A comparison of routing sets for robust network design

Size: px
Start display at page:

Download "A comparison of routing sets for robust network design"

Transcription

1 A omprison of routing sts for roust ntwork sign Mihl Poss Astrt Dsigning ntwork l to rout st of non-simultnous mn vtors is n importnt prolm rising in tlommunitions. Th prolm n sn two-stg roust progrm whr th rours funtion onsists in hoosing th routing for h mn vtor. Allowing th routing to hng ritrrily s th mn vris yils vry iffiult optimiztion prolm so tht iffrnt susts of missil routings hv n isuss in th litrtur. In this ppr, w ompr thortilly th optiml pity llotion osts for six of ths routing sts: ffin routing, volum routing n its two simplifitions, th routing s on n ritrry -ovr of th unrtinty st, n th routing s on ovr limit y hyprpln. W show tht th two routing sts s on ovrs of th unrtinty st yil th sm optiml osts. W show thn tht th two simplifi volum routings r spil ss of ffin routings. Finlly, ssuming tht th unrtinty st is th on stui y Brtsims n Sim (004), w show tht th optiml ost provi y volum routing is not lss thn th osts provi y th simplifi volum routings. Kywors: Roust optimiztion; Ntwork sign; Routing st; Routing tmplt; Affin routing. Introution Givn grph n st of point-to-point ommoitis with known mn vlus, th trministi ntwork sign prolm ims t instlling nough pity on th rs of th grph so tht th rsulting ntwork is l to rout ll ommoitis. In prti it is howvr vry iffiult to know with prision th xt vlus of th mns t th tim th sign isions r tkn. In th st s, w n stimt st tht ontins most likly vlus for th mn. Th introution of th unrtinty st ls to roust optimiztion prolm. In this ontxt, solution is si to fsil for th prolm if it is fsil for ll mn vtors tht long to th stimt unrtinty st D, s Soystr [8] n Bn-Tl n Nmirovski [9, 0], mong othrs. This rigi frmwork is omputtionlly sy ut it os not llow th mol to rt ginst th unrtinty. To rss this rwk, Bn-Tl t l. [8] introu two-stg roust optimiztion mols tht llows to just sust of th prolm vrils only ftr osrving th tul rliztion of th t. This justing prour is oftn ll rours. This two-stg pproh pplis nturlly to ntwork sign sin first stg pity sign isions r usully m in th long trm whil th routing isions pn on th rliztion of th mn. Hn, th routing isions n sn s th rours. Fr rours is ll ynmi routing in th ontxt of roust ntwork sign prolms. It hs n shown y Chkuri t l. [4] n Gupt t l. [8] tht th roust ntwork sign with ynmi routing is intrtl. Alry iing whthr or not fix pity sign llows for ynmi routing of mns in givn polytop is o-n P-omplt (on irt grphs). It is known lry tht two-stg roust progrmming with ritrry rours is omputtionlly intrtl [8]. For this rson, Bn-Tl t l. [8] limit th rours to ffin funtions of th unrtintis whih mks th prolm trtl. Furthr works y Chn n Zhng [5] n Goh n Sim [7] suggst to xtn th son stg to pi-wis linr funtions of th unrtintis. In ft, onsiring spil typs of rourss h n us lry in th ontxt of ntwork sign. Bn-Amur n Krivin [4, 5] introu th onpt of stti routing: ftr fixing th sign, th routing of ommoity is llow to hng ut only linrly with th vrition of th ommoity. Stti routing n lso sn s singl stg roust progrm whr th st of routings pths togthr with th prntl splitting mong th pths r hosn t th sm tim th sign isions Dprtmnt of Computr Sin, Fulté s Sins, Univrsité Lir Bruxlls, Brussls, Blgium, mposs@ul..

2 r m. Th rsulting st of pths n prntl splitting is oftn ll routing tmplt, whih is us y ll mn vtors in th unrtinty st. Th us of stti routing mks th roust ntwork sign prolm trtl ut it yils mor xpnsiv pity llotions thn th prolm with ynmi routing. Stti routing hs n us y vrious uthors sin its introution y Bn-Amur n Krivin, inluing Altin t l. [], Kostr t l. [9], Oróñz n Zho []. Svrl uthors tri to introu routing shms tht r mor flxil thn stti routing whil still ing omputtionlly sir thn ynmi routing. Bn-Amur [3] ovrs th mn unrtinty st y two (or mor) susts using sprting hyprplns n uss spifi routings tmplts for h sust. Th rsulting optimiztion prolm is N P-hr whn no ssumptions is m on th hyprplns. Sutllà [7] gnrlizs this i to ritrry ovrs of th unrtinty st. Sh llows st of routing tmplts to us onjointly so tht h mn vtor n rout y t lst on of th routing tmplts. Sh lso introus prour tht works in two stps. First, n optiml pity llotion with stti routing is omput. Thn, sh llows llows to rrout prt of th mn vtors oring to son routing tmplt. Bn-Amur n Zotkiwiz [6] introu volum routing, frmwork tht shrs th mn twn two routing tmplts, oring to thrshols. Thy prov tht th rsulting optimiztion prolm is N P- hr n introu two simplifitions. Finlly, pplying th ffin rours from Bn-Tl t l. [8] to roust ntwork sign prolms, Ouorou n Vil [4] introu th onpt of ffin routing. Rntly, Poss n Rk [6, 5] stuy th proprtis of ffin routing, n ompr th ltr to th stti n ynmi routings, oth thortilly n mpirilly. Thy onlu tht ffin routing tns to yil vry goo pproximtions of ynmi routing whil ing omputtionlly trtl. In this ppr, w ompr thortilly th optiml pity llotion osts provi y th ffin routings from Ouorou n Vil [4], th volum routings from Bn-Amur n Zotkiwiz [6], n th routings s on ovrs of th unrtinty st in two susts (Bn-Amur [3] n Sutllà [7]). In th nxt stion, w introu th roust ntwork sign prolm n fin routing st. W mol th roust ntwork sign prolm with th xpliit pnny on th routing st n formliz h of th routing frmworks stui hrin. Our min rsults r stt in Stion.3. In Stion 3, w try to unrstn how goo is th ost of th optiml pity llotion provi y h of th routing sts, n w ompr ths osts mong th iffrnt routing sts. W strt y ompring th osts otin with routings s on ovrs of th unrtinty st in Stion 3.. Thn, in Stion 3.3, w turn to ffin n volum routings. In Stion 4, w prsnt xmpls showing tht it is not possil, in gnrl, to ompr som of ths osts. Finlly, w onlu th ppr in Stion 5. Roust ntwork sign. Prolm formultion Th prolm is fin low for irt grph G = (V, A) n st of ommoitis K. W formliz first th onpt of routing. Thn, w introu th roust ntwork sign prolm. Eh ommoity k K hs sour s(k) V, stintion t(k) V, n mn vlu k 0. A multi-ommoity flow is vtor f R A K + tht stisfis th flow onsrvtion onstrints t h no of th ntwork: δ + (v) f k δ (v) k if v = s(k) f k = k if v = t(k) 0 ls for h v V, () whr δ + (v) n δ (v) rsptivly not th st of outgoing rs n inoming rs t no v. In this work th vlus of th mn vtor r unrtin n long to th los, onvx, n oun st D R K +. W ll suh st n unrtinty st n ny D is ll rliztion of th mn. W not y (D, R A K ) th st of ll funtions from D to R A K. Thn, routing is funtion f (D, R A K ) tht stisfis () for ll rliztions of th mn, tht is f k () k if v = s(k) f k () = k if v = t(k) for ll v V, D, () δ + (v) δ (v) 0 ls n tht is non-ngtiv f k () 0 for ll D. (3)

