Copyright 2000, Kevin Wayne 1

Size: px
Start display at page:

Download "Copyright 2000, Kevin Wayne 1"

Transcription

1 Extnsions: Mtin Rsints to Hospitls CS 580: Aloritm Dsin n Anlysis Ex: Mn ospitls, Womn m sool rsints. Vrint 1. Som prtiipnts lr otrs s unptl. 1.2 Fiv Rprsnttiv Prolms Vrint 2. Unqul numr o mn n womn. rsint A unwillin to work in Clvln Jrmi Bloki Puru Univrsity Sprin 18 Vrint. Limit polymy. ospitl X wnts to ir rsints Gl-Sply Aloritm Still Works. Minor moiitions to o to nl vritions! Rp: Stl Mtin Prolm Extnsions: Mtin Rsints to Hospitls Dinition o Stl Mtin Stl Roomt Mtin Prolm Stl mtin os not lwys xist! Gl Sply Aloritm (Propos-An-Rjt) Proo tt Aloritm Trmints in stps Proo tt Aloritm Outputs Stl Mtin Mtin is ml-optiml I tr r multipl irnt stl mtins mn t s is st vli prtnr Mtin is ml-pssiml I tr r multipl irnt stl mtins mn t s r worst vli prtnr 2 4 Ex: Mn ospitls, Womn m sool rsints. Vrint 1. Som prtiipnts lr otrs s unptl. Vrint 2. Unqul numr o mn n womn. Vrint. Limit polymy. ospitl X wnts to ir rsints D. Mtin S unstl i tr is ospitl n rsint r su tt: n r r ptl to otr; n itr r is unmt, or r prrs to r ssin ospitl; n itr os not v ll its pls ill, or prrs r to t lst on o its ssin rsints. rsint A unwillin to work in Clvln jos on't ovrlp Tim Copyrit 00, Kvin Wyn 1

2 jos on't ovrlp jos on't ovrlp jos on't ovrlp Gry Coi. Slt jo wit rlist inis tim n limint inomptil jos. Gry Coi. Slt jo wit rlist inis tim n limint inomptil jos. Cptr 4: W will prov tt tis ry loritm lwys ins t optiml solution! Tim Tim Tim 7 9 Wit Input. St o jos wit strt tims, inis tims, n wits. Gol. Fin mximum wit sust o mutully omptil jos. jos on't ovrlp Gry Coi. Slt jo wit rlist inis tim n limint inomptil jos. jos on't ovrlp Gry Coi. Slt jo wit rlist inis tim n limint inomptil jos. Gry Aloritm No Lonr Works! Tim Tim Tim Copyrit 00, Kvin Wyn 2

3 Wit Biprtit Mtin Inpnnt St Input. St o jos wit strt tims, inis tims, n wits. Gol. Fin mximum wit sust o mutully omptil jos. Input. Biprtit rp. Gol. Fin mximum rinlity mtin. Input. Grp. Gol. Fin mximum rinlity inpnnt st. Gry Aloritm No Lonr Works! 2 A 1 1 sust o nos su tt no two join y n 2 12 B C 7 1 D 4 Brut-For Aloritm: Ck vry possil sust. RunninTim: 2 stps Tim E 5 Dirnt rom Stl Mtin Prolm! How? NP-Complt: Unlikly tt iint loritm xists! Positiv: Cn sily k tt tr is n inpnnt st o siz k Wit Biprtit Mtin Comptitiv Fility Lotion Input. St o jos wit strt tims, inis tims, n wits. Gol. Fin mximum wit sust o mutully omptil jos. Input. Biprtit rp. Gol. Fin mximum rinlity mtin. Input. Grp wit wit on no. Gm. Two omptin plyrs ltrnt in sltin nos. Not llow to slt no i ny o its niors v n slt. Prolm n solv usin tniqu ll Dynmi Prormmin A 1 Gol. Slt mximum wit sust o nos. 2 B 2 12 C 2 1 D 4 E 5 Son plyr n urnt, ut not Tim Prolm n solv usin Ntwork Flow Aloritms Copyrit 00, Kvin Wyn

4 Comptitiv Fility Lotion Comptitiv Fility Lotion Comptitiv Fility Lotion Input. Grp wit wit on no. Gm. Two omptin plyrs ltrnt in sltin nos. Not llow to slt no i ny o its niors v n slt. Gol. Slt mximum wit sust o nos. Input. Grp wit wit on no. Gm. Two omptin plyrs ltrnt in sltin nos. Not llow to slt no i ny o its niors v n slt. Gol. Slt mximum wit sust o nos. Input. Grp wit wit on no. Gm. Two omptin plyrs ltrnt in sltin nos. Not llow to slt no i ny o its niors v n slt. Gol. Slt mximum wit sust o nos. PSPACE-Complt: Evn rr tn NP-Complt! No sort proo tt plyr n urnt vlu B. (Unlik prvious prolm) Son plyr n urnt, ut not 25. Son plyr n urnt, ut not 25. Son plyr n urnt, ut not Comptitiv Fility Lotion Comptitiv Fility Lotion Fiv Rprsnttiv Prolms Input. Grp wit wit on no. Gm. Two omptin plyrs ltrnt in sltin nos. Not llow to slt no i ny o its niors v n slt. Gol. Slt mximum wit sust o nos. Input. Grp wit wit on no. Gm. Two omptin plyrs ltrnt in sltin nos. Not llow to slt no i ny o its niors v n slt. Gol. Slt mximum wit sust o nos. Vritions on tm: inpnnt st. Intrvl sulin: n lo n ry loritm. Wit intrvl sulin: n lo n ynmi prormmin loritm. Biprtit mtin: n k mx-low s loritm. Inpnnt st: NP-omplt. Comptitiv ility lotion: PSPACE-omplt. Son plyr n urnt, ut not 25. Son plyr n urnt, ut not Copyrit 00, Kvin Wyn 4

5 Computtionl Trtility Worst-Cs Anlysis Cptr 2 Bsis o Aloritm Anlysis As soon s n Anlyti Enin xists, it will nssrily ui t utur ours o t sin. Wnvr ny rsult is sout y its i, t qustion will ris - By wt ours o lultion n ts rsults rriv t y t min in t sortst tim? - Crls B Worst s runnin tim. Otin oun on lrst possil runnin tim o loritm on input o ivn siz N. Gnrlly pturs iiny in prti. Dronin viw, ut r to in tiv ltrntiv. Avr s runnin tim. Otin oun on runnin tim o loritm on rnom input s untion o input siz N. Hr (or impossil) to urtly mol rl instns y rnom istriutions. Aloritm tun or rtin istriution my prorm poorly on otr inputs. Slis y Kvin Wyn. Copyrit 05 Prson-Aison Wsly. All rits rsrv. Crls B (184) Anlyti Enin (smti) Polynomil-Tim Worst-Cs Polynomil-Tim 2.1 Computtionl Trtility "For m, rt loritms r t potry o omputtion. Just lik vrs, ty n trs, llusiv, ns, n vn mystrious. But on unlok, ty st rillint nw lit on som spt o omputin." - Frnis Sullivn Brut or. For mny non-trivil prolms, tr is nturl rut or sr loritm tt ks vry possil solution. Typilly tks 2 N tim or wors or inputs o siz N. Unptl in prti. n! or stl mtin wit n mn n n womn Dsirl slin proprty. Wn t input siz ouls, t loritm soul only slow own y som onstnt tor C. Tr xists onstnts > 0 n > 0 su tt on vry input o siz N, its runnin tim is oun y N stps. D. An loritm is poly-tim i t ov slin proprty ols. oos C = 2 D. An loritm is iint i its runnin tim is polynomil. Justiition: It rlly works in prti! Altou N is tnilly poly-tim, it woul uslss in prti. In prti, t poly-tim loritms tt popl vlop lmost lwys v low onstnts n low xponnts. Brkin trou t xponntil rrir o rut or typilly xposs som ruil strutur o t prolm. Exptions. Som poly-tim loritms o v i onstnts n/or xponnts, n r uslss in prti. Som xponntil-tim (or wors) loritms r wily us us t worst-s instns sm to rr. simplx mto Unix rp 28 0 Copyrit 00, Kvin Wyn 5

