CSC2542 Representations for Classical Planning

Size: px
Start display at page:

Download "CSC2542 Representations for Classical Planning"

Transcription

1 1 Aknowlegements CSC2542 Representtions for Clssil Plnning Sheil MIlrith Deprtment of Computer Siene University of Toronto Winter 2009 Some the slies use in this ourse re moifitions of Dn Nu s leture slies for the textook Automte Plnning, liense uner the Cretive Commons Attriution-NonCommeril-ShreAlike Liense: Other slies re moifitions of slies evelope y Mlte Helmert, Bernhr Neel, n Jussi Rintnen. I hve lso use some mteril prepre y P@trik Hslum n Ro Kmhmpti. I woul like to grtefully knowlege the ontriutions of these reserhers, n thnk them for generously permitting me to use spets of their presenttion mteril. 2 Quik Review of Clssil Plnning 8 restritive ssumptions req : A0: Finite A1: Fully oservle A2: Deterministi A3: Stti A4: Attinment gols A5: Sequentil plns A6: Impliit time A7: Offline plnning lotion 1 lotion 2 move2 lotion 1 lotion 2 unlo move1 lo s 1 s 3 s 4 put tke put tke move2 move1 lotion 1 lotion 2 move2 move1 lotion 1 lotion 2 lotion 1 lotion 2 lotion 1 lotion 2 s 0 s 2 s 5 4 Representtion: Motivtion for Approh Defult view: represent stte expliitly represent tions s trnsition system (e.g., s n iniene mtrix) Prolem: expliit grph orresponing to trnsition system is huge iret mnipultion of trnsition system is umersome Solution: Provie ompt representtion of trnsition system & inue grph 1. Explite the struture of the sttes e.g., sttes speifie in terms of stte vriles 2. Represent tions not s trnsition system/iniene mtries ut s funtions (e.g., opertors) speifie in terms of the stte vriles An tion is pplile to stte when some stte vriles hve ertin vlues. When pplile, it will hnge the vlues of ertin (other) stte vriles 3. To pln, Just give the initil stte 6 Use the opertors to generte the other sttes s neee Why is this more ompt? Why is this more ompt thn n expliit trnsition system? In n expliit trnsition system, tions re represente s stte-tostte trnsitions. Eh tion will e represente y n iniene mtrix of size S x S In the propose moel, tions re represente only in terms of stte vriles whose vlues they re out, n whose vlue they ffet. (It exploits the struture of the prolem!) Consier stte spe of 1024 sttes. It n e represente y log =10 stte vriles. If n tion nees vrile v1 to e true n mkes v7 to e flse, it n e represente y just 2 its (inste of 1024x1024 mtrix) Of ourse, if the tion hs omplite mpping from sttes to sttes, in the worst se the tion rep will e just s lrge The ssumption eing me here is tht the tions will hve effets on smll numer of stte vriles. 8 Outline Representtion shemes 1. Clssil representtion 2. Set-theoreti representtion 3. Stte-vrile representtion Exmples: DWR n the Bloks Worl Comprisons 9

2 10 1. Clssil Representtion Strt with funtion-free first-orer lnguge Finitely mny preite symols n onstnt symols, ut no funtion symols Exmple: the DWR omin Lotions: l1, l2, Continers: 1, 2, Piles: p1, p2, Root rts: r1, r2, Crnes: k1, k2, Clssil Representtion Atom: preite symol n rgs Use these to represent oth fixe n ynmi ( fluent ) reltions jent(l,l ) tthe(p,l) elong(k,l) oupie(l) t(r,l) loe(r,) unloe(r) holing(k,) empty(k) in(,p) on(, ) top(,p) top(pllet,p) Groun expression: ontins no vrile symols - e.g., in(1,p3) Ungroun expression: t lest one vrile symol - e.g., in(1,x) Sustitution: θ = {x 1 v 1, x 2 v 2,, x n v n } Eh x i is vrile symol; eh v i is term Instne of e: result of pplying sustitution θ to e Reple vriles of e simultneously, not sequentilly 11 Sttes Stte: set s of groun toms The toms represent the things tht re true in one of Σ s sttes Only finitely mny groun toms, so only finitely mny possile sttes Opertors Opertor: triple o=(nme(o), preon(o), effets(o)) nme(o) is syntti expression of the form n(x 1,,x k ) n: opertor symol - must e unique for eh opertor x 1,,x k : vrile symols (prmeters) must inlue every vrile symol in o preon(o): preonitions literls tht must e true in orer to use the opertor effets(o): effets literls the opertor will mke true Ations Ation: groun instne (vi sustitution) of n opertor Nottion Let e n opertor or tion. Then E.g., preon + () = {toms tht pper positively in s preonitions} preon () = {toms tht pper negtively in s preonitions} effets + () = {toms tht pper positively in s effets} effets () = {toms tht pper negtively in s effets} effets + (tke(k,l,,,p) = {holing(k,), top(,p)} effets (tke(k,l,,,p) = {empty(k), in(,p), top(,p), on(,)} 14 15

3 16 Appliility An tion is pplile to stte s if s stisfies preon(), i.e., if preon + () s n preon () s = Here re n tion n stte tht it s pplile to: Result of Performing n Ation If is pplile to s, the result of performing it is γ(s,) = (s effets ()) effets + () Delete negtive effets, n positive ones 17 Opertors for the DWR Domin Plnning omin: lnguge & opertors Opertors orrespons to set of stte-trnsition systems Plnning Prolems Given plnning omin (lnguge L, opertors O) Sttement of plnning prolem: triple P=(O,s 0,g) O is the olletion of opertors s 0 is stte (the initil stte) g is set of literls (the gol formul) The tul plnning prolem: P = (Σ,s 0, g) s 0 n g re s ove Σ = (S,A,γ) is stte-trnsition system S = {ll sets of groun toms in L} A = {ll groun instnes of opertors in O} γ = stte-trnsition funtion etermine y the opertors Plns n Solutions Pln: ny sequene of tions σ = 1, 2,, n suh tht eh i is groun instne of n opertor in O The pln is solution for P=(O,s 0,g) if it is exeutle n hieves g i.e., if there re sttes s 0, s 1,, s n suh tht γ (s 0, 1 ) = s 1 γ (s 1, 2 ) = s 2 γ (s n 1, n ) = s n s n stisfies g Exmple Let P 1 = (O, s 1, g 1 ), where O is the set of opertors given erlier g 1 ={loe(r1,3), t(r1,lo2)} 20 21

4 22 Exmple (ont.) Exmple (ont.) Here re three solutions for P 1 : tke(rne1,lo1,3,1,p1), move(r1,lo2,lo1), move(r1,lo1,lo2), move(r1,lo2,lo1), lo(rne1,lo1,3,r1), move(r1,lo1,lo2) tke(rne1,lo1,3,1,p1), move(r1,lo2,lo1), lo(rne1,lo1,3,r1), move(r1,lo1,lo2) move(r1,lo2,lo1), tke(rne1,lo1,3,1,p1), lo(rne1,lo1,3,r1), move(r1,lo1,lo2) Eh of them proues the stte shown here: First is reunnt: n remove tions n still hve solution tke(rne1,lo1,3,1,p1), move(r1,lo2,lo1), move(r1,lo1,lo2), move(r1,lo2,lo1), lo(rne1,lo1,3,r1), move(r1,lo1,lo2) tke(rne1,lo1,3,1,p1), move(r1,lo2,lo1), lo(rne1,lo1,3,r1), move(r1,lo1,lo2) move(r1,lo2,lo1), tke(rne1,lo1,3,1,p1), lo(rne1,lo1,3,r1), move(r1,lo1,lo2) The 2n n 3r re irreunnt n shortest Set-Theoreti Representtion Inste of opertors like this one, Like lssil representtion, ut restrite to propositionl logi Sttes: Inste of olletion of groun toms {on(1,pllet), on(1,r1), on(1,2),, t(r1,l1), t(r1,l2), } use olletion of propositions (oolen vriles): {on-1-pllet, on-1-r1, on-1-2,, t-r1-l1, t-r1-l2, } tke ll of the opertor instnes, e.g., this one, n rewrite groun toms s propositions tke-rne1-lo1-3-1-p1 preon: elong-rne1-lo1, tthe-p1-lo1, empty-rne1, top-3-p1, on-3-1 elete: empty-rne1, in-3-p1, top-3-p1, on-3-p1 : holing-rne1-3, top-1-p Comprison A set-theoreti representtion is equivlent to lssil representtion in whih ll of the toms re groun Prolem: Exponentil lowup If lssil opertor ontins n toms n eh tom hs rity k, then it orrespons to nk tions where = {onstnt symols} 3. Stte-Vrile Representtion Non-fluents (properties tht on t hnge) re groun reltions: e.g., jent(lo1,lo2) Fluents re funtions: i.e., for properties tht n hnge, ssign vlues to stte vriles Clssil n stte-vrile rep ns tke similr mounts of spe eh n e trnslte into the other in low-orer polynomil time {top(p1)=3, pos(3)=1, pos(1)=pllet, holing(rne1)=nil, rlo(r1)=lo2, loe(r1)=nil, } 26 27

5 28 Exmple: The Bloks Worl Infinitely wie tle, finite numer of hilren s loks Ignore where lok is lote on the tle A lok n sit on the tle or on nother lok Wnt to move loks from one onfigurtion to nother e.g., initil stte e gol Clssil, set-theoreti, n stte-vrile formultions for the se of FIVE BLOCKS follow. 1. Exmple Clssil Representtion Constnt symols: The loks:,,,, e Preites: ontle(x) - lok x is on the tle on(x,y) - lok x is on lok y ler(x) - lok x hs nothing on it holing(x) - the root hn is holing lok x hnempty- the root hn isn t holing nything e 29 Clssil Opertors unstk(x,y) Preon: on(x,y), ler(x), hnempty Effets: ~on(x,y), ~ler(x), ~hnempty, holing(x), ler(y) stk(x,y) Preon: holing(x), ler(y) Effets: ~holing(x), ~ler(y), on(x,y), ler(x), hnempty pikup(x) Preon: ontle(x), ler(x), hnempty Effets: ~ontle(x), ~ler(x), ~hnempty, holing(x) putown(x) Preon: holing(x) Effets: ~holing(x), ontle(x), ler(x), hnempty Exmple Set-Theoreti Representtion For five loks, 36 propositions, 50 tions E.g., ontle- - lok is on the tle on-- - lok is on lok ler- - lok hs nothing on it holing- - the root hn is holing lok hnempty - the root hn isn t holing nything (31 more) e 31 Set-Theoreti Ations E.g., unstk-- Pre: on-,, ler-, hnempty Del: on-,, ler-, hnempty A: holing-, ler- stk-- Pre: holing-, ler- Del: holing-, ler- A: on--, ler-, hnempty pikup- Pre: ontle-, ler-, hnempty Del: ontle-, ler-, hnempty A: holing- putown- Pre: holing- Del: holing- A: ontle-, ler-, hnempty 3. Exmple Stte-Vrile Representtion Constnt symols:,,,, e of type lok 0, 1, tle, nil of type other Stte vriles: pos(x) = y if lok x is on lok y e pos(x) = tle if lok x is on the tle pos(x) = nil if lok x is eing hel ler(x) = 1 if lok x hs nothing on it ler(x) = 0 if lok x is eing hel or hs lok on it holing = x if the root hn is holing lok x holing = nil if the root hn is holing nothing... (46 more) 32 33

6 34 Stte-Vrile Opertors unstk(x : lok, y : lok) Preon: pos(x)=y, ler(y)=0, ler(x)=1, holing=nil Effets: pos(x)=nil, ler(x)=0, holing=x, ler(y)=1 stk(x : lok, y : lok) Preon: holing=x, ler(x)=0, ler(y)=1 Effets: holing=nil, ler(y)=0, pos(x)=y, ler(x)=1 pikup(x : lok) Preon: pos(x)=tle, ler(x)=1, holing=nil Effets: pos(x)=nil, ler(x)=0, holing=x putown(x : lok) Preon: holing=x Effets: holing=nil, pos(x)=tle, ler(x)=1 Representtionl Equivlene Any prolem tht n e represente in one representtion n lso e represente in the other two Cn onvert in liner time n spe, exept when onverting to set-theoreti (where we get n exponentil lowup) Set-theoreti representtion trivil write ll of the groun instnes Clssil representtion P(x 1,,x n ) eomes f P (x 1,,x n )=1 Stte-vrile representtion f(x 1,,x n )=y eomes P f (x 1,,x n,y) 35 Comprison Clssil representtion Most populr for lssil plnning, sis of PDDL Set-theoreti representtion Cn tke muh more spe thn lssil representtion Useful in lgorithms tht mnipulte groun toms iretly e.g., plnning grphs, SAT Useful for ertin kins of theoretil stuies Stte-vrile representtion Equivlent to lssil representtion in expressive power Less nturl for logiins, more nturl for engineers Useful in non-lssil plnning prolems s wy to hnle numers, funtions, time Riher Speifition Lnguges: ADL The previous representtions were so-lle STRIPS representtions. ADL is riher, n thus more ompt, representtion lnguge tht llows for Disjuntion n Quntifition in preonitions n gols Effets tht re Quntifie, n/or Conitionl (effet is onitione on stte) PDDL supports STRIPS n ADL, ut not ll plnners support ADL, n not ll plnners even support so-lle Clssil Representtion Compiling to Cnonil Ation Rep n PROS & CONS: It is possile to ompile own ADL tions into STRIPS tions Quntifition is written s onjuntions/isjuntions over finite universes Ations with onitionl effets re ompile into multiple (exponentilly more) tions without onitionl effets Ations with isjuntive effets re ompile into multiple tions, eh of whih tke one of the isjunts s their preonitions (Domin xioms n e ompile own into the iniviul effets of the tions; so ll tions stisfy STRIPS ssumption) Compiltion is not lwys win-win. By ompiling own to nonil form, we n onentrte on highly effiient plnning for nonil tions However, often ompiltion les to n exponentil lowup n mkes it hrer to exploit the struture of the omin By leving tions in non-nonil form, we n often o more ompt enoing of the omins s well s more effiient serh However, we will hve to ontinully exten plnning lgorithms to hnle these representtions 51

CSC2542 State-Space Planning

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

More information

Chapter 4 State-Space Planning

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

More information

Situation Calculus. Situation Calculus Building Blocks. Sheila McIlraith, CSC384, University of Toronto, Winter Situations Fluents Actions

Situation Calculus. Situation Calculus Building Blocks. Sheila McIlraith, CSC384, University of Toronto, Winter Situations Fluents Actions Plnning gent: single gent or multi-gent Stte: complete or Incomplete (logicl/probbilistic) stte of the worl n/or gent s stte of knowlege ctions: worl-ltering n/or knowlege-ltering (e.g. sensing) eterministic

More information

Lecture 6: Coding theory

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

More information

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

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

More information

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

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

More information

Project 6: Minigoals Towards Simplifying and Rewriting Expressions

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

More information

2.4 Theoretical Foundations

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

More information

CS 573 Automata Theory and Formal Languages

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

More information

Compression of Palindromes and Regularity.

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

More information

Algebra 2 Semester 1 Practice Final

Algebra 2 Semester 1 Practice Final Alger 2 Semester Prtie Finl Multiple Choie Ientify the hoie tht est ompletes the sttement or nswers the question. To whih set of numers oes the numer elong?. 2 5 integers rtionl numers irrtionl numers

More information

Unit 4. Combinational Circuits

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

More information

Nondeterminism and Nodeterministic Automata

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

More information

CS 491G Combinatorial Optimization Lecture Notes

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

More information

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

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

More information

Automatic Synthesis of New Behaviors from a Library of Available Behaviors

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

More information

Linear Inequalities. Work Sheet 1

Linear Inequalities. Work Sheet 1 Work Sheet 1 Liner Inequlities Rent--Hep, cr rentl compny,chrges $ 15 per week plus $ 0.0 per mile to rent one of their crs. Suppose you re limited y how much money you cn spend for the week : You cn spend

More information

Lecture 11 Binary Decision Diagrams (BDDs)

Lecture 11 Binary Decision Diagrams (BDDs) C 474A/57A Computer-Aie Logi Design Leture Binry Deision Digrms (BDDs) C 474/575 Susn Lyseky o 3 Boolen Logi untions Representtions untion n e represente in ierent wys ruth tle, eqution, K-mp, iruit, et

More information

Lecture 2: Cayley Graphs

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

More information

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

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

More information

Coalgebra, Lecture 15: Equations for Deterministic Automata

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

More information

Finite State Automata and Determinisation

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

More information

Eigenvectors and Eigenvalues

Eigenvectors and Eigenvalues MTB 050 1 ORIGIN 1 Eigenvets n Eigenvlues This wksheet esries the lger use to lulte "prinipl" "hrteristi" iretions lle Eigenvets n the "prinipl" "hrteristi" vlues lle Eigenvlues ssoite with these iretions.

More information

Factorising FACTORISING.

Factorising FACTORISING. Ftorising FACTORISING www.mthletis.om.u Ftorising FACTORISING Ftorising is the opposite of expning. It is the proess of putting expressions into rkets rther thn expning them out. In this setion you will

More information

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

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

More information

Propositional models. Historical models of computation. Application: binary addition. Boolean functions. Implementation using switches.

Propositional models. Historical models of computation. Application: binary addition. Boolean functions. Implementation using switches. Propositionl models Historil models of omputtion Steven Lindell Hverford College USA 1/22/2010 ISLA 2010 1 Strt with fixed numer of oolen vriles lled the voulry: e.g.,,. Eh oolen vrile represents proposition,

More information

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

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

More information

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

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

More information

1 Nondeterministic Finite Automata

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

More information

Solutions to Problem Set #1

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

More information

Implication Graphs and Logic Testing

Implication Graphs and Logic Testing Implition Grphs n Logi Testing Vishwni D. Agrwl Jmes J. Dnher Professor Dept. of ECE, Auurn University Auurn, AL 36849 vgrwl@eng.uurn.eu www.eng.uurn.eu/~vgrwl Joint reserh with: K. K. Dve, ATI Reserh,

More information

Nondeterministic Automata vs Deterministic Automata

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

More information

Logic, Set Theory and Computability [M. Coppenbarger]

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

More information

Convert the NFA into DFA

Convert 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 information

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

The University of Nottingham SCHOOL OF COMPUTER SCIENCE A LEVEL 2 MODULE, SPRING SEMESTER LANGUAGES AND COMPUTATION ANSWERS The University of Nottinghm SCHOOL OF COMPUTER SCIENCE LEVEL 2 MODULE, SPRING SEMESTER 2016 2017 LNGUGES ND COMPUTTION NSWERS Time llowed TWO hours Cndidtes my complete the front cover of their nswer ook

More information

CIT 596 Theory of Computation 1. Graphs and Digraphs

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

More information

Minimal DFA. minimal DFA for L starting from any other

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

More information

The Regulated and Riemann Integrals

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

More information

Analysis of Temporal Interactions with Link Streams and Stream Graphs

Analysis of Temporal Interactions with Link Streams and Stream Graphs Anlysis of Temporl Intertions with n Strem Grphs, Tiphine Vir, Clémene Mgnien http:// ltpy@ LIP6 CNRS n Soronne Université Pris, Frne 1/23 intertions over time 0 2 4 6 8,,, n for 10 time units time 2/23

More information

Logic Synthesis and Verification

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

More information

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

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

More information

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

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

More information

BİL 354 Veritabanı Sistemleri. Relational Algebra (İlişkisel Cebir)

BİL 354 Veritabanı Sistemleri. Relational Algebra (İlişkisel Cebir) BİL 354 Veritnı Sistemleri Reltionl lger (İlişkisel Ceir) Reltionl Queries Query lnguges: llow mnipultion nd retrievl of dt from dtse. Reltionl model supports simple, powerful QLs: Strong forml foundtion

More information

CS311 Computational Structures Regular Languages and Regular Grammars. Lecture 6

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

More information

Homework 3 Solutions

Homework 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 information

NON-DETERMINISTIC FSA

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

More information

Lecture 2 : Propositions DRAFT

Lecture 2 : Propositions DRAFT CS/Mth 240: Introduction to Discrete Mthemtics 1/20/2010 Lecture 2 : Propositions Instructor: Dieter vn Melkeeek Scrie: Dlior Zelený DRAFT Lst time we nlyzed vrious mze solving lgorithms in order to illustrte

More information

CS 360 Exam 2 Fall 2014 Name

CS 360 Exam 2 Fall 2014 Name CS 360 Exm 2 Fll 2014 Nme 1. The lsses shown elow efine singly-linke list n stk. Write three ifferent O(n)-time versions of the reverse_print metho s speifie elow. Eh version of the metho shoul output

More information

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

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

More information

CS103B Handout 18 Winter 2007 February 28, 2007 Finite Automata

CS103B 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 information

Hardware Verification 2IMF20

Hardware Verification 2IMF20 Hrdwre Verifition 2IMF20 Julien Shmltz Leture 02: Boolen Funtions, ST, CEC Course ontent - Forml tools Temporl Logis (LTL, CTL) Domin Properties System Verilog ssertions demi & Industrils Proessors Networks

More information

Bisimulation, Games & Hennessy Milner logic

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

More information

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

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

More information

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

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

More information

Linear Algebra Introduction

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

More information

The Evaluation Theorem

The Evaluation Theorem These notes closely follow the presenttion of the mteril given in Jmes Stewrt s textook Clculus, Concepts nd Contexts (2nd edition) These notes re intended primrily for in-clss presenttion nd should not

More information

Nondeterministic Finite Automata

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

More information

Alpha Algorithm: Limitations

Alpha Algorithm: Limitations Proess Mining: Dt Siene in Ation Alph Algorithm: Limittions prof.dr.ir. Wil vn der Alst www.proessmining.org Let L e n event log over T. α(l) is defined s follows. 1. T L = { t T σ L t σ}, 2. T I = { t

More information

Review of Gaussian Quadrature method

Review 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 information

Chapter 5 Plan-Space Planning

Chapter 5 Plan-Space Planning Lecture slides for Automted Plnning: Theory nd Prctice Chpter 5 Pln-Spce Plnning Dn S. Nu CMSC 722, AI Plnning University of Mrylnd, Spring 2008 1 Stte-Spce Plnning Motivtion g 1 1 g 4 4 s 0 g 5 5 g 2

More information

QUADRATIC EQUATION. Contents

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

More information

Bases for Vector Spaces

Bases 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 information

Lecture 3: Equivalence Relations

Lecture 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 information

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

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

More information

Tutorial Worksheet. 1. Find all solutions to the linear system by following the given steps. x + 2y + 3z = 2 2x + 3y + z = 4.

Tutorial Worksheet. 1. Find all solutions to the linear system by following the given steps. x + 2y + 3z = 2 2x + 3y + z = 4. Mth 5 Tutoril Week 1 - Jnury 1 1 Nme Setion Tutoril Worksheet 1. Find ll solutions to the liner system by following the given steps x + y + z = x + y + z = 4. y + z = Step 1. Write down the rgumented mtrix

More information

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

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

More information

CS 330 Formal Methods and Models

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

More information

p-adic Egyptian Fractions

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

More information

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

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

More information

Lecture 8: Abstract Algebra

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

More information

Improper Integrals, and Differential Equations

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

More information

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

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

More information

Let s divide up the interval [ ab, ] into n subintervals with the same length, so we have

Let s divide up the interval [ ab, ] into n subintervals with the same length, so we have III. INTEGRATION Eonomists seem muh more intereste in mrginl effets n ifferentition thn in integrtion. Integrtion is importnt for fining the epete vlue n vrine of rnom vriles, whih is use in eonometris

More information

For a, b, c, d positive if a b and. ac bd. Reciprocal relations for a and b positive. If a > b then a ab > b. then

For a, b, c, d positive if a b and. ac bd. Reciprocal relations for a and b positive. If a > b then a ab > b. then Slrs-7.2-ADV-.7 Improper Definite Integrls 27.. D.dox Pge of Improper Definite Integrls Before we strt the min topi we present relevnt lger nd it review. See Appendix J for more lger review. Inequlities:

More information

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

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

More information

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

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

More information

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

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

More information

Chapter 2 Finite Automata

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

More information

Abstraction of Nondeterministic Automata Rong Su

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

More information

for all x in [a,b], then the area of the region bounded by the graphs of f and g and the vertical lines x = a and x = b is b [ ( ) ( )] A= f x g x dx

for all x in [a,b], then the area of the region bounded by the graphs of f and g and the vertical lines x = a and x = b is b [ ( ) ( )] A= f x g x dx Applitions of Integrtion Are of Region Between Two Curves Ojetive: Fin the re of region etween two urves using integrtion. Fin the re of region etween interseting urves using integrtion. Desrie integrtion

More information

Lecture 9: LTL and Büchi Automata

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

More information

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

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

More information

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

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

More information

Part 4. Integration (with Proofs)

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

More information

CS 310 (sec 20) - Winter Final Exam (solutions) SOLUTIONS

CS 310 (sec 20) - Winter Final Exam (solutions) SOLUTIONS CS 310 (sec 20) - Winter 2003 - Finl Exm (solutions) SOLUTIONS 1. (Logic) Use truth tles to prove the following logicl equivlences: () p q (p p) (q q) () p q (p q) (p q) () p q p q p p q q (q q) (p p)

More information

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

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

More information

Vidyalankar S.E. Sem. III [CMPN] Discrete Structures Prelim Question Paper Solution

Vidyalankar S.E. Sem. III [CMPN] Discrete Structures Prelim Question Paper Solution S.E. Sem. III [CMPN] Discrete Structures Prelim Question Pper Solution 1. () (i) Disjoint set wo sets re si to be isjoint if they hve no elements in common. Exmple : A = {0, 4, 7, 9} n B = {3, 17, 15}

More information

First Midterm Examination

First Midterm Examination 24-25 Fll Semester First Midterm Exmintion ) Give the stte digrm of DFA tht recognizes the lnguge A over lphet Σ = {, } where A = {w w contins or } 2) The following DFA recognizes the lnguge B over lphet

More information

CARLETON UNIVERSITY. 1.0 Problems and Most Solutions, Sect B, 2005

CARLETON UNIVERSITY. 1.0 Problems and Most Solutions, Sect B, 2005 RLETON UNIVERSIT eprtment of Eletronis ELE 2607 Swithing iruits erury 28, 05; 0 pm.0 Prolems n Most Solutions, Set, 2005 Jn. 2, #8 n #0; Simplify, Prove Prolem. #8 Simplify + + + Reue to four letters (literls).

More information

Finite-State Automata: Recap

Finite-State Automata: Recap Finite-Stte Automt: Recp Deepk D Souz Deprtment of Computer Science nd Automtion Indin Institute of Science, Bnglore. 09 August 2016 Outline 1 Introduction 2 Forml Definitions nd Nottion 3 Closure under

More information

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

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

More information

Behavior Composition in the Presence of Failure

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

More information

MCH T 111 Handout Triangle Review Page 1 of 3

MCH T 111 Handout Triangle Review Page 1 of 3 Hnout Tringle Review Pge of 3 In the stuy of sttis, it is importnt tht you e le to solve lgeri equtions n tringle prolems using trigonometry. The following is review of trigonometry sis. Right Tringle:

More information

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

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

More information

Total score: /100 points

Total score: /100 points Points misse: Stuent's Nme: Totl sore: /100 points Est Tennessee Stte University Deprtment of Computer n Informtion Sienes CSCI 2710 (Trnoff) Disrete Strutures TEST 2 for Fll Semester, 2004 Re this efore

More information

6.5 Improper integrals

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

More information

CS415 Compilers. Lexical Analysis and. These slides are based on slides copyrighted by Keith Cooper, Ken Kennedy & Linda Torczon at Rice University

CS415 Compilers. Lexical Analysis and. These slides are based on slides copyrighted by Keith Cooper, Ken Kennedy & Linda Torczon at Rice University CS415 Compilers Lexicl Anlysis nd These slides re sed on slides copyrighted y Keith Cooper, Ken Kennedy & Lind Torczon t Rice University First Progrmming Project Instruction Scheduling Project hs een posted

More information

Model Reduction of Finite State Machines by Contraction

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

More information

Polynomials. Polynomials. Curriculum Ready ACMNA:

Polynomials. Polynomials. Curriculum Ready ACMNA: Polynomils Polynomils Curriulum Redy ACMNA: 66 www.mthletis.om Polynomils POLYNOMIALS A polynomil is mthemtil expression with one vrile whose powers re neither negtive nor frtions. The power in eh expression

More information

Chapter Five - Eigenvalues, Eigenfunctions, and All That

Chapter Five - Eigenvalues, Eigenfunctions, and All That Chpter Five - Eigenvlues, Eigenfunctions, n All Tht The prtil ifferentil eqution methos escrie in the previous chpter is specil cse of more generl setting in which we hve n eqution of the form L 1 xux,tl

More information