3 A routing with no furthr rstritions is ll ynmi routing. Hn, th st of ll ynmi routings is th st of ll funtions from D to R A K tht stisfy () n (3): F f (D, R A K ) f stisfis () n (3). (4) In this ppr, w r intrst in using spil kins of routings. This orrspons to using spifi susts F F. Ths susts r sri in th nxt stion. In wht follows, w sri th roust ntwork sign prolm mking xpliit th st of missil routings. A vtor x R A + is ll pity llotion. A pity llotion is si to support th st D if thr xists ynmi routing f F srving D suh tht for vry D th orrsponing multi-ommoity flow f() os not x th pitis sri y x. Similrly, w sy tht (x, f) supports D whn oth th routing f n th pity llotion x r givn. Mor gnrlly, w sy tht (x, F ) supports D whn thr xists routing f F suh tht (x, f) supports D. Givn n unrtinty st D n routing st F F, roust ntwork sign now ims t proviing th ost miniml pity llotion x suh tht (x, F ) supports D: min κ x A RND(F ) s.t. f F (5) f k () x, D (6) k K x 0, whr κ R is th ost for instlling on unit of pity on r A. Noti tht in rl pplitions, ths osts r usully non-ngtiv. W shll not th optiml ost of RND(F ) y opt(f ). Prolm RND(F ) ontins n infinit numr of vrils f() for ll D s wll s n infinit numr of pity onstrints (6). Morovr, th prolm my not vn linr, pning on th onstrints fining st F. Consiring th st of ll routings F, RND(F) is two-stg roust progrm with rours following th mor gnrl frmwork sri y Bn-Tl t l. [8]. Th pity sign hs to fix in th first stg, n osrving mn rliztion D, w r llow to just th routing f() ritrrily in th son stg. In tht s, (5) is rpl y () n (3) so tht RND(F) is linr progrm, yt infinit. Whnvr D is polytop, Poss n Rk [6], mong othrs, show how to provi finit linr progrmming formultion for RN D(F). Th formultion is s on numrting th xtrm points of D, so tht its siz tns to inrs xponntilly with th numr of ommoitis. In ft, th prolm is vry iffiult to solv givn tht only iing whthr givn pity llotion vtor x supports D is on P-omplt for gnrl polytops D, s Chkuri t l. [4] n Gupt t l. [8]. Morovr, th us of ynmi routings suffrs from nothr rwk. It my iffiult in prti to hng ritrrily th routing oring to th mn rliztion. For ths rsons, vrious uthors stuy rstritions on th routings tht n us, introuing iffrnt susts of routings F F. Thir hop is tht opt(f ) provis goo pproximtion of opt(f) whil yiling n sir optimiztion prolm RND(F ). For instn, Frngioni t l. [6] n Poss n Rk [6] show unr vry strong ssumptions on D tht th optiml pity llotions provi y ynmi routings r quivlnt to th ons provi y stti routings n ffin routings, rsptivly, whih r polynomilly solvl whn D hs ompt formultion. In th nxt stion, w prsnt iffrnt hois of F isuss in th litrtur, inluing stti n ffin routings. Thn, w summriz th min ontriutions of this ppr in Stion.3. Not tht if thr xists only on pth from s(k) to t(k) for ommoity k K, thn ll routings oini for tht ommoity. Unlss stt othrwis, in th following w ssum tht for ll k K thr xist t lst two istint pths p, p in G from s(k) to t(k), tht is, two pths tht iffr y on r t lst.. Routings frmworks In th nxt stions, w fin formlly th st of stti routings n th routing sts from Ouorou n Vil [4], Bn-Amur [3], Sutllà [7] n Bn-Amur n Zotkiwiz [6]. 3

