Exercises with (Some) Solutions
|
|
- Lionel Perry
- 5 years ago
- Views:
Transcription
1 Exercises with (Some) Solutions Techer: Luc Tesei Mster of Science in Computer Science - University of Cmerino Contents 1 Strong Bisimultion nd HML 2 2 Wek Bisimultion 31 3 Complete Lttices nd Fix Points 33 1
2 1 Strong Bisimultion nd HML Exercise 1.1 Consider the following LTS: 1. Tell whether or not s1 is strongly isimilr to t1. Justify your nswer formlly. 2. Determine ll the sttes of the LTS tht stisfy the following formuls: []hitt []hcitt hihitt hcihi[c]ff [][]hcitt Exercise 1.2 Consider the following lelled trnsition system. so s1 / s2 Compute for which sets of sttes [[X]] {s, s1, s2 } the following formule re true. X = hitt []X X = hitt ([]X hitt) Exercise 1.3 Consider the following LTS: 2
3 1. Tell whether or not s 1 is strongly isimilr to t 1. Justify your nswer formlly. 2. Determine ll the sttes of the LTS tht stisfy the following formuls: [] tt ( tt c tt) [][c]ff Exercise 1.4 Consider the following LTS: 1. Tell whether or not s 1 is strongly isimilr to u 1. Justify your nswer formlly. 2. Determine ll the sttes of the LTS tht stisfy the following formuls: ϕ 1 = [] c tt ϕ 2 = c tt ϕ 3 = [] [c]ff ϕ 4 = [c]ff Exercise 1.5 Consider the following lelled trnsition system. s t t 3 t 4 s 1 s 2 t 1 s 3 s 4 t 2 Show tht s t y finding strong isimultion R contining the pir (s, t). 3
4 Exercise 1.6 Consider the CCS processes P nd Q defined y: P P 1 def =.P 1 def =.P + c.p nd Q Q 1 Q 2 Q 3 def =.Q 1 def =.Q 2 + c.q def =.Q 3 def =.Q + c.q 2. Show tht P Q holds y finding n pproprite strong isimultion. Exercise 1.7 Consider the following lelled trnsition system. s t u v s 1 t 1 u 1 u 2 v 1 v 2 s 2 t 2 u 3 v 3 Decide whether s? t, s? u, nd s? v. Support your clims y giving universl winning strtegy either for the ttcker (in the negtive cse) or the defender (in the positive cse). In the positive cse you cn lso define strong isimultion relting the pir in question. Exercise 1.8 Prove tht for ny CCS processes P nd Q the following lws hold: P Nil P P + Nil P Exercise 1.9 rgue tht ny two strongly isimilr processes hve the sme sets of trces, i.e., tht s t implies Trces(s) = Trces(t). Hint: you cn find useful the gme chrcteriztion of strong isimilrity. 4
5 Exercise 1.10 Is it true tht ny reltion R tht is strong isimultion must e reflexive, trnsitive nd symmetric? If yes then prove it, if not then give counter exmples, i.e. define n LTS nd inry reltion on sttes which is not reflexive ut it is strong isimultion define n LTS nd inry reltion on sttes which is not symmetric ut it is strong isimultion define n LTS nd inry reltion on sttes which is not trnsitive ut it is strong isimultion. Exercise 1.11 Find (one) lelled trnsition system with n initil stte s such tht it stisfies (t the sme time) the following properties: s = ( c tt c tt) s = ([]ff []ff [c]ff) s = [] ([c]ff tt) Exercise 1.12 ssume n ritrry CCS defining eqution K def = P where K is process constnt nd P is CCS expression. Prove tht K P. (Hint: y using SOS rules for CCS, exmine the possile trnsitions from K nd P.) Exercise 1.13 Decide whether the following clims re true or flse. Support your clims either y using isimultion gmes or directly the definition of strong/wek isimilrity..τ.nil? τ..nil τ.. +.B? τ.(. +.B) τ.nil + (.Nil.Nil) {, }? τ.nil.(τ.nil +.B)?.Nil +..B The sme processes ut wek isimilrity insted of the strong one..τ.nil? τ..nil τ.. +.B? τ.(. +.B) τ.nil + (.Nil.Nil) {, }? τ.nil.(τ.nil +.B)?.Nil +..B Hint: drw first the LTS generted y the CCS processes. Home exercise: try to verify your clims y using the tool CWB. 5
6 Exercise 1.14 Prove tht for ny CCS process P the following lw (clled idempotency) holds. P + P P By using the fct tht conclude tht lso P + P P. Exercise 1.15 Consider the tiny communiction protocol from Lecture 4. Drw the lelled trnsition system generted y the processes Spec nd Impl. Prove (y hnd) tht Spec Impl. Hint: define wek isimultion reltion contining (Spec, Impl). Exercise 1.16 Consider the following LTSs: Consider lso the following HML formuls: φ def = [](< > tt < c > tt) ψ def = [](< > tt < c > tt) ϕ def =< > []ff 1. Clculte [ φ ], [ ψ ] nd [ ϕ ] in the LTSs (1), (2), (3) nd (4). 2. Determine if p1 = φ, p1 = ψ, p1 = ϕ, q4 = φ, q4 = ψ, q4 = ϕ. 6
7 Exercise 1.17 Consider the following lelled trnsition system. s s 1 s 2 s 3 s 4 1. Decide whether the stte s stisfies the following formule of Hennessy-Milner logic: s? = tt s? = tt s? = []ff s? = []ff s? = [] tt s? = tt s? = [] [][]ff s? = ( tt tt ) s? = [] ( tt tt ) s? = ( [][]ff tt ) s? = ( []( tt []ff) ff ) 2. Compute the following sets ccording to the denottionl semntics for Hennessy-Milner logic. [[][]ff ] =? [ ( tt tt ) ] =? [[][][]ff ] =? [[] ( tt tt ) ] =? 7
8 Exercise 1.18 Consider the following lelled trnsition system. s t v s 1 t 1 v 1 v 2 s 2 t 2 v 3 It it true tht s t, s v nd t v. Find distinguishing formul of Hennessy-Milner logic for the pirs s nd t s nd v t nd v. Exercise 1.19 For ech of the following CCS expressions decide whether they re strongly isimilr nd if no, find distinguishing formul in Hennessy-Milner logic...nil +.Nil nd.(.nil +.Nil).(.c.Nil +.d.nil) nd..c.nil +..d.nil.nil.nil nd..nil +..Nil (.Nil.Nil) + c..nil nd.nil (.Nil + c.nil) Home exercise: verify your clims in CWB (use the strongeq nd checkprop commnds) nd check whether you found the shortest distinguishing formul (use the dfstrong commnd). Exercise 1.20 Prove tht for every Hennessy-Milner formul F nd every stte p Proc: p = F if nd only if p [F ]. Hint: use structurl induction on the structure of the formul F. 8
9 Exercise 1.21 Consider the following lelled trnsition system. s s 1 c s 3 s 2 Using the gme chrcteriztion for recursive Hennessy-Milner formule decide whether the following clims re true or flse nd discuss wht properties the formule descrie: s? = X where X min = c tt ct X s? = X where X min = c tt [ct]x s? = X where X mx = X s? = X where X mx = tt []X []X Exercise 1.22 Consider the following LTS: 1. (2 points) Tell whether or not s is strongly isimilr to t. Justify your nswer formlly. 2. (2 points) Tell whether or not t stisfies the formul [c] c tt. Justify your nswer formlly. 3. (3 points) Determine ll the sttes of the LTS tht stisfy the following formuls: [][c]ff [c]ff [c]tt 9
