισσότερα

The challenges of non-stable predicates

The challenges of non-stable predicates The challenges of non-stable predicates Consider a non-stable predicate Φ encoding, say, a safety property. We want to determine whether Φ holds for our program. The challenges of non-stable predicates

Διαβάστε περισσότερα

One and two particle density matrices for single determinant HF wavefunctions. (1) = φ 2. )β(1) ( ) ) + β(1)β * β. (1)ρ RHF

One and two particle density matrices for single determinant HF wavefunctions. (1) = φ 2. )β(1) ( ) ) + β(1)β * β. (1)ρ RHF One and two partcle densty matrces for sngle determnant HF wavefunctons One partcle densty matrx Gven the Hartree-Fock wavefuncton ψ (,,3,!, = Âϕ (ϕ (ϕ (3!ϕ ( 3 The electronc energy s ψ H ψ = ϕ ( f ( ϕ

Διαβάστε περισσότερα

Multi-dimensional Central Limit Theorem

Multi-dimensional Central Limit Theorem Mult-dmensonal Central Lmt heorem Outlne () () () t as () + () + + () () () Consder a sequence of ndependent random proceses t, t, dentcal to some ( t). Assume t 0. Defne the sum process t t t t () t ();

Διαβάστε περισσότερα

Partial Differential Equations in Biology The boundary element method. March 26, 2013

Partial Differential Equations in Biology The boundary element method. March 26, 2013 The boundary element method March 26, 203 Introduction and notation The problem: u = f in D R d u = ϕ in Γ D u n = g on Γ N, where D = Γ D Γ N, Γ D Γ N = (possibly, Γ D = [Neumann problem] or Γ N = [Dirichlet

Διαβάστε περισσότερα

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

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

Διαβάστε περισσότερα

The Simply Typed Lambda Calculus

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

Διαβάστε περισσότερα

Lecture 2. Soundness and completeness of propositional logic

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

Διαβάστε περισσότερα

Homework 3 Solutions

Homework 3 Solutions Homework 3 Solutions Igor Yanovsky (Math 151A TA) Problem 1: Compute the absolute error and relative error in approximations of p by p. (Use calculator!) a) p π, p 22/7; b) p π, p 3.141. Solution: For

Διαβάστε περισσότερα

Πανεπιστήµιο Κρήτης - Τµήµα Επιστήµης Υπολογιστών. ΗΥ-570: Στατιστική Επεξεργασία Σήµατος. ιδάσκων : Α. Μουχτάρης. εύτερη Σειρά Ασκήσεων.

Πανεπιστήµιο Κρήτης - Τµήµα Επιστήµης Υπολογιστών. ΗΥ-570: Στατιστική Επεξεργασία Σήµατος. ιδάσκων : Α. Μουχτάρης. εύτερη Σειρά Ασκήσεων. Πανεπιστήµιο Κρήτης - Τµήµα Επιστήµης Υπολογιστών ΗΥ-570: Στατιστική Επεξεργασία Σήµατος 2015 ιδάσκων : Α. Μουχτάρης εύτερη Σειρά Ασκήσεων Λύσεις Ασκηση 1. 1. Consder the gven expresson for R 1/2 : R 1/2

Διαβάστε περισσότερα

Homomorphism in Intuitionistic Fuzzy Automata

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

Διαβάστε περισσότερα

4.6 Autoregressive Moving Average Model ARMA(1,1)

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

Διαβάστε περισσότερα

Exam Statistics 6 th September 2017 Solution

Exam Statistics 6 th September 2017 Solution Exam Statstcs 6 th September 17 Soluto Maura Mezzett Exercse 1 Let (X 1,..., X be a raom sample of... raom varables. Let f θ (x be the esty fucto. Let ˆθ be the MLE of θ, θ be the true parameter, L(θ be

Διαβάστε περισσότερα

Matrices and Determinants

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

Διαβάστε περισσότερα

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

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

Διαβάστε περισσότερα

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

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

Διαβάστε περισσότερα

On Generating Relations of Some Triple. Hypergeometric Functions

On Generating Relations of Some Triple. Hypergeometric Functions It. Joural of Math. Aalysis, Vol. 5,, o., 5 - O Geeratig Relatios of Some Triple Hypergeometric Fuctios Fadhle B. F. Mohse ad Gamal A. Qashash Departmet of Mathematics, Faculty of Educatio Zigibar Ade