4 .. Stti routing Th simplst ltrntiv to ynmi routing hs n introu y Bn-mur [5] n hs n us xtnsivly sin thn, s Altin t l. [, ], Kostr t l. [9], Muhntongsuk t l. [], n Oróñz n Zho []. This frmwork onsirs rstrition on th son stg rours known s stti routing (lso ll olivious routing). Eh omponnt f k : D R A + is for to linr funtion of k : f k () := y k k A, k K, D. (7) Noti tht (7) implis tht th flow for k is not hnging if w prtur th mn for h k. By omining () n (7) it follows tht th multiplirs y R A K + stisfy to y k if v = s(k) y k = if v = t(k) for h v V. (8) 0 ls δ + (v) δ (v) Th flow y is ll routing tmplt sin it is, for vry ommoity, whih pths r us to rout th mn n wht is th prntl splitting mong ths pths. W fin formlly th th st of ll routing tmplts s Y y R A K + y stisfis (8), (9) n th st of ll stti routings s F stt f (D, R A K ) y Y : f k () = y k k A, k K, D. An importnt rsult is tht ompt linr formultion n provi for RND(F stt ) s long s th sription of D is ompt (s Altin t l. [] mong othrs). Hn, th rsulting optimiztion prolm is polynomilly solvl. In th following, w rviw ltrntiv routing sts F tht r lss rstritiv thn stti routings whil not ing s flxil s ynmi routings. Si iffrntly, F stt F F... Covrs of th unrtinty st limit y hyprpln Givn st D, olltion of susts of D forms ovr of D if D is sust of th union of sts in th olltion. Bn-Amur [3] introus th i of ovring th unrtinty st y two (or mor) susts using hyprplns n proposs to us routing tmplt for h sust. This yils th following st of routings: F f (D, R A K ) y, y Y n α R K, β R : f k () = y k k D, α β y k k D, α β A, k K, D. Th finition ov implis tht oth routing tmplts y n y must l to rout mn vtors tht li in th hyprpln, α = β without xing th pity. H provs tht RND(F ) is N P-hr in gnrl n sris simplifition shms, whr α is givn. H furthr works on th frmwork in Bn-Amur n Zotkiwiz [7]...3 Aritrry ovrs of th unrtinty st Sutllà [7] introus th i of using onjointly two routing tmplts. Formlly, sh proposs to us two routing tmplts y n y suh tht h D n srv ithr y y or y y (or oth). This yils th following st of routings: F f (D, R A K ) y, y Y n D, D D, D = D D : f k () = y k k D y k k D A, k K, D. 4

5 Sh mntions tht th omplxity of RND(F ) is unknown. W show in this ppr tht this optimiztion prolm is N P-hr, us it is gnrliztion of RND(F ), prov to N P- hr y Bn-Amur [3]. Th frmwork sri y F hs n inpnntly propos for gnrl roust progrms y Brtsims n Crmnis [] (s lso Brtsims t l. []) whr th uthors propos to ovr th unrtinty sts with k susts n vis inpnnt sts of rours vrils for h of ths susts...4 Volum routings Mor rntly, Bn-Amur n Zotkiwiz [6] introu frmwork tht shrs th mn twn two routing tmplts, oring to thrshols h k for h k K. Formlly, thy us th following st of routings: F V f (D, R A K ) y, y Y, h R K + : f k () = y k min( k, h k ) + y k mx( k h k, 0) A, k K, D. Thy prov tht RND(F V ) is n N P-hr optimiztion prolm. Hn, thy introu simplr frmworks sri low. Dfining k min = min D k n k mx = mx D k, th st of routings oms on of th following F VS F VG f (D, R A K ) y, y Y : f k () = y k k min + y k ( k k min) f (D, R A K ) y, y Y : f k () = y k k min k mx k k mx k + y k min k mx k k min k mx k min A, k K, D, A, k K, D, whih r oth wll-fin whnvr k min < k mx for h k K. Whn k min = k mx for som k K, th k-th omponnt of f F VG is fin y f k () = y k k...5 Affin routings Bn-Tl t l. [8] introu Affin Ajustl Roust Countrprts rstriting th rours to n ffin funtion of th unrtintis. Ourou n Vil [4] pply this frmwork to roust ntwork sign y rstriting f k to n ffin funtion of ll omponnts of giving F ff f (D, R A K ) f 0 R K, y R A K : f k () = f 0k + h K y kh h A, k K, D, f stisfis () n (3). This frmwork hs n ompr thortilly n numrilly to stti n ynmi routings y Poss n Rk [6]. In prtiulr, th uthors show tht ompt formultion n sri for RND(F ff ) s long s D hs ompt sription, gnrlizing th rsult otin for stti routing lry. W point out tht mjor iffrn twn F ff n th routing sri in Stion is tht th formrs r uil up using routing tmplts, so tht it is impliitly ssum tht flow onsrvtion onstrints () n non-ngtivity onstrints (3) r stisfi. In opposition, routings in F ff r uil up using orinry vtors so tht tht stisftion of () n (3) must stt xpliitly..3 Contriutions of this ppr Th ojtiv of this ppr is to ompr opt(f ) mong th routing sts rll in prvious stions. This omprison is rri out in Stion 3. Our min rsults r stt nxt. () Lt D n unrtinty st. It hols tht opt(f ) = opt(f ), opt(f ff ) opt(f VG ) opt(f VS ), n opt(f V ) opt(f VS ) for ny ost vtor κ R A. () Lt D n unrtinty polytop suh tht for h k K, thr xists non-ngtiv numrs 0 k min k mx suh tht k k min, k mx for h xtrm point of D. It hols tht opt(f V ) = opt(f VS ) for ny ost vtor κ R A. 5