10 Solutions Solution of Exercise 1.1 We show tht s 1 t 1 using the gme chrcteriztion of isimilrity. In prticulr we show tht the ttcker hs the universl winning strtegy tht follows: 1. The configurtion of the gme is (s 1, t 1 ). The ttcker selects s 1 nd mkes the move: s 1 s 4. The Defender cn only reply mking the move t 1 t The configurtion of the gme is (s 4, t 2 ). The ttcker selects t 2 nd mkes the move: t 2 t 1. The Defender is stuck: there exists no stte s such tht s 4 s. We clculte the semntics of the three formuls in the given LTS. [] tt [] c tt = [] tt [] c tt = [ ]( tt) [ ]( c tt) = [ ]({s 2, s 3, t 2 }) [ ]({s 2, s 4, t 2, t 3 }) = {s 2, s 3, s 4, s 5, s 6, t 1, t 2, t 3, t 4 } {s 2, s 3, s 4, s 5, s 6, t 1, t 2, t 3, t 4 } = {s 2, s 3, s 4, s 5, s 6, t 1, t 2, t 3, t 4 } tt c [c]ff = tt c [c]ff = ( tt) c ( [c]ff) = ({s 2, s 3, t 2 }) c ( ([c]ff)) = {s 1, t 1 } c ( ({s 1, s 3, s 5, s 6, t 1, t 4 }) = {s 1, t 1 } c ({s 2, t 2 }) = {s 1, t 1 } {} = {s 1, t 1 } [][] c tt = [ ]([] c tt) = [ ]([ ]( c tt)) = [ ]([ ]({s 2, s 4, t 2, t 3 })) = [ ]({s 1, s 3, s 4, s 5, s 6, t 1, t 3, t 4 }) = {s 2, s 3, s 4, s 5, s 6, t 2, t 3, t 4 } Solution of Exercise 1.2 Consider the following lelled trnsition system. s s 1 s 2 Compute for which sets of sttes [X ] {s, s 1, s 2 } the following formule re true. X = tt []X The eqution holds for the following sets of sttes: {s 2, s}, {s 2, s 1, s}. X = tt ([]X tt) 10
11 The eqution holds only for the set {s 2 }. 11
12 Solution of Exercise
13 Solution of Exercise
14 Solution of Exercise 1.5 If we cn show tht R = {(s, t), (s 1, t 1 ), (s 3, t 2 ), (s 4, t 2 ), (s 2, t 3 ), (s 4, t 4 )} is strong isimultion, then s t. Indeed R is strong isimultion since: Consider (s, t) R. Trnsitions from s: If s s 1, mtch y doing t t 1, nd (s 1, t 1 ) R. If s s 2, mtch y doing t t 3, nd (s 2, t 3 ) R. These re ll trnsitions from s. Trnsitions from t: If t t 1, mtch y doing s s 1, nd (s 1, t 1 ) R. If t t 3, mtch y doing s s 2, nd (s 2, t 3 ) R. These re ll trnsitions from t. Consider (s 1, t 1 ) R. Trnsitions from s 1 : If s 1 s 3, mtch y doing t 1 t 2 nd (s 3, t 2 ) R. If s 1 s 4, mtch y doing t 1 t 2 nd (s 4, t 2 ) R. Trnsitions from t 1 : If t 1 t 2, mtch y doing s 1 s 3 nd (s 3, t 2 ) R. If t 1 t 2, mtch y doing s 1 s 4 nd (s 4, t 2 ) R. Consider (s 3, t 2 ) R. Trnsitions from s 3 : If s 3 s, mtch y doing t 2 t nd (s, t) R. Trnsitions from t 2 : If t 2 t, mtch y doing s 3 s nd (s, t) R. Consider (s 4, t 2 ) R. Trnsitions from s 4 : If s 4 s, mtch y doing t 2 t nd (s, t) R. Trnsitions from t 2 : If t 2 t, mtch y doing s 4 s nd (s, t) R. Consider (s 2, t 3 ) R. Trnsitions from s 2 : If s 2 s 4, mtch y doing t 3 t 4 nd (s 4, t 4 ) R. Trnsitions from t 3 : If t 3 t 4, mtch y doing s 2 s 4 nd (s 4, t 4 ) R. Consider (s 4, t 4 ) R. Trnsitions from s 4 : If s 4 s, mtch y t 4 t nd (s, t) R. Trnsitions from t 4 : If t 4 t, mtch y s 4 s nd (s, t) R. 14
15 Solution of Exercise 1.6 Let R = {(P, Q), (P 1, Q 1 ), (P, Q 2 ), (P 1, Q 3 )}. We only outline the proof; it follows long the lines s the proof in Exercise??. You should complete the detils. From (P, Q) R either P or Q cn do n trnsition. In either cse the response is to mtch y mking n trnsition from the remining stte, so we end up in (P 1, Q 1 ) R. From (P 1, Q 1 ) R we end up in either (P, Q) R or (P, Q 2 ) R. From (P, Q 2 ) R we cn only end up in (P 1, Q 3 ) R. From (P 1, Q 3 ) R we end up in either (P, Q) R or (P, Q 2 ) R. Solution of Exercise 1.7 In this exercise you re sked to trin yourself in the use of the gme chrcteriztion for strong isimultion. We therefore give universl winning strtegy for the ttcker or the defender in order to prove strong nonisimilrity or isimilrity. Let denote the ttcker nd D the defender. Clim: s t. The universl winning strtegy for is s follows. In configurtion (s, t), chooses s nd mkes the move s s 1. D s only possile response is to choose t nd mke the move t configurtion is now (s 1, t 1 ) In configurtion (s 1, t 1 ), chooses s 1 nd mkes the move s 1 s 2. t 1. The current Now the winning strtegy depends on D s next move nd is s follows. D cn only choose the stte t 1, ut hs two possile moves. Suppose D chooses t 1 t 1. Then the current configurtion ecomes (s 2, t 1 ). Now choose s 2 nd mkes the move s 2 s. Then D looses since there re no -trnsitions from t 1. If D uses the other possile move, nmely t 1 t 2, the current configurtion ecomes (s 2, t 2 ). But then chooses s 2 nd mkes the move s 2 s 2. gin D looses since there re no -trnsitions from t 2. Remrk: there is nother winning strtegy for the ttcker which is esier to descrie; try to find it. Clim: s u: The universl winning strtegy for D is s follows. Strting in (s, u), hs two possile moves. Either () s s 1 or () u u 1. If chooses (), then D tkes the move u (s 1, u 1 ). If chooses (), then D tkes the move s ecomes (s 1, u 1 ). u 1, nd the current configurtion ecomes s 1, nd the current configurtion gin In configurtion (s 1, u 1 ), cn choose either () s 1 s 2, or () u 1 u 3. If chooses (), then D tkes the move u 1 u 3, nd the current configurtion ecomes (s 2, u 3 ). If chooses (), then D tkes the move s 1 ecomes (s 2, u 3 ). 15 s 2, nd the current configurtion gin
16 In configurtion (s 2, u 3 ), cn choose either () s 2 s 2 or () s 2 u 2. or (d) u 3 If chooses (), then D tkes the move u 3 (s 2, u 2 ). If chooses (), then D tkes the move u 3 (s, u) which is exctly the strt configurtion. If chooses (c), then D tkes the move s 2 (s, u) which is the strt configurtion. s or (c) u 3 u u 2 nd the current configurtion ecomes u nd the current configurtion ecomes s nd the current configurtion ecomes If chooses (d), then D tkes the move s 2 s 2 nd the current configurtion ecomes (s 2, u 2 ) s when the ttcker plyed (). Hence from now we only need to consider gmes form the stte (s 2, u 2 ). Now we cn rgue tht D hs winning strtegy. From (s 2, u 2 ), D s response to ny move from will e to tke the sme trnsition. This mens tht the next configurtion is either (s 2, u 2 ) or (s, u). The gme will e infinite, nd hence D is the winner. Clim: s v: The universl winning strtegy for is s follows. In configurtion (s, v), mkes the move s s 1. Now D must mke the move v v 1 nd the current configurtion ecomes (s 1, v 1 ). In configurtion (s 1, v 1 ), chooses v 1 v 2. D must mke the move s 1 s 2. The current configurtion is (s 2, v 2 ). Now wins since from (s 2, v 2 ) s he cn choose to mke the move s 2 re no -trnsitions from v 2, D looses. s 2. Since there Solution of Exercise 1.8 The generl ide in this exercise is tht in order to prove tht P Q you define some inry reltion R such tht (P, Q) R, nd then proceed to prove tht R is indeed strong isimultion. Define R = {(P Nil, P ) P is CCS process}. We show tht R is strong isimultion. α Suppose for some α ct tht P Nil P Nil. We now hve to find some process P such tht P α P nd (P Nil, P ) R. Now use the trnsition reltion. The only rule tht could hve een used is the COM1-rule. α P P α P Nil P Nil Now set P = P. Then we re finished since we now know tht P of R, (P Nil, P ) = (P Nil, P ) R. Symmetriclly we must prove tht when P α P, then some P exists so tht P Nil nd ( P, P ) R. But this is esy. By using the COM1-rule we hve. α P nd y the definition α P α P P α P Nil P Nil. So we simply let P = P Nil. nd gin y definition of R, we hve tht ( P, P ) = (P Nil, P ) R. This proves tht R, is isimultion. nd since (P Nil, P ) R, this mens tht P Nil P. 16
17 This time we show tht P + Nil P y giving universl winning strtegy for the defender. Rememer tht the gme is plyed on the LTS, so we will just denote the sttes of the LTS y the CCS-expression. If the ttcker chooses P + Nil, then the only possile moves re those of P since Nil hs no trnsitions. So if P P, the ttcker cn mke the move P + Nil P. But then the defender cn mke the move P P. The current configurtion is now (P, P ). From now on the defenders strtegy is do to the sme s the ttcker. Either the gme is infinite, in which cse the defender wins. Or the gme is finite. But then the defender wins, since the ttcker cnnot mke ny move ecuse oth processes re stuck. Similrly if the ttcker plys P P. Then the defender moves P + Nil P, nd the configurtion gin ecomes (P, P ). We show now tht R = {(P Q, Q P ) P, QreCCS expressions} is strong isimultion. We only give n outline of the proof, the method is the sme s in the first ullet. Suppose P Q P Q. If COM3-rule ws pplied, we cn rgue s follows: P P Q Q P Q τ P Q But then since = we cn use the sme rule to derive: Q Q P P. Q P τ Q P nd y the definition of R, we know tht (P Q, Q P ) R. If COM1 or COM2 rule ws used, we do the following nlysis. Suppose the COM1-rule ws the one used. Then we know tht P P P Q P Q. gin one cn now pply the COM2-rule nd derive P Q P P, Q P nd (P Q, Q P ) R. In order to finish the proof we need to rgue for the symmmetric cse (i.e. when the rule COM2 ws used from P Q). The rgument for this cse is similr s efore. The cse when Q P Q P is completely symmetric. Solution of Exercise 1.9 ssume tht s t. We will show oth trce inclusions s follows. Trces(s) Trces(t): Let w = n e trce from Trces(s). The ttcker will ply the sequence w in n-rounds of the strong isimultion gme, lwys from the left processes s. s s t, the defender hs to e le to nswer to such n ttck nd hence he hs to e le to do the sme sequence w from the right process t. This mens tht w Trces(t). Trces(t) Trces(s): The rgument is completely symmetric, the ttcker plys the whole sequence from the right process t nd the defender hs to e le to mtch it in the left process. 17
18 This implies tht Trces(s) = Trces(t). Solution of Exercise 1.10 The nswer is no for ll the cses nd the reltion R of strong isimultion from Exercise?? cn serve s counter exmple for reflexivity nd symmetry. Solution of Exercise 1.11 One possile solution is s follows. s s 1 c s 2 s 4 Solution of Exercise 1.12 Let K def = P. We define R = {(K, P )} {(P, P ) P is CCS process}. We will rgue tht R is strong isimultion. We nlyze only the pir (K, P ) from R s ny pir of the form (P, P ) cn e sfely dded to R (why?). Let K P. We must find P such tht P P nd (P, P ) R. The trnsition K P must hve een derived using the CON-rule with the premise P P. Then we cn just let P = P s we know tht P P, nd (P, P ) R. Let P P. Then using the SOS rule CON we know tht lso K P nd gin (P, P ) R. Solution of Exercise 1.13 Decide whether the following clims re true or flse. Support your clims either y using isimultion gmes or directly the definition of strong/wek isimilrity..τ.nil τ..nil The ttcker plys the ction in the left process nd the defender does not hve ny -move ville in the right process nd looses. τ.. +.B τ.(. +.B) The ttcker plys the ction from the left process, there is no ction ville in the right process in the first round. The ttcker clerly wins. τ.nil + (.Nil.Nil) {, } τ.nil 18
19 R = {(τ.nil + (.Nil.Nil) {, }, τ.nil), (Nil, Nil), ((Nil Nil) {, }, Nil)} is strong isimultion..(τ.nil +.B).Nil +..B In the first round the ttcker plys from the left the ction nd in the second round he plys gin from left the ction τ. The defender looses s he cn never ply the sme sequence of followed y τ from the right process. The sme processes ut wek isimilrity insted of the strong one..τ.nil τ..nil R = {(.τ.nil, τ..nil), (τ.nil, Nil), (Nil, Nil), (.τ.nil,.nil)} is wek isimultion. τ.. +.B τ.(. +.B) The ttcker plys the ction τ from the left nd reches the process.. The defender cn either nswer y (i) doing nothing on the right nd stying in the process τ.(. +.B) or (ii) y plying the ction τ nd reching. +.B. In cse (i) the ttcker will ply in second round on the right the ction τ, the defender cn only sty in. nd in the next round the ttcker wins y mking the -move on the right. In cse (ii) the ttcker wins lredy in the second round y plying from the right process. τ.nil + (.Nil.Nil) {, } τ.nil These two processes re even strongly isimilr so they must e lso wekly isimilr..(τ.nil +.B).Nil +..B The ttcker plys.nil +..B.B on the right, the defender cn nswer either y.(τ.nil +.B) = τ.nil +.B or y.(τ.nil +.B) = Nil. In the first cse the ttcker plys τ.nil +.B τ Nil nd the defender cn only do nothing nd will loose in the next round. In the second cse, the ttcker plys the ction from the left nd the defender looses. Home exercise: try to verify your clims y using the tool CWB. Solution of Exercise 1.14 We now rgue tht P + P P using the gme chrcteriztion. We strt from the configurtion (P + P, P ). Suppose the ttcker chooses P + P P. Then we know (from the SOS trnsition rules) tht this trnsition cn only hve een derived if P P. So, of course, the defender replies y doing P P. The current configurtion ecomes (P, P ) from which the defender lwys hs winning strtegy y simply doing exctly the sme s the ttcker. Conversely, if the ttcker from (P + P, P ) chooses P P then the defender responds y plying P + P P nd the current configurtion ecomes gin (P, P ). 19
20 Solution of Exercise
21 Solution of Exercise
22 22
23 23
24 24
25 Solution of Exercise 1.17 Consider the following lelled trnsition system. s s 1 s 2 s 3 s 4 1. Decide whether the stte s stisfies the following formule of Hennessy-Milner logic: s = tt s = tt s = []ff s = []ff s = [] tt s = tt s = [] [][]ff s = ( tt tt ) s = [] ( tt tt ) s = ( [][]ff tt ) s = ( []( tt []ff) ff ) 2. Compute the following sets ccording to the denottionl semntics for Hennessy-Milner logic. [[][]ff ] = [ ][[]ff ] = [ ][ ][ff ] = [ ][ ] = [ ]{P P.P P P } = [ ]{s, s 3, s 2, s 4 } = { P P.P P P {s, s 3, s 2, s 4 } } = {s 1, s 2, s 3, s 4 } [ ( tt tt ) ] = [ tt tt] = ( [ tt] [ tt] ) = ( Proc Proc) = ( {s, s 1, s 2, s 3, s 4 } {s 1 } ) = {s 1 } = {s} 25
26 [[][][]ff ] = [ ][ ][ ] = [ ][ ]{s, s 2, s 3, s 4 } = [ ]{s 1, s 2, s 3, s 4 } = {s, s 1, s 2 } [[] ( tt tt ) ] = [ ][ tt tt] = [ ] ( Proc Proc ) = [ ]{s, s 1, s 2, s 3, s 4 } = {s, s 1, s 2, s 3, s 4 } Solution of Exercise 1.18 Distingushing HML-formule re s follows. Let F 1 = [] tt. Then s = F 1, ut t = F 1. Let F 2 = [] tt. Then s = F 2 ut v = F 3. Let F 3 = ( tt tt ). Then t = F 3 ut v = F 3. Solution of Exercise 1.19 For ech of the following CCS expressions decide whether they re strongly isimilr nd if not, find distinguishing formul in Hennessy-Milner logic...nil +.Nil nd.(.nil +.Nil) They re not isimilr. Let F 1 = [] tt. Then..Nil+.Nil = F 1 ut.(.nil+.nil) = F 1..(.c.Nil +.d.nil) nd..c.nil +..d.nil They re not isimilr. Let F 2 = [] ( c tt d tt ). Then.(.c.Nil +.d.nil) = F 2 ut..c.nil +..d.nil = F 2..Nil.Nil nd..nil +..Nil They re isimilr. (.Nil.Nil) + c..nil nd.nil (.Nil + c.nil) They re not isimilr. Let F 3 = [] c tt. Then (.Nil.Nil)+c..Nil = F 3 ut.nil (.Nil+ c.nil) = F 3. Home exercise: verify your clims in CWB (use the strongeq nd checkprop commnds) nd check whether you found the shortest distinguishing formul (use the dfstrong commnd). 26
27 Solution of Exercise 1.21 Consider the following lelled trnsition system. s s 1 c s 3 s 2 Using the gme chrcteriztion for recursive Hennessy-Milner formule decide whether the following clims re true or flse nd discuss wht properties the formule descrie: s = X where X min = c tt ct X universl winning strtegy for the defender strting from (s, X) is s follows: (s, X) (s, c tt ct X) (s 1, c tt ct X) (s 2, c tt ct X) (s 3, c tt ct X) D D (s, ct X) (s 1, X) D D (s 1, ct X) (s 2, X) D D (s 2, ct X) (s 3, X) D D (s 3, c tt) (s, tt), where (s, tt) y definition is winning configurtion for the defender. s = X where X min = c tt [ct]x universl winning strtegy for the ttcker is s follows: (s, X) (s, c tt [ct]x) Then if the defender plys c tt, he loses since there re no c-trnsitions from s, thus the defender must D ply (s, c tt [ct]x) (s, [ct]x). Then the ttcker plys (s, [ct]x) (s 1, X). nd we hve (s 1, X) (s 1, c tt [ct]x). Now for similr resons s ove the defender D must choose to ply (s 1, c tt [ct]x) (s 1, [ct]x). The ttcker plys (s 1, [ct]x) (s 1, X) which is configurtion we hve seen erlier. Thus either the ply is infinite, in which cse the ttcker wins since X is defined s the lest fixed-point. Or the ply is finite, in which cse the ttcker lso wins. s = X where X mx = X universl winning strtegy for the defender is: (s, X) (s, X) D D (s 1, X) (s 1, X) (s 1, X). Thus the ply is infinite, nd since X is defined s the gretest fixed-point, the defender wins. s = X where X mx = tt []X []X Universl winning strtegy for the defender: We hve (s, X) (s, tt []X []X). Now if the ttcker plys (s, tt []X []X) (s, tt) he loses since the defender cn then ply D (s, tt) (s 1, tt). Furthermore if the ttcker plys (s, tt []X []X) (s, []X), then he lso loses since he is stuck in the configurtion (s, []X). The third option for the ttcker is to choose (s, tt []X []X) (s, []X) (s 1, X). Expnding X we get (s 1, X) (s 1, tt []X []X). From here if the ttcker plys D (s 1, tt []X []X) (s 1, tt) he loses since the defender cn ply (s 1, tt) (s 1, tt). 27
28 If the ttcker plys (s 1, tt []X []X) (s 1, []X), then the only possile next move is (s 1, []X) (s 1, X) which is previously encountered configurtion. The lst option for the ttcker is to ply (s 1, tt []X []X) (s 1, []X) (s 2, X). Expnding the encoding we get (s 2, X) (s 2, tt []X []X). gin if the ttcker plys D (s 2, tt []X []X) (s 2, tt) he loses y the defenders move (s 2, tt) (s 3, tt). If the ttcker plys (s 2, tt []X []X) (s 2, []X) he loses since he is stuck. Finlly he cn ply (s 2, tt []X []X) (s 2, []X) (s 3, X). Expnding X we otin (s 3, X) (s 3, tt []X []X). Now plying (s 3, tt []X D []X) (s 3, tt) he loses y the defenders move (s 3, tt) (s 3, tt). If the ttcker plys (s 3, tt []X []X) (s 3, []X) he is stuck. Finlly the ttcker cn ply (s 3, tt []X []X) (s 3, []X) (s 3, X) which is previously encountered configurtion. Thus either the ttcker loses in finite ply, or the ply is infinite in which cse the defender wins since X is defined s the gretest fixed-point. 28
29 Solution of Exercise
30 30
31 2 Wek Bisimultion Exercise 2.1 Consider the following lelled trnsition system. s τ s 1 τ s 2 t τ t 1 τ τ τ τ s 3 s 4 s 5 t 2 t 3 Show tht s t y finding wek isimultion R contining the pir (s, t). Exercise 2.2 In the wek isimultion gme the ttcker is llowed to use moves for the ttcks nd the defender cn use = in response. rgue tht if we modify the gme rules so tht the ttcker cn lso use the long moves = then this does not provide ny dditionl power for the ttcker. Conclude tht oth versions of the gme provide the sme nswer out isimilrity/nonisimilrity of two processes. 31
32 Solutions Solution of Exercise 2.1 Let R = {(s, t), (s 1, t), (s 2, t), (s 3, t 2 ), (s 4, t 3 ), (s 5, t 1 )}. Now one cn rgue tht R is wek isimultion s follows. Trnsitions from the pir (s, t): if s τ s 3 then t t = t nd (s 1, t) R. If t t 2 then s = s 3 nd (s 3, t 2 ) R. If t τ (s 4, t 3 ) R. If t t 1 then s = τ s 5 nd (s 5, t 1 ) R. = t 2 nd (s 3, t 2 ) R. If s t 3 then s The trnsitions from the remining pirs cn e checked in similr wy. τ s 1 then = s 4 nd Solution of Exercise 2.2 Oserve tht ech long ttck cn e simulted (in more rounds) y doing in series ll single steps tht re contined in the long move, so the defender in fct hs n nswer even to the long move y comining the nswers to the series 32
33 3 Complete Lttices nd Fix Points Exercise 3.1 Drw grphicl representtion of the complete lttice (2 {,,c}, ) nd compute supremum nd infimum of the following sets: {{}, {}} =? {{}, {}} =? {{}, {, }, {, c}} =? {{}, {, }, {, c}} =? {{}, {}, {c}} =? {{}, {}, {c}} =? {{}, {, }, {}, } =? {{}, {, }, {}, } =? Exercise 3.2 Prove tht for ny prtilly ordered set (D, ) nd ny X D, if supremum of X ( X) nd infimum of X ( X) exist then they re uniquely defined. (Hint: use the definition of supremum nd infimum nd ntisymmetry of.) Exercise 3.3 Let (D, ) e complete lttice. Wht re nd equl to? Exercise 3.4 Consider the complete lttice (2 {,,c}, ). Define function f : 2 {,,c} 2 {,,c} such tht f is monotonic. Compute the gretest fixed point y using directly the Trski s fixed point theorem. Compute the lest fixed point y using the Trski s fixed point theorem for finite lttices (i.e. y strting from nd y pplying repetedly the function f until the fixed point is reched). 33
34 Solutions Solution of Exercise 3.1 Drw grphicl representtion of the complete lttice (2 {,,c}, ) nd compute supremum nd infimum of the sets elow. The complete lttice: {,, c} {, } {, c} {, c} {} {} {c} {{}, {}} = {{}, {}} = {, } {{}, {, }, {, c}} = {} {{}, {, }, {, c}} = {,, c} {{}, {}, {c}} = {{}, {}, {c}} = {,, c} {{}, {, }, {}, } = {{}, {, }, {}, } = {, } Solution of Exercise 3.2 Prove tht for ny prtilly ordered set (D, ) nd ny X D, if supremum of X ( X) nd infimum of X ( X) exist then they re uniquely defined. (Hint: use the definition of supremum nd infimum nd ntisymmetry of.) We prove the clim for the supremum (lest upper ound) of X. The rguments for the infimum re symmetric. Let d 1, d 2 D e two supremums of given set X. This mens tht X d 1 nd X d 2 s oth d 1 nd d 2 re upper ounds of X. Now ecuse d 1 is the lest upper ound nd d 2 is n upper ound we get d 1 d 2. Similrly, d 2 is the lest upper ound nd d 1 is n upper ound so d 2 d 1. However, from ntisymmetry nd d 1 d 2 nd d 2 d 1 we get tht d 1 = d 2. 34
35 S f(s) {} {} {} {} {} {c} {} {,, c} {, } {, } {, } {, c} {, } {, c} {, } Tle 1: Definition of monotonic function f in Exercise??. Solution of Exercise 3.3 Let (D, ) e complete lttice. Wht re nd equl to? = = D. = = D. Solution of Exercise 3.4 Consider the complete lttice (2 {,,c}, ). Define function f : 2 {,,c} 2 {,,c} such tht f is monotonic. For exmple we define f s in Tle 1 (note tht there re mny possiilites). The function f is monotonic which we cn verify y cse inspection. Compute the gretest fixed point y using directly the Trski s fixed point theorem. ccording to Trski s fixed point theorem the lrgest fixed point z mx is given y z mx =, where = { x 2 {,,c} x f(x) }. In our cse, y the definition of f we get = {, {}, {, } }. The supremum of in 2 {,,c} is {, } so y Trski s fixed point theorem, the lrgest fixed point of f is {, }. Compute the lest fixed point y using the Trski s fixed point theorem for finite lttices (i.e. y strting from nd y pplying repetedly the function f until the fixed point is reched). First note tht = 2 {,,c} =. We now repetedly pply f until it stilizes f( ) f(f( )) = f({}) = {} = {} nd hence the lest fixed point of f is {}. 35
Strong Bisimulation. Overview. References. Actions Labeled transition system Transition semantics Simulation Bisimulation
Strong Bisimultion Overview Actions Lbeled trnsition system Trnsition semntics Simultion Bisimultion References Robin Milner, Communiction nd Concurrency Robin Milner, Communicting nd Mobil Systems 32
More informationCoalgebra, Lecture 15: Equations for Deterministic Automata
Colger, Lecture 15: Equtions for Deterministic Automt Julin Slmnc (nd Jurrin Rot) Decemer 19, 2016 In this lecture, we will study the concept of equtions for deterministic utomt. The notes re self contined
More information1 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 informationMore on automata. Michael George. March 24 April 7, 2014
More on utomt Michel George Mrch 24 April 7, 2014 1 Automt constructions Now tht we hve forml model of mchine, it is useful to mke some generl constructions. 1.1 DFA Union / Product construction Suppose
More informationIntermediate 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 informationLecture 3: Equivalence Relations
Mthcmp Crsh Course Instructor: Pdric Brtlett Lecture 3: Equivlence Reltions Week 1 Mthcmp 2014 In our lst three tlks of this clss, we shift the focus of our tlks from proof techniques to proof concepts
More informationConvert the NFA into DFA
Convert the NF into F For ech NF we cn find F ccepting the sme lnguge. The numer of sttes of the F could e exponentil in the numer of sttes of the NF, ut in prctice this worst cse occurs rrely. lgorithm:
More informationDesigning 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 informationCMPSCI 250: Introduction to Computation. Lecture #31: What DFA s Can and Can t Do David Mix Barrington 9 April 2014
CMPSCI 250: Introduction to Computtion Lecture #31: Wht DFA s Cn nd Cn t Do Dvid Mix Brrington 9 April 2014 Wht DFA s Cn nd Cn t Do Deterministic Finite Automt Forml Definition of DFA s Exmples of DFA
More informationp-adic Egyptian Fractions
p-adic Egyptin Frctions Contents 1 Introduction 1 2 Trditionl Egyptin Frctions nd Greedy Algorithm 2 3 Set-up 3 4 p-greedy Algorithm 5 5 p-egyptin Trditionl 10 6 Conclusion 1 Introduction An Egyptin frction
More informationI1 = I2 I1 = I2 + I3 I1 + I2 = I3 + I4 I 3
2 The Prllel Circuit Electric Circuits: Figure 2- elow show ttery nd multiple resistors rrnged in prllel. Ech resistor receives portion of the current from the ttery sed on its resistnce. The split is
More informationset is not closed under matrix [ multiplication, ] and does not form a group.
Prolem 2.3: Which of the following collections of 2 2 mtrices with rel entries form groups under [ mtrix ] multipliction? i) Those of the form for which c d 2 Answer: The set of such mtrices is not closed
More informationLecture 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 informationQuadratic Forms. Quadratic Forms
Qudrtic Forms Recll the Simon & Blume excerpt from n erlier lecture which sid tht the min tsk of clculus is to pproximte nonliner functions with liner functions. It s ctully more ccurte to sy tht we pproximte
More information1. 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 information378 Relations Solutions for Chapter 16. Section 16.1 Exercises. 3. Let A = {0,1,2,3,4,5}. Write out the relation R that expresses on A.
378 Reltions 16.7 Solutions for Chpter 16 Section 16.1 Exercises 1. Let A = {0,1,2,3,4,5}. Write out the reltion R tht expresses > on A. Then illustrte it with digrm. 2 1 R = { (5,4),(5,3),(5,2),(5,1),(5,0),(4,3),(4,2),(4,1),
More information2.4 Linear Inequalities and Interval Notation
.4 Liner Inequlities nd Intervl Nottion We wnt to solve equtions tht hve n inequlity symol insted of n equl sign. There re four inequlity symols tht we will look t: Less thn , Less thn or
More informationSummer School Verification Technology, Systems & Applications
VTSA 2011 Summer School Verifiction Technology, Systems & Applictions 4th edition since 2008: Liège (Belgium), Sep. 19 23, 2011 free prticiption, limited number of prticipnts ppliction dedline: July 22,
More informationUNIFORM CONVERGENCE. Contents 1. Uniform Convergence 1 2. Properties of uniform convergence 3
UNIFORM CONVERGENCE Contents 1. Uniform Convergence 1 2. Properties of uniform convergence 3 Suppose f n : Ω R or f n : Ω C is sequence of rel or complex functions, nd f n f s n in some sense. Furthermore,
More informationMTH 505: Number Theory Spring 2017
MTH 505: Numer Theory Spring 207 Homework 2 Drew Armstrong The Froenius Coin Prolem. Consider the eqution x ` y c where,, c, x, y re nturl numers. We cn think of $ nd $ s two denomintions of coins nd $c
More informationFarey Fractions. Rickard Fernström. U.U.D.M. Project Report 2017:24. Department of Mathematics Uppsala University
U.U.D.M. Project Report 07:4 Frey Frctions Rickrd Fernström Exmensrete i mtemtik, 5 hp Hledre: Andres Strömergsson Exmintor: Jörgen Östensson Juni 07 Deprtment of Mthemtics Uppsl University Frey Frctions
More informationMinimal 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 informationChapter 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 informationExercises Chapter 1. Exercise 1.1. Let Σ be an alphabet. Prove wv = w + v for all strings w and v.
1 Exercises Chpter 1 Exercise 1.1. Let Σ e n lphet. Prove wv = w + v for ll strings w nd v. Prove # (wv) = # (w)+# (v) for every symol Σ nd every string w,v Σ. Exercise 1.2. Let w 1,w 2,...,w k e k strings,
More informationHomework Solution - Set 5 Due: Friday 10/03/08
CE 96 Introduction to the Theory of Computtion ll 2008 Homework olution - et 5 Due: ridy 10/0/08 1. Textook, Pge 86, Exercise 1.21. () 1 2 Add new strt stte nd finl stte. Mke originl finl stte non-finl.
More informationLecture 3. In this lecture, we will discuss algorithms for solving systems of linear equations.
Lecture 3 3 Solving liner equtions In this lecture we will discuss lgorithms for solving systems of liner equtions Multiplictive identity Let us restrict ourselves to considering squre mtrices since one
More information(e) if x = y + z and a divides any two of the integers x, y, or z, then a divides the remaining integer
Divisibility In this note we introduce the notion of divisibility for two integers nd b then we discuss the division lgorithm. First we give forml definition nd note some properties of the division opertion.
More informationParse trees, ambiguity, and Chomsky normal form
Prse trees, miguity, nd Chomsky norml form In this lecture we will discuss few importnt notions connected with contextfree grmmrs, including prse trees, miguity, nd specil form for context-free grmmrs
More informationReview of Gaussian Quadrature method
Review of Gussin Qudrture method Nsser M. Asi Spring 006 compiled on Sundy Decemer 1, 017 t 09:1 PM 1 The prolem To find numericl vlue for the integrl of rel vlued function of rel vrile over specific rnge
More informationCS 373, Spring Solutions to Mock midterm 1 (Based on first midterm in CS 273, Fall 2008.)
CS 373, Spring 29. Solutions to Mock midterm (sed on first midterm in CS 273, Fll 28.) Prolem : Short nswer (8 points) The nswers to these prolems should e short nd not complicted. () If n NF M ccepts
More informationAdvanced Calculus: MATH 410 Notes on Integrals and Integrability Professor David Levermore 17 October 2004
Advnced Clculus: MATH 410 Notes on Integrls nd Integrbility Professor Dvid Levermore 17 October 2004 1. Definite Integrls In this section we revisit the definite integrl tht you were introduced to when
More informationA study of Pythagoras Theorem
CHAPTER 19 A study of Pythgors Theorem Reson is immortl, ll else mortl. Pythgors, Diogenes Lertius (Lives of Eminent Philosophers) Pythgors Theorem is proly the est-known mthemticl theorem. Even most nonmthemticins
More informationCS 301. Lecture 04 Regular Expressions. Stephen Checkoway. January 29, 2018
CS 301 Lecture 04 Regulr Expressions Stephen Checkowy Jnury 29, 2018 1 / 35 Review from lst time NFA N = (Q, Σ, δ, q 0, F ) where δ Q Σ P (Q) mps stte nd n lphet symol (or ) to set of sttes We run n NFA
More informationLecture 2: January 27
CS 684: Algorithmic Gme Theory Spring 217 Lecturer: Év Trdos Lecture 2: Jnury 27 Scrie: Alert Julius Liu 2.1 Logistics Scrie notes must e sumitted within 24 hours of the corresponding lecture for full
More informationCS103B Handout 18 Winter 2007 February 28, 2007 Finite Automata
CS103B ndout 18 Winter 2007 Ferury 28, 2007 Finite Automt Initil text y Mggie Johnson. Introduction Severl childrens gmes fit the following description: Pieces re set up on plying ord; dice re thrown or
More informationCS 311 Homework 3 due 16:30, Thursday, 14 th October 2010
CS 311 Homework 3 due 16:30, Thursdy, 14 th Octoer 2010 Homework must e sumitted on pper, in clss. Question 1. [15 pts.; 5 pts. ech] Drw stte digrms for NFAs recognizing the following lnguges:. L = {w
More information1 From NFA to regular expression
Note 1: How to convert DFA/NFA to regulr expression Version: 1.0 S/EE 374, Fll 2017 Septemer 11, 2017 In this note, we show tht ny DFA cn e converted into regulr expression. Our construction would work
More informationThe area under the graph of f and above the x-axis between a and b is denoted by. f(x) dx. π O
1 Section 5. The Definite Integrl Suppose tht function f is continuous nd positive over n intervl [, ]. y = f(x) x The re under the grph of f nd ove the x-xis etween nd is denoted y f(x) dx nd clled the
More information20 MATHEMATICS POLYNOMIALS
0 MATHEMATICS POLYNOMIALS.1 Introduction In Clss IX, you hve studied polynomils in one vrible nd their degrees. Recll tht if p(x) is polynomil in x, the highest power of x in p(x) is clled the degree of
More informationFinite Automata-cont d
Automt Theory nd Forml Lnguges Professor Leslie Lnder Lecture # 6 Finite Automt-cont d The Pumping Lemm WEB SITE: http://ingwe.inghmton.edu/ ~lnder/cs573.html Septemer 18, 2000 Exmple 1 Consider L = {ww
More informationHow do we solve these things, especially when they get complicated? How do we know when a system has a solution, and when is it unique?
XII. LINEAR ALGEBRA: SOLVING SYSTEMS OF EQUATIONS Tody we re going to tlk out solving systems of liner equtions. These re prolems tht give couple of equtions with couple of unknowns, like: 6= x + x 7=
More informationCM10196 Topic 4: Functions and Relations
CM096 Topic 4: Functions nd Reltions Guy McCusker W. Functions nd reltions Perhps the most widely used notion in ll of mthemtics is tht of function. Informlly, function is n opertion which tkes n input
More informationHarvard University Computer Science 121 Midterm October 23, 2012
Hrvrd University Computer Science 121 Midterm Octoer 23, 2012 This is closed-ook exmintion. You my use ny result from lecture, Sipser, prolem sets, or section, s long s you quote it clerly. The lphet is
More informationConverting Regular Expressions to Discrete Finite Automata: A Tutorial
Converting Regulr Expressions to Discrete Finite Automt: A Tutoril Dvid Christinsen 2013-01-03 This is tutoril on how to convert regulr expressions to nondeterministic finite utomt (NFA) nd how to convert
More informationHomework 3 Solutions
CS 341: Foundtions of Computer Science II Prof. Mrvin Nkym Homework 3 Solutions 1. Give NFAs with the specified numer of sttes recognizing ech of the following lnguges. In ll cses, the lphet is Σ = {,1}.
More informationHandout: Natural deduction for first order logic
MATH 457 Introduction to Mthemticl Logic Spring 2016 Dr Json Rute Hndout: Nturl deduction for first order logic We will extend our nturl deduction rules for sententil logic to first order logic These notes
More information3 Regular expressions
3 Regulr expressions Given n lphet Σ lnguge is set of words L Σ. So fr we were le to descrie lnguges either y using set theory (i.e. enumertion or comprehension) or y n utomton. In this section we shll
More information1B40 Practical Skills
B40 Prcticl Skills Comining uncertinties from severl quntities error propgtion We usully encounter situtions where the result of n experiment is given in terms of two (or more) quntities. We then need
More informationdx dt dy = G(t, x, y), dt where the functions are defined on I Ω, and are locally Lipschitz w.r.t. variable (x, y) Ω.
Chpter 8 Stility theory We discuss properties of solutions of first order two dimensionl system, nd stility theory for specil clss of liner systems. We denote the independent vrile y t in plce of x, nd
More informationImproper Integrals, and Differential Equations
Improper Integrls, nd Differentil Equtions October 22, 204 5.3 Improper Integrls Previously, we discussed how integrls correspond to res. More specificlly, we sid tht for function f(x), the region creted
More informationThe Regulated and Riemann Integrals
Chpter 1 The Regulted nd Riemnn Integrls 1.1 Introduction We will consider severl different pproches to defining the definite integrl f(x) dx of function f(x). These definitions will ll ssign the sme vlue
More informationReview: set theoretic definition of the numbers. Natural numbers:
Review: reltions A inry reltion on set A is suset R Ñ A ˆ A, where elements p, q re written s. Exmple: A Z nd R t pmod nqu. A inry reltion on set A is... (R) reflexive if for ll P A; (S) symmetric if implies
More informationBases for Vector Spaces
Bses for Vector Spces 2-26-25 A set is independent if, roughly speking, there is no redundncy in the set: You cn t uild ny vector in the set s liner comintion of the others A set spns if you cn uild everything
More informationAssignment 1 Automata, Languages, and Computability. 1 Finite State Automata and Regular Languages
Deprtment of Computer Science, Austrlin Ntionl University COMP2600 Forml Methods for Softwre Engineering Semester 2, 206 Assignment Automt, Lnguges, nd Computility Smple Solutions Finite Stte Automt nd
More informationLecture 3: Curves in Calculus. Table of contents
Mth 348 Fll 7 Lecture 3: Curves in Clculus Disclimer. As we hve textook, this lecture note is for guidnce nd supplement only. It should not e relied on when prepring for exms. In this lecture we set up
More informationBeginning Darboux Integration, Math 317, Intro to Analysis II
Beginning Droux Integrtion, Mth 317, Intro to Anlysis II Lets strt y rememering how to integrte function over n intervl. (you lerned this in Clculus I, ut mye it didn t stick.) This set of lecture notes
More informationPhysics 116C Solution of inhomogeneous ordinary differential equations using Green s functions
Physics 6C Solution of inhomogeneous ordinry differentil equtions using Green s functions Peter Young November 5, 29 Homogeneous Equtions We hve studied, especilly in long HW problem, second order liner
More informationNondeterminism and Nodeterministic Automata
Nondeterminism nd Nodeterministic Automt 61 Nondeterminism nd Nondeterministic Automt The computtionl mchine models tht we lerned in the clss re deterministic in the sense tht the next move is uniquely
More informationCS 275 Automata and Formal Language Theory
CS 275 utomt nd Forml Lnguge Theory Course Notes Prt II: The Recognition Prolem (II) Chpter II.5.: Properties of Context Free Grmmrs (14) nton Setzer (Bsed on ook drft y J. V. Tucker nd K. Stephenson)
More informationBisimulation, Games & Hennessy Milner logic
Bisimultion, Gmes & Hennessy Milner logi Leture 1 of Modelli Mtemtii dei Proessi Conorrenti Pweł Soboiński Univeristy of Southmpton, UK Bisimultion, Gmes & Hennessy Milner logi p.1/32 Clssil lnguge theory
More informationThings to Memorize: A Partial List. January 27, 2017
Things to Memorize: A Prtil List Jnury 27, 2017 Chpter 2 Vectors - Bsic Fcts A vector hs mgnitude (lso clled size/length/norm) nd direction. It does not hve fixed position, so the sme vector cn e moved
More informationBridging the gap: GCSE AS Level
Bridging the gp: GCSE AS Level CONTENTS Chpter Removing rckets pge Chpter Liner equtions Chpter Simultneous equtions 8 Chpter Fctors 0 Chpter Chnge the suject of the formul Chpter 6 Solving qudrtic equtions
More informationCSCI 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 information5. (±±) Λ = fw j w is string of even lengthg [ 00 = f11,00g 7. (11 [ 00)± Λ = fw j w egins with either 11 or 00g 8. (0 [ ffl)1 Λ = 01 Λ [ 1 Λ 9.
Regulr Expressions, Pumping Lemm, Right Liner Grmmrs Ling 106 Mrch 25, 2002 1 Regulr Expressions A regulr expression descries or genertes lnguge: it is kind of shorthnd for listing the memers of lnguge.
More informationLecture 9: LTL and Büchi Automata
Lecture 9: LTL nd Büchi Automt 1 LTL Property Ptterns Quite often the requirements of system follow some simple ptterns. Sometimes we wnt to specify tht property should only hold in certin context, clled
More informationImproper Integrals. The First Fundamental Theorem of Calculus, as we ve discussed in class, goes as follows:
Improper Integrls The First Fundmentl Theorem of Clculus, s we ve discussed in clss, goes s follows: If f is continuous on the intervl [, ] nd F is function for which F t = ft, then ftdt = F F. An integrl
More informationFormal languages, automata, and theory of computation
Mälrdlen University TEN1 DVA337 2015 School of Innovtion, Design nd Engineering Forml lnguges, utomt, nd theory of computtion Thursdy, Novemer 5, 14:10-18:30 Techer: Dniel Hedin, phone 021-107052 The exm
More information1 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 informationWorked 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 informationCS12N: The Coming Revolution in Computer Architecture Laboratory 2 Preparation
CS2N: The Coming Revolution in Computer Architecture Lortory 2 Preprtion Ojectives:. Understnd the principle of sttic CMOS gte circuits 2. Build simple logic gtes from MOS trnsistors 3. Evlute these gtes
More informationTHE EXISTENCE-UNIQUENESS THEOREM FOR FIRST-ORDER DIFFERENTIAL EQUATIONS.
THE EXISTENCE-UNIQUENESS THEOREM FOR FIRST-ORDER DIFFERENTIAL EQUATIONS RADON ROSBOROUGH https://intuitiveexplntionscom/picrd-lindelof-theorem/ This document is proof of the existence-uniqueness theorem
More informationLinear Systems with Constant Coefficients
Liner Systems with Constnt Coefficients 4-3-05 Here is system of n differentil equtions in n unknowns: x x + + n x n, x x + + n x n, x n n x + + nn x n This is constnt coefficient liner homogeneous system
More informationLecture 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 informationCS 330 Formal Methods and Models
CS 330 Forml Methods nd Models Dn Richrds, George Mson University, Spring 2017 Quiz Solutions Quiz 1, Propositionl Logic Dte: Ferury 2 1. Prove ((( p q) q) p) is tutology () (3pts) y truth tle. p q p q
More informationSeptember 13 Homework Solutions
College of Engineering nd Computer Science Mechnicl Engineering Deprtment Mechnicl Engineering 5A Seminr in Engineering Anlysis Fll Ticket: 5966 Instructor: Lrry Cretto Septemer Homework Solutions. Are
More informationLecture 1. Functional series. Pointwise and uniform convergence.
1 Introduction. Lecture 1. Functionl series. Pointwise nd uniform convergence. In this course we study mongst other things Fourier series. The Fourier series for periodic function f(x) with period 2π is
More informationBoolean Algebra. Boolean Algebra
Boolen Alger Boolen Alger A Boolen lger is set B of vlues together with: - two inry opertions, commonly denoted y + nd, - unry opertion, usully denoted y ˉ or ~ or, - two elements usully clled zero nd
More informationSecond Lecture: Basics of model-checking for finite and timed systems
Second Lecture: Bsics of model-checking for finite nd timed systems Jen-Frnçois Rskin Université Lire de Bruxelles Belgium Artist2 Asin Summer School - Shnghi - July 28 Pln of the tlk Lelled trnsition
More information1. 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 informationDesigning Information Devices and Systems I Spring 2018 Homework 7
EECS 16A Designing Informtion Devices nd Systems I Spring 2018 omework 7 This homework is due Mrch 12, 2018, t 23:59. Self-grdes re due Mrch 15, 2018, t 23:59. Sumission Formt Your homework sumission should
More informationThe Dirichlet Problem in a Two Dimensional Rectangle. Section 13.5
The Dirichlet Prolem in Two Dimensionl Rectngle Section 13.5 1 Dirichlet Prolem in Rectngle In these notes we will pply the method of seprtion of vriles to otin solutions to elliptic prolems in rectngle
More informationChapter 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 informationMathematics Number: Logarithms
plce of mind F A C U L T Y O F E D U C A T I O N Deprtment of Curriculum nd Pedgogy Mthemtics Numer: Logrithms Science nd Mthemtics Eduction Reserch Group Supported y UBC Teching nd Lerning Enhncement
More informationQUADRATIC RESIDUES MATH 372. FALL INSTRUCTOR: PROFESSOR AITKEN
QUADRATIC RESIDUES MATH 37 FALL 005 INSTRUCTOR: PROFESSOR AITKEN When is n integer sure modulo? When does udrtic eution hve roots modulo? These re the uestions tht will concern us in this hndout 1 The
More informationUnit #9 : Definite Integral Properties; Fundamental Theorem of Calculus
Unit #9 : Definite Integrl Properties; Fundmentl Theorem of Clculus Gols: Identify properties of definite integrls Define odd nd even functions, nd reltionship to integrl vlues Introduce the Fundmentl
More informationFinite Automata Theory and Formal Languages TMV027/DIT321 LP4 2018
Finite Automt Theory nd Forml Lnguges TMV027/DIT321 LP4 2018 Lecture 10 An Bove April 23rd 2018 Recp: Regulr Lnguges We cn convert between FA nd RE; Hence both FA nd RE ccept/generte regulr lnguges; More
More informationName Ima Sample ASU ID
Nme Im Smple ASU ID 2468024680 CSE 355 Test 1, Fll 2016 30 Septemer 2016, 8:35-9:25.m., LSA 191 Regrding of Midterms If you elieve tht your grde hs not een dded up correctly, return the entire pper to
More informationCompiler Design. Fall Lexical Analysis. Sample Exercises and Solutions. Prof. Pedro C. Diniz
University of Southern Cliforni Computer Science Deprtment Compiler Design Fll Lexicl Anlysis Smple Exercises nd Solutions Prof. Pedro C. Diniz USC / Informtion Sciences Institute 4676 Admirlty Wy, Suite
More informationFinite state automata
Finite stte utomt Lecture 2 Model-Checking Finite-Stte Systems (untimed systems) Finite grhs with lels on edges/nodes set of nodes (sttes) set of edges (trnsitions) set of lels (lhet) Finite Automt, CTL,
More informationState Minimization for DFAs
Stte Minimiztion for DFAs Red K & S 2.7 Do Homework 10. Consider: Stte Minimiztion 4 5 Is this miniml mchine? Step (1): Get rid of unrechle sttes. Stte Minimiztion 6, Stte is unrechle. Step (2): Get rid
More informationRiemann is the Mann! (But Lebesgue may besgue to differ.)
Riemnn is the Mnn! (But Lebesgue my besgue to differ.) Leo Livshits My 2, 2008 1 For finite intervls in R We hve seen in clss tht every continuous function f : [, b] R hs the property tht for every ɛ >
More informationVectors , (0,0). 5. A vector is commonly denoted by putting an arrow above its symbol, as in the picture above. Here are some 3-dimensional vectors:
Vectors 1-23-2018 I ll look t vectors from n lgeric point of view nd geometric point of view. Algericlly, vector is n ordered list of (usully) rel numers. Here re some 2-dimensionl vectors: (2, 3), ( )
More informationCSE396 Prelim I Answer Key Spring 2017
Nme nd St.ID#: CSE96 Prelim I Answer Key Spring 2017 (1) (24 pts.) Define A to e the lnguge of strings x {, } such tht x either egins with or ends with, ut not oth. Design DFA M such tht L(M) = A. A node-rc
More informationPolynomials and Division Theory
Higher Checklist (Unit ) Higher Checklist (Unit ) Polynomils nd Division Theory Skill Achieved? Know tht polynomil (expression) is of the form: n x + n x n + n x n + + n x + x + 0 where the i R re the
More informationHennessy-Milner Logic 1.
Hennessy-Milner Logic 1. Colloquium in honor of Robin Milner. Crlos Olrte. Pontifici Universidd Jverin 28 April 2010. 1 Bsed on the tlks: [1,2,3] Prof. Robin Milner (R.I.P). LIX, Ecole Polytechnique. Motivtion
More informationImproper Integrals. Introduction. Type 1: Improper Integrals on Infinite Intervals. When we defined the definite integral.
Improper Integrls Introduction When we defined the definite integrl f d we ssumed tht f ws continuous on [, ] where [, ] ws finite, closed intervl There re t lest two wys this definition cn fil to e stisfied:
More informationAUTOMATA AND LANGUAGES. Definition 1.5: Finite Automaton
25. Finite Automt AUTOMATA AND LANGUAGES A system of computtion tht only hs finite numer of possile sttes cn e modeled using finite utomton A finite utomton is often illustrted s stte digrm d d d. d q
More informationa,b a 1 a 2 a 3 a,b 1 a,b a,b 2 3 a,b a,b a 2 a,b CS Determinisitic Finite Automata 1
CS4 45- Determinisitic Finite Automt -: Genertors vs. Checkers Regulr expressions re one wy to specify forml lnguge String Genertor Genertes strings in the lnguge Deterministic Finite Automt (DFA) re nother
More informationChapter 14. Matrix Representations of Linear Transformations
Chpter 4 Mtrix Representtions of Liner Trnsformtions When considering the Het Stte Evolution, we found tht we could describe this process using multipliction by mtrix. This ws nice becuse computers cn
More informationSection 4: Integration ECO4112F 2011
Reding: Ching Chpter Section : Integrtion ECOF Note: These notes do not fully cover the mteril in Ching, ut re ment to supplement your reding in Ching. Thus fr the optimistion you hve covered hs een sttic
More information