a ( b ) ( a ) a ( c ) ( d )

Size: px
Start display at page:

Download "a ( b ) ( a ) a ( c ) ( d )"

Transcription

1 Lzy Complton o Prtl Orr to t Smllst Ltt Krll Brtt 1, Ml Morvn 1, Lour Nourn 2 1 LITP/IBP - Unvrst Dns Drot Prs 7 Cs , pl Jussu Prs Cx 05 Frn. ml: (rtt,morvn)@ltp.p.r. 2 Dprtmnt 'Inormtqu Fonmntl LIRMM, Unvrst Montpllr II CNRS UMR C ru A, Montpllr Cx 5, Frn. ml: nourn@lrmm.r. Astrt. Ltt struturs r otn us n knowlg prossng, ut strtng rom prtl orr, omplton nto ltt poss ny prolms. W us som rnt rsults on ltt tory to propos n onln lgortm or ntly omputng o t smllst ltt ontnng gvn prtl orr, ll t Dkn-MNll omplton. By onln w mn tt t lmnts r nssry wn w omput t grtst lowr oun or t lst uppr oun o two lmnts tt o not lry xst. Ts rsult n us n knowlg prossng or mntnng t rry o typs n n on-ln son. Kywors: lgortm, prtl orr, ltt, Dkn-MNll omplton, nong. 1 Introuton n Motvtons Ltts r n mportnt lss o prtl orrs us o tr struturl proprts n tr ntrst n mny rs s knowlgs prossng wt t rry o typs [4,6,1,13,7]. T prolm o mnmlly xtnng prtl orr to t smllst ltt pprs n mny ppltons. Su n pplton s t rry o typs w s rt yl grp wr ntrprtton pns on ppltons (nrt, susum, unton,...). Mntnng t grp o typs s rul n knowlg prossng. Ts grp s usully ltt, ut t s rlz ynmlly,. wl prossng nw knowlg. Most o t tm, t pplton supposs tt only prtl grp s known n tt ts grp s omplt wn nssry y ng nw typ. Tt s, w r gvn som typs n w wnt to omput t gnrlzton or t splzton. But omputng t st o ll gnrl typs s not lwys prtl, sn som typs my not not sgnnt or ts pplton. Formlly, gvn prtl orr P = (X; E), w v to omput t Dkn- MNll omplton o P, not y DM(P ), w s t smllst ltt w ms P s suorr. For mor tls on Dkn-MNll omplton s Bout [3] n Dvy n Prstly [5]. Fgur 1 sows n xmpl

2 o su omplton. From Bout n Wll [3,13]w know tt DM(P) s somorp to t ltt o mxml ntns o t prtt orr (X; X; 6), n lso s somorp to t Glos ltt o (X; X; ). So wy to omput DM(P ) s smply to us lgortms or omputng t ltt o mxml ntns or t Glos ltt o gvn prtl orr. Tr r two nt lgortms to o ts [2,9]. In ts ppr, w prsnt n lgortm or on-ln omputng DM(P ), nmly t lzy Dkn-MNll omplton: W ynmlly omput DM(P ) y ng only lmnts wn w n tm s t lowr or uppr oun o two gvn lmnts. In Ston 3 w sow t mn onstrnt to rspt n orr to nsur orrt mnml onstruton. To o so, w propos n nong o P w gvs wy w s su n lmnt n n nt wy s on n rsult [1,11] n ltt tory. Fnlly, w prsnt n lgortm w s su n lmnt wt t us o tr. P DM(P) Fg. 1. Dkn-MNll omplton o post 2 Dntons n Nottons A prtl orr P = (X; P ) s rxv, ntsymmtr n trnstv nry rlton on st X. Two stnt lmnts x n y r s to omprl x P y or y P x. Otrws, ty r s nomprl, not y xjj P y. P s s prtt orr wn X = X1 [ X2 wt X1 \ X2 = ; n x P y mpls x 2 X1 n y 2 X2. A prtt orr s otn not y P = (X1; X2; P ). W n t ollowng sts or n lmnt x o X : P r(x) = y 2 Xjy < P xg t st o prssors o x, Su(x) = y 2 Xjx < P yg t st o sussors o x. A prtl orr Q = (X 0 ; Q ) s suorr o P X 0 X n t rlton n Q s t sm s t rlton n P on t lmnts o X 0. An lmnt z 2 X s n uppr oun o x; y 2 X x P z n y P z. Lt z n uppr oun o x n y, z s ll t lst uppr oun or t jon o x n y z P t, or ll uppr ouns t o x n y. T grtst lowr oun, or mt s n ully. W not y LUB(x; y) (rsp GLB(x; y)) t lst uppr oun (rsp t grtst lowr oun) o x n y.