6 Th polytop introu y Brtsims n Sim [3], us for roust ntwork sign prolms in [6, 3, 4, 0, 6], stisfis th ssumption of () whn th numr of vitions llow is intgr. W prsnt xmpls in Stion 4 showing tht it is not possil, in gnrl, to orr opt(f ), opt(f V ) n opt(f ff ). 3 Optiml osts Th ojtiv of this stion is to ompr th ost of th optiml pity llotions otin for RND(F ) using iffrnt routing sts F. W prov in Stion 3. tht it lwys hols tht opt(f ) = opt(f ). In Stion 3.3, w prov tht it lwys hols tht opt(f ff ) opt(f VG ) opt(f VS ). W show lso tht unr itionl ssumptions on D, it hols tht opt(f VS ) = opt(f V ), opt(f VG ) opt(f V ) n opt(f ff ) opt(f V ). In Stion 3., w sri th mthoology us hrin to otin th sir rltions. 3. Mthoology Givn two routing sts F n F, w prov tht opt(f ) opt(f ) using two iffrnt pprohs. Th first pproh onsists in ompring irtly th routing sts thmslvs, y showing tht F F. Proving this inlusion is vry strong rsult, whih hols only for losly rlt routing sts. In suh sitution, w sy tht F is spil s of F. Bus it is not lwys possil to ompr irtly th routing sts thmslvs, th son pproh is s on ompring th sts of ll pity llotions tht support D whn onsiring spifi routing st. Ths sts r fin formlly s X (F ) x R A + (x, F ) supports D, (0) for ny routing st F. To ttr unrstn th link twn X (F ) n opt(f ), RND(F ) n quivlntly writtn s min κ x s.t. x X (F ). () A Th son pproh is wkr thn th first on in th sns tht F F implis tht X (F ) X (F ). Hn, it n ppli to mor pirs of routing sts. W prov nxt proprty stisfi y (0). W sy tht st F (D, R A K ) is onvx if th lin sgmnt twn ny two lmnts of F lis in F, tht is, if for ny f, f F n ny 0 λ w hv tht λf + ( λ)f F. Lmm. If F is onvx sust of (D, R A K ) thn X (F ) is onvx sust of R A +. Proof. Consir x, x X (F ). Hn, thr xists f, f F suh tht oth (x, f) n (x, f) support D. Bus (6) is onstitut of linr qutions, w s tht (λx + ( λ)x, λf + ( λ)f) supports D for ll 0 λ. Thrfor, λf + ( λ)f F implis tht λx + ( λ)x X (F ). Th two pprohs r formliz in th rsult low. Proposition. Lt F n F two routing sts. Th following hols:. If F F, thn opt(f ) opt(f ) for ny ost vtor κ R A.. If X (F ) X (F ) thn opt(f ) opt(f ) for ny ost vtor κ R A. 3. If opt(f ) opt(f ) for ny ost vtor κ R A n F is onvx sust of (D, R A K ) thn X (F ) X (F ). Proof. : Follows immitly from th finition of RND(F ). : Follows from th ft tht opt(f ) is th ost of th optiml solution of (). 3: Suppos thr xists x X (F )\X (F ). Applying Lmm, X (F ) is onvx st so tht thr xists hyprpln H R A suh tht H X (F ) is mpty n x H. Thrfor, lt κ th vtor in R A orthogonl to H n pointing towrs th hlf-sp ontining X (F ). By finition of κ, A κ x < A κ x for ll x X (F ). Hn, opt(f ) < opt(f ). In th following stions, w will us Proposition to rlt th optiml pity llotion osts mong th routing sts introu in Stion.. 6

7 3. Routings tht ovr D D D, α = β D D () Covr ssoit with routing f F. () Consiring sts whr y or y stisfis (6). () Th rsulting hyprpln. In this stion w fous on F n F. Figur : Constrution of sprting hyprpln. Thorm. Lt D n unrtinty st. It hols tht X (F ) = X (F ). To prov Thorm, w n th following Lmm. Lmm. Lt D onvx st in R K +. Lt D n D two los n onvx susts of D suh tht D D, D D, n D = D D. Thn thr xists hyprpln H R K suh tht H D D D. Proof. Consir th sts onv(d \D ) n onv(d \D ). Thr xists hyprpln H R K suh tht H sprts onv(d \D ) n onv(d \D ). Sin D D n D D, H D is non mpty n longs to l(d D ) = D D. Proof. of Thorm. : Follows from th ft tht F is th sust of F whr th intrstion of D n D must hyprpln. : Consir pity llotion x n routing f F suh tht (x, f) supports D. Routing f is fin y th ovr D = D D, s Figur (), n th routing tmplts y n y. W shll prov tht f n lwys trnsform to routing ˆf F suh tht (x, ˆf) supports D. Lt D (rsp. D ) fin s th sust of D whr y (rsp. y ) stisfis (6) for pity x. Sin w ssum tht (x, f) supports D, (x, f) stisfis (6) so tht D D n D D, s Figur (). Hn, D = D D. In ition, D n D r onvx. To s tht D is onvx, onsir, D, so tht thy stisfy th inqulitis y k x n y k x for h A n k K. Thn, for ny 0 λ, y k (λ + ( λ) ) = λ y k + ( λ) y k λx + ( λ)x = x, so tht λ + ( λ) D. Th proof is th sm for D. Thn, sin th inqulitis in (6) r not strit, w s sily tht D n D r los. Suppos tht D D. Thn, w n fin routing ˆf F y onsiring hyprpln tht os not intrst D so tht th only routing tmplt us is y, whih provs th rsult. W n pro similrly if D D. Hn, w n ssum tht D D n D D. Thrfor, D, D n D stisfy ll th hypothsis of Lmm. Hn, thr xists hyprpln, α = β suh tht, α = β D D D, s Figur (). Assum w.l.o.g. tht D, α β D n D, α β D. Hn, w n onstrut ˆf F through th hyprpln, α = β, tht is, ˆf () := y k k for h D, α β n f ˆk () := y k k for h D, α β. It follows from Proposition tht th osts of optiml pity llotions r lwys qul. Corollry. Lt D n unrtinty st. It hols tht opt(f ) = opt(f ) for ny ost vtor κ R A. 7