Διαβάστε περισσότερα

CS 1675 Introduction to Machine Learning Lecture 7. Density estimation. Milos Hauskrecht 5329 Sennott Square

CS 1675 Introduction to Machine Learning Lecture 7. Density estimation. Milos Hauskrecht 5329 Sennott Square CS 675 Itroducto to Mache Learg Lecture 7 esty estmato Mlos Hausrecht mlos@cs.tt.edu 539 Seott Square ata: esty estmato {.. } a vector of attrbute values Objectve: estmate the model of the uderlyg robablty

Διαβάστε περισσότερα

n r f ( n-r ) () x g () r () x (1.1) = Σ g() x = Σ n f < -n+ r> g () r -n + r dx r dx n + ( -n,m) dx -n n+1 1 -n -1 + ( -n,n+1)

n r f ( n-r ) () x g () r () x (1.1) = Σ g() x = Σ n f < -n+ r> g () r -n + r dx r dx n + ( -n,m) dx -n n+1 1 -n -1 + ( -n,n+1) 8 Higher Derivative of the Product of Two Fuctios 8. Leibiz Rule about the Higher Order Differetiatio Theorem 8.. (Leibiz) Whe fuctios f ad g f g are times differetiable, the followig epressio holds. r

Διαβάστε περισσότερα

Homework for 1/27 Due 2/5

Homework for 1/27 Due 2/5 Name: ID: Homework for /7 Due /5. [ 8-3] I Example D of Sectio 8.4, the pdf of the populatio distributio is + αx x f(x α) =, α, otherwise ad the method of momets estimate was foud to be ˆα = 3X (where

Διαβάστε περισσότερα

Statistical Inference I Locally most powerful tests

Statistical Inference I Locally most powerful tests Statistical Inference I Locally most powerful tests Shirsendu Mukherjee Department of Statistics, Asutosh College, Kolkata, India. shirsendu st@yahoo.co.in So far we have treated the testing of one-sided

Διαβάστε περισσότερα

Other Test Constructions: Likelihood Ratio & Bayes Tests

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

Διαβάστε περισσότερα

Solution Series 9. i=1 x i and i=1 x i.

Solution Series 9. i=1 x i and i=1 x i. Lecturer: Prof. Dr. Mete SONER Coordinator: Yilin WANG Solution Series 9 Q1. Let α, β >, the p.d.f. of a beta distribution with parameters α and β is { Γ(α+β) Γ(α)Γ(β) f(x α, β) xα 1 (1 x) β 1 for < x

Διαβάστε περισσότερα

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

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

Διαβάστε περισσότερα

derivation of the Laplacian from rectangular to spherical coordinates

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

Διαβάστε περισσότερα

Ψηφιακή Επεξεργασία Εικόνας

Ψηφιακή Επεξεργασία Εικόνας ΠΑΝΕΠΙΣΤΗΜΙΟ ΙΩΑΝΝΙΝΩΝ ΑΝΟΙΚΤΑ ΑΚΑΔΗΜΑΪΚΑ ΜΑΘΗΜΑΤΑ Ψηφιακή Επεξεργασία Εικόνας Φιλτράρισμα στο πεδίο των συχνοτήτων Διδάσκων : Αναπληρωτής Καθηγητής Νίκου Χριστόφορος Άδειες Χρήσης Το παρόν εκπαιδευτικό

Διαβάστε περισσότερα

HOMEWORK 4 = G. In order to plot the stress versus the stretch we define a normalized stretch:

HOMEWORK 4 = G. In order to plot the stress versus the stretch we define a normalized stretch: HOMEWORK 4 Problem a For the fast loading case, we want to derive the relationship between P zz and λ z. We know that the nominal stress is expressed as: P zz = ψ λ z where λ z = λ λ z. Therefore, applying