3 A non-mpty prtl orr P s ll ltt LUB(x; y) n GLB(x; y) xst or ll x; y 2 X. It s lr tt nt ltt s on mnml lmnt n on mxml lmnt not rsptvly y > n?. Sn w r lng wt lgortms t ltts w onsr r suppos to nt. Lt L = (X; L ) ltt. An lmnt x 2 X s s to jon-rrul x 6=? n x = LUB(y; z) mpls x = y or x = z or ll y; z 2 X. Mtrrul lmnts r n ully. W not t st o jon-rrul lmnts o L y J(L) n t st o mt-rrul lmnts y M(L). W not y 2 X t st o ll susts o X. Dnton 1. [10] Lt P = (X; P ) prtl orr. T Dkn-MNll omplton o P, not y DM(P ), s t smllst ltt ontnng P s suorr. 3 Dkn-MNll Complton o Prtl Orr Sn P s suorr o DM(P ), w v to lmnts n P n orr to omput DM(P ). A nv lgortm onssts n ng n lmnt n P wn t GLB o two lmnts x n y os not xst n P, wt x n y s sussors n P r(x) \ P r(y) s prssors. But ts lgortm os not omput t Dkn-MNll omplton s t ollowng xmpl llustrts t. Exmpl 2. Lt P t prtl orr n Fgur 2(). Suppos tt t pplton sks or t GLB o n. T nv lgortm wll n lmnt low n s n () us GLB(; ) os not lry xst. Lkws, t pplton sks or t GLB o n g, n lmnt wll low n g s n (). In ts wy, ts two lmnts r not n t Dkn-MNll omplton o P sown n (). Clrly ts lgortm nnot us. Lt L ltt n (L) t st o ll prtl orrs vng L s Dkn- MNll omplton. W know tt t prtl orr nu y J(L) [ M(L) s suorr o orr o (L) [5]. Consr t st o lmnts o L w r not rrul lmnts. E sust o ts st orrspons to st o lmnts tt w v to to J(L) [ M(L) to otn prtl orr o (L). Sn ll t susts o ts st orr y nluson orms ooln ltt, w otn n somorp ooln ltt wt t st (L) orr y suorr (. P P 0 P s suorr o P 0 ). Clrly, t > lmnt o (L) s L n t? s t prtl orr nu y J(L) [ M(L). Fgur 3 sows t st (L) or ltt L. In (DM(P )), ny pt rom prtl orr P to > o DM(P ) orrspons to wy o nrmntlly omputng DM(P ). T o o su wy n tkn rltvly to t pplton, or xmpl ng lmnts rom? to > on y lvl t stp. In t ollowng, w gv n lgortm tt ollows pt orng to qustons sk y t pplton. T lmnts r nssry wn w omput GLB or LUB tt os not lry xst.

4 g g ( ) ( ) g g ( ) ( ) Fg. 2. Crton o ummy vrts 4 Enong Prsrvng LUB n GLB W now rll n nong o prtl orr y sts, w llows us to vo t rton o xtr lmnts s sown n Fgur 2. Ts nong s s on rsult o Mrkowsky [11] on ltts, w pprs mpltly n At-K t l[1] ppr on ltts nong. Dnton 3. Lt T n L ltts. Lt x n y two lmnts o T. A unton ' : T! L s s to : 1. Mt-prsrvng '(GLB(x; y)) = GLB('(x); '(y)). 2. Jon-prsrvng '(LUB(x; y)) = LUB('(x); '(y)). Torm 4. [11] Lt L = (X; L ) ltt. Tn t mppng 1. ' : X! 2 J(L) wt '(x) = j 2 J(L) su tt j L xg s mt-prsrvng,. '(GLB(x; y)) = '(x) \ '(y). 2. : X! 2 M(L) wt (x) = m 2 M(L) su tt m L xg s jonprsrvng,. (LUB(x; y)) = (x) \ (y). Ts two nongs r optml (. t szs o ' n r mnml). Not tt or strutv ltt ' n r ot jon-prsrvng n mt-prsrvng[11]. Lmm 5. Lt L = (X; L ) ltt. Tn ny lmnt o L ut t? (rsp. >), s t LU B (rsp.glb) o jon-rrul (rsp. mt-rrul) lmnts.

5 L : Fg. 3. St (L) or ltt L In t ollowng, w sow ow to omput t jon-rrul n mtrrul lmnts o DM(P ) not y J(P ) n M(P ) rom t prtl orr P. Ts n on wtout omputng DM(P ). For tt, w n to ntrou t ollowng notons: Lt P = (X; P ) prtl orr. W omput t ollowng prtt orr B = (X; X 0 ; B ), wt X 0 opy o X n x B y x s n X, y s n X 0 n x 6 P y. Dnton 6. [12] A prtt orr B = (X; X 0 ; P ) s s to ru : C0 : For ll x 2 X, or ll y 2 X 0, w v Su B (x) 6= ;, n P r S B (y) 6= ; k C1 : For ll x 2 X, or ll x1; : : : ; x k 2 Xnxg, w v Su B (x) 6= S =1 Su B(x ) C2 : For ll y 2 X 0, or ll y1; : : : ; y k 2 X 0 k nyg, w v P r B (y) 6= =1 P r B(y ) Conton C1 s not rspt n t ollowng ss: ) Tr xst x n x 0 n X su tt x 6= x 0 n Su B (x) = Su B (x 0 ) ) Tr xst x n X n x1; : : : ; x k n Xnxg wt k > 1 su tt Su B (x) = S k =1 Su B(x ) T symmtrl rmrk n m or C2: ') Tr xst y n y 0 n X 0 su tt y 6= y 0 n P r B (y) = P r B (y 0 ) ') Tr xst y n X 0 n y1; : : : ; y k n X 0 nyg wt k > 1 su tt P r B (y) = S k =1 P r B(y ) So w n trnsorm prtt orr B = (X; X 0 ; B ) n ru prtt orr Bp(P ) = (J; M; B ) y ltng sngltons (C0), ltng on o t two lmnts x or x 0 (rtrrly) n s ), ltng x n s ), ltng on o t two lmnts y or y 0 (rtrrly) n s 0 ), ltng y n s 0 ).

6 It n sown tt J (rsp. M) s t st o jon-rrul (rsp. mtrrul) lmnts o DM(P ). Exmpl 7. Fgur 4 llustrts t nongs or t prtl orr o Fgur 2: () t prtt orr (X; X 0 ; 6 P ), () ts ruton Bp(P ) = (J; M; 6 P ) wr s lt us P r() = P r() [ P r() n s lt us P r() = ;, () t nongs ' n o t lmnts n P. g g ( ) g M(P) J(P) ( ) ' g g g g g ; () Fg. 4. Enongs ' n Now w n pply Torm 4 to v t jon-prsrvng n mt-prsrvng nong or DM(P ) usng J(P ) n M(P ). Sn P s suorr o DM(P ) tn w no only lmnts or P n t otrs n otn usng GLB or LUB sk y t pplton. T ollowng orollry s rt onsqun o Torm 1. Corollry 8. T nongs ' n omput y Algortm 1 r rsptvly mt-prsrvng n jon-prsrvng. W r now gong to sow ow to omput t o o t GLB o two gvn lmnts o P n tn ow to o ts normton to otn t no lmnt(. to sr '?1 ). W suss only GLB, or LUB n otn ully. Lmm 9. Lt x n y two lmnts o prtl orr P. Tn '(GLB(x; y)) = '(x) \ '(y), urtrmor GLB(x; y) = '?1 ('(x) \ '(y)). T omplxty o t lgortm omputng GLB(x; y) pns on t t strutur us or storng t o '. To v goo omplxty, w us t tr t strutur ntrou n [8] or n optml rprsntton o strutv ltt. Ts tr llows us to v st tm omplxty to omput '?1.

7 Algortm 1: Jon-prsrvng n mt-prsrvng nongs Dt : A prtl orr P = (X; P ) Rsult : T mt-prsrvng nong ' : P! 2 J(P ). t jon-prsrvng nong : P! 2 M(P ). gn Comput B = (X; X 0 ; 6 P ); Comput t ruton Bp(P ) = (J; M; B) o B. n Enong ll lmnts n P g or ll x 2 X o '(x) = S j2j; jp x jg; (x) = S m2m; mp x mg; 5 Algortm In ts ston, w gv n lgortm s on tr t strutur to omput GLB(x; y). To o so, onsr prtl orr P = (X; P ) n t nong ' o P omput prvously. For lmnt x 2 P, w suppos tt '(x) s sort orng to topologl sortng o J(P ). Lt us n t tr t strutur, not y T ' or t nong '. { Lt Root ' t root o T ' (ts orrspons to t mxml lmnt n DM(P )). { E no o t tr orrspons to n lmnt o DM(P ). { Evry g o t tr s ll y lmnts o J(P ), su tt t unon o lls rom ny no x to Root ' orrspons '(x) sort orng to. { T sons o ntrnl no r sort orng to?1 Not tt or strutv ltt, vry g s ll y on lmnt o J(P ). Most oprtons on ts tr r sr n [8]. W us ts tr to stor t o ': or o '(x) o n lmnt x, w v to sr t tr n rursv wy to vry x s n t tr. I not, w n sly t. Exmpl 10. Fgur 5 sows T ' or t nong o t prtl orr o Fgur 2, wt ' = < < < < <. Lt us now sr t omputng o GLB(x; y) usng t tr. Exmpl 11. Consr t prtl orr n Fgur 2. I t pplton sks or GLB(; ), Algortm 2 omputs '(GLB(; )) = '() \ '() = w s n t tr o t nong ' s Fgur 6(). Tn, nw lmnt pprs n P (not xpltlly n P ut rprsnt y ts o) s n Fgur 6(). Now, t pplton sks or GLB(; g), Algortm 2 omputs '(GLB(; g)) = '() \ '(g) = w lry xsts n T '.

8 g Algortm 2: GLB(x; y) Fg. 5. Tr o t nong ' Dt : A prtl orr P = (X; P ); Two lmnts x n y o P ; T root T '; Rsult : GLB(x; y) gn o = '(x) \ '(y) ; tr xst z n T ' wt '(z) = o tn Rturn( z); n ls GLB(x; y) o not xst Hr t progrm sks t usr GLB(x; y) s sgnton I ys w nw lmnt z n T '. Corollry 12. Lt P = (X; P ) prtl orr. { T ntl omputton o t nongs ' n T ' rqurs O(jJ(P )j jxj) tm. { T omputton o GLB rqurs O(jJ(P )j). O ours, t s sy to moy t lgortm to xpltly nw lmnt to P. Ts nturlly nrss t omplxty o t GLB omputton to O(jJ(P )j + jxj) T LU B oprton n on ully. For smultnous omputton o GLB n LUB w must o som upt on T ' wn ng nw lmnt tr LUB n v vrs. 6 Conluson In ts ppr w v sr nw lgortm to omput t Dkn MNll omplton o prtl orr, s on GLB n LU B oprtons. Ts sms to ntrstng or ppltons n knowlgs prossng.

9 g g ( ) ( ) Fg. 6. Computton o GLB(; ) Furtrmor our lgortm n us to omput t Glos ltt or t onpt ltt rom Wll trmnologs. Our rgumnts or ts r s on quvlns twn Dkn MNll Complton, Glos ltt n t ltt o Mxml ntns [12]. Rmrk 13. Ts lgortm ws mplmnt unr Mpl lngug (ttp://wwwltp.p.r:= rtt/). Rrns 1. H. At-K, R. Boyr, P. Lnoln, n R. Nsr. Ent mplmntton o ltt oprtons. ACM Trnstons on Progrmmng Lnggs n Systms, 11(1):115{ 146, jnury J.P. Bort. Clul prtqu u trlls gllos 'un orrsponn. In Mt. S. Hum, 96, pgs 31{47, A. Bout. Cogs t mnsons rltons nrs. Annls o Dsrt Mtmts 23, Orrs: Dsrpton n Rols, (M. Pouzt, D. Rr s), Yvs Csu. Ent nlng o multpl nrtn rrs. In OOP- SLA'93, pgs 271{287, B. A. Dvy n H. A. Prstly. Introuton to ltts n orrs. Cmrg Unvrsty Prss, son ton, R. Gon n H. Ml. Bulng n mntnng nlyss-lvl lss rrs usng glos ltts. In OOPSLA'93, pgs 394{410, M. H n L. Nourn. Bt-vtor nong or prtlly orr sts. In Pro. o Intrntonl Worksop ORDAL`94, tor, Orrs, Algortms n Appltons, numr 831, pgs 1{12, Lyon, Frn, July Sprngr Vrlg. 8. M. H n L. Nourn. Tr strutur or strutv ltts n ts ppltons. TCS, 165(2):391{405, otor C. Jr, G.-V. Journ, n J.-X. Rmpon. Computng on-ln t ltt o mxml ntns o posts. Tnl rport, IRISA, Rnns, Frn, Frury To ppr n Orr. 10. H.M. MNll. Prtlly orr sts. Trns. Amr. So., 42:416{460, G. Mrkowsky. T rprsntton o posts n ltts y sts. Algr Unvrsls, 11:173{192, 1980.

10 12. M. Morvn n L. Nourn. Smpll lmnton sms, xtrml ltts n mxml ntn ltts. Orr 13, No 2:159{173, R. Wll. Rstruturng ltt tory: An ppro s on rrs o ontxts. n Orr sts,i. Rvl, Es. NATO ASI No 83, Rl, Dort, Holln, pgs 445{470, 1982.

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

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

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

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

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

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

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

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

(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

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

Minimum Spanning Trees (CLRS 23)

Minimum Spanning Trees (CLRS 23) Mnmum Spnnn Trs (CLRS 3) T prolm Rll t nton o spnnn tr: Gvn onnt, unrt rp G = (V, E), sust o s o G su tt ty onnt ll vrts n G n orm no yls s ll spnnn tr (ST) o G. Any unrt, onnt rp s spnnn tr. Atully, rp

More information

Applications of trees

Applications of trees Trs Apptons o trs Orgnzton rts Attk trs to syst Anyss o tr ntworks Prsng xprssons Trs (rtrv o norton) Don-n strutur Mutstng Dstnton-s orwrng Trnsprnt swts Forwrng ts o prxs t routrs Struturs or nt pntton

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

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

The R-Tree. Yufei Tao. ITEE University of Queensland. INFS4205/7205, Uni of Queensland

The R-Tree. Yufei Tao. ITEE University of Queensland. INFS4205/7205, Uni of Queensland Yu To ITEE Unvrsty o Qunsln W wll stuy nw strutur ll t R-tr, w n tout o s mult-mnsonl xtnson o t B-tr. T R-tr supports ntly vrty o qurs (s w wll n out ltr n t ours), n s mplmnt n numrous ts systms. Our

More information

Closed Monochromatic Bishops Tours

Closed Monochromatic Bishops Tours Cos Monoromt Bsops Tours Jo DMo Dprtmnt o Mtmts n Sttsts Knnsw Stt Unvrsty, Knnsw, Gor, 0, USA mo@nnsw.u My, 00 Astrt In ss, t sop s unqu s t s o to sn oor on t n wt or. Ts ms os tour n w t sop vsts vry

More information

SAMPLE CSc 340 EXAM QUESTIONS WITH SOLUTIONS: part 2

SAMPLE CSc 340 EXAM QUESTIONS WITH SOLUTIONS: part 2 AMPLE C EXAM UETION WITH OLUTION: prt. It n sown tt l / wr.7888l. I Φ nots orul or pprotng t vlu o tn t n sown tt t trunton rror o ts pproton s o t or or so onstnts ; tt s Not tt / L Φ L.. Φ.. /. /.. Φ..787.

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

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

Minimum Spanning Trees (CLRS 23)

Minimum Spanning Trees (CLRS 23) Mnmum Spnnn Trs (CLRS 3) T prolm Gvn onnt, unrt rp G = (V, E), sust o s o G su tt ty onnt ll vrts n G n orm no yls s ll spnnn tr (ST) o G. Clm: Any unrt, onnt rp s spnnn tr (n nrl rp my v mny spnnn trs).

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

2 Trees and Their Applications

2 Trees and Their Applications Trs n Tr Appltons. Proprts o trs.. Crtrzton o trs Dnton. A rp s ll yl (or orst) t ontns no yls. A onnt yl rp s ll tr. Quston. Cn n yl rp v loops or prlll s? Notton. I G = (V, E) s rp n E, tn G wll not

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

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

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

Graph Search (6A) Young Won Lim 5/18/18

Graph Search (6A) Young Won Lim 5/18/18 Grp Sr (6A) Youn Won Lm Copyrt () 2015 2018 Youn W. Lm. Prmon rnt to opy, trut n/or moy t oumnt unr t trm o t GNU Fr Doumntton Ln, Vron 1.2 or ny ltr vron pul y t Fr Sotwr Founton; wt no Invrnt Ston, no

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

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

The Constrained Longest Common Subsequence Problem. Rotem.R and Rotem.H

The Constrained Longest Common Subsequence Problem. Rotem.R and Rotem.H T Constrn Lonst Common Susqun Prolm Rotm.R n Rotm.H Prsntton Outln. LCS Alortm Rmnr Uss o LCS Alortm T CLCS Prolm Introuton Motvton For CLCS Alortm T CLCS Prolm Nïv Alortm T CLCS Alortm A Dynm Prormmn

More information

Lecture II: Minimium Spanning Tree Algorithms

Lecture II: Minimium Spanning Tree Algorithms Ltur II: Mnmum Spnnn Tr Alortms Dr Krn T. Hrly Dprtmnt o Computr Sn Unvrsty Coll Cork Aprl 0 KH (/0/) Ltur II: Mnmum Spnnn Tr Alortms Aprl 0 / 5 Mnmum Spnnn Trs Mnmum Spnnn Trs Spnnn Tr tr orm rom rp s

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

CSE 332. Data Structures and Parallelism

CSE 332. Data Structures and Parallelism Am Blnk Ltur 20 Wntr 2017 CSE 332 Dt Struturs n Prlllsm CSE 332: Dt Struturs n Prlllsm Grps 1: Wt s Grp? DFS n BFS LnkLsts r to Trs s Trs r to... 1 Wr W v Bn Essntl ADTs: Lsts, Stks, Quus, Prorty Quus,

More information

CSE 332. Graphs 1: What is a Graph? DFS and BFS. Data Abstractions. CSE 332: Data Abstractions. A Graph is a Thingy... 2

CSE 332. Graphs 1: What is a Graph? DFS and BFS. Data Abstractions. CSE 332: Data Abstractions. A Graph is a Thingy... 2 Am Blnk Ltur 19 Summr 2015 CSE 332: Dt Astrtons CSE 332 Grps 1: Wt s Grp? DFS n BFS Dt Astrtons LnkLsts r to Trs s Trs r to... 1 A Grp s Tny... 2 Wr W v Bn Essntl ADTs: Lsts, Stks, Quus, Prorty Quus, Hps,

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

On Hamiltonian Tetrahedralizations Of Convex Polyhedra

On Hamiltonian Tetrahedralizations Of Convex Polyhedra O Ht Ttrrzts O Cvx Pyr Frs C 1 Q-Hu D 2 C A W 3 1 Dprtt Cputr S T Uvrsty H K, H K, C. E: @s.u. 2 R & TV Trsss Ctr, Hu, C. E: q@163.t 3 Dprtt Cputr S, Mr Uvrsty Nwu St. J s, Nwu, C A1B 35. E: w@r.s.u. Astrt

More information

Planar convex hulls (I)

Planar convex hulls (I) Covx Hu Covxty Gv st P o ots 2D, tr ovx u s t sst ovx oyo tt ots ots o P A oyo P s ovx or y, P, t st s try P. Pr ovx us (I) Coutto Gotry [s 3250] Lur To Bowo Co ovx o-ovx 1 2 3 Covx Hu Covx Hu Covx Hu

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

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

An Introduction to Clique Minimal Separator Decomposition

An Introduction to Clique Minimal Separator Decomposition Alortms 2010, 3, 197-215; o:10.3390/3020197 Rvw OPEN ACCESS lortms ISSN 1999-4893 www.mp.om/ournl/lortms An Introuton to Clqu Mnml Sprtor Domposton Ann Brry 1,, Romn Poorln 1 n Gnvèv Smont 2 1 LIMOS UMR

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

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

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

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

Isomorphism In Kinematic Chains

Isomorphism In Kinematic Chains Intrntonl Journl o Rsr n Ennrn n Sn (IJRES) ISSN (Onln): 0-, ISSN (Prnt): 0- www.rs.or Volum Issu ǁ My. 0 ǁ PP.0- Isomorpsm In Knmt Cns Dr.Al Hsn Asstt.Prossor, Dprtmnt o Mnl Ennrn, F/O- Ennrn & Tnoloy,

More information

Single Source Shortest Paths (with Positive Weights)

Single Source Shortest Paths (with Positive Weights) Snl Sour Sortst Pts (wt Postv Wts) Yuf To ITEE Unvrsty of Qunslnd In ts ltur, w wll rvst t snl sour sortst pt (SSSP) problm. Rll tt w v lrdy lrnd tt t BFS lortm solvs t problm ffntly wn ll t ds v t sm

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

A Simple Method for Identifying Compelled Edges in DAGs

A Simple Method for Identifying Compelled Edges in DAGs A Smpl Mto or Intyn Compll Es n DAGs S.K.M. Won n D. Wu Dprtmnt o Computr Sn Unvrsty o Rn Rn Ssktwn Cn S4S 0A2 Eml: {won, nwu}@s.urn. Astrt Intyn ompll s s mportnt n lrnn t strutur (.., t DAG) o Bysn ntwork.

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

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

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

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

Graphs Depth First Search

Graphs Depth First Search Grp Dpt Frt Sr SFO 337 LAX 1843 1743 1233 802 DFW ORD - 1 - Grp Sr Aort - 2 - Outo Ø By unrtnn t tur, you ou to: q L rp orn to t orr n w vrt r ovr, xpor ro n n n pt-rt r. q Cy o t pt-rt r tr,, orwr n ro

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

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

CHELOURANYAN CALENDAR FOR YEAR 3335 YEAR OF SAI RHAVË

CHELOURANYAN CALENDAR FOR YEAR 3335 YEAR OF SAI RHAVË CHELOURANYAN CALENDAR FOR YEAR YEAR OF SAI RHAVË I tou woust n unon wt our Motr, now tt tou st nvr t Hr. I tou woust sp t v o mttr, now tt tr s no mttr n no v. ~Cry Mry KEY TO CALENDAR T Dys o t W In t

More information

Phylogenetic Tree Inferences Using Quartet Splits. Kevin Michael Hathcock. Bachelor of Science Lenoir-Rhyne University 2010

Phylogenetic Tree Inferences Using Quartet Splits. Kevin Michael Hathcock. Bachelor of Science Lenoir-Rhyne University 2010 Pylont Tr Inrns Usn Qurtt Splts By Kvn Ml Htok Blor o Sn Lnor-Ryn Unvrsty 2010 Sumtt n Prtl Fulllmnt o t Rqurmnts or t Dr o Mstr o Sn n Mtmts Coll o Arts n Sns Unvrsty o Sout Croln 2012 Apt y: Év Czrk,

More information

23 Minimum Spanning Trees

23 Minimum Spanning Trees 3 Mnmum Spnnn Trs Eltron rut sns otn n to mk t pns o svrl omponnts ltrlly quvlnt y wrn tm totr. To ntronnt st o n pns, w n us n rrnmnt o n wrs, onntn two pns. O ll su rrnmnts, t on tt uss t lst mount o

More information

More Foundations. Undirected Graphs. Degree. A Theorem. Graphs, Products, & Relations

More Foundations. Undirected Graphs. Degree. A Theorem. Graphs, Products, & Relations Mr Funtins Grphs, Pruts, & Rltins Unirt Grphs An unirt grph is pir f 1. A st f ns 2. A st f gs (whr n g is st f tw ns*) Friy, Sptmr 2, 2011 Ring: Sipsr 0.2 ginning f 0.4; Stughtn 1.1.5 ({,,,,}, {{,}, {,},

More information

SAMPLE LITANY OF THE SAINTS E/G. Dadd9/F. Aadd9. cy. Christ, have. Lord, have mer cy. Christ, have A/E. Dadd9. Aadd9/C Bm E. 1. Ma ry and. mer cy.

SAMPLE LITANY OF THE SAINTS E/G. Dadd9/F. Aadd9. cy. Christ, have. Lord, have mer cy. Christ, have A/E. Dadd9. Aadd9/C Bm E. 1. Ma ry and. mer cy. LTNY OF TH SNTS Cntrs Gnt flwng ( = c. 100) /G Ddd9/F ll Kybrd / hv Ddd9 hv hv Txt 1973, CL. ll rghts rsrvd. Usd wth prmssn. Musc: D. Bckr, b. 1953, 1987, D. Bckr. Publshd by OCP. ll rghts rsrvd. SMPL

More information

DOCUMENT STATUS: RELEASE

DOCUMENT STATUS: RELEASE RVSON STORY RV T SRPTON O Y 0-4-0 RLS OR PROUTON 5 MM -04-0 NS TRU PLOT PROUTON -- S O O OR TLS 30 MM 03-3-0 3-044 N 3-45, TS S T TON O PROTTV RM OVR. 3 05--0 LT 3-004, NOT, 3-050 3 0//00 UPT ST ROM SN,

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

A Multi Objective Graph Based Model for Analyzing Survivability of Vulnerable Networks. Saeedeh Javanmardi 1, Ahmad Makui 2.

A Multi Objective Graph Based Model for Analyzing Survivability of Vulnerable Networks. Saeedeh Javanmardi 1, Ahmad Makui 2. Journl of Inustrl n Systms Engnrng Vol., No., pp Summr 00 Mult Otv Grp Bs Mol for nlyzng Survvlty of Vulnrl Ntworks S Jvnmr, m Mku Dprtmnt of Inustrl Engnrng, Irn Unvrsty of Sn n Tnology, Trn, Irn mku@ust..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

New Biomaterials from Renewable Resources - Amphiphilic Block Copolymers from δ-decalactone. Figure S4 DSC plot of Propargyl PDL...

New Biomaterials from Renewable Resources - Amphiphilic Block Copolymers from δ-decalactone. Figure S4 DSC plot of Propargyl PDL... Eltron Supplmntry Mtrl (ESI) or Polymr Cmstry. Ts ournl s T Royl Soty o Cmstry 2015 Polymr Cmstry RSCPulsng Supportng Inormton Nw Bomtrls rom Rnwl Rsours - Amppl Blo Copolymrs rom δ-dlton Kulp K. Bnsl,

More information

Priority Search Trees - Part I

Priority Search Trees - Part I .S. 252 Pro. Rorto Taassa oputatoal otry S., 1992 1993 Ltur 9 at: ar 8, 1993 Sr: a Q ol aro Prorty Sar Trs - Part 1 trouto t last ltur, w loo at trval trs. or trval pot losur prols, ty us lar spa a optal

More information

CSE 332. Graphs 1: What is a Graph? DFS and BFS. Data Abstractions. CSE 332: Data Abstractions. A Graph is a Thingy... 2

CSE 332. Graphs 1: What is a Graph? DFS and BFS. Data Abstractions. CSE 332: Data Abstractions. A Graph is a Thingy... 2 Am Blnk Ltur 0 Autumn 0 CSE 33: Dt Astrtons CSE 33 Grps : Wt s Grp? DFS n BFS Dt Astrtons LnkLsts r to Trs s Trs r to... A Grp s Tny... Wr W v Bn Essntl ADTs: Lsts, Stks, Quus, Prorty Quus, Hps, Vnll Trs,

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

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

Dental PBRN Study: Reasons for replacement or repair of dental restorations

Dental PBRN Study: Reasons for replacement or repair of dental restorations Dntl PBRN Stuy: Rsons or rplmnt or rpr o ntl rstortons Us ts Dt Collton Form wnvr stuy rstorton s rpl or rpr. For nrollmnt n t ollton you my rpl or rpr up to 4 rstortons, on t sm ptnt, urn snl vst. You

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

Integration Continued. Integration by Parts Solving Definite Integrals: Area Under a Curve Improper Integrals

Integration Continued. Integration by Parts Solving Definite Integrals: Area Under a Curve Improper Integrals Intgrtion Continud Intgrtion y Prts Solving Dinit Intgrls: Ar Undr Curv Impropr Intgrls Intgrtion y Prts Prticulrly usul whn you r trying to tk th intgrl o som unction tht is th product o n lgric prssion

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

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

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

Extension Formulas of Lauricella s Functions by Applications of Dixon s Summation Theorem

Extension Formulas of Lauricella s Functions by Applications of Dixon s Summation Theorem Avll t http:pvu.u Appl. Appl. Mth. ISSN: 9-9466 Vol. 0 Issu Dr 05 pp. 007-08 Appltos Appl Mthts: A Itrtol Jourl AAM Etso oruls of Lurll s utos Appltos of Do s Suto Thor Ah Al Atsh Dprtt of Mthts A Uvrst

More information

PR D NT N n TR T F R 6 pr l 8 Th Pr d nt Th h t H h n t n, D D r r. Pr d nt: n J n r f th r d t r v th tr t d rn z t n pr r f th n t d t t. n

PR D NT N n TR T F R 6 pr l 8 Th Pr d nt Th h t H h n t n, D D r r. Pr d nt: n J n r f th r d t r v th tr t d rn z t n pr r f th n t d t t. n R P RT F TH PR D NT N N TR T F R N V R T F NN T V D 0 0 : R PR P R JT..P.. D 2 PR L 8 8 J PR D NT N n TR T F R 6 pr l 8 Th Pr d nt Th h t H h n t n, D.. 20 00 D r r. Pr d nt: n J n r f th r d t r v th

More information

Functions and Graphs 1. (a) (b) (c) (f) (e) (d) 2. (a) (b) (c) (d)

Functions and Graphs 1. (a) (b) (c) (f) (e) (d) 2. (a) (b) (c) (d) Functions nd Grps. () () (c) - - - O - - - O - - - O - - - - (d) () (f) - - O - 7 6 - - O - -7-6 - - - - - O. () () (c) (d) - - - O - O - O - - O - -. () G() f() + f( ), G(-) f( ) + f(), G() G( ) nd G()

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

MINI POST SERIES BALUSTRADE SYSTEM INSTALLATION GUIDE PRODUCT CODE: MPS-RP

MINI POST SERIES BALUSTRADE SYSTEM INSTALLATION GUIDE PRODUCT CODE: MPS-RP MN POST SRS LUSTR SYSTM NSTLLTON U PROUT O: MPS-RP 0 R0 WLL LN 0 RONT LVTON VW R0 N P 0 T RUR LOK LOT ON LSS. SLON SL TYP. OT SS 000 LSS T 0 00 SRS LSS WT 00/00 (0mm NRMNTS VLL) MX. 000 00-0 (ROMMN) 00

More information

COMP108 Algorithmic Foundations

COMP108 Algorithmic Foundations Grdy mthods Prudn Wong http://www.s.liv..uk/~pwong/thing/omp108/01617 Coin Chng Prolm Suppos w hv 3 typs of oins 10p 0p 50p Minimum numr of oins to mk 0.8, 1.0, 1.? Grdy mthod Lrning outoms Undrstnd wht

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

Graph Search Algorithms

Graph Search Algorithms Grp Sr Aortms 1 Grp 2 No ~ ty or omputr E ~ ro or t Unrt or Drt A surprsny r numr o omputton proms n xprss s rp proms. 3 Drt n Unrt Grps () A rt rp G = (V, E), wr V = {1,2,3,4,5,6} n E = {(1,2), (2,2),

More information

17 Basic Graph Properties

17 Basic Graph Properties Ltur 17: Bs Grp Proprts [Sp 10] O look t t sn y o. Tn t t twnty-svn 8 y 10 olor lossy pturs wt t rls n rrows n prrp on t k o on... n tn look t t sn y o. An tn t t twnty-svn 8 y 10 olor lossy pturs wt t

More information

n r t d n :4 T P bl D n, l d t z d th tr t. r pd l

n r t d n :4 T P bl D n, l d t z d   th tr t. r pd l n r t d n 20 20 :4 T P bl D n, l d t z d http:.h th tr t. r pd l 2 0 x pt n f t v t, f f d, b th n nd th P r n h h, th r h v n t b n p d f r nt r. Th t v v d pr n, h v r, p n th pl v t r, d b p t r b R

More information

46 D b r 4, 20 : p t n f r n b P l h tr p, pl t z r f r n. nd n th t n t d f t n th tr ht r t b f l n t, nd th ff r n b ttl t th r p rf l pp n nt n th

46 D b r 4, 20 : p t n f r n b P l h tr p, pl t z r f r n. nd n th t n t d f t n th tr ht r t b f l n t, nd th ff r n b ttl t th r p rf l pp n nt n th n r t d n 20 0 : T P bl D n, l d t z d http:.h th tr t. r pd l 46 D b r 4, 20 : p t n f r n b P l h tr p, pl t z r f r n. nd n th t n t d f t n th tr ht r t b f l n t, nd th ff r n b ttl t th r p rf l

More information

FL/VAL ~RA1::1. Professor INTERVI of. Professor It Fr recru. sor Social,, first of all, was. Sys SDC? Yes, as a. was a. assumee.

FL/VAL ~RA1::1. Professor INTERVI of. Professor It Fr recru. sor Social,, first of all, was. Sys SDC? Yes, as a. was a. assumee. B Pror NTERV FL/VAL ~RA1::1 1 21,, 1989 i n or Socil,, fir ll, Pror Fr rcru Sy Ar you lir SDC? Y, om um SM: corr n 'd m vry ummr yr. Now, y n y, f pr my ry for ummr my 1 yr Un So vr ummr cour d rr o l

More information

4 4 N v b r t, 20 xpr n f th ll f th p p l t n p pr d. H ndr d nd th nd f t v L th n n f th pr v n f V ln, r dn nd l r thr n nt pr n, h r th ff r d nd

4 4 N v b r t, 20 xpr n f th ll f th p p l t n p pr d. H ndr d nd th nd f t v L th n n f th pr v n f V ln, r dn nd l r thr n nt pr n, h r th ff r d nd n r t d n 20 20 0 : 0 T P bl D n, l d t z d http:.h th tr t. r pd l 4 4 N v b r t, 20 xpr n f th ll f th p p l t n p pr d. H ndr d nd th nd f t v L th n n f th pr v n f V ln, r dn nd l r thr n nt pr n,

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

Fun sheet matching: towards automatic block decomposition for hexahedral meshes

Fun sheet matching: towards automatic block decomposition for hexahedral meshes DOI 10.1007/s00366-010-0207-5 ORIGINAL ARTICLE Fun st mtn: towrs utomt lok omposton or xrl mss Nols Kowlsk Frnk Loux Mttw L. Sttn Stv J. Own Rv: 19 Frury 2010 / Apt: 22 Dmr 2010 Ó Sprnr-Vrl Lonon Lmt 2011

More information

Computer Graphics. Viewing & Projections

Computer Graphics. Viewing & Projections Vw & Ovrvw rr : rss r t -vw trsrt: st st, rr w.r.t. r rqurs r rr (rt syst) rt: 2 trsrt st, rt trsrt t 2D rqurs t r y rt rts ss Rr P usuy st try trsrt t wr rts t rs t surs trsrt t r rts u rt w.r.t. vw vu

More information

D t r l f r th n t d t t pr p r d b th t ff f th l t tt n N tr t n nd H n N d, n t d t t n t. n t d t t. h n t n :.. vt. Pr nt. ff.,. http://hdl.handle.net/2027/uiug.30112023368936 P bl D n, l d t z d

More information

828.^ 2 F r, Br n, nd t h. n, v n lth h th n l nd h d n r d t n v l l n th f v r x t p th l ft. n ll n n n f lt ll th t p n nt r f d pp nt nt nd, th t

828.^ 2 F r, Br n, nd t h. n, v n lth h th n l nd h d n r d t n v l l n th f v r x t p th l ft. n ll n n n f lt ll th t p n nt r f d pp nt nt nd, th t 2Â F b. Th h ph rd l nd r. l X. TH H PH RD L ND R. L X. F r, Br n, nd t h. B th ttr h ph rd. n th l f p t r l l nd, t t d t, n n t n, nt r rl r th n th n r l t f th f th th r l, nd d r b t t f nn r r pr

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

A ' / 1 6 " 5 ' / 4 " A4.2 48' - 0" 3 12' - 7" 13' - 11" 10' - 0" 9' - 0" 2' - 6" 1. 2: 12 INDICATES SHOW MELT TYP ABV ABV

A ' / 1 6  5 ' / 4  A4.2 48' - 0 3 12' - 7 13' - 11 10' - 0 9' - 0 2' - 6 1. 2: 12 INDICATES SHOW MELT TYP ABV ABV 4. 4. 4. K ' - / " ' - / 4 " 0 ' - / " ' - 0 " ' - 0 " ' - / " 4 ' - 0 " 4. M U PPR 48' - 0" ' - ' - " 0' - 0" ' - 0" ' - ". : WOM ' - 0 " OT: PROV URROU TR OUT SVS OR UTUR SP UTTY T OR QUSTR MPUS OTO

More information

L.3922 M.C. L.3922 M.C. L.2996 M.C. L.3909 M.C. L.5632 M.C. L M.C. L.5632 M.C. L M.C. DRIVE STAR NORTH STAR NORTH NORTH DRIVE

L.3922 M.C. L.3922 M.C. L.2996 M.C. L.3909 M.C. L.5632 M.C. L M.C. L.5632 M.C. L M.C. DRIVE STAR NORTH STAR NORTH NORTH DRIVE N URY T NORTON PROV N RRONOUS NORTON NVRTNTY PROV. SPY S NY TY OR UT T TY RY OS NOT URNT T S TT T NORTON PROV S ORRT, NSR S POSS, VRY ORT S N ON N T S T TY RY. TS NORTON S N OP RO RORS RT SU "" YW No.

More information

Jonathan Turner Exam 2-10/28/03

Jonathan Turner Exam 2-10/28/03 CS Algorihm n Progrm Prolm Exm Soluion S Soluion Jonhn Turnr Exm //. ( poin) In h Fioni hp ruur, u wn vrx u n i prn v u ing u v i v h lry lo hil in i l m hil o om ohr vrx. Suppo w hng hi, o h ing u i prorm

More information

Minimum Spanning Trees

Minimum Spanning Trees Mnmum Spnnng Trs Spnnng Tr A tr (.., connctd, cyclc grph) whch contns ll th vrtcs of th grph Mnmum Spnnng Tr Spnnng tr wth th mnmum sum of wghts 1 1 Spnnng forst If grph s not connctd, thn thr s spnnng

More information

T h e C S E T I P r o j e c t

T h e C S E T I P r o j e c t T h e P r o j e c t T H E P R O J E C T T A B L E O F C O N T E N T S A r t i c l e P a g e C o m p r e h e n s i v e A s s es s m e n t o f t h e U F O / E T I P h e n o m e n o n M a y 1 9 9 1 1 E T

More information

Data-Parallel Primitives for Spatial Operations Using PM. Quadtrees* primitives that are used to construct the data. concluding remarks.

Data-Parallel Primitives for Spatial Operations Using PM. Quadtrees* primitives that are used to construct the data. concluding remarks. Dt-rlll rmtvs or Sptl Oprtons Usn M Qutrs* Erk G. Hol Hnn Smt Computr Sn Dprtmnt Computr Sn Dprtmnt Cntr or Automton Rsr Cntr or Automton Rsr Insttut or Avn Computr Sns Insttut or Avn Computr Sns Unvrsty

More information