8 Thorm os not imply F = F, sin w my f routings in F with omins D n D tht o not rsult from hyprpln sprtion of D, s for instn Figur (). W rmrk tht Sutllá [7] mntions tht th omplxity of RND(F ) is unknown. Th omplxity of RND(F ) follows irtly from th suffiiny onition of Thorm n th ft tht Bn-Amur [3] provs RND(F ) to N P-hr. Corollry. Th optimiztion prolm RND(F ) is N P-hr. 3.3 Volum n ffin routings In this stion w ompr volum n ffin routings. Bn-Amur n Zotkiwiz [6] mntion tht F VS is spil s of F V, tht is, F VS F V. Th inlusion is sily vrifi for F VS, y hoosing h k = k min. Lmm 3. [6] It hols tht F VS F V. Howvr, w xplin in wht follows tht it is is not tru tht F VG F V. Th routings in F V n only inrs th mount of flow snt on ny r of G for ommoity k whn k riss. Si iffrntly, th flow for ny f F V, fin y f k () = y k min( k, h k ) + y k mx( k h k, 0) A, k K, is non-rsing funtion in. In opposition, routings in F VG n lso rs th flow snt on som of th rs sin ny routing f F V is fin y f k () = y k k min k mx k k mx k + y k min k mx k k min k mx k min A, k K, whih is sum of rsing trm n n inrsing trm. Th vntg of rsing th flow snt on som rs whn th mn for ommoity riss llows to ttr omin iffrnt ommoitis within th vill pity. W provi in Stion 4. n xmpl showing tht, in gnrl, it hols tht F VG F V. Routing sts F VS or F VG r nvrthlss spil ss of ffin routings. Bus ny routing in F VS or F VG is sri y n ffin funtions from D to R A K +, it must lso long to F ff. Th thorm low provs, morovr, tht F VS is spil s of F VG. Thorm. Lt D n unrtinty st. Th following hols:. F VS F VG. Th inlusion is strit if n only if 0 < k min < k mx for t lst on k K.. F VG F ff. Th inlusion is strit if n only if im(d) > or im(d) = n D is orthogonl to on of th oorint xs of R K +. Proof.. Lt th routing tmplts y n y sri ny routing f F VS. In wht follows, w st up routing tmplts y n y tht yil routing f F VG quivlnt to f. W onsir inpnntly h omponnt f k of f. If k mx = 0, thn k = 0 for h D n w n hoos y k n y k ritrrily sin th rsulting f k n f k r lwys null. Similrly, if k min = k mx, thn ny routing in F VS or F VG is uniquly trmin y uniqu routing tmplt for k. Suppos tht 0 < k min < k mx. Thn, w intify f k n f k t k = k min n k = k mx, otining th following tmplts for f F VG : y k y k = y k = y k k min k mx + y k k mx k min k mx, for h A, k K. () To s tht th inlusion is strit, hoos routing f F VG fin y routing tmplts y n y suh tht y k y k k min y k k mx woul stritly lss thn zro so tht y woul not routing tmplt. < 0 for som A. Using gin th intifition from (), w s tht. Lt y n y sri ny routing f F VG. Th omponnts of f 0 n y of th orrsponing ffin routing r otin y grouping th trms of f oring to thir gr in. W otin 8

9 tht y kh = 0 for h k h K, n f 0k = k min k mx k mx k min y kk = k mx k min (y k y k ) ( k mxy k k miny k ), for h A, k K, whih provs th inlusion F VG F ff. Suppos tht im(d) = n tht D is not orthogonl to ny of th oorint xs, n onsir ny routing f F ff n ommoity k K. Th flow for k is givn y f k () = f 0k + h K y kh h. (3) Sin D is not orthogonl to th k-th xis, w n prmtriz D through its orthogonl projtion on th xis. Nmly, thr xists positiv rls λ h R + for h h K\k suh tht h = λ h k for ll D. Hn, (3) oms f 0k + y kk + h K\k λ h y kh k = f 0k + y kk k. Thn, intifying f n f F VG t k = k min n k = k mx, ny ffin routing f is quivlnt to th routing f F VG with y k y k = f 0k = f 0k / k min + y kk / k mx + y kk, for h A, k K, (4) whih provs th inlusion F VG F ff whn im(d) = n D is not orthogonl to ny of th oorint xs. Th quntitis in (4) r non-ngtiv us f stisfis non-ngtivity onstrints from (3). Suppos now tht im(d) = n tht D is orthogonl to th k-th xis. Thrfor, k min = k mx = k for ny D so tht f k () = y k k is onstnt for ny f F VG. Th prolm must ontin mor thn on ommoity us im(d) = n D is orthogonl to th k-th xis. Thus, thr xists h K\k suh tht D is not orthogonl to th h-th xis. Thrfor, w n fin n ffin routing f F ff suh tht f k is not onstnt y hoosing propr y kh 0. Finlly, if im(d) >, D ontins smll ll B of imnsion t lst two. Hn, thr xists pir, B suh tht k = k n h h for som k, h K. As for, ll routings in F VG for ommoity k yil intil flows for n, whil w n fin n ffin routing f F ff yiling iffrnt flows y hoosing propr y kh 0. Th strit inlusion of Thorm.. hs n vrifi numrilly y Bn-Amur n Zotkiwiz [6], whr it is shown tht opt(f VG ) n stritly smllr thn opt(f VS ). W show nxt tht F VS is lwys t lst s ffiint s F V whnvr D stisfis th ssumption low. Givn onvx st D R K +, w not y xt(d) th st of its xtrm points. Assumption. Th unrtinty st D is polytop suh tht for h k K, thr xists nonngtiv numrs 0 k min k mx suh tht k k min, k mx for ll xt(d). Assumption is stisfi y wll-known fmily of unrtinty polytops, s Exmpl. Exmpl. Brtsims n Sim [3] onsir gnrl linr progrms whr th offiints of h linr inqulity long to intrvls suh tht th numr of offiints tking onjointly thir mximum vlu is oun y onstnt Γ. Consiring upwrs vitions only, thir unrtinty st n formliz in R K + s follows D Γ R K + k [ k min, k mx] for h k K, k k min k mx k Γ min Whn Γ is intgr, it is sy to s tht D Γ fulfills Assumption. Morovr, D Γ hs n frquntly us s th unrtinty st for roust ntwork sign prolms, s [6, 3, 4, 0, 6], mong othrs. k K. 9