Διαβάστε περισσότερα

CHAPTER 25 SOLVING EQUATIONS BY ITERATIVE METHODS

CHAPTER 25 SOLVING EQUATIONS BY ITERATIVE METHODS CHAPTER 5 SOLVING EQUATIONS BY ITERATIVE METHODS EXERCISE 104 Page 8 1. Find the positive root of the equation x + 3x 5 = 0, correct to 3 significant figures, using the method of bisection. Let f(x) =

Διαβάστε περισσότερα

A Class of Orthohomological Triangles

A Class of Orthohomological Triangles A Class of Orthohomologcal Trangles Prof. Claudu Coandă Natonal College Carol I Craova Romana. Prof. Florentn Smarandache Unversty of New Mexco Gallup USA Prof. Ion Pătraşcu Natonal College Fraţ Buzeşt

Διαβάστε περισσότερα

ST5224: Advanced Statistical Theory II

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

Διαβάστε περισσότερα

α & β spatial orbitals in

α & β spatial orbitals in The atrx Hartree-Fock equatons The most common method of solvng the Hartree-Fock equatons f the spatal btals s to expand them n terms of known functons, { χ µ } µ= consder the spn-unrestrcted case. We

Διαβάστε περισσότερα

Solutions to Exercise Sheet 5

Solutions to Exercise Sheet 5 Solutions to Eercise Sheet 5 jacques@ucsd.edu. Let X and Y be random variables with joint pdf f(, y) = 3y( + y) where and y. Determine each of the following probabilities. Solutions. a. P (X ). b. P (X

Διαβάστε περισσότερα

Concrete Mathematics Exercises from 30 September 2016

Concrete Mathematics Exercises from 30 September 2016 Concrete Mathematics Exercises from 30 September 2016 Silvio Capobianco Exercise 1.7 Let H(n) = J(n + 1) J(n). Equation (1.8) tells us that H(2n) = 2, and H(2n+1) = J(2n+2) J(2n+1) = (2J(n+1) 1) (2J(n)+1)

Διαβάστε περισσότερα

Econ 2110: Fall 2008 Suggested Solutions to Problem Set 8 questions or comments to Dan Fetter 1

Econ 2110: Fall 2008 Suggested Solutions to Problem Set 8  questions or comments to Dan Fetter 1 Eon : Fall 8 Suggested Solutions to Problem Set 8 Email questions or omments to Dan Fetter Problem. Let X be a salar with density f(x, θ) (θx + θ) [ x ] with θ. (a) Find the most powerful level α test

Διαβάστε περισσότερα

6.1. Dirac Equation. Hamiltonian. Dirac Eq.

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

Διαβάστε περισσότερα

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

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

Διαβάστε περισσότερα

Reminders: linear functions

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

Διαβάστε περισσότερα

Optimal stopping under nonlinear expectation

Optimal stopping under nonlinear expectation Avalable ole at www.scecedrect.com SceceDrect Stochastc Processes ad ther Applcatos 124 (2014) 3277 3311 www.elsever.com/locate/spa Optmal stoppg uder olear expectato Ibrahm Ekre a, Nzar Touz b, Jafeg

Διαβάστε περισσότερα

Finite Field Problems: Solutions

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

Διαβάστε περισσότερα

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

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

Διαβάστε περισσότερα

2. THEORY OF EQUATIONS. PREVIOUS EAMCET Bits.

2. THEORY OF EQUATIONS. PREVIOUS EAMCET Bits. EAMCET-. THEORY OF EQUATIONS PREVIOUS EAMCET Bits. Each of the roots of the equation x 6x + 6x 5= are increased by k so that the new transformed equation does not contain term. Then k =... - 4. - Sol.

Διαβάστε περισσότερα

Last Lecture. Biostatistics Statistical Inference Lecture 19 Likelihood Ratio Test. Example of Hypothesis Testing.

Last Lecture. Biostatistics Statistical Inference Lecture 19 Likelihood Ratio Test. Example of Hypothesis Testing. Last Lecture Biostatistics 602 - Statistical Iferece Lecture 19 Likelihood Ratio Test Hyu Mi Kag March 26th, 2013 Describe the followig cocepts i your ow words Hypothesis Null Hypothesis Alterative Hypothesis

Διαβάστε περισσότερα

EE512: Error Control Coding

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

Διαβάστε περισσότερα

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

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

Διαβάστε περισσότερα

Chap. 6 Pushdown Automata

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

Διαβάστε περισσότερα

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

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

Διαβάστε περισσότερα

New bounds for spherical two-distance sets and equiangular lines

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

Διαβάστε περισσότερα

ANSWERSHEET (TOPIC = DIFFERENTIAL CALCULUS) COLLECTION #2. h 0 h h 0 h h 0 ( ) g k = g 0 + g 1 + g g 2009 =?

ANSWERSHEET (TOPIC = DIFFERENTIAL CALCULUS) COLLECTION #2. h 0 h h 0 h h 0 ( ) g k = g 0 + g 1 + g g 2009 =? Teko Classes IITJEE/AIEEE Maths by SUHAAG SIR, Bhopal, Ph (0755) 3 00 000 www.tekoclasses.com ANSWERSHEET (TOPIC DIFFERENTIAL CALCULUS) COLLECTION # Question Type A.Single Correct Type Q. (A) Sol least

Διαβάστε περισσότερα

Example Sheet 3 Solutions

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

Διαβάστε περισσότερα

ORDINAL ARITHMETIC JULIAN J. SCHLÖDER

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

Διαβάστε περισσότερα

Models for Probabilistic Programs with an Adversary

Models for Probabilistic Programs with an Adversary Models for Probabilistic Programs with an Adversary Robert Rand, Steve Zdancewic University of Pennsylvania Probabilistic Programming Semantics 2016 Interactive Proofs 2/47 Interactive Proofs 2/47 Interactive

Διαβάστε περισσότερα

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

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

Διαβάστε περισσότερα

SOLUTIONS TO MATH38181 EXTREME VALUES AND FINANCIAL RISK EXAM

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

Διαβάστε περισσότερα

Μηχανική Μάθηση Hypothesis Testing

Μηχανική Μάθηση Hypothesis Testing ΕΛΛΗΝΙΚΗ ΔΗΜΟΚΡΑΤΙΑ ΠΑΝΕΠΙΣΤΗΜΙΟ ΚΡΗΤΗΣ Μηχανική Μάθηση Hypothesis Testing Γιώργος Μπορμπουδάκης Τμήμα Επιστήμης Υπολογιστών Procedure 1. Form the null (H 0 ) and alternative (H 1 ) hypothesis 2. Consider

Διαβάστε περισσότερα

Markov Processes and Applications

Markov Processes and Applications Markov Processes ad Alcatos Dscrete-Tme Markov Chas Cotuous-Tme Markov Chas Alcatos Queug theory Performace aalyss Dscrete-Tme Markov Chas Books - Itroducto to Stochastc Processes (Erha Clar), Cha. 5,

Διαβάστε περισσότερα

On Hypersurface of Special Finsler Spaces. Admitting Metric Like Tensor Field

On Hypersurface of Special Finsler Spaces. Admitting Metric Like Tensor Field It J otem Mat Sceces Vo 7 0 o 9 99-98 O Hyersurface of Seca Fser Saces Admttg Metrc Lke Tesor Fed H Wosoug Deartmet of Matematcs Isamc Azad Uversty Babo Brac Ira md_vosog@yaoocom Abstract I te reset work

Διαβάστε περισσότερα

Generalized Fibonacci-Like Polynomial and its. Determinantal Identities

Generalized Fibonacci-Like Polynomial and its. Determinantal Identities Int. J. Contemp. Math. Scences, Vol. 7, 01, no. 9, 1415-140 Generalzed Fbonacc-Le Polynomal and ts Determnantal Identtes V. K. Gupta 1, Yashwant K. Panwar and Ompraash Shwal 3 1 Department of Mathematcs,

Διαβάστε περισσότερα

SCHOOL OF MATHEMATICAL SCIENCES G11LMA Linear Mathematics Examination Solutions

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

Διαβάστε περισσότερα

A Note on Intuitionistic Fuzzy. Equivalence Relation

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

Διαβάστε περισσότερα

Local Approximation with Kernels

Local Approximation with Kernels Local Approximation with Kernels Thomas Hangelbroek University of Hawaii at Manoa 5th International Conference Approximation Theory, 26 work supported by: NSF DMS-43726 A cubic spline example Consider