6 Wy It Mttrs Asymptoti Orr o Growt Proprtis Uppr ouns. T(n) is O((n)) i tr xist onstnts > 0 n n 0 0 su tt or ll n n 0 w v T(n) (n). Lowr ouns. T(n) is ((n)) i tr xist onstnts > 0 n n 0 0 su tt or ll n n 0 w v T(n) (n). Tit ouns. T(n) is ((n)) i T(n) is ot O((n)) n ((n)). Ex: T(n) = 2n n + 2. T(n) is O(n 2 ), O(n ), (n 2 ), (n), n (n 2 ). T(n) is not O(n), (n ), (n), or (n ). Trnsitivity. I = O() n = O() tn = O(). I = () n = () tn = (). I = () n = () tn = (). Aitivity. I = O() n = O() tn + = O(). I = () n = () tn + = (). I = () n = O() tn + = (). 1 5 Nottion Asymptoti Bouns or Som Common Funtions 2.2 Asymptoti Orr o Growt Slit us o nottion. T(n) = O((n)). Not trnsitiv: (n) = 5n ; (n) = n 2 (n) = O(n ) = (n) ut (n) (n). Bttr nottion: T(n) O((n)). Mninlss sttmnt. Any omprison-s sortin loritm rquirs t lst O(n lo n) omprisons. Sttmnt osn't "typ-k." Us or lowr ouns. Polynomils n + + n is (n ) i > 0. Polynomil tim. Runnin tim is O(n ) or som onstnt inpnnt o t input siz n. Loritms. O(lo n) = O(lo n) or ny onstnts, > 0. n voi spiyin t s Loritms. For vry x > 0, lo n = O(n x ). lo rows slowr tn vry polynomil Exponntils. For vry r > 1 n vry > 0, n = O(r n ). vry xponntil rows str tn vry polynomil 4 Copyrit 00, Kvin Wyn

7 Linr Tim: O(n) Qurti Tim: O(n 2 ) 2.4 A Survy o Common Runnin Tims Mr. Comin two sort lists A = 1, 2,, n wit B = 1, 2,, n into sort wol. Qurti tim. Enumrt ll pirs o lmnts. Closst pir o points. Givn list o n points in t pln (x 1, y 1 ),, (x n, y n ), in t pir tt is losst. i = 1, j = 1 wil (ot lists r nonmpty) { i ( i j ) ppn i to output list n inrmnt i ls( i j )ppn j to output list n inrmnt j ppn rminr o nonmpty list to output list Clim. Mrin two lists o siz n tks O(n) tim. P. Atr omprison, t lnt o output list inrss y 1. O(n 2 ) solution. Try ll pirs o points. min (x 1 - x 2 ) 2 + (y 1 - y 2 ) 2 or i = 1 to n { or j = i+1 to n { (x i - x j ) 2 + (y i - y j ) 2 i ( < min) min Rmrk. (n 2 ) sms invitl, ut tis is just n illusion. s ptr 5 on't n to tk squr roots 9 41 Linr Tim: O(n) O(n lo n) Tim Cui Tim: O(n ) Linr tim. Runnin tim is proportionl to input siz. Computin t mximum. Comput mximum o n numrs 1,, n. mx 1 or i = 2 to n { i ( i > mx) mx i O(n lo n) tim. Ariss in ivi-n-onqur loritms. lso rrr to s linritmi tim Sortin. Mrsort n psort r sortin loritms tt prorm O(n lo n) omprisons. Lrst mpty intrvl. Givn n tim-stmps x 1,, x n on wi opis o il rriv t srvr, wt is lrst intrvl o tim wn no opis o t il rriv? O(n lo n) solution. Sort t tim-stmps. Sn t sort list in orr, intiyin t mximum p twn sussiv tim-stmps. Cui tim. Enumrt ll tripls o lmnts. St isjointnss. Givn n sts S 1,, S n o wi is sust o 1, 2,, n, is tr som pir o ts wi r isjoint? O(n ) solution. For pirs o sts, trmin i ty r isjoint. or st S i { or otr st S j { or lmnt p o S i { trmin wtr p lso lons to S j i (no lmnt o S i lons to S j ) rport tt S i n S j r isjoint Copyrit 00, Kvin Wyn 7

8 Polynomil Tim: O(n k ) Tim Hp Dt Strutur Hp Insrtion Inpnnt st o siz k. Givn rp, r tr k nos su tt no two r join y n? k is onstnt Hp.Insrt() O(n k ) solution. Enumrt ll susts o k nos. or sust S o k nos { k wtr S in n inpnnt st i (S is n inpnnt st) rport S is n inpnnt st Ck wtr S is n inpnnt st = O(k 2 ). Numr o k lmnt susts = n O(k 2 n k / k!) = O(n k ). n (n 1) (n 2) (n k 1) k k (k 1) (k 2) (2) (1) poly-tim or k=17, ut not prtil nk k! Nxt Min Hp Orr: For no v in t tr Prnt v. Vlu v. Vlu Mx Hp Orr: For no v in t tr Prnt v. Vlu v. Vlu Min Hp Orr: For no v in t tr Prnt v. Vlu v. Vlu Exponntil Tim Hp Insrtion Hp Insrtion Inpnnt st. Givn rp, wt is mximum siz o n inpnnt st? Hp.Insrt() O(n 2 2 n ) solution. Enumrt ll susts. Hp.Insrt() 9 S* or sust S o nos { k wtr S in n inpnnt st i (S is lrst inpnnt st sn so r) upt S* S Min Hp Orr: For no v in t tr Prnt v. Vlu v. Vlu Min Hp Orr: For no v in t tr Prnt v. Vlu v. Vlu Copyrit 00, Kvin Wyn 8

9 Hp Insrtion Hp Extrt Minimum Hp Extrt Minimum Hp.Insrt() Hp.ExtrtMin() Hp.ExtrtMin() Nxt Min Hp Orr: For no v in t tr Prnt v. Vlu v. Vlu Torm 2.12 [KT]: T prour Hpiy-up ixs t p proprty n llows us to insrt nw lmnt into p o n lmnts in O(lo n) tim. Nxt Min Hp Orr: For no v in t tr Prnt v. Vlu v. Vlu Torm 2.1 [KT]: T prour Hpiy-own ixs t p proprty n llows us to lt n lmnt in p o n lmnts in O(lo n) tim. Nxt Min Hp Orr: For no v in t tr Prnt v. Vlu v. Vlu Torm 2.1 [KT]: T prour Hpiy-own ixs t p proprty n llows us to lt n lmnt in p o n lmnts in O(lo n) tim Hp Extrt Minimum Hp Extrt Minimum Hp Extrt Minimum Hp.ExtrtMin() Hp.ExtrtMin() Hp.ExtrtMin() Nxt Min Hp Orr: For no v in t tr Prnt v. Vlu v. Vlu Torm 2.1 [KT]: T prour Hpiy-own ixs t p proprty n llows us to lt n lmnt in p o n lmnts in O(lo n) tim. Nxt Min Hp Orr: For no v in t tr Prnt v. Vlu v. Vlu Torm 2.1 [KT]: T prour Hpiy-own ixs t p proprty n llows us to lt n lmnt in p o n lmnts in O(lo n) tim. Nxt Min Hp Orr: For no v in t tr Prnt v. Vlu v. Vlu Torm 2.1 [KT]: T prour Hpiy-own ixs t p proprty n llows us to lt n lmnt in p o n lmnts in O(lo n) tim Copyrit 00, Kvin Wyn 9

10 Hp Extrt Minimum Hp.ExtrtMin() Nxt Min Hp Orr: For no v in t tr Prnt v. Vlu v. Vlu Torm 2.1 [KT]: T prour Hpiy-own ixs t p proprty n llows us to lt n lmnt in p o n lmnts in O(lo n) tim. 55 Hp Summry Insrt: O(lo n) FinMin: O(1) Dlt: O(lo n) tim ExtrtMin: O(lo n) tim Tout Qustion: O(n lo n) tim sortin loritm usin ps? 5 Copyrit 00, Kvin Wyn 10

4.1 Interval Scheduling. Chapter 4. Greedy Algorithms. Interval Scheduling: Greedy Algorithms. Interval Scheduling. Interval scheduling.

4.1 Interval Scheduling. Chapter 4. Greedy Algorithms. Interval Scheduling: Greedy Algorithms. Interval Scheduling. Interval scheduling. Cptr 4 4 Intrvl Suln Gry Alortms Sls y Kvn Wyn Copyrt 005 Prson-Ason Wsly All rts rsrv Intrvl Suln Intrvl Suln: Gry Alortms Intrvl suln! Jo strts t s n nss t! Two os omptl ty on't ovrlp! Gol: n mxmum sust

More information

16.unified Introduction to Computers and Programming. SOLUTIONS to Examination 4/30/04 9:05am - 10:00am

16.unified Introduction to Computers and Programming. SOLUTIONS to Examination 4/30/04 9:05am - 10:00am 16.unii Introution to Computrs n Prormmin SOLUTIONS to Exmintion /30/0 9:05m - 10:00m Pro. I. Kristin Lunqvist Sprin 00 Grin Stion: Qustion 1 (5) Qustion (15) Qustion 3 (10) Qustion (35) Qustion 5 (10)

More information

learning objectives learn what graphs are in mathematical terms learn how to represent graphs in computers learn about typical graph algorithms

learning objectives learn what graphs are in mathematical terms learn how to represent graphs in computers learn about typical graph algorithms rp loritms lrnin ojtivs loritms your sotwr systm sotwr rwr lrn wt rps r in mtmtil trms lrn ow to rprsnt rps in omputrs lrn out typil rp loritms wy rps? intuitivly, rp is orm y vrtis n s twn vrtis rps r

More information

OpenMx Matrices and Operators

OpenMx Matrices and Operators OpnMx Mtris n Oprtors Sr Mln Mtris: t uilin loks Mny typs? Dnots r lmnt mxmtrix( typ= Zro", nrow=, nol=, nm="" ) mxmtrix( typ= Unit", nrow=, nol=, nm="" ) mxmtrix( typ= Int", nrow=, nol=, nm="" ) mxmtrix(

More information

CS 103 BFS Alorithm. Mark Redekopp

CS 103 BFS Alorithm. Mark Redekopp CS 3 BFS Aloritm Mrk Rkopp Brt-First Sr (BFS) HIGHLIGHTED ALGORITHM 3 Pt Plnnin W'v sn BFS in t ontxt o inin t sortst pt trou mz? S?? 4 Pt Plnnin W xplor t 4 niors s on irtion 3 3 3 S 3 3 3 3 3 F I you

More information

1 Introduction to Modulo 7 Arithmetic

1 Introduction to Modulo 7 Arithmetic 1 Introution to Moulo 7 Arithmti Bor w try our hn t solvin som hr Moulr KnKns, lt s tk los look t on moulr rithmti, mo 7 rithmti. You ll s in this sminr tht rithmti moulo prim is quit irnt rom th ons w

More information

d e c b a d c b a d e c b a a c a d c c e b

d e c b a d c b a d e c b a a c a d c c e b FLAT PEYOTE STITCH Bin y mkin stoppr -- sw trou n pull it lon t tr until it is out 6 rom t n. Sw trou t in witout splittin t tr. You soul l to sli it up n own t tr ut it will sty in pl wn lt lon. Evn-Count

More information

MAT3707. Tutorial letter 201/1/2017 DISCRETE MATHEMATICS: COMBINATORICS. Semester 1. Department of Mathematical Sciences MAT3707/201/1/2017

MAT3707. Tutorial letter 201/1/2017 DISCRETE MATHEMATICS: COMBINATORICS. Semester 1. Department of Mathematical Sciences MAT3707/201/1/2017 MAT3707/201/1/2017 Tutoril lttr 201/1/2017 DISCRETE MATHEMATICS: COMBINATORICS MAT3707 Smstr 1 Dprtmnt o Mtmtil Sins SOLUTIONS TO ASSIGNMENT 01 BARCODE Din tomorrow. univrsity o sout ri SOLUTIONS TO ASSIGNMENT

More information

, each of which is a tree, and whose roots r 1. , respectively, are children of r. Data Structures & File Management

, each of which is a tree, and whose roots r 1. , respectively, are children of r. Data Structures & File Management nrl tr T is init st o on or mor nos suh tht thr is on sint no r, ll th root o T, n th rminin nos r prtition into n isjoint susts T, T,, T n, h o whih is tr, n whos roots r, r,, r n, rsptivly, r hilrn o

More information

Lecture 20: Minimum Spanning Trees (CLRS 23)

Lecture 20: Minimum Spanning Trees (CLRS 23) Ltur 0: Mnmum Spnnn Trs (CLRS 3) Jun, 00 Grps Lst tm w n (wt) rps (unrt/rt) n ntrou s rp voulry (vrtx,, r, pt, onnt omponnts,... ) W lso suss jny lst n jny mtrx rprsntton W wll us jny lst rprsntton unlss

More information

Weighted Graphs. Weighted graphs may be either directed or undirected.

Weighted Graphs. Weighted graphs may be either directed or undirected. 1 In mny ppltons, o rp s n ssot numrl vlu, ll wt. Usully, t wts r nonntv ntrs. Wt rps my tr rt or unrt. T wt o n s otn rrr to s t "ost" o t. In ppltons, t wt my msur o t lnt o rout, t pty o ln, t nry rqur

More information

Cycles and Simple Cycles. Paths and Simple Paths. Trees. Problem: There is No Completely Standard Terminology!

Cycles and Simple Cycles. Paths and Simple Paths. Trees. Problem: There is No Completely Standard Terminology! Outlin Computr Sin 331, Spnnin, n Surphs Mik Joson Dprtmnt o Computr Sin Univrsity o Clry Ltur #30 1 Introution 2 3 Dinition 4 Spnnin 5 6 Mik Joson (Univrsity o Clry) Computr Sin 331 Ltur #30 1 / 20 Mik

More information

5/1/2018. Huffman Coding Trees. Huffman Coding Trees. Huffman Coding Trees. Huffman Coding Trees. Huffman Coding Trees. Huffman Coding Trees

5/1/2018. Huffman Coding Trees. Huffman Coding Trees. Huffman Coding Trees. Huffman Coding Trees. Huffman Coding Trees. Huffman Coding Trees /1/018 W usully no strns y ssnn -lnt os to ll rtrs n t lpt (or mpl, 8-t on n ASCII). Howvr, rnt rtrs our wt rnt rquns, w n sv mmory n ru trnsmttl tm y usn vrl-lnt non. T s to ssn sortr os to rtrs tt our

More information

Outline. 1 Introduction. 2 Min-Cost Spanning Trees. 4 Example

Outline. 1 Introduction. 2 Min-Cost Spanning Trees. 4 Example Outlin Computr Sin 33 Computtion o Minimum-Cost Spnnin Trs Prim's Alorithm Introution Mik Joson Dprtmnt o Computr Sin Univrsity o Clry Ltur #33 3 Alorithm Gnrl Constrution Mik Joson (Univrsity o Clry)

More information

Complete Solutions for MATH 3012 Quiz 2, October 25, 2011, WTT

Complete Solutions for MATH 3012 Quiz 2, October 25, 2011, WTT Complt Solutions or MATH 012 Quiz 2, Otor 25, 2011, WTT Not. T nswrs ivn r r mor omplt tn is xpt on n tul xm. It is intn tt t mor omprnsiv solutions prsnt r will vlul to stunts in stuyin or t inl xm. In

More information

QUESTIONS BEGIN HERE!

QUESTIONS BEGIN HERE! Points miss: Stunt's Nm: Totl sor: /100 points Est Tnnss Stt Univrsity Dprtmnt o Computr n Inormtion Sins CSCI 2710 (Trno) Disrt Struturs TEST or Sprin Smstr, 2005 R this or strtin! This tst is los ook

More information

Math 61 : Discrete Structures Final Exam Instructor: Ciprian Manolescu. You have 180 minutes.

Math 61 : Discrete Structures Final Exam Instructor: Ciprian Manolescu. You have 180 minutes. Nm: UCA ID Numr: Stion lttr: th 61 : Disrt Struturs Finl Exm Instrutor: Ciprin nolsu You hv 180 minuts. No ooks, nots or lultors r llow. Do not us your own srth ppr. 1. (2 points h) Tru/Fls: Cirl th right

More information

(2) If we multiplied a row of B by λ, then the value is also multiplied by λ(here lambda could be 0). namely

(2) If we multiplied a row of B by λ, then the value is also multiplied by λ(here lambda could be 0). namely . DETERMINANT.. Dtrminnt. Introution:I you think row vtor o mtrix s oorint o vtors in sp, thn th gomtri mning o th rnk o th mtrix is th imnsion o th prlllppi spnn y thm. But w r not only r out th imnsion,

More information

Outline. Computer Science 331. Computation of Min-Cost Spanning Trees. Costs of Spanning Trees in Weighted Graphs

Outline. Computer Science 331. Computation of Min-Cost Spanning Trees. Costs of Spanning Trees in Weighted Graphs Outlin Computr Sin 33 Computtion o Minimum-Cost Spnnin Trs Prim s Mik Joson Dprtmnt o Computr Sin Univrsity o Clry Ltur #34 Introution Min-Cost Spnnin Trs 3 Gnrl Constrution 4 5 Trmintion n Eiiny 6 Aitionl

More information

Improving Union. Implementation. Union-by-size Code. Union-by-Size Find Analysis. Path Compression! Improving Find find(e)

Improving Union. Implementation. Union-by-size Code. Union-by-Size Find Analysis. Path Compression! Improving Find find(e) POW CSE 36: Dt Struturs Top #10 T Dynm (Equvln) Duo: Unon-y-Sz & Pt Comprsson Wk!! Luk MDowll Summr Qurtr 003 M! ZING Wt s Goo Mz? Mz Construton lortm Gvn: ollton o rooms V Conntons twn t rooms (ntlly

More information

Paths. Connectivity. Euler and Hamilton Paths. Planar graphs.

Paths. Connectivity. Euler and Hamilton Paths. Planar graphs. Pths.. Eulr n Hmilton Pths.. Pth D. A pth rom s to t is squn o gs {x 0, x 1 }, {x 1, x 2 },... {x n 1, x n }, whr x 0 = s, n x n = t. D. Th lngth o pth is th numr o gs in it. {, } {, } {, } {, } {, } {,

More information

b. How many ternary words of length 23 with eight 0 s, nine 1 s and six 2 s?

b. How many ternary words of length 23 with eight 0 s, nine 1 s and six 2 s? MATH 3012 Finl Exm, My 4, 2006, WTT Stunt Nm n ID Numr 1. All our prts o this prolm r onrn with trnry strings o lngth n, i.., wors o lngth n with lttrs rom th lpht {0, 1, 2}.. How mny trnry wors o lngth

More information

CS September 2018

CS September 2018 Loil los Distriut Systms 06. Loil los Assin squn numrs to msss All ooprtin prosss n r on orr o vnts vs. physil los: rport tim o y Assum no ntrl tim sour Eh systm mintins its own lol lo No totl orrin o

More information

Math 166 Week in Review 2 Sections 1.1b, 1.2, 1.3, & 1.4

Math 166 Week in Review 2 Sections 1.1b, 1.2, 1.3, & 1.4 Mt 166 WIR, Sprin 2012, Bnjmin urisp Mt 166 Wk in Rviw 2 Stions 1.1, 1.2, 1.3, & 1.4 1. S t pproprit rions in Vnn irm tt orrspon to o t ollowin sts. () (B ) B () ( ) B B () (B ) B 1 Mt 166 WIR, Sprin 2012,

More information

Algorithmic and NP-Completeness Aspects of a Total Lict Domination Number of a Graph

Algorithmic and NP-Completeness Aspects of a Total Lict Domination Number of a Graph Intrntionl J.Mth. Comin. Vol.1(2014), 80-86 Algorithmi n NP-Compltnss Aspts of Totl Lit Domintion Numr of Grph Girish.V.R. (PES Institut of Thnology(South Cmpus), Bnglor, Krntk Stt, Ini) P.Ush (Dprtmnt

More information

Grade 7/8 Math Circles March 4/5, Graph Theory I- Solutions

Grade 7/8 Math Circles March 4/5, Graph Theory I- Solutions ulty o Mtmtis Wtrloo, Ontrio N ntr or ution in Mtmtis n omputin r / Mt irls Mr /, 0 rp Tory - Solutions * inits lln qustion. Tr t ollowin wlks on t rp low. or on, stt wtr it is pt? ow o you know? () n

More information

Tangram Fractions Overview: Students will analyze standard and nonstandard

Tangram Fractions Overview: Students will analyze standard and nonstandard ACTIVITY 1 Mtrils: Stunt opis o tnrm mstrs trnsprnis o tnrm mstrs sissors PROCEDURE Skills: Dsriin n nmin polyons Stuyin onrun Comprin rtions Tnrm Frtions Ovrviw: Stunts will nlyz stnr n nonstnr tnrms

More information

ECE COMBINATIONAL BUILDING BLOCKS - INVEST 13 DECODERS AND ENCODERS

ECE COMBINATIONAL BUILDING BLOCKS - INVEST 13 DECODERS AND ENCODERS C 24 - COMBINATIONAL BUILDING BLOCKS - INVST 3 DCODS AND NCODS FALL 23 AP FLZ To o "wll" on this invstition you must not only t th riht nswrs ut must lso o nt, omplt n onis writups tht mk ovious wht h

More information

Outline. Binary Tree

Outline. Binary Tree Outlin Similrity Srh Th Binry Brnh Distn Nikolus Austn nikolus.ustn@s..t Dpt. o Computr Sins Univrsity o Slzur http://rsrh.uni-slzur.t 1 Binry Brnh Distn Binry Rprsnttion o Tr Binry Brnhs Lowr Boun or

More information

CSE 373: More on graphs; DFS and BFS. Michael Lee Wednesday, Feb 14, 2018

CSE 373: More on graphs; DFS and BFS. Michael Lee Wednesday, Feb 14, 2018 CSE 373: Mor on grphs; DFS n BFS Mihl L Wnsy, F 14, 2018 1 Wrmup Wrmup: Disuss with your nighor: Rmin your nighor: wht is simpl grph? Suppos w hv simpl, irt grph with x nos. Wht is th mximum numr of gs

More information

Multipoint Alternate Marking method for passive and hybrid performance monitoring

Multipoint Alternate Marking method for passive and hybrid performance monitoring Multipoint Altrnt Mrkin mtho or pssiv n hyri prormn monitorin rt-iool-ippm-multipoint-lt-mrk-00 Pru, Jul 2017, IETF 99 Giuspp Fiool (Tlom Itli) Muro Coilio (Tlom Itli) Amo Spio (Politnio i Torino) Riro

More information

In which direction do compass needles always align? Why?

In which direction do compass needles always align? Why? AQA Trloy Unt 6.7 Mntsm n Eltromntsm - Hr 1 Complt t p ll: Mnt or s typ o or n t s stronst t t o t mnt. Tr r two typs o mnt pol: n. Wrt wt woul ppn twn t pols n o t mnt ntrtons low: Drw t mnt l lns on

More information

Why the Junction Tree Algorithm? The Junction Tree Algorithm. Clique Potential Representation. Overview. Chris Williams 1.

Why the Junction Tree Algorithm? The Junction Tree Algorithm. Clique Potential Representation. Overview. Chris Williams 1. Why th Juntion Tr lgorithm? Th Juntion Tr lgorithm hris Willims 1 Shool of Informtis, Univrsity of Einurgh Otor 2009 Th JT is gnrl-purpos lgorithm for omputing (onitionl) mrginls on grphs. It os this y

More information

Divided. diamonds. Mimic the look of facets in a bracelet that s deceptively deep RIGHT-ANGLE WEAVE. designed by Peggy Brinkman Matteliano

Divided. diamonds. Mimic the look of facets in a bracelet that s deceptively deep RIGHT-ANGLE WEAVE. designed by Peggy Brinkman Matteliano RIGHT-ANGLE WEAVE Dv mons Mm t look o ts n rlt tt s ptvly p sn y Py Brnkmn Mttlno Dv your mons nto trnls o two or our olors. FCT-SCON0216_BNB66 2012 Klm Pulsn Co. Ts mtrl my not rprou n ny orm wtout prmsson

More information

Strongly connected components. Finding strongly-connected components

Strongly connected components. Finding strongly-connected components Stronly onnt omponnts Fnn stronly-onnt omponnts Tylr Moor stronly onnt omponnt s t mxml sust o rp wt rt pt twn ny two vrts SE 3353, SMU, Dlls, TX Ltur 9 Som sls rt y or pt rom Dr. Kvn Wyn. For mor normton

More information

10. EXTENDING TRACTABILITY

10. EXTENDING TRACTABILITY Coping with NP-compltnss 0. EXTENDING TRACTABILITY ining small vrtx covrs solving NP-har problms on trs circular arc covrings vrtx covr in bipartit graphs Q. Suppos I n to solv an NP-complt problm. What

More information

Planar Upward Drawings

Planar Upward Drawings C.S. 252 Pro. Rorto Tmssi Computtionl Gomtry Sm. II, 1992 1993 Dt: My 3, 1993 Sri: Shmsi Moussvi Plnr Upwr Drwings 1 Thorm: G is yli i n only i it hs upwr rwing. Proo: 1. An upwr rwing is yli. Follow th

More information

Spanning Trees. BFS, DFS spanning tree Minimum spanning tree. March 28, 2018 Cinda Heeren / Geoffrey Tien 1

Spanning Trees. BFS, DFS spanning tree Minimum spanning tree. March 28, 2018 Cinda Heeren / Geoffrey Tien 1 Spnnn Trs BFS, DFS spnnn tr Mnmum spnnn tr Mr 28, 2018 Cn Hrn / Gory Tn 1 Dpt-rst sr Vsts vrts lon snl pt s r s t n o, n tn ktrks to t rst junton n rsums own notr pt Mr 28, 2018 Cn Hrn / Gory Tn 2 Dpt-rst

More information

Depth First Search. Yufei Tao. Department of Computer Science and Engineering Chinese University of Hong Kong

Depth First Search. Yufei Tao. Department of Computer Science and Engineering Chinese University of Hong Kong Dprtmnt o Computr Sn n Ennrn Cns Unvrsty o Hon Kon W v lry lrn rt rst sr (BFS). Toy, w wll suss ts sstr vrson : t pt rst sr (DFS) lortm. Our susson wll on n ous on rt rps, us t xtnson to unrt rps s strtorwr.

More information

Problem solving by search

Problem solving by search Prolm solving y srh Tomáš voo Dprtmnt o Cyrntis, Vision or Roots n Autonomous ystms Mrh 5, 208 / 3 Outlin rh prolm. tt sp grphs. rh trs. trtgis, whih tr rnhs to hoos? trtgy/algorithm proprtis? Progrmming

More information

Exam 1 Solution. CS 542 Advanced Data Structures and Algorithms 2/14/2013

Exam 1 Solution. CS 542 Advanced Data Structures and Algorithms 2/14/2013 CS Avn Dt Struturs n Algorithms Exm Solution Jon Turnr //. ( points) Suppos you r givn grph G=(V,E) with g wights w() n minimum spnning tr T o G. Now, suppos nw g {u,v} is to G. Dsri (in wors) mtho or

More information

Graph Isomorphism. Graphs - II. Cayley s Formula. Planar Graphs. Outline. Is K 5 planar? The number of labeled trees on n nodes is n n-2

Graph Isomorphism. Graphs - II. Cayley s Formula. Planar Graphs. Outline. Is K 5 planar? The number of labeled trees on n nodes is n n-2 Grt Thortil Is In Computr Sin Vitor Amhik CS 15-251 Ltur 9 Grphs - II Crngi Mllon Univrsity Grph Isomorphism finition. Two simpl grphs G n H r isomorphi G H if thr is vrtx ijtion V H ->V G tht prsrvs jny

More information

QUESTIONS BEGIN HERE!

QUESTIONS BEGIN HERE! Points miss: Stunt's Nm: Totl sor: /100 points Est Tnnss Stt Univrsity Dprtmnt of Computr n Informtion Sins CSCI 710 (Trnoff) Disrt Struturs TEST for Fll Smstr, 00 R this for strtin! This tst is los ook

More information

Graph Algorithms and Combinatorial Optimization Presenters: Benjamin Ferrell and K. Alex Mills May 7th, 2014

Graph Algorithms and Combinatorial Optimization Presenters: Benjamin Ferrell and K. Alex Mills May 7th, 2014 Grp Aloritms n Comintoril Optimiztion Dr. R. Cnrskrn Prsntrs: Bnjmin Frrll n K. Alx Mills My 7t, 0 Mtroi Intrstion In ts ltur nots, w mk us o som unonvntionl nottion or st union n irn to kp tins lnr. In

More information

# 1 ' 10 ' 100. Decimal point = 4 hundred. = 6 tens (or sixty) = 5 ones (or five) = 2 tenths. = 7 hundredths.

# 1 ' 10 ' 100. Decimal point = 4 hundred. = 6 tens (or sixty) = 5 ones (or five) = 2 tenths. = 7 hundredths. How os it work? Pl vlu o imls rprsnt prts o whol numr or ojt # 0 000 Tns o thousns # 000 # 00 Thousns Hunrs Tns Ons # 0 Diml point st iml pl: ' 0 # 0 on tnth n iml pl: ' 0 # 00 on hunrth r iml pl: ' 0

More information

CSE 373: AVL trees. Warmup: Warmup. Interlude: Exploring the balance invariant. AVL Trees: Invariants. AVL tree invariants review

CSE 373: AVL trees. Warmup: Warmup. Interlude: Exploring the balance invariant. AVL Trees: Invariants. AVL tree invariants review rmup CSE 7: AVL trs rmup: ht is n invrint? Mihl L Friy, Jn 9, 0 ht r th AVL tr invrints, xtly? Disuss with your nighor. AVL Trs: Invrints Intrlu: Exploring th ln invrint Cor i: xtr invrint to BSTs tht

More information

Seven-Segment Display Driver

Seven-Segment Display Driver 7-Smnt Disply Drivr, Ron s in 7-Smnt Disply Drivr, Ron s in Prolm 62. 00 0 0 00 0000 000 00 000 0 000 00 0 00 00 0 0 0 000 00 0 00 BCD Diits in inry Dsin Drivr Loi 4 inputs, 7 outputs 7 mps, h with 6 on

More information

S i m p l i f y i n g A l g e b r a SIMPLIFYING ALGEBRA.

S i m p l i f y i n g A l g e b r a SIMPLIFYING ALGEBRA. S i m p l i y i n g A l g r SIMPLIFYING ALGEBRA www.mthltis.o.nz Simpliying SIMPLIFYING Algr ALGEBRA Algr is mthmtis with mor thn just numrs. Numrs hv ix vlu, ut lgr introus vrils whos vlus n hng. Ths

More information

Designing A Concrete Arch Bridge

Designing A Concrete Arch Bridge This is th mous Shwnh ri in Switzrln, sin y Rort Millrt in 1933. It spns 37.4 mtrs (122 t) n ws sin usin th sm rphil mths tht will monstrt in this lsson. To pro with this lsson, lik on th Nxt utton hr

More information

Minimum Spanning Trees

Minimum Spanning Trees Yufi Tao ITEE Univrsity of Qunslan In tis lctur, w will stuy anotr classic prolm: finin a minimum spannin tr of an unirct wit rap. Intrstinly, vn tou t prolm appars ratr iffrnt from SSSP (sinl sourc sortst

More information

Using the Printable Sticker Function. Using the Edit Screen. Computer. Tablet. ScanNCutCanvas

Using the Printable Sticker Function. Using the Edit Screen. Computer. Tablet. ScanNCutCanvas SnNCutCnvs Using th Printl Stikr Funtion On-o--kin stikrs n sily rt y using your inkjt printr n th Dirt Cut untion o th SnNCut mhin. For inormtion on si oprtions o th SnNCutCnvs, rr to th Hlp. To viw th

More information

COMPLEXITY OF COUNTING PLANAR TILINGS BY TWO BARS

COMPLEXITY OF COUNTING PLANAR TILINGS BY TWO BARS OMPLXITY O OUNTING PLNR TILINGS Y TWO RS KYL MYR strt. W show tht th prolm o trmining th numr o wys o tiling plnr igur with horizontl n vrtil r is #P-omplt. W uil o o th rsults o uquir, Nivt, Rmil, n Roson

More information

(Minimum) Spanning Trees

(Minimum) Spanning Trees (Mnmum) Spnnn Trs Spnnn trs Kruskl's lortm Novmr 23, 2017 Cn Hrn / Gory Tn 1 Spnnn trs Gvn G = V, E, spnnn tr o G s onnt surp o G wt xtly V 1 s mnml sust o s tt onnts ll t vrts o G G = Spnnn trs Novmr

More information

Announcements. Not graphs. These are Graphs. Applications of Graphs. Graph Definitions. Graphs & Graph Algorithms. A6 released today: Risk

Announcements. Not graphs. These are Graphs. Applications of Graphs. Graph Definitions. Graphs & Graph Algorithms. A6 released today: Risk Grphs & Grph Algorithms Ltur CS Spring 6 Announmnts A6 rls toy: Risk Strt signing with your prtnr sp Prlim usy Not grphs hs r Grphs K 5 K, =...not th kin w mn, nywy Applitions o Grphs Communition ntworks

More information

DFA Minimization. DFA minimization: the idea. Not in Sipser. Background: Questions: Assignments: Previously: Today: Then:

DFA Minimization. DFA minimization: the idea. Not in Sipser. Background: Questions: Assignments: Previously: Today: Then: Assinmnts: DFA Minimiztion CMPU 24 Lnu Tory n Computtion Fll 28 Assinmnt 3 out toy. Prviously: Computtionl mols or t rulr lnus: DFAs, NFAs, rulr xprssions. Toy: How o w in t miniml DFA or lnu? Tis is t

More information

COMP 250. Lecture 29. graph traversal. Nov. 15/16, 2017

COMP 250. Lecture 29. graph traversal. Nov. 15/16, 2017 COMP 250 Ltur 29 rp trvrsl Nov. 15/16, 2017 1 Toy Rursv rp trvrsl pt rst Non-rursv rp trvrsl pt rst rt rst 2 Hs up! Tr wr w mstks n t sls or S. 001 or toy s ltur. So you r ollown t ltur rorns n usn ts

More information

CSE 373. Graphs 1: Concepts, Depth/Breadth-First Search reading: Weiss Ch. 9. slides created by Marty Stepp

CSE 373. Graphs 1: Concepts, Depth/Breadth-First Search reading: Weiss Ch. 9. slides created by Marty Stepp CSE 373 Grphs 1: Conpts, Dpth/Brth-First Srh ring: Wiss Ch. 9 slis rt y Mrty Stpp http://www.s.wshington.u/373/ Univrsity o Wshington, ll rights rsrv. 1 Wht is grph? 56 Tokyo Sttl Soul 128 16 30 181 140

More information

12/3/12. Outline. Part 10. Graphs. Circuits. Euler paths/circuits. Euler s bridge problem (Bridges of Konigsberg Problem)

12/3/12. Outline. Part 10. Graphs. Circuits. Euler paths/circuits. Euler s bridge problem (Bridges of Konigsberg Problem) 12/3/12 Outlin Prt 10. Grphs CS 200 Algorithms n Dt Struturs Introution Trminology Implmnting Grphs Grph Trvrsls Topologil Sorting Shortst Pths Spnning Trs Minimum Spnning Trs Ciruits 1 Ciruits Cyl 2 Eulr

More information

5/9/13. Part 10. Graphs. Outline. Circuits. Introduction Terminology Implementing Graphs

5/9/13. Part 10. Graphs. Outline. Circuits. Introduction Terminology Implementing Graphs Prt 10. Grphs CS 200 Algorithms n Dt Struturs 1 Introution Trminology Implmnting Grphs Outlin Grph Trvrsls Topologil Sorting Shortst Pths Spnning Trs Minimum Spnning Trs Ciruits 2 Ciruits Cyl A spil yl

More information

Having a glimpse of some of the possibilities for solutions of linear systems, we move to methods of finding these solutions. The basic idea we shall

Having a glimpse of some of the possibilities for solutions of linear systems, we move to methods of finding these solutions. The basic idea we shall Hvn lps o so o t posslts or solutons o lnr systs, w ov to tos o nn ts solutons. T s w sll us s to try to sply t syst y lntn so o t vrls n so ts qutons. Tus, w rr to t to s lnton. T prry oprton nvolv s

More information

Exam 2 Solutions. Jonathan Turner 4/2/2012. CS 542 Advanced Data Structures and Algorithms

Exam 2 Solutions. Jonathan Turner 4/2/2012. CS 542 Advanced Data Structures and Algorithms CS 542 Avn Dt Stutu n Alotm Exm 2 Soluton Jontn Tun 4/2/202. (5 ont) Con n oton on t tton t tutu n w t n t 2 no. Wt t mllt num o no tt t tton t tutu oul ontn. Exln you nw. Sn n mut n you o u t n t, t n

More information

Theorem 1. An undirected graph is a tree if and only if there is a unique simple path between any two of its vertices.

Theorem 1. An undirected graph is a tree if and only if there is a unique simple path between any two of its vertices. Cptr 11: Trs 11.1 - Introuton to Trs Dnton 1 (Tr). A tr s onnt unrt rp wt no sp ruts. Tor 1. An unrt rp s tr n ony tr s unqu sp pt twn ny two o ts vrts. Dnton 2. A root tr s tr n w on vrtx s n snt s t

More information

0.1. Exercise 1: the distances between four points in a graph

0.1. Exercise 1: the distances between four points in a graph Mth 707 Spring 2017 (Drij Grinrg): mitrm 3 pg 1 Mth 707 Spring 2017 (Drij Grinrg): mitrm 3 u: W, 3 My 2017, in lss or y mil (grinr@umn.u) or lss S th wsit or rlvnt mtril. Rsults provn in th nots, or in

More information

Organization. Dominators. Control-flow graphs 8/30/2010. Dominators, control-dependence. Dominator relation of CFGs

Organization. Dominators. Control-flow graphs 8/30/2010. Dominators, control-dependence. Dominator relation of CFGs Orniztion Domintors, ontrol-pnn n SSA orm Domintor rltion o CFGs postomintor rltion Domintor tr Computin omintor rltion n tr Dtlow lorithm Lnur n Trjn lorithm Control-pnn rltion SSA orm Control-low rphs

More information

Face Detection and Recognition. Linear Algebra and Face Recognition. Face Recognition. Face Recognition. Dimension reduction

Face Detection and Recognition. Linear Algebra and Face Recognition. Face Recognition. Face Recognition. Dimension reduction F Dtto Roto Lr Alr F Roto C Y I Ursty O solto: tto o l trs s s ys os ot. Dlt to t to ltpl ws. F Roto Aotr ppro: ort y rry s tor o so E.. 56 56 > pot 6556- stol sp A st o s t ps to ollto o pots ts sp. F

More information

CS 461, Lecture 17. Today s Outline. Example Run

CS 461, Lecture 17. Today s Outline. Example Run Prim s Algorithm CS 461, Ltur 17 Jr Si Univrsity o Nw Mxio In Prim s lgorithm, th st A mintin y th lgorithm orms singl tr. Th tr strts rom n ritrry root vrtx n grows until it spns ll th vrtis in V At h

More information

(4, 2)-choosability of planar graphs with forbidden structures

(4, 2)-choosability of planar graphs with forbidden structures 1 (4, )-oosility o plnr rps wit orin struturs 4 5 Znr Brikkyzy 1 Cristopr Cox Mil Diryko 1 Kirstn Honson 1 Moit Kumt 1 Brnr Liiký 1, Ky Mssrsmit 1 Kvin Moss 1 Ktln Nowk 1 Kvin F. Plmowski 1 Drrik Stol

More information

The University of Sydney MATH 2009

The University of Sydney MATH 2009 T Unvrsty o Syny MATH 2009 APH THEOY Tutorl 7 Solutons 2004 1. Lt t sonnt plnr rp sown. Drw ts ul, n t ul o t ul ( ). Sow tt s sonnt plnr rp, tn s onnt. Du tt ( ) s not somorp to. ( ) A onnt rp s on n

More information

12. Traffic engineering

12. Traffic engineering lt2.ppt S-38. Introution to Tltrffi Thory Spring 200 2 Topology Pths A tlommunition ntwork onsists of nos n links Lt N not th st of nos in with n Lt J not th st of nos in with j N = {,,,,} J = {,2,3,,2}

More information

Present state Next state Q + M N

Present state Next state Q + M N Qustion 1. An M-N lip-lop works s ollows: I MN=00, th nxt stt o th lip lop is 0. I MN=01, th nxt stt o th lip-lop is th sm s th prsnt stt I MN=10, th nxt stt o th lip-lop is th omplmnt o th prsnt stt I

More information

Chapter 7 Conformance Checking

Chapter 7 Conformance Checking Cptr 7 Conormn Ckin pro.r.ir. Wil vn r Alst www.prossminin.or Ovrviw Cptr 1 Introution Prt I: Prliminris Cptr 2 Pross Molin n Anlysis Cptr 3 Dt Minin Prt II: From Evnt Los to Pross Mols Cptr 4 Gttin t

More information

An undirected graph G = (V, E) V a set of vertices E a set of unordered edges (v,w) where v, w in V

An undirected graph G = (V, E) V a set of vertices E a set of unordered edges (v,w) where v, w in V Unirt Grphs An unirt grph G = (V, E) V st o vrtis E st o unorr gs (v,w) whr v, w in V USE: to mol symmtri rltionships twn ntitis vrtis v n w r jnt i thr is n g (v,w) [or (w,v)] th g (v,w) is inint upon

More information

CS200: Graphs. Graphs. Directed Graphs. Graphs/Networks Around Us. What can this represent? Sometimes we want to represent directionality:

CS200: Graphs. Graphs. Directed Graphs. Graphs/Networks Around Us. What can this represent? Sometimes we want to represent directionality: CS2: Grphs Prihr Ch. 4 Rosn Ch. Grphs A olltion of nos n gs Wht n this rprsnt? n A omputr ntwork n Astrtion of mp n Soil ntwork CS2 - Hsh Tls 2 Dirt Grphs Grphs/Ntworks Aroun Us A olltion of nos n irt

More information

Chapter Finding Small Vertex Covers. Extending the Limits of Tractability. Coping With NP-Completeness. Vertex Cover

Chapter Finding Small Vertex Covers. Extending the Limits of Tractability. Coping With NP-Completeness. Vertex Cover Coping With NP-Compltnss Chaptr 0 Extning th Limits o Tractability Q. Suppos I n to solv an NP-complt problm. What shoul I o? A. Thory says you'r unlikly to in poly-tim algorithm. Must sacriic on o thr

More information

VLSI Testing Assignment 2

VLSI Testing Assignment 2 1. 5-vlu D-clculus trut tbl or t XOR unction: XOR 0 1 X D ~D 0 0 1 X D ~D 1 1 0 X ~D D X X X X X X D D ~D X 0 1 ~D ~D D X 1 0 Tbl 1: 5-vlu D-clculus Trut Tbl or t XOR Function Sinc 2-input XOR t wors s

More information

V={A,B,C,D,E} E={ (A,D),(A,E),(B,D), (B,E),(C,D),(C,E)}

V={A,B,C,D,E} E={ (A,D),(A,E),(B,D), (B,E),(C,D),(C,E)} s s of s Computr Sin & Enginring 423/823 Dsign n Anlysis of Ltur 03 (Chptr 22) Stphn Sott (Apt from Vinohnrn N. Vriym) s of s s r strt t typs tht r pplil to numrous prolms Cn ptur ntitis, rltionships twn

More information

V={A,B,C,D,E} E={ (A,D),(A,E),(B,D), (B,E),(C,D),(C,E)}

V={A,B,C,D,E} E={ (A,D),(A,E),(B,D), (B,E),(C,D),(C,E)} Introution Computr Sin & Enginring 423/823 Dsign n Anlysis of Algorithms Ltur 03 Elmntry Grph Algorithms (Chptr 22) Stphn Sott (Apt from Vinohnrn N. Vriym) I Grphs r strt t typs tht r pplil to numrous

More information

Platform Controls. 1-1 Joystick Controllers. Boom Up/Down Controller Adjustments

Platform Controls. 1-1 Joystick Controllers. Boom Up/Down Controller Adjustments Ston 7 - Rpr Prours Srv Mnul - Son Eton Pltorm Controls 1-1 Joystk Controllrs Mntnn oystk ontrollrs t t propr sttns s ssntl to s mn oprton. Evry oystk ontrollr soul oprt smootly n prov proportonl sp ontrol

More information

Constructive Geometric Constraint Solving

Constructive Geometric Constraint Solving Construtiv Gomtri Constrint Solving Antoni Soto i Rir Dprtmnt Llngutgs i Sistms Inormàtis Univrsitt Politèni Ctluny Brlon, Sptmr 2002 CGCS p.1/37 Prliminris CGCS p.2/37 Gomtri onstrint prolm C 2 D L BC

More information

Graphs. Graphs. Graphs: Basic Terminology. Directed Graphs. Dr Papalaskari 1

Graphs. Graphs. Graphs: Basic Terminology. Directed Graphs. Dr Papalaskari 1 CSC 00 Disrt Struturs : Introuon to Grph Thory Grphs Grphs CSC 00 Disrt Struturs Villnov Univrsity Grphs r isrt struturs onsisng o vrs n gs tht onnt ths vrs. Grphs n us to mol: omputr systms/ntworks mthml

More information

Decimals DECIMALS.

Decimals DECIMALS. Dimls DECIMALS www.mthltis.o.uk ow os it work? Solutions Dimls P qustions Pl vlu o imls 0 000 00 000 0 000 00 0 000 00 0 000 00 0 000 tnths or 0 thousnths or 000 hunrths or 00 hunrths or 00 0 tn thousnths

More information

G-001 CHATHAM HARBOR AUNT LYDIA'S COVE CHATHAM ATLANTIC OCEAN INDEX OF NAVIGATION AIDS GENERAL NOTES: GENERAL PLAN A6 SCALE: 1" = 500' CANADA

G-001 CHATHAM HARBOR AUNT LYDIA'S COVE CHATHAM ATLANTIC OCEAN INDEX OF NAVIGATION AIDS GENERAL NOTES: GENERAL PLAN A6 SCALE: 1 = 500' CANADA TR ISL ROR UST 8 O. R-2,4-3 R-4 IX O VITIO IS STT PL ORPI OORITS POSITIO 27698 4-39'-" 88 69-6'-4."W 278248 4-4'-" 8968 69-6'-4"W 27973 4-4'-2" 88 69-6'-"W W MPSIR OOR UUST PORTL MI OR 27 8-OOT OR L -

More information

CS150 Sp 98 R. Newton & K. Pister 1

CS150 Sp 98 R. Newton & K. Pister 1 Outin Cok Synronous Finit- Mins Lst tim: Introution to numr systms: sin/mnitu, ons ompmnt, twos ompmnt Rviw o ts, ip ops, ountrs Tis tur: Rviw Ts & Trnsition Dirms Impmnttion Usin D Fip-Fops Min Equivn

More information

Edge-Triggered D Flip-flop. Formal Analysis. Fundamental-Mode Sequential Circuits. D latch: How do flip-flops work?

Edge-Triggered D Flip-flop. Formal Analysis. Fundamental-Mode Sequential Circuits. D latch: How do flip-flops work? E-Trir D Flip-Flop Funamntal-Mo Squntial Ciruits PR A How o lip-lops work? How to analys aviour o lip-lops? R How to sin unamntal-mo iruits? Funamntal mo rstrition - only on input an an at a tim; iruit

More information

EE1000 Project 4 Digital Volt Meter

EE1000 Project 4 Digital Volt Meter Ovrviw EE1000 Projt 4 Diitl Volt Mtr In this projt, w mk vi tht n msur volts in th rn o 0 to 4 Volts with on iit o ury. Th input is n nlo volt n th output is sinl 7-smnt iit tht tlls us wht tht input s

More information

Self-Adjusting Top Trees

Self-Adjusting Top Trees Th Polm Sl-jsting Top Ts ynmi ts: ol: mintin n n-tx ost tht hngs o tim. link(,w): ts n g twn tis n w. t(,w): lts g (,w). pplition-spii t ssoit with gs n/o tis. ont xmpls: in minimm-wight g in th pth twn

More information

Outlines: Graphs Part-4. Applications of Depth-First Search. Directed Acyclic Graph (DAG) Generic scheduling problem.

Outlines: Graphs Part-4. Applications of Depth-First Search. Directed Acyclic Graph (DAG) Generic scheduling problem. Outlins: Graps Part-4 Applications o DFS Elmntary Grap Aloritms Topoloical Sort o Dirctd Acyclic Grap Stronly Connctd Componnts PART-4 1 2 Applications o Dpt-First Sarc Topoloical Sort: Usin dpt-irst sarc

More information

Register Allocation. How to assign variables to finitely many registers? What to do when it can t be done? How to do so efficiently?

Register Allocation. How to assign variables to finitely many registers? What to do when it can t be done? How to do so efficiently? Rgistr Allotion Rgistr Allotion How to ssign vrils to initly mny rgistrs? Wht to o whn it n t on? How to o so iintly? Mony, Jun 3, 13 Mmory Wll Disprity twn CPU sp n mmory ss sp improvmnt Mony, Jun 3,

More information

Applications: The problem has several applications, for example, to compute periods of maximum net expenses for a design department.

Applications: The problem has several applications, for example, to compute periods of maximum net expenses for a design department. A Gntl Introution to Aloritms: Prt III Contnts o Prt I: 1. Mr: (to mr two sort lists into sinl sort list.). Bul Sort 3. Mr Sort: 4. T Bi-O, Bi-Θ, Bi-Ω nottions: symptoti ouns Contnts o Prt II: 5. Bsis

More information

Graphs. CSC 1300 Discrete Structures Villanova University. Villanova CSC Dr Papalaskari

Graphs. CSC 1300 Discrete Structures Villanova University. Villanova CSC Dr Papalaskari Grphs CSC 1300 Disrt Struturs Villnov Univrsity Grphs Grphs r isrt struturs onsis?ng of vr?s n gs tht onnt ths vr?s. Grphs n us to mol: omputr systms/ntworks mthm?l rl?ons logi iruit lyout jos/prosss f

More information

Similarity Search. The Binary Branch Distance. Nikolaus Augsten.

Similarity Search. The Binary Branch Distance. Nikolaus Augsten. Similrity Srh Th Binry Brnh Distn Nikolus Augstn nikolus.ugstn@sg..t Dpt. of Computr Sins Univrsity of Slzurg http://rsrh.uni-slzurg.t Vrsion Jnury 11, 2017 Wintrsmstr 2016/2017 Augstn (Univ. Slzurg) Similrity

More information

Instruction Scheduling

Instruction Scheduling Instrution Sulin Not y Bris Aktmur: Our slis r pt from Coopr n Torzon s slis tt ty prpr for COMP 412 t Ri. Copyrit 20, Kit D. Coopr & Lin Torzon, ll rits rsrv. Stunts nroll in Comp 412 t Ri Univrsity v

More information

Outline. Circuits. Euler paths/circuits 4/25/12. Part 10. Graphs. Euler s bridge problem (Bridges of Konigsberg Problem)

Outline. Circuits. Euler paths/circuits 4/25/12. Part 10. Graphs. Euler s bridge problem (Bridges of Konigsberg Problem) 4/25/12 Outlin Prt 10. Grphs CS 200 Algorithms n Dt Struturs Introution Trminology Implmnting Grphs Grph Trvrsls Topologil Sorting Shortst Pths Spnning Trs Minimum Spnning Trs Ciruits 1 2 Eulr s rig prolm

More information

Walk Like a Mathematician Learning Task:

Walk Like a Mathematician Learning Task: Gori Dprtmnt of Euction Wlk Lik Mthmticin Lrnin Tsk: Mtrics llow us to prform mny usful mthmticl tsks which orinrily rquir lr numbr of computtions. Som typs of problms which cn b on fficintly with mtrics

More information

A Simple Code Generator. Code generation Algorithm. Register and Address Descriptors. Example 3/31/2008. Code Generation

A Simple Code Generator. Code generation Algorithm. Register and Address Descriptors. Example 3/31/2008. Code Generation A Simpl Co Gnrtor Co Gnrtion Chptr 8 II Gnrt o for singl si lok How to us rgistrs? In most mhin rhitturs, som or ll of th oprnsmust in rgistrs Rgistrs mk goo tmporris Hol vlus tht r omput in on si lok

More information

Last time: introduced our first computational model the DFA.

Last time: introduced our first computational model the DFA. Lctur 7 Homwork #7: 2.2.1, 2.2.2, 2.2.3 (hnd in c nd d), Misc: Givn: M, NFA Prov: (q,xy) * (p,y) iff (q,x) * (p,) (follow proof don in clss tody) Lst tim: introducd our first computtionl modl th DFA. Tody

More information

NP-Completeness. CS3230 (Algorithm) Traveling Salesperson Problem. What s the Big Deal? Given a Problem. What s the Big Deal? What s the Big Deal?

NP-Completeness. CS3230 (Algorithm) Traveling Salesperson Problem. What s the Big Deal? Given a Problem. What s the Big Deal? What s the Big Deal? NP-Compltnss 1. Polynomil tim lgorithm 2. Polynomil tim rution 3.P vs NP 4.NP-ompltnss (som slis y P.T. Um Univrsity o Txs t Dlls r us) Trvling Slsprson Prolm Fin minimum lngth tour tht visits h ity on

More information

10/30/12. Today. CS/ENGRD 2110 Object- Oriented Programming and Data Structures Fall 2012 Doug James. DFS algorithm. Reachability Algorithms

10/30/12. Today. CS/ENGRD 2110 Object- Oriented Programming and Data Structures Fall 2012 Doug James. DFS algorithm. Reachability Algorithms 0/0/ CS/ENGRD 0 Ojt- Orint Prormmin n Dt Strutur Fll 0 Dou Jm Ltur 9: DFS, BFS & Shortt Pth Toy Rhility Dpth-Firt Srh Brth-Firt Srh Shortt Pth Unwiht rph Wiht rph Dijktr lorithm Rhility Alorithm Dpth Firt

More information

XML and Databases. Outline. Recall: Top-Down Evaluation of Simple Paths. Recall: Top-Down Evaluation of Simple Paths. Sebastian Maneth NICTA and UNSW

XML and Databases. Outline. Recall: Top-Down Evaluation of Simple Paths. Recall: Top-Down Evaluation of Simple Paths. Sebastian Maneth NICTA and UNSW Smll Pth Quiz ML n Dtss Cn you giv n xprssion tht rturns th lst / irst ourrn o h istint pri lmnt? Ltur 8 Strming Evlution: how muh mmory o you n? Sstin Mnth NICTA n UNSW

More information

Numbering Boundary Nodes

Numbering Boundary Nodes Numring Bounry Nos Lh MBri Empori Stt Univrsity August 10, 2001 1 Introution Th purpos of this ppr is to xplor how numring ltril rsistor ntworks ffts thir rspons mtrix, Λ. Morovr, wht n lrn from Λ out

More information