10 Th proof of th thorm low rquirs th following simpl proprty. For ny x R A + n f F VS, (x, f) supports D (x, f) supports xt(d). (5) Proprty (5) follows irtly from th ft tht ny routing in F VS is linr funtion. Thorm 3. Lt D n unrtinty st tht fulfills Assumption. It hols tht X (F VS ) = X (F V ). Proof. : Follows irtly from th ft tht F VS F V y tking h k = k min. : Consir pity llotion x n routing f F V suh tht (x, f) supports D. W show nxt tht thr xists routing f F VS suh tht f() = f() for h xt(d). Thrfor, (x, f) supports xt(d). Sin (5) is stisfi, w hv tht (x, f) supports D, proving th rsult. Noti tht th flow for ommoity k of ny routing in F V or F VS is funtion tht only pns on th k-th omponnt of D. Thus, lt us fin D k s th orthogonl projtion of D into its k-th omponnt so tht funtions f k n f k r fin on D k. In th following, w show how to onstrut f F VS suh tht f() = f() for h xt(d) inpnntly for h ommoity k K. By Assumption, xt(d k ),. First, suppos tht xt(d k ) = so tht D k = k is singlton. If k = 0, thn w n hoos y k n y k ritrrily sin f k (0) will null nywy. If k > 0, thn w tk y k = f k ( k )/ k n y k = 0. Suppos now tht xt(d k ) =. If k min = 0, thn w n hoos y k ritrrily (sin it will multipli y k min = 0) n w st yk = f k ( k mx)/ k mx. If k min > 0, thn w st yk = f k ( k min )/k min n yk = f k ( k mx)/( k mx k min ) for h k K. Bus of Assumption, ny xt(d) is suh tht k k min, k mx. Thrfor, f k () = f k () for h xt(d), so tht (x, f) supports xt(d). Thorm 3 stts tht whnvr D stisfis Assumption, on shoul not try to us th omplx st of routings F V, sin opt(f V ) will nvr t opt(f VS ). This is of prtiulr intrst us RND(F V ) is N P-hr in gnrl whil Bn-Amur n Zotkiwiz [6] show tht RND(F VS ) is ssntilly of th sm iffiulty s RND(F stt ). 4 Non-omprl routings In this stion, w ompr opt(f ), opt(f V ) n opt(f ff ) for gnrl unrtinty sts. W show tht it is not possil to orr ths osts y prsnting thr xmpls whr on of th osts is stritly lss thn th two othrs. To vis xmpls showing tht F ff my yil mor xpnsiv pity llotions thn F n F V, w shll us th following rsult. Lt k th k-th unit vtor in R K +. Proposition. [6, Proposition 8] Lt D mn polytop. If 0 D n for h k K thr is ɛ k > 0 suh tht ɛ k k D, thn opt(f ff ) = opt(f stt ). Noti tht in our xmpls som of th ommoitis hv uniqu pths from thir sours to thir sinks, so tht ll routings r qul for ths ommoitis. This nls us to prou simpl grphs tht prsnt th proprtis rquir y our xmpls. On n sily xtn ths xmpls to lrgr grphs for whih h ommoity k K hs t lst two iffrnt pths from its sour s(k) to its sink t(k). 4. opt(f V ) n stritly smllr thn opt(f ff ) n opt(f ) Consir th ntwork sign prolm for th grph pit in Figur () with two ommoitis k : n k :. Th unrtinty st D is fin y th xtrm points = (, ), = (, ), 3 = (, 0), 4 = (0, ), n 5 = (0, 0), n th pity unitry osts r th g lls of Figur (). Eg lls from Figur () n Figur () rprsnt optiml pity llotions with ynmi n stti routing, rsptivly. Thy hv osts of 7 n 8, rsptivly. A routing f F tht stisfis th pity from Figur () is pit on Figur () n Figur (), for n, rsptivly. W show nxt tht th optiml pity llotions for F V, F, n F ff r 7, 8 n 8, rsptivly. Th routing f from Figur () n Figur () n xtn to routing in f F V suh tht (x, f) supports D y fixing h k =, h k =, y k = f k ( ), y k = f k ( ) f k ( ), n y k = y k = f k ( ). 0

11 0 () g osts () optiml pity llotion with F V () optiml pity llotion with F or F ff (,) (0,) (,0) (,0) (,0) (0,0) () f( ) () f( ) Figur : Exmpl showing tht opt(f V ) n n stritly smllr thn opt(f ff ) n opt(f ) () g osts () optiml pity llotion with F VG or F ff () optiml pity llotion with F V or F (3,0,0) (0,0,0) (0,3,0) (0,3,0) (0,,0) (0,,0) (0,0,) (0,0,0) (0,0,0) (0,0,) () f( ) () f( ) Figur 3: Exmpl showing tht opt(f VG ) (thus opt(f ff )) n n stritly smllr thn opt(f V ) n opt(f ). Howvr, w xplin nxt why it nnot xtn to routing in F within th pity x from Figur (). W rstrit our ttntion to th sust of D tht onsists of th lin sgmnt D = onv(, ) n show tht f n lry not xtn to routing in F for D. Consir flow f( ) pit in Figur (). This flow uss th routing tmplt y fin s y k = f k ( k )/ k for k = k, k. Similrly, flow f( ) uss th routing tmplt y k = f k ( k )/ k for k = k, k. Thn, w s tht (rsp. ) is th uniqu mn vtor in D tht n rout within th pity x from Figur () using routing tmplt y (rsp. y ). Thrfor, fining D (rsp. D ) s th sust of D tht ontins ll mn vtors tht n rout long routing tmplt y (rsp. y ), w hv tht D D D. This shows tht it is not possil to xtn f to routing in F for D, so tht it is not possil to o so for D ithr. In ft, w hv tht th optiml pity llotion for F is otin whn D is ovr only y itslf, yiling opt(f ) = 8. For F ff, w n pply Proposition (us (0, 0), (, 0), (0, ) D) so tht opt(f ff ) = opt(f stt ) = opt(f VG ) n opt(f ff ) n stritly smllr thn opt(f V ) n opt(f ) Consir th ntwork sign prolm for th grph pit in Figur 3() with thr ommoitis k :, k : n k 3 :. Th unrtinty st D is fin y th xtrm points