Διαβάστε περισσότερα

MINIMAL CLOSED SETS AND MAXIMAL CLOSED SETS

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

Διαβάστε περισσότερα

6.3 Forecasting ARMA processes

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

Διαβάστε περισσότερα

George S. A. Shaker ECE477 Understanding Reflections in Media. Reflection in Media

George S. A. Shaker ECE477 Understanding Reflections in Media. Reflection in Media Geoge S. A. Shake C477 Udesadg Reflecos Meda Refleco Meda Ths hadou ages a smplfed appoach o udesad eflecos meda. As a sude C477, you ae o equed o kow hese seps by hea. I s jus o make you udesad how some

Διαβάστε περισσότερα

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

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

Διαβάστε περισσότερα

Homework 8 Model Solution Section

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

Διαβάστε περισσότερα

Degenerate Perturbation Theory

Degenerate Perturbation Theory R.G. Griffi BioNMR School page 1 Degeerate Perturbatio Theory 1.1 Geeral Whe cosiderig the CROSS EFFECT it is ecessary to deal with degeerate eergy levels ad therefore degeerate perturbatio theory. The

Διαβάστε περισσότερα

Homework 4.1 Solutions Math 5110/6830

Homework 4.1 Solutions Math 5110/6830 Homework 4. Solutios Math 5/683. a) For p + = αp γ α)p γ α)p + γ b) Let Equilibria poits satisfy: p = p = OR = γ α)p ) γ α)p + γ = α γ α)p ) γ α)p + γ α = p ) p + = p ) = The, we have equilibria poits

Διαβάστε περισσότερα

IIT JEE (2013) (Trigonomtery 1) Solutions

IIT JEE (2013) (Trigonomtery 1) Solutions L.K. Gupta (Mathematic Classes) www.pioeermathematics.com MOBILE: 985577, 677 (+) PAPER B IIT JEE (0) (Trigoomtery ) Solutios TOWARDS IIT JEE IS NOT A JOURNEY, IT S A BATTLE, ONLY THE TOUGHEST WILL SURVIVE

Διαβάστε περισσότερα

Srednicki Chapter 55

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

Διαβάστε περισσότερα

Commutative Monoids in Intuitionistic Fuzzy Sets

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

Διαβάστε περισσότερα

2. Let H 1 and H 2 be Hilbert spaces and let T : H 1 H 2 be a bounded linear operator. Prove that [T (H 1 )] = N (T ). (6p)

2. Let H 1 and H 2 be Hilbert spaces and let T : H 1 H 2 be a bounded linear operator. Prove that [T (H 1 )] = N (T ). (6p) Uppsala Universitet Matematiska Institutionen Andreas Strömbergsson Prov i matematik Funktionalanalys Kurs: F3B, F4Sy, NVP 2005-03-08 Skrivtid: 9 14 Tillåtna hjälpmedel: Manuella skrivdon, Kreyszigs bok

Διαβάστε περισσότερα

Problem Set 3: Solutions

Problem Set 3: Solutions CMPSCI 69GG Applied Information Theory Fall 006 Problem Set 3: Solutions. [Cover and Thomas 7.] a Define the following notation, C I p xx; Y max X; Y C I p xx; Ỹ max I X; Ỹ We would like to show that C

Διαβάστε περισσότερα

( y) Partial Differential Equations

( y) Partial Differential Equations Partial Dierential Equations Linear P.D.Es. contains no owers roducts o the deendent variables / an o its derivatives can occasionall be solved. Consider eamle ( ) a (sometimes written as a ) we can integrate

Διαβάστε περισσότερα

SOLUTIONS TO MATH38181 EXTREME VALUES AND FINANCIAL RISK EXAM

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

Διαβάστε περισσότερα

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

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

Διαβάστε περισσότερα

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

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

Διαβάστε περισσότερα

Galatia SIL Keyboard Information

Galatia SIL Keyboard Information Galatia SIL Keyboard Information Keyboard ssignments The main purpose of the keyboards is to provide a wide range of keying options, so many characters can be entered in multiple ways. If you are typing

Διαβάστε περισσότερα

DESIGN OF MACHINERY SOLUTION MANUAL h in h 4 0.

DESIGN OF MACHINERY SOLUTION MANUAL h in h 4 0. DESIGN OF MACHINERY SOLUTION MANUAL -7-1! PROBLEM -7 Statement: Design a double-dwell cam to move a follower from to 25 6, dwell for 12, fall 25 and dwell for the remader The total cycle must take 4 sec

Διαβάστε περισσότερα

Uniform Convergence of Fourier Series Michael Taylor

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

Διαβάστε περισσότερα

Math 446 Homework 3 Solutions. (1). (i): Reverse triangle inequality for metrics: Let (X, d) be a metric space and let x, y, z X.

Math 446 Homework 3 Solutions. (1). (i): Reverse triangle inequality for metrics: Let (X, d) be a metric space and let x, y, z X. Math 446 Homework 3 Solutions. (1). (i): Reverse triangle inequalit for metrics: Let (X, d) be a metric space and let x,, z X. Prove that d(x, z) d(, z) d(x, ). (ii): Reverse triangle inequalit for norms:

Διαβάστε περισσότερα

Introduction of Numerical Analysis #03 TAGAMI, Daisuke (IMI, Kyushu University)

Introduction of Numerical Analysis #03 TAGAMI, Daisuke (IMI, Kyushu University) Itroductio of Numerical Aalysis #03 TAGAMI, Daisuke (IMI, Kyushu Uiversity) web page of the lecture: http://www2.imi.kyushu-u.ac.jp/~tagami/lec/ Strategy of Numerical Simulatios Pheomea Error modelize

Διαβάστε περισσότερα

1. Introduction and Preliminaries.

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

Διαβάστε περισσότερα

ΕΠΙΧΕΙΡΗΣΙΑΚΗ ΑΛΛΗΛΟΓΡΑΦΙΑ ΚΑΙ ΕΠΙΚΟΙΝΩΝΙΑ ΣΤΗΝ ΑΓΓΛΙΚΗ ΓΛΩΣΣΑ

ΕΠΙΧΕΙΡΗΣΙΑΚΗ ΑΛΛΗΛΟΓΡΑΦΙΑ ΚΑΙ ΕΠΙΚΟΙΝΩΝΙΑ ΣΤΗΝ ΑΓΓΛΙΚΗ ΓΛΩΣΣΑ Ανοικτά Ακαδημαϊκά Μαθήματα στο ΤΕΙ Ιονίων Νήσων ΕΠΙΧΕΙΡΗΣΙΑΚΗ ΑΛΛΗΛΟΓΡΑΦΙΑ ΚΑΙ ΕΠΙΚΟΙΝΩΝΙΑ ΣΤΗΝ ΑΓΓΛΙΚΗ ΓΛΩΣΣΑ Ενότητα 1: Elements of Syntactic Structure Το περιεχόμενο του μαθήματος διατίθεται με άδεια

Διαβάστε περισσότερα

Differentiation exercise show differential equation

Differentiation exercise show differential equation Differentiation exercise show differential equation 1. If y x sin 2x, prove that x d2 y 2 2 + 2y x + 4xy 0 y x sin 2x sin 2x + 2x cos 2x 2 2cos 2x + (2 cos 2x 4x sin 2x) x d2 y 2 2 + 2y x + 4xy (2x cos

Διαβάστε περισσότερα

Generating Set of the Complete Semigroups of Binary Relations

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

Διαβάστε περισσότερα

The ε-pseudospectrum of a Matrix

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

Διαβάστε περισσότερα

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

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

Διαβάστε περισσότερα

Article Multivariate Extended Gamma Distribution

Article Multivariate Extended Gamma Distribution axoms Artcle Multvarate Exteded Gamma Dstrbuto Dhaya P. Joseph Departmet of Statstcs, Kurakose Elas College, Maaam, Kottayam, Kerala 686561, Ida; dhayapj@gmal.com; Tel.: +91-9400-733-065 Academc Edtor:

Διαβάστε περισσότερα