c 2009 Society for Industrial and Applied Mathematics

Size: px
Start display at page:

Download "c 2009 Society for Industrial and Applied Mathematics"

Transcription

1 SIAM J. DISCRETE MATH. Vol. 0, No. 0, pp Soity or Inustril n Appli Mtmtis THE TWO-COLORING NUMBER AND DEGENERATE COLORINGS OF PLANAR GRAPHS HAL KIERSTEAD, BOJAN MOHAR, SIMON ŠPACAPAN, DAQING YANG, AND XUDING ZHU Astrt. T two-olorin numr o rps, wi ws oriinlly introu in t stuy o t m romti numr, lso ivs n uppr oun on t nrt romti numr s introu y Boroin. It is prov tt t two-olorin numr o ny plnr rp is t most nin. As onsqun, t nrt list romti numr o ny plnr rp is t most nin. It is lso sown tt t nrt ionl romti numr is t most 11 n t nrt ionl list romti numr is t most 12 or ll plnr rps. Ky wors. two-olorin numr, nrt olorin, plnr rp AMS sujt lssiition. 05C15 DOI / Introution. T two-olorin numr o rp ws introu y Cn n Slp [6] in t stuy o Rmsy proprtis o rps n us ltr in t stuy o t m romti numr [9, 10, 15, 16]. In [10] it ws sown tt t two-olorin numr is rlt to t yli romti numr. It turns out tt t two-olorin numr is lso rlt to t notion o nrt olorins introu y Boroin [1], wi ws t strtin motivtion or t rsults o tis ppr. Lt G rp, n lt L linr orrin o V (G). A vrtx x is k-rl rom y i x< L y n tr is n xy-pt P o lnt t most k su tt y< L z or ll intrior vrtis z o P.LtR L,k (y) t st o ll vrtis x tt r k-rl rom y wit rspt to t linr orr L. Tk-olorin numr o G is in s ol k (G) =1+min L mx R L,k(y), y V (G) wr t minimum is tkn ovr ll linr orrins L o V (G). I k =1,tn ol 1 (G) islsoknownstolorin numr o G sin it provis n uppr oun Riv y t itors Sptmr 25, 2007; pt or pulition (in rvis orm) July 7, 2009; pulis ltronilly DATE. ttp:// Dprtmnt o Mtmtis n Sttistis, Arizon Stt Univrsity, Tmp, AZ (kirst@su.u). Tis utor s rsr ws support in prt y NSA rnt H Dprtmnt o Mtmtis, Simon Frsr Univrsity, Burny, BC V5A 1S6, Cn (mor@su.). On lv rom Dprtmnt o Mtmtis, IMFM & FMF, Univrsity o Ljuljn, Ljuljn, Slovni. Tis utor s rsr ws support in prt y t ARRS (Slovni), Rsr Prorm P1 0297, y n NSERC Disovry rnt, n y t Cn Rsr Cir prorm. Univrsity o Mrior, FME, Smtnov 17, 2000 Mrior, Slovni (simon.sppn@unim.si). Tis utor s rsr ws support in prt y t ARRS (Slovni), Rsr Prorm P Cntr or Disrt Mtmtis, Fuzou Univrsity, Fuzou, Fujin , Cin (qin85@ yoo.om). Tis utor s rsr ws support in prt y NSFC unr rnts n SX o t oll o Fujin. Dprtmnt o Appli Mtmtis, Ntionl Sun Yt-sn Univrsity, Kosiun 80424, Tiwn, Popl s Rpuli o Cin, n Ntionl Cntr or Tortil Sins, Ntionl Tiwn Univrsity, Tipi 10617, Tiwn, Popl s Rpuli o Cin (zu@mt.nsysu.u.tw). Tis utor s rsr ws support y Tiwn rnt NSC M MY3. 1

2 2 KIERSTEAD, MOHAR, ŠPACAPAN, YANG, AND ZHU or t romti numr o G. T ir k-olorin numrs provi uppr ouns or som otr olorin prmtrs [17]. Lt k positiv intr. A rp G is k-nrt i vry surp o G s vrtx o r lss tn k. A olorin o rp su tt or vry k 1, t union o ny k olor lsss inus k-nrt surp, is nrt olorin. Not tt tis strntns t notion o yli olorins, or wi it is rquir tt vry olor lss is 1-nrt n t union o ny two olor lsss inus 2-nrt rp ( orst). T nrt romti numr o G, not s χ (G), is t lst n su tt G mits nrt olorin wit n olors. Suppos tt or vrtx v V (G) w ssin list L(v) N o missil olors wi n us to olor t vrtx v. A list olorin o G is untion : V (G) N, su tt (v) L(v) orv V (G) n(u) (v) wnvr u n v r jnt in G. I t olorin is lso nrt, w sy tt is nrt list olorin. I or ny oi o lists L(v),v V (G), su tt L(v) k, trxists list olorin o G, tn w sy tt G is k-oosl. T list romti numr (or t oi numr) og, not s (G), is t lst k, su tt G is k-oosl. Anloously w in t nrt oi numr n not it y (G). In 1976 Boroin prov [2, 3] tt vry plnr rp mits n yli 5-olorin n tus solv onjtur propos y Grünum [7]. At t sm tim, propos t ollowin onjtur. Conjtur 1 (Boroin [2, 3]). Evry plnr rp s 5-olorin su tt t union o vry k olor lsss wit 1 k 4 inus k-nrt rp. Tomssn sttl wknin o Conjtur 1 y provin tt t vrtx st o vry plnr rp n ompos into two sts tt, rsptivly, inu 2-nrt rp n 3-nrt rp [13], n tt t vrtx st o vry plnr rp n ompos into n inpnnt st n st tt inus 4-nrt rp [14]. Howvr, Conjtur 1 rmin silly untou sin tr r no tools to l wit nrt olorins. Vry rntly, t rrir ws ovrrin y Rutn [11] wo prov tt vry plnr rp mits nrt olorin usin t most 18 olors. In tis ppr w introu two irnt ppros or lin wit nrt olorins. Bot r s on t ollowin osrvtion. Osrvtion 1. Lt G rp, n lt nrt olorin o vrtxlt surp G v. I t niors o v r olor y pirwis istint olors n w olor v y olor wi is irnt rom ll o tos olors, tn t rsultin olorin o G is nrt. T osrvtion ov, wos sy proo is lt to t rr, is link twn t two-olorin numr n t nrt romti numr, sin it implis t ollowin proposition. Proposition 1. For ny rp G, (G) ol 2 (G). Proo. Suppos tt L is linr orrin o V (G), n suppos tt vrtx y s list o R L,2 (y) + 1 olors. Tn w n olor t vrtis o G, onyon, orin to t linr orrin L, sottvrtxy is olor y olor irnt rom t olors o vrtis in R L,2 (y). Tis strty urnts tt ll niors o y, wi r lry olor, v pirwis irnt olors. To s tis, lt y 1 < L y 2 ny two niors o y, wry 1,y 2 < L y. Tn y 1 R L,2 (y 2 ), so y 2 s n olor irntly rom y 1. It ollows rom Osrvtion 1 tt t otin olorin o G is nrt n n (G) ol 2 (G). In tis ppr w sll us two-olorins to t n iint oun on t nrt

3 THE TWO-COLORING NUMBER AND DEGENERATE COLORINGS 3 romti numr. It is prov in [9] tt plnr rps v two-olorin numr t most 10. Currntly, our min rsult ivs t st uppr oun or t two-olorin numr o vry plnr rp G, n nort oun on (G). Torm 1. I G is plnr rp, tn ol 2 (G) 9, n tror lso (G) 9. Torm 1 will prov in stion 2. In t, w will prov slitly stronr sttmnt tt tr is linr orrin L o V (G) su tt or vrtx x, R L,1 (x) 5n R L,2 (x) 8. S Torm 2. An xmpl ivn in [9] sows tt tr r plnr rps wos 2-olorin numr is 8. Tt xmpl is quit omplit. Hr w iv mu simplr on. Consir iv-onnt trinultion T o t pln in wi no two vrtis o r 5 r jnt. It is wll known n sy to s tt tr r ininitly mny su trinultions. Lt L linr orrin o vrtis o T,nltx t lst vrtx tt s lrr nior wit rspt to L. Suppos tt y 1,...,y k r t niors o x su tt x< L y i or i =1,...,k,nltN (x) tsto niors o x istint rom y 1,...,y k. By t oi o x, ll niors o y 1,...,y k r 2-rl rom x. Not tt y i stmosttwoniorsinn (x). I k = 1, it is sy to s tt N (x) n t niors o y 1 ontin t lst svn vrtis istint rom x. Tus, R L,2 (x) 7. On t otr n, i k 2, tn y 1 n y 2 v only x n possily on nior o x s ommon niors. I on o tm s r t lst 6, tn ty v t lst svn niors istint rom x, n n R L,2 (x) 7. I ty ot v r 5, tn x s r t lst 6, n it is in sy to s tt R L,2 (x) 7. Tis sows tt ol 2 (T ) 8. T ollowin rmins llnin opn prolm. Qustion 1. Is it tru tt vry plnr rp G stisis ol 2 (G) 8? u y F 1 F 2 x v Fi. 1. x n y r opposit wit rspt to = uv. T sontool win us to ontrolnrywn lin wit rps m in surs is s on t notion o ionl olorins in low. Lt G pln rp, n lt = uv n o G. Suppos tt t s F 1 n F 2 inint wit r ot trinls. I x, y r t vrtis istint rom u, v on t ounry o F 1 n F 2, rsptivly, tn w sy tt x, y r opposit wit rspt to (s Fiur 1). Vrtis x n y o G r si to opposit i ty r opposit wit rspt to som o G. Not tt or vrtx o r k, tr r t most k vrtis tt r opposit to it. Bout t l. [5] introu t notion o ionl olorins o pln rps, or wi on rquirs tt ny two jnt or opposit vrtis riv irnt olors. Ty propos t ollowin onjtur. Conjtur 2 (Bout t l. [5]). Evry pln rp s ionl 9-olorin.

4 4 KIERSTEAD, MOHAR, ŠPACAPAN, YANG, AND ZHU Fi. 2. A trinultion wi ns nin olors. T rp sown in Fiur 2 is n xmpl wi ns nin olors. Bout t l. prov in [5] tt 12 olors sui in nrl. Boroin [4] sow ow to sv on olor, n Snrs n Zo [12] prov tt 10 olors sui. S lso [8, Prolm 2.15]. 2. T two-olorin numr. First w n som proprtis o pln trinultions. Suppos G =(V,E) is trinultion wos vrtis r prtition into two susts U n C, wrc is n inpnnt st n vrtx in C s r 4. For vrtx x, lt U (x) t numr o niors o x in U, nlt C (x) t numr o niors o x in C. Osrvtt C (x) =4 C. x U Lt w(x) = U (x)+ C (x)/2 twit o x. Tn (1) 2 E = G (x) = G (x)+4 C = w(x)+6 C. x V x U x U Eulr s ormul implis tt E < 3 V =3 U +3 C. Tis implis tt 6 U > x U w(x), so tr is vrtx x U wit w(x) < 6 (n w(x) 5.5). ForrpG n x, y V (G) wwritx y wn x is jnt to y in G n x y otrwis. W sy tt x U is vrtx o typ (, ) i U (x) = n C (x) =. Ix y, x is o typ (5,0) or (5,1), n y is o typ (5,0), (5,1), or (5,2), tn (x, y) is ll oo pir. I xyz is trinl in G, z is o typ (5,0) or (5,1), n x, y r ot o typ (6,0), tn (x, y, z) isoo tripl. Ix, y r o typs (5,0) or (5,1) n z is o typ (6,1) n x z,y z,x y, tn(x, y, z) is lso ll oo tripl. Lmm 1. Lt G =(V,E) plnr trinultion. Suppos tt C V is n inpnnt st wr vrtx o C s r 4, ltu = V C, n suppos tt U (x) 5 or ll x U. TnG s oo pir or oo tripl. Proo. Assum tt tr is no oo pir n no oo tripl. Osrv tt U. For x U, lt(x) =w(x) 6tinitil r o x. A vrtx x is ll mjor vrtx i (x) > 0, n vrtx x wit (x) < 0 is ll minor vrtx. Eulr s ormul implis tt E =3 V 6, n totr wit (1) w onlu tt (2) (x) = w(x) 6 U =2 E 6 C 6 U = 12. x U x U

5 THE TWO-COLORING NUMBER AND DEGENERATE COLORINGS 5 I x is minor vrtx o typ (5,0), tn w lt mjor nior o x sn r o 1/3 tox. I x is minor vrtx o typ (5,1), tn mjor nior o x is sk to sn r o 1/6 tox. Lt us onsir t rsultin r (x) or ll vrtis o G. W sll sow tt (x) 0orx, wi will ontrit (2) n omplt t proo. Not tt vry vrtx, wi is not mjor vrtx, is o typ (5, 0), (5, 1), (5, 2), or (6, 0). I x is minor vrtx, tn its typ is (5, 0) or (5, 1). Sin tr r no oo pirs, x is not jnt to vrtx o typ (5, 0), (5, 1), or (5, 2). Aitionlly, sin tr r no oo tripls, x is not jnt to two jnt vrtis o typ (6, 0). It ollows tt vry minor vrtx s t lst tr mjor niors. Tror (x) 0 or ll minor vrtis x. Suppos now tt x is mjor vrtx. Sin tr r no oo pirs, no two minor vrtis r jnt, n n x sns r only to nonniorin vrtis. I minoru-nior y o x is jnt to C-nior o x, tny is o typ (5,1), n n x sns to y only 1/6 r. A U-nior jnt to two C-niors o x rivs 0. Tror t totl r snt out rom x is t most U (x)/2 /3. Hn (x) 0i U (x) 7. Assum now tt U (x) =6. I C (x) 2, tn it is sy to s tt t r snt out rom x is t most 2/3, n n (x) 0. So, ssum U (x) =6n C (x) =1. Ix s t lst two minor niors, tn ts two minor niors r not jnt (or otrwis w v oo pir). But tn w v oo tripl, ontrry to our ssumption. Tus x s t most on minor nior, n n t r snt out rom x is t most 1/3. Sin (x) =1/2 w onlu tt (x) 1/2 1/3 > 0. I (x) = 0, tn no r is snt out rom x, n n (x) =0. Nowvry vrtx s nonntiv r, wi is ontrition. Lt G =(V,E) rp n C V.AC-orrin o G is prtil orrin L o V (G) su tt t ollowin onitions ol: (i) T rstrition o L to C is linr orrin o C. (ii) T vrtis in V C r inomprl (tt is, nitr x< L y nor y< L x or x, y V C). (iii) y< L x or vry x C n y V C. Lt L C-orrin o G n x C. TstR L,2 (x) iststolly< L x, su tt itr y x or tr xists z C wit x z,y z, nx< L z.int lttr s w sy tt y is two-rl rom x wit rspt to L. Suppos tt v is vrtx o G n C = C {v}. IL is C -orrin o G n L is C-orrin o G su tt L n L r qul on C n v< L u or ll u C, tn lrly R L,2 (x) = R L,2(x) or ll x C. Tus, xtnin t C-orrin to C -orrin os not inrs t siz o ny st R L,2 (x). Torm 2. I G is plnr rp, tn tr is linr orrin L o V (G) su tt or vrtx x, R L,1 (x) 5 n R L,2 (x) 8. Inprtiulr,ol 2 (G) 9. Proo. W n to in linr orrin L o V (G) su tt or vrtx x, R L,1 (x) 5n R L,2 (x) 8. Lt C V n U = V C. W sy tt C-orrin L o G is vli i vrtx x C s t most our niors in U n R L,2 (x) 8. In t ollowin w sll prou squn o vli orrins o G so tt t orr st C oms lrr n lrr, n vntully prous linr orrin o V (G). At t innin C = (wi is rtinly vli orrin). Suppos w v

6 6 KIERSTEAD, MOHAR, ŠPACAPAN, YANG, AND ZHU vli C-orrin L o G n U. To prou lrr vli orrin w n t rp G s ollows. First w lt ll s twn vrtis o C. Ix C s t most tr niors in U, tn lt x n s twn pir o t U-niors o x. Osrv tt tis oprtion prsrvs plnrity o t rp. I x C s our niors in U, tn s in t yli orr (orin to t pln min) so tt t our niors orm 4-yl. T rsultin rp G 1 is plnr n vry vrtx o G 1 in C is o r 4 n is ontin only in trinulr s. Tror w n s only mon vrtis o U to otin pln trinultion G in wi t vrtx st F = C V (G ) is inpnnt n ll its vrtis v r 4. Sin G is trinultion n F is n inpnnt st o G, w know tt or vrtx x U, U (x) F (x). I tr is vrtx x U wit U (x) 4, tn xtn t orrin L y lttin x t nxt orr vrtx. T otin orrin L is n orrin wit x< L y or ll y C n u< L x or ll u U {x}. Lt z nior o x, wi is in C. Iz F, tn it s our niors in U n t most on o tm is not x or nior o x. Consquntly t most on nw vrtx is two-rl rom x trou z. On t otr n i z C F,tnz s t most tr niors in U n ll o tm r itr x or its niors in G. W inr tt R L,2 (x) 8 ols in t oriinl rp G. By ssumption, x s t most our niors in U, so t xtn orrin is vli. j x y i r q p x k z o y j i n m n k s m l l Fi. 3. A s o n xtnsion o orrin, wr ot x n y r o typ (5,1). Dionl vrtis r init y rokn lins. Vrtis o F r ull; vrtis o U r ollow. Suppos now tt U (x) 5inG or x U. W will pply Lmm 1 or G n t st F plyin t rol o C. T lmm implis tt tr is itr oo pir (x, y) or oo tripl (x, y, z). I tr is oo pir (x, y), tn w xtn t orrin y rqustin tt or ny u U {x, y} n v C w v u< L x< L y< L v. W lim tt t otin orrin L is vli in G or t prorr st C 1 = C {x, y}. W trt t s wn x n y r o typ (5,1) in til, n lv ll otr ss to t rr. Lt t nior o x in F,nlt t nior o y in F (possily = ). Not tt n ror4ing n tt o tm s t most on nior tt is two-rl rom x (rsp., y) n is not nior o x (rsp., y). An xmpl o tis s is sown in Fiur 3. Not tt t typ (5,1) n t rwin in Fiur 3 r wit rspt to G. T uniqu niors o x n y in F r osn in Fiur 3 s vrtis n, rsptivly, ut ty n ny otr niors, inluin possily in on o t ommon niors o x n y. Sin t xtnsion

7 THE TWO-COLORING NUMBER AND DEGENERATE COLORINGS 7 L o L is in so tt x< L y, w s tt t numr o niors u o x wit u< L x is our (in Fiur 3 ts r,,, n). Morovr, t numr o vrtis tt r two-rl in G rom x (n r irnt rom t U-niors o x in G ) is t most tr, on trou n two mor trou y (ts r j,, n). On t otr n, y s iv niors u wit u< L y (nmly, x,,, n) n on itionl two-rl vrtx trou tt is not its nior in G,ttisi. W prov tt R L,2(x), R L,2(y) 7nox, y s t most our niors in U {x, y}. Tror t xtn orrin is vli. Otr oniurtions, wn (x, y) is oo pir, r trt similrly. I (x, y, z) is oo tripl su tt x n y r o typs (5,0) or (5,1) n z is o typ (6,1),tnxtntC-orrin L to L y inin positions or x, y, nz s ollows: or ny u U {x, y, z} n v C, wstu< L x< L z< L y< L v. I (x, y, z) is oo tripl su tt x n y r o typ (6,0) n z is o typ (5,0) or (5,1),tnxtntC-orrin L to L y sttin u< L x< L y< L z< L v or u U {x, y, z} n v C. As or, it is sy to vriy tt R L,2(x), R L,2(y), R L,2(z) 8nox, y, z s t most our niors in U {x, y, z}. W onlu tt L is vli orrin. Not lso tt t nwly in prtil orr L stisis R L,1(x) 5 or ll x. On o t ss is sown in Fiur 3 or t onvnin o t rr. A slit n in t ov proo yils n pplition to nrt ionl list olorins usin t most 12 olors t vrtx. Corollry 1. Evry pln rp s nrt ionl list olorin rom lists o siz t lst 12. Proo. Lt L C-orrin, n lt RL,2 (y) t st o ll vrtis tt r two-rl rom y, totr wit ll vrtis x, su tt x< L y n x is opposit y. For t purpos o tis proo w sy tt L is vli orrin i or vrtx x C, RL,2 (x) 11, n x s t most our niors in U. Wprovtxistn o linr orr L so tt RL,2 (y) 11 or vrtx y V (G). W pro nloously s in t proo o Torm 1, tt is, w onstrut squn o vli orrins until w vntully t linr orrin o V (G). So suppos tt w r ivn vli C-orrin L, wrc V (G). Tn n t rp G s in t proo o Torm 1 to otin pln rp G. It is sy to s tt t onstrution o G ws on so tt i x, y U n x is opposit y in G, tnx is itr jnt to or opposit y in G. Ain w rriv t w ss, wr w v to trmin ow to xtn t orrin L. I tr is vrtx x wit U (x) 4inG, tn xtn t orrin L to L so tt t nxt orr vrtx is x n osrv tt ny vrtx tt is two-rl rom x n is not nior o x is lso opposit to x. It ollows tt RL,2(y) 8 ols in G. Otrwis G s itr oo pir or oo tripl. I (x, y) is oo pir, tn lt x< L y. I x, y r o typ (5,1) (s Fiur 3), tn osrv tt x s our jnt vrtis smllr tn x (ts r,,, ), two vrtis wi r tworl n ionl t t sm tim (ts r j n ), two ionl vrtis wi r not two-rl (ts r m n n) n on otr two-rl vrtx (). Tis is nin ltotr, n n RL,2(x) 9. It is sy to k tt RL,2(y) ={, x,,,, i, k, l, }, so its siz is 9. Otr ss o oo pirs r trt similrly ut RL,2 (x) n R L,2(y) r lwys t most 10. I (x, y, z) is oo tripl su tt x n y r o typs (5,0) or (5,1) n z is o typ (6, 1), w xtn L to L so tt z < L x< L y (s Fiur 3). Tn

8 8 KIERSTEAD, MOHAR, ŠPACAPAN, YANG, AND ZHU w v RL,2 (z) ={, k,,, l, s,, j,, }. Similrly R L,2(x) ={k, z,,,,, q, r, p} n RL,2(y) ={,, z, k, j, m, n, k, o}, n n in ny s t siz is lss tn 10. I (x, y, z) is oo tripl wr z is o typ (5,0) or (5,1) n x, y r o typ (6,0), tn lt x < L y < L z, n it is sy to k tt RL,2 (x), R L,2 (y), RL,2(z) Dnrt ionl 11-olorins. T ollowin torm is n improvmnt o Corollry 1 y on olor or t nonlist vrsion o nrt n ionl olorins. W v i to inlu it spit t t tt it is slitly lonr sin t mtos us in t proo o tis rsult r irnt rom t mtos o t prvious stion. Torm 3. Evry pln rp s nrt ionl olorin wit 11 olors. Proo. Suppos tt t torm is not tru. Lt G minimum ountrxmpl (wit rspt to t numr o vrtis). W my ssum tt G is trinultion o t pln. Clim 0. G s no vrtis o r 3. Proo. Suppos, on t ontrry, tt v V (G) is vrtx o r 3. Sin G is minimum ountrxmpl, tr is nrt olorin o G v, su tt no two opposit vrtis r olor wit t sm olor. A sir olorin o G is otin rom t olorin o G v, y olorin t vrtx v wit olor irnt rom t olors o its niors n its opposit vrtis. Not tt tis is lwys possil, sin tr r t most six niors or opposit vrtis o v; tror in t st o 11 olors tr r 5 possil olors or v. Tis is ontrition to t oi o G. Clim 1. G s no vrtis o r 4. Proo. Suppos, on t ontrry, tt v is vrtx o r 4. S Fiur 4. Witout loss o nrlity ssum tt is not n in G. Dlt t vrtx v, t, n ll t otin rp G. By t minimlity o G, tris nrt olorin o G su tt n r olor wit istint olors (sin ty r opposit). Tis olorin inus nrt olorin o G v. InG tr r t most our vrtis opposit to v n tr r our vrtis jnt to v. W olor t vrtx v wit olor irnt rom olors o t vrtis jnt or opposit to v. Sin,,, n r olor y pirwis istint olors, w inr rom Osrvtion 1 tt t otin olorin is nrt. Morovr, ny two opposit vrtis r olor wit istint olors. v Fi. 4. A vrtx o r 4. Lt us now introu som nottion. I G s sprtin 3-yl, tn lt C sprtin 3-yl wit t lst numr o vrtis in its intrior. Otrwis lt C t 3-yl on t ounry o t ininit. Dnot y C t rp inu y vrtis o C n tos in t intrior o C. I C ontins 4-yl or 5-yl wit t lst two vrtis in its intrior, tn lt D t on wit t lst numr o vrtis in its intrior; otrwis, lt D = C. Lt D t rp inu y t

9 THE TWO-COLORING NUMBER AND DEGENERATE COLORINGS 9 vrtis o D n tos in t intrior o D. W sll not y int(d) tsto intrnl vrtis o D. Clim 2. In C, no two intrnl vrtis o r 5 r jnt. Proo. Suppos, on t ontrry, tt u, v r jnt intrnl vrtis o r 5; s Fiur 5 or nottion. Sin C s no sprtin 3-yls,,, n r not s o G. Dlt t vrtis u n v, t s,, n, nllt otin rp G (s Fiur 5). u v Fi. 5. Two intrnl jnt vrtis o r 5. Osrv tt,, n, r two pirs o opposit vrtis in G ; tror tr xists nrt olorin o G su tt t vrtx sts {,,, } n {,,, } r olor y pirwis istint olors. Sin ot u n v r vrtis o r 5, tr r t most nin (iv on opposit n our on jnt vrtis) olors proiit or u n v. Tror, olorin o G n otin rom t olorin o G y olorin u n v y istint vill olors. Osrv tt t otin olorin is nrt (y Osrvtion 1) n tt ny two opposit vrtis r olor y istint olors. v u z x, i i Fi. 6. u, v, z, nx r vrtis o rs 5, 6, 6, n6, rsptivly. Clim 3. An intrnl vrtx u o D o r 5 nnot jnt to tr intrnl vrtis v, x, z o D o r 6, wrz is jnt to x n v. Proo. Suppos tt u, v, z, nx r intrnl vrtis o D ontritin t lim (s Fiur 6). W lim tt vrtis i n r nitr jnt nor v ommon nior. Assum (or ontrition) tt ty r jnt n tt t i is m s sown in Fiur 6. Consir t 4-yl E = iuv n osrv tt it ontins t lst two vrtis in t intrior. By t minimlity o D, t 4-yl E is not ontin in D. So ssum tt t is vrtx o D ontin in t intrior o E. I t is jnt to ot n i, tntvuit is 5-yl ontritin t minimlity o D. Otrwis, tr r two vrtis o D ontin in t intrior o E n vrtx t V (D) in t xtrior o E; intisst is jnt to n

10 10 KIERSTEAD, MOHAR, ŠPACAPAN, YANG, AND ZHU i. Ain w onlu tt t vuit is 5-yl, ontritin t minimlity o D. Anloous rumnts prov tt n i nnot v ommon nior. Not lso tt t vrtis,,...,i sown in Fiur 6 r pirwis istint, sin otrwis D woul ontin sprtin 3-yl or 4-yl (irnt rom D). Morovr, t sm rumnts sow tt non o,,,, n is n o G. Lt us lt vrtis u, v, z, nx n intiy i n. Furtr, t s,, n s sown in Fiur 6, n ll t otin rp G. As prov ov, G is rp witout loops or multipl s. By t minimlity o G, tr is nrt olorin o G, su tt o t vrtx sts {i,,, }, {,, }, {,,, }, n{, i} is olor y pirwis istint olors (not tt i,, n, r pirs o opposit vrtis in G ). Tis olorin inus olorin o ( surp o) G, wri n riv t sm olor. Sin n i r t istn t lst 3inG, tis olorin is nrt n s opposit vrtis olor wit istint olors. W now olor t vrtis x, z, v, nu in tis orr. Sin x is olor or u, v, nz n t olor o quls t olor o i, w in tt tr r t most nin proiit olors or x; ts r t olors us or t vrtis,,,,, i, n tr itionl opposit vrtis istint rom, v, n. W olor t vrtx x wit on o t two rminin olors not proiit or x. Nxt w olor z, wi s 10 proiit olors; ts r t olors us or t vrtis,,, x,,,,, nt two opposit vrtis wit rspt to s n. Notttwvrqust tt t olor o z irnt rom in orr to l to pply Osrvtion 1 wn olorin v. So tr is olor not proiit or z, n w us tis olor to olor it. Sin i n v t sm olor, t olor o x is istint rom t olor o. Hn, t olors on t niors o z r ll istint. Tror t olorin is still nrt. Nxt w olor v, wi s t most 10 proiit olors: it s six niors n six opposit vrtis, ut u is not yt olor, n, i v t sm olor. Finlly, w olor u, wi s lso t most nin proiit olors (t olors o iv opposit n iv jnt vrtis, wr two o tm oini, nmly t olors o n i). By pplyin Osrvtion 1 t stp, w onlu tt t rsultin olorin o G is nrt. It ws onstrut in su wy tt ll opposit vrtis v irnt olors. Tis ontrition to nonolorility o G omplts t proo o Clim 3. Clim 4. An intrnl vrtx u o D o r 7 nnot jnt to tr intrnl vrtis v, x, z o D o r 5 n two vrtis y n t o r 6, su tt y is jnt to v n x n t is jnt to x n z. Proo. Suppos tt u, v, x, z, y, t r vrtis o D ontritin t lim. S Fiur 7 or itionl nottion. Sin u, v, z, nx r intrnl vrtis o D, w in tt n r nitr jnt nor v ommon nior (sin otrwis on woul in 4- or 5-yl wit wr vrtis in t intrior wn ompr to D). Lt us rmov vrtis t, u, v, x, y, z. Tn intiy vrtis n, s i, i,, n (or ), n ll t otin rp G. T nrt olorin o G inus nrt olorin o surp o G, wr n riv t sm olor. W will olor t vrtis u, y, t, x, v, z in tis orr. Osrv tt or t vrtx u, tr r t most 10 = proiit olors: 14 olors or jnt n opposit vrtis, +2 olors or j n (wi w wnt to olor irnt rom u to us Osrvtion 1 wn ltr olorin y n t), 5 or not yt olor jnt vrtis, n 1 us t olor o is qul to t olor o. Similrly on n

11 THE TWO-COLORING NUMBER AND DEGENERATE COLORINGS 11 i v y u t z i j x j Fi. 7. u, v, z, nx r vrtis o rs 7, 5, 5, n5, rsptivly. ru tt or y tr r t most it rstritions, n or t, v, x, z, tr r t most 10 proiit olors. Sin w olor wit 11 olors n or vrtx u, y, t, x, v, z tr r wr tn 11 proiit olors, w n xtn t prtil olorin o G inu y t olorin o G to nrt ionl olorin o G. Clim 4 implis tt, i u is vrtx o r 7 wit tr (nonjnt) niors v, x, z o r 5 n istriut s sown in Fiur 7, tn itr y or t s r t lst 7. W omplt t proo o t torm y pplyin t isrin mto in D. W in t r (v) ovrtxv V ( D) s { D(v) 6 i v int(d), (3) (v) = D(v) 3 i v V (D). It ollows rom t Eulr ormul tt ( D(v) 6) = 6 2 D, n tus (4) v V ( D) (v) = v V (D) v V ( D) ( D(v) 3) + v int(d) ( D(v) 6) = D 6. W now prorm t isrin prour s ollows: (i) Evry vrtx o D ivs 1 to nior o r 5 in int(d). (ii) Evry vrtx in int(d) o r t lst 8 ivs 1/2 to nior o r 5inint(D). (iii) Evry vrtx in int(d) o r 7 wit t most two niors o r 5 in int(d) ivs 1/2 to nior o r 5 in int(d). (iv) A vrtx u int(d) o r 7 wit tr niors o r 5 in int(d) s its nioroo s sown in Fiur 7, xpt tt y n t r not nssrily o r 6. () I (y) 7 n (t) 7, tn u ivs 1/2 to x n 1/4 to v n z. () I (y) 7 n (t) =6,tnu ivs 1/2 to z n 1/4 to v n x. () I (y) = 6 n (t) 7, tn u ivs 1/2 to v n 1/4 to z n x. Not tt vrtx o D nnot v r 3in D, sin tis woul imply tt D is not 3-, 4-, or 5-yl wit minimum numr o vrtis in its intrior.

12 12 KIERSTEAD, MOHAR, ŠPACAPAN, YANG, AND ZHU Morovr, i v is vrtx o D n s α>1 niors o r 5 in int(d), tn t r o v in D is t lst 2α + 1 y Clim 2. Tis implis tt vry vrtx o D rtins (tr isrin) t lst 0 o its r. Also, y Clim 2, vrtx o r α 7 s t most α 2 niors o r 5. So w inr rom t isrin ruls tt vry vrtx o int(d) or 7 s nonntiv r tr t isrin prour. Sin intrnl vrtis o r 6 kp tir r t zro, n tr r no vrtis o r lss tn 5; t only nits or vin ntiv r r intrnl vrtis o r 5. Osrv tt vry vrtx v int(d) o r 5 is itr jnt to vrtx o D or s, y Clims 0, 1, 2, n 3, t lst two niors o r 7. I v s our or iv niors o r 7, tn, tr isrin, its r will t lst 0, sin vry vrtx o r 7 ivs t lst 1/4 to o its niors o r 5. I v s tr niors o r 7 n on o tm is not o r 7 or os not v tr niors o r 5, tn tis nior will iv 1/2 to v n tus v will v r o t lst 0. Otrwis v s tr niors o r 7, n o tm s tr niors o r 5. It ollows rom rul (iv) tt on o t niors o v will iv 1/2 to v, so t inl r o v will nonntiv. In t rminin s, wr v s xtly two niors o r 7, w s tt ts two niors r not jnt y Clim 3. Tror t isrin rul (iv) implis tt ot o tm iv 1/2 to v. Tus, t inl r t v is nonntiv. Tis isrin pross provs tt t lt-n si o (4) is nonntiv, wil t rit-n si is ntiv, ontrition. Tis omplts t proo o Torm 3. Finlly w turn to t proo o t ionl list olorin rsult. Osrv tt intiition o vrtis nnot on wit list olorins, so t proos o Clims 3 n 4 rom t ov proo nnot xtn to list olorins. W nxt iv irnt proo o Corollry 1. T proo is similr to t proo o Torm 3. W strt y ssumin t ontrry, n lt G minimum ountrxmpl. Din C, C,D,n D t sm wy s in t proo o Torm 3. T proos o Clims 0, 1, n 2 rom t proo o Torm 3 lso ol or list olorins. Clim 5. An intrnl vrtx o D o r 5 nnot jnt to two jnt intrnl vrtis o D o r 6. Proo. Suppos, on t ontrry, tt u, v, z r vrtis o rs 5, 6, n 6, rsptivly. S Fiur 8 or urtr nottion. W o t rution s ollows. v u z Fi. 8. u, v, nz r vrtis o rs 5, 6, n6, rsptivly. Dlt t vrtis u, v, nz n s,,, n. Not tt ts r not s o G, sin D s no sprtin 3-yls. Lt us ll t otin rp

13 THE TWO-COLORING NUMBER AND DEGENERATE COLORINGS 13 G. T lists or vrtis o G r inrit G. By t minimlity o G, tris nrt list olorin o G, su tt,,, n,,,, rsptivly, r olor y pirwis istint olors (sin,, n, r pirs o opposit vrtis in G ). Tis olorin inus olorin o surp o G. W nxt olor v, z, nu (in tis orr). Osrv tt v n z v t most 11 proiit olors (6 opposit vrtis n 4, rsptivly 5, niors, n t olor o is proiit or v); t vrtx u is o r 5, n tus it s t most 10 proiit olors. Tus, or ll tr rminin vrtis tr is r olor so tt w n xtn t list olorin o G to nrt list olorin o G; tis is ontrition provin t lim. Finlly, t ollowin isrin prour ls to ontrition. (i) Evry vrtx o D ivs 1 to nior o r 5 in int(d). (ii) Evry vrtx o r t lst 7 o int(d) ivs 1/3 to nior o r 5inint(D). Sin vry vrtx o r 5 o int(d) s (y Clims 0, 1, 2, n 5) t lst tr niors o r 7 or s nior in D, n vry vrtx o r α 7o int(d) s t most α 2 niors o r 5 in int(d), w onlu tt t lt-n si o (4) is nonntiv, wil t rit-n si is ntiv, ontrition. REFERENCES [1] O. V. Boroin, On omposition o rps into nrt surps, Diskrtny Anliz., 28 (1976), pp (in Russin). [2] O. V. Boroin, A proo o Grünum s onjtur on t yli 5-olorility o plnr rps, Sovit Mt. Dokl., 17 (1976), pp [3] O. V. Boroin, On yli olorins o plnr rps, Disrt Mt., 25 (1979), pp [4] O. V. Boroin, Dionl 11-olorin o pln trinultions, J. Grp Tory, 14 (1990), pp [5] A. Bout, J.-L. Fouqut, J.-L. Jolivt, n M. Riviér, On spil olourin o ui rps, Ars Comin., 24 (1987), pp [6] G. Cn n R. H. Slp, Grps wit linrly oun Rmsy numrs, J. Comin. Tory Sr. B, 57 (1993), pp [7] B. Grünum, Ayli olorins o plnr rps, Isrl J. Mt., 14 (1973), pp [8] T. R. Jnsn n B. Tot, Grp Colorin Prolms, Jon Wily & Sons, Nw York, [9] H. A. Kirst n W. T. Trottr, Plnr rp olorin wit n unooprtiv prtnr, J. Grp Tory, 18 (1994), pp [10] H. A. Kirst n D. Yn, Orrins on rps n m olorin numr, Orr, 20 (2003), pp [11] D. Rutn, A onjtur o Boroin n olorin o Grünum, Fit Crow Conrn on Grp Tory, Ustron 06, Eltron. Nots Disrt Mt., 24 (2006), pp [12] D. P. Snrs n Y. Zo, On ionlly 10-olorin pln trinultions, J. Grp Tory, 20 (1995), pp [13] C. Tomssn, Domposin plnr rp into nrt rps, J. Comin. Tory Sr. B, 65 (1995), pp [14] C. Tomssn, Domposin plnr rp into n inpnnt st n 3-nrt rp, J. Comin. Tory Sr. B, 83 (2001), pp [15] X. Zu, T m olorin numr o plnr rps, J. Comin. Tory Sr. B, 75 (1999), pp [16] X. Zu, Rin tivtion strty or t mrkin m, J. Comin. Tory Sr. B, 98 (2008), pp [17] X. Zu, Colourin rps wit oun nrliz olourin numr, Disrt Mt., to ppr.

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

(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

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

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

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

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

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

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

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

The University of Sydney MATH2969/2069. Graph Theory Tutorial 5 (Week 12) Solutions 2008

The University of Sydney MATH2969/2069. Graph Theory Tutorial 5 (Week 12) Solutions 2008 Th Univrsity o Syny MATH2969/2069 Grph Thory Tutoril 5 (Wk 12) Solutions 2008 1. (i) Lt G th isonnt plnr grph shown. Drw its ul G, n th ul o th ul (G ). (ii) Show tht i G is isonnt plnr grph, thn G is

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Steinberg s Conjecture is false

Steinberg s Conjecture is false Stinrg s Conjtur is als arxiv:1604.05108v2 [math.co] 19 Apr 2016 Vinnt Cohn-Aa Mihal Hig Danil Král Zhntao Li Estan Salgao Astrat Stinrg onjtur in 1976 that vry planar graph with no yls o lngth our or

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 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

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

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

(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

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

MCS100. One can begin to reason only when a clear picture has been formed in the imagination.

MCS100. One can begin to reason only when a clear picture has been formed in the imagination. 642 ptr 10 Grps n Trs 46. Imin tt t irmsown low is mp wit ountris ll. Is it possil to olor t mp wit only tr olors so tt no two jnt ountris v t sm olor? To nswr tis qustion, rw n nlyz rp in wi ountry is

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

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

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

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

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

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

Trees as operads. Lecture A formalism of trees

Trees as operads. Lecture A formalism of trees Ltur 2 rs s oprs In this ltur, w introu onvnint tgoris o trs tht will us or th inition o nroil sts. hs tgoris r gnrliztions o th simpliil tgory us to in simpliil sts. First w onsir th s o plnr trs n thn

More information

CMPS 2200 Fall Graphs. Carola Wenk. Slides courtesy of Charles Leiserson with changes and additions by Carola Wenk

CMPS 2200 Fall Graphs. Carola Wenk. Slides courtesy of Charles Leiserson with changes and additions by Carola Wenk CMPS 2200 Fll 2017 Grps Crol Wnk Sls ourtsy o Crls Lsrson wt ns n tons y Crol Wnk 10/23/17 CMPS 2200 Intro. to Alortms 1 Grps Dnton. A rt rp (rp) G = (V, E) s n orr pr onsstn o st V o vrts (snulr: vrtx),

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

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

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

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

Solutions for HW11. Exercise 34. (a) Use the recurrence relation t(g) = t(g e) + t(g/e) to count the number of spanning trees of v 1

Solutions for HW11. Exercise 34. (a) Use the recurrence relation t(g) = t(g e) + t(g/e) to count the number of spanning trees of v 1 Solutions for HW Exris. () Us th rurrn rltion t(g) = t(g ) + t(g/) to ount th numr of spnning trs of v v v u u u Rmmr to kp multipl gs!! First rrw G so tht non of th gs ross: v u v Rursing on = (v, u ):

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

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

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

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

STRUCTURAL GENERAL NOTES

STRUCTURAL GENERAL NOTES UILIN OS: SIN LOS: RUTURL NRL NOTS NRL NOTS: US ROUP: - SSMLY USS INTN OR PRTIIPTION IN OR VIWIN OUTOOR TIVITIS PR MIIN UILIN O STION. SSONL. T UNTION O TIS ILITY IS NOT OR QUIPP OR OUPNY URIN WINTR/ TIN

More information

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

Solutions to Homework 5

Solutions to Homework 5 Solutions to Homwork 5 Pro. Silvia Frnánz Disrt Mathmatis Math 53A, Fall 2008. [3.4 #] (a) Thr ar x olor hois or vrtx an x or ah o th othr thr vrtis. So th hromati polynomial is P (G, x) =x (x ) 3. ()

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

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

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

Combinatorial Optimization

Combinatorial Optimization Cominoril Opimizion Prolm : oluion. Suppo impl unir rp mor n on minimum pnnin r. Cn Prim lorim (or Krukl lorim) u o in ll o m? Explin wy or wy no, n iv n xmpl. Soluion. Y, Prim lorim (or Krukl lorim) n

More information

CMSC 451: Lecture 4 Bridges and 2-Edge Connectivity Thursday, Sep 7, 2017

CMSC 451: Lecture 4 Bridges and 2-Edge Connectivity Thursday, Sep 7, 2017 Rn: Not ovr n or rns. CMSC 451: Ltr 4 Brs n 2-E Conntvty Trsy, Sp 7, 2017 Hr-Orr Grp Conntvty: (T ollown mtrl ppls only to nrt rps!) Lt G = (V, E) n onnt nrt rp. W otn ssm tt or rps r onnt, t somtms t

More information

Module graph.py. 1 Introduction. 2 Graph basics. 3 Module graph.py. 3.1 Objects. CS 231 Naomi Nishimura

Module graph.py. 1 Introduction. 2 Graph basics. 3 Module graph.py. 3.1 Objects. CS 231 Naomi Nishimura Moul grph.py CS 231 Nomi Nishimur 1 Introution Just lik th Python list n th Python itionry provi wys of storing, ssing, n moifying t, grph n viw s wy of storing, ssing, n moifying t. Bus Python os not

More information

Slide-and-swap permutation groups. Onyebuchi Ekenta, Han Gil Jang and Jacob A. Siehler. (Communicated by Joseph A. Gallian)

Slide-and-swap permutation groups. Onyebuchi Ekenta, Han Gil Jang and Jacob A. Siehler. (Communicated by Joseph A. Gallian) msp INVOLVE 7:1 (2014) x.oi.or/10.2140/involv.2014.7.41 Sli-n-swp prmuttion roups Onyui Eknt, Hn Gil Jn n Jo A. Silr (Communit y Josp A. Gllin) W prsnt simpl til-sliin m tt n ply on ny 3-rulr rp, nrtin

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

BASIC CAGE DETAILS SHOWN 3D MODEL: PSM ASY INNER WALL TABS ARE COINED OVER BASE AND COVER FOR RIGIDITY SPRING FINGERS CLOSED TOP

BASIC CAGE DETAILS SHOWN 3D MODEL: PSM ASY INNER WALL TABS ARE COINED OVER BASE AND COVER FOR RIGIDITY SPRING FINGERS CLOSED TOP MO: PSM SY SI TIS SOWN SPRIN INRS OS TOP INNR W TS R OIN OVR S N OVR OR RIIITY. R TURS US WIT OPTION T SINS. R (UNOMPRSS) RR S OPTION (S T ON ST ) IMNSIONS O INNR SIN TO UNTION WIT QU SM ORM-TOR (zqsp+)

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

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

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

CS 241 Analysis of Algorithms

CS 241 Analysis of Algorithms CS 241 Anlysis o Algorithms Prossor Eri Aron Ltur T Th 9:00m Ltur Mting Lotion: OLB 205 Businss HW6 u lry HW7 out tr Thnksgiving Ring: Ch. 22.1-22.3 1 Grphs (S S. B.4) Grphs ommonly rprsnt onntions mong

More information

Revisiting Decomposition Analysis of Geometric Constraint Graphs

Revisiting Decomposition Analysis of Geometric Constraint Graphs Rvisitin Domposition Anlysis o Gomtri Constrint Grps R. Jon-Arinyo A. Soto-Rir S. Vil-Mrt J. Vilpln-Pstó Univrsitt Politèni Ctluny Dprtmnt Llnuts i Sistms Inormàtis Av. Dionl 647, 8, E 08028 Brlon [rort,

More information

Garnir Polynomial and their Properties

Garnir Polynomial and their Properties Univrsity of Cliforni, Dvis Dprtmnt of Mthmtis Grnir Polynomil n thir Proprtis Author: Yu Wng Suprvisor: Prof. Gorsky Eugny My 8, 07 Grnir Polynomil n thir Proprtis Yu Wng mil: uywng@uvis.u. In this ppr,

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

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

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

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

BASIC CAGE DETAILS D C SHOWN CLOSED TOP SPRING FINGERS INNER WALL TABS ARE COINED OVER BASE AND COVER FOR RIGIDITY

BASIC CAGE DETAILS D C SHOWN CLOSED TOP SPRING FINGERS INNER WALL TABS ARE COINED OVER BASE AND COVER FOR RIGIDITY SI TIS SOWN OS TOP SPRIN INRS INNR W TS R OIN OVR S N OVR OR RIIITY. R IMNSIONS O INNR SIN TO UNTION WIT QU SM ORM-TOR (zqsp+) TRNSIVR. R. RR S OPTION (S T ON ST ) TURS US WIT OPTION T SINS. R (INSI TO

More information

A 4-state solution to the Firing Squad Synchronization Problem based on hybrid rule 60 and 102 cellular automata

A 4-state solution to the Firing Squad Synchronization Problem based on hybrid rule 60 and 102 cellular automata A 4-stt solution to th Firing Squ Synhroniztion Prolm s on hyri rul 60 n 102 llulr utomt LI Ning 1, LIANG Shi-li 1*, CUI Shung 1, XU Mi-ling 1, ZHANG Ling 2 (1. Dprtmnt o Physis, Northst Norml Univrsity,

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

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

24.1 Sex-Linked Inheritance. Chapter 24 Chromosomal Basis of Inheritance Sex-Linked Inheritance Sex-Linked Inheritance

24.1 Sex-Linked Inheritance. Chapter 24 Chromosomal Basis of Inheritance Sex-Linked Inheritance Sex-Linked Inheritance ptr 24 romosoml sis o Inritn 24. Sx-Link Inritn Normlly, ot mls n mls v 23 pirs o romosoms 22 pirs r ll utosoms On pir is t sx romosoms Mls r XY mls r XX opyrit T Mrw-Hill ompnis, In. Prmission rquir or

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

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

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

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

(4, 2)-choosability of planar graphs with forbidden structures (4, )-ooslty o plnr rps wt orn struturs Znr Brkkyzy 1 Crstopr Cox Ml Dryko 1 Krstn Honson 1 Mot Kumt 1 Brnr Lký 1, Ky Mssrsmt 1 Kvn Moss 1 Ktln Nowk 1 Kvn F. Plmowsk 1 Drrk Stol 1,4 Dmr 11, 015 Astrt All

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

Physics 222 Midterm, Form: A

Physics 222 Midterm, Form: A Pysis 222 Mitrm, Form: A Nm: Dt: Hr r som usul onstnts. 1 4πɛ 0 = 9 10 9 Nm 2 /C 2 µ0 4π = 1 10 7 tsl s/c = 1.6 10 19 C Qustions 1 5: A ipol onsistin o two r point-lik prtils wit q = 1 µc, sprt y istn

More information

5/7/13. Part 10. Graphs. Theorem Theorem Graphs Describing Precedence. Outline. Theorem 10-1: The Handshaking Theorem

5/7/13. Part 10. Graphs. Theorem Theorem Graphs Describing Precedence. Outline. Theorem 10-1: The Handshaking Theorem Thorm 10-1: Th Hnshkin Thorm Lt G=(V,E) n unirt rph. Thn Prt 10. Grphs CS 200 Alorithms n Dt Struturs v V (v) = 2 E How mny s r thr in rph with 10 vrtis h of r six? 10 * 6 /2= 30 1 Thorm 10-2 An unirt

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

Proof of Pumping Lemma. PL Use. Example. Since there are only n different states, two of q 0, must be the same say q i

Proof of Pumping Lemma. PL Use. Example. Since there are only n different states, two of q 0, must be the same say q i COSC 2B 22 Summr Proo o Pumpin Lmm Sin w lim L is rulr, tr must DFA A su tt L = L(A) Lt A v n stts; oos tis n or t pumpin lmm Lt w strin o lnt n in L, sy w = 2 m, wr m n Lt i t stt A is in tr rin t irst

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

Continuous Flattening of Convex Polyhedra

Continuous Flattening of Convex Polyhedra Continuous Flttnin o Conv Polr Jin-ii Ito 1, Ci Nr 2, n Costin Vîlu 3 1 Fult o Eution, Kummoto Univrsit, Kummoto, 860-8555, Jpn. j-ito@kummoto-u..jp 2 Lirl Arts Eution Cntr, Aso Cmpus, Toki Univrsit, Aso,

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

CSC Design and Analysis of Algorithms. Example: Change-Making Problem

CSC Design and Analysis of Algorithms. Example: Change-Making Problem CSC 801- Dsign n Anlysis of Algorithms Ltur 11 Gry Thniqu Exmpl: Chng-Mking Prolm Givn unlimit mounts of oins of nomintions 1 > > m, giv hng for mount n with th lst numr of oins Exmpl: 1 = 25, 2 =10, =

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

24CKT POLARIZATION OPTIONS SHOWN BELOW ARE REPRESENTATIVE FOR 16 AND 20CKT

24CKT POLARIZATION OPTIONS SHOWN BELOW ARE REPRESENTATIVE FOR 16 AND 20CKT 0 NOTS: VI UNSS OTRWIS SPII IRUIT SMT USR R PORIZTION OPTION IRUIT SMT USR R PORIZTION OPTION IRUIT SMT USR R PORIZTION OPTION. NR: a. PPITION SPIITION S: S--00 b. PROUT SPIITION S: PS--00 c. PIN SPIITION

More information

Indices. Indices. Curriculum Ready ACMNA: 209, 210, 212,

Indices. Indices. Curriculum Ready ACMNA: 209, 210, 212, Inis Inis Curriulum Ry ACMNA: 09, 0,, 6 www.mtltis.om Inis INDICES Inis is t plurl or inx. An inx is us to writ prouts o numrs or pronumrls sily. For xmpl is tully sortr wy o writin #. T is t inx. Anotr

More information

Two Approaches to Analyzing the Permutations of the 15 Puzzle

Two Approaches to Analyzing the Permutations of the 15 Puzzle Two Approhs to Anlyzin th Prmuttions o th 15 Puzzl Tom How My 2017 Astrt Th prmuttions o th 15 puzzl hv n point o ous sin th 1880 s whn Sm Lloy sin spin-o o th puzzl tht ws impossil to solv. In this ppr,

More information