12 () g osts () optiml pity llotion with F () optiml pity llotion with F V or F ff (,0,0) (0,0,0) (0,,0) (0,,0) (0,,0) (0,,0) (0,0,) (0,0,0) (0,0,0) (0,0,) () f( ) () f( ) Figur 4: Exmpl showing tht opt(f ) n n stritly smllr thn opt(f ff ) n opt(f V ). = (3,, 0) n = (0, 3, ), n th pity unitry osts r th g lls of Figur 3(). Eg lls from Figur 3() rprsnt n optiml pity llotion for ynmi routing, with ost. A routing f F tht stisfis th pity from Figur 3() is pit on Figur 3() n Figur 3(), for n, rsptivly. This routing n xtn to routing f F VG suh tht (x, f) supports D y stting y k = y k = f k ( )/3, y k = f k ( )/, y k = f k ( )/3 n y k3 = y k3 = f k3 ( )/. Applying Thorm.., f lso longs to F ff. Howvr, f nnot xtn to routing in F V lry us k > k n f k ( ) < f k ( ), tht is, f is not non-rsing funtion. W n show in ition tht, using rsoning similr to th on us in th prvious stion, f nnot xtn to routing in F within th xisting pity. W n s tht n optiml pity llotion using F V or F is lso n optiml pity llotion using F stt, n it rquirs two mor units of pity on n no pity on, s Figur 3(), whih yils totl ost of opt(f ) n stritly smllr thn opt(f ff ) n opt(f V ) Consir th ntwork sign prolm for th grph pit in Figur 4() with thr ommoitis k :, k : n k 3 :. Th unrtinty st D is fin y th xtrm points = (,, 0), = (0,, ), 3 = (, 0, 0), 4 = (0,, 0), 5 = (0, 0, ), 6 = (0, 0, 0) n th pity unitry osts r th g lls of Figur 4() (it is th sm s Figur 3()). Eg lls from Figur 4() n Figur 4() rprsnt optiml pity llotions with ynmi n stti routing, rsptivly. Thy hv osts of 5 n 7, rsptivly. A routing f F tht stisfis th pity from Figur 4() is pit on Figur 4() n Figur 4(), for n, rsptivly. This routing n xtn to routing f F suh tht (x, f) supports D y onsiring th ovr through hyprpln, k = n stting y k = y k = f k ( )/, y k = f k ( ), y k = f k ( ), n y k3 = y k3 = f k3 ( )/. Howvr, f nnot xtn to routing in F V lry us k > k n f k ( ) < f k ( ), tht is, f is not non-rsing funtion. In ft, w hv tht opt(f V ) = opt(f stt ) = 9. Thn, w n pply Proposition (us (, 0, 0), (0,, 0), (0, 0, ), (0, 0, 0) D) to th prolm with F ff, so tht opt(f ff ) = opt(f stt ) = 9. 5 Conluing rmrks This ppr stuis th optiml pity llotion ost provi y roust ntwork sign mols rstrit to us spifi routing sts. Ths routing sts r: ffin routing, volum routing n its two simplifitions, n th routings s on ovrs of th mn unrtinty st. W show tht th routing st s on n ritrry ovr of th unrtinty is quivlnt to th routing st tht uss sprtion hyprpln. W show thn tht th simplifi volum routings r spil

13 ss of ffin routings. Finlly, w show tht th gnrl volum routing is no mor flxil thn its simplifitions whnvr th unrtinty st is th polytop introu y Brtsims n Sim. An importnt hrtristi of ths routing sts is th omplxity of th rsulting ntwork sign prolm. In this rspt, th gnrl volum routings n th routing sts s on ovrs of th unrtinty st l to N P-hr optimiztion prolms. Morovr, whil finit linr progrmming formultion n provi for th roust ntwork sign prolm with ynmi routing unr polyhrl unrtinty (y onsiring only th xtrm points of th mn polytop), no suh formultions r known for th prolms tht us th gnrl volum routings or th routings s on ovrs of th unrtinty st. In this sns, ths two routing sts yil optimiztion prolms tht r omputtionlly vn mor iffiult thn th roust ntwork sign with ynmi routing. In opposition, ffin routing n th two simplifi volum routings l to polynomilly solvl optimiztion prolms, givn tht th unrtinty polytop hs ompt sription. 6 Aknowlgmnts Th uthor is grtful to Christin Rk for numrous ommnts tht hlp in improving th prsnttion of th ppr. Rfrns [] A. Altin, E. Amli, P. Blotti, n M. Ç. Pinr. Provisioning virtul privt ntworks unr trffi unrtinty. Ntworks, 49():00 5, 007. [] A. Altin, H. Ymn, n M. Ç. Pinr. Th roust ntwork loing prolm unr hos mn unrtinty: Formultion, polyhrl nlysis n omputtions. INFORMS Journl on Computing, 3():75 89, 0. [3] W. Bn-Amur. Btwn fully ynmi routing n roust stl routing. In 6th Intrntionl Workshop on Dsign n Rlil Communition Ntworks, 007. DRCN 007, 007. [4] W. Bn-Amur n H. Krivin. Nw onomil virtul privt ntworks. Communitions of th ACM, 46(6):69 73, 003. [5] W. Bn-Amur n H. Krivin. Routing of unrtin mns. Optimiztion n Enginring, 3:83 33, 005. [6] W. Bn-Amur n M. Zotkiwiz. Volum orint routing. In 4th Intrntionl Tlommunitions Ntwork Strtgy n Plnning Symposium (NETWORKS), pgs 7, 00. [7] W. Bn-Amur n M. Zotkiwiz. Roust routing n optiml prtitioning of trffi mn polytop. Intrntionl Trnstions in Oprtionl Rsrh, 8(3): , 0. [8] A. Bn-Tl, A. Goryshko, E. Guslitzr, n A. Nmirovski. Ajustl roust solutions of unrtin linr progrms. Mthmtil Progrmming, 99():35 376, 004. [9] A. Bn-Tl n A. Nmirovski. Roust solutions of linr progrmming prolms ontmint with unrtin t. Mthmtil Progrmming, 88:4 44, 000. [0] A. Bn-Tl n A. Nmirovski. Roust optimiztion mthoology n pplitions. Mthmtil Progrmming, 9: , 00. [] D. Brtsims n C. Crmnis. Finit ptility in multistg linr optimiztion. Automti Control, IEEE Trnstions on, 55():75 766, 00. [] D. Brtsims, V. Goyl, n A. Sun. Chrtriztion of th powr of finit ptility in multi-stg stohsti n ptiv optimiztion. To ppr in Mthmtis of Oprtions Rsrh., 0. [3] D. Brtsims n M. Sim. Th Pri of Roustnss. Oprtions Rsrh, 5():35 53, Jn 004. [4] C. Chkuri, G. Oriolo, M. G. Sutllà, n F. B. Shphr. Hrnss of roust ntwork sign. Ntworks, 50():50 54,

14 [5] X. Chn n Y. Zhng. Unrtin Linr Progrms: Extn Affinly Ajustl Roust Countrprts. Oprtions Rsrh, 57(6):469 48, 009. [6] A. Frngioni, F. Psli, n M. G. Sutllà. Stti n ynmi routing unr isjoint ominnt xtrm mns. Oprtions Rsrh Lttrs, 39():36 39, 0. [7] J. Goh n M. Sim. Distriutionlly roust optimiztion n its trtl pproximtions. Oprtions Rsrh, 58(4-Prt-):90 97, 00. [8] A. Gupt, J. Klinrg, A. Kumr, R. Rstogi, n B. Ynr. Provisioning virtul privt ntwork: ntwork sign prolm for multiommoity flow. In STOC 0: Proings of th thirty-thir nnul ACM symposium on Thory of omputing, pgs , 00. [9] A. M. C. A. Kostr, M. Kutshk, n C. Rk. Towrs roust ntwork sign using intgr linr progrmming thniqus. In Proings of th NGI 00, Pris, Frn, Jun 00. Nxt Gnrtion Intrnt. [0] A.M.C.A. Kostr, M. Kutshk, n C. Rk. Ajustl roust ntwork sign: Mols, inqulitis, n omputtions, 00. [] S. Muhntongsuk, F. Oronz, n J. Liu. Roust solutions for ntwork sign unr trnsporttion ost n mn unrtinty. Journl of th Oprtions Rsrh Soity, 59:55 56, 008. [] F. Oróñz n J. Zho. Roust pity xpnsion of ntwork flows. Ntworks, 50():36 45, 007. [3] A. Ouorou. Affin ision ruls for trtl pproximtions to roust pity plnning in tlommunitions. Tlk t th Intrntionl Ntwork Optimiztion Confrn, 0. [4] A. Ouorou n J.-P. Vil. A mol for roust pity plnning for tlommunitions ntworks unr mn unrtinty. In 6th Intrntionl Workshop on Dsign n Rlil Communition Ntworks, 007. DRCN 007, pgs 4, 007. [5] M. Poss n C. Rk. Affin rours for th roust ntwork sign prolm: twn stti n ynmi routing. In Proings of INOC 0, Hmurg, Grmny, numr 6703 in LNCS, pgs Springr. [6] M. Poss n C. Rk. Affin rours for th roust ntwork sign prolm: twn stti n ynmi routing. ZIB Rport -03, Zus Institut Brlin, Frury 0. [7] M. G. Sutllà. On improving optiml olivious routing. Oprtions Rsrh Lttrs, 37(3):97 00, 009. [8] A. L. Soystr. Convx progrmming with st-inlusiv onstrints n pplitions to inxt linr progrmming. Oprtions Rsrh, :54 57,

, 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

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

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

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

(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

12. Traffic engineering

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

More information

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

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 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

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

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

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

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

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

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

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

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

Numbering Boundary Nodes

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

More information

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

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

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

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

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

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

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

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

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

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

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

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

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

Register Allocation. Register Allocation. Principle Phases. Principle Phases. Example: Build. Spills 11/14/2012

Register Allocation. Register Allocation. Principle Phases. Principle Phases. Example: Build. Spills 11/14/2012 Rgistr Allotion W now r l to o rgistr llotion on our intrfrn grph. W wnt to l with two typs of onstrints: 1. Two vlus r liv t ovrlpping points (intrfrn grph) 2. A vlu must or must not in prtiulr rhitturl

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

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

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

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

More information

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

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

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

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

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

More information

Similarity Search. The Binary Branch Distance. Nikolaus Augsten.

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

More information

Section 10.4 Connectivity (up to paths and isomorphism, not including)

Section 10.4 Connectivity (up to paths and isomorphism, not including) Toy w will isuss two stions: Stion 10.3 Rprsnting Grphs n Grph Isomorphism Stion 10.4 Conntivity (up to pths n isomorphism, not inluing) 1 10.3 Rprsnting Grphs n Grph Isomorphism Whn w r working on n lgorithm

More information

TOPIC 5: INTEGRATION

TOPIC 5: INTEGRATION TOPIC 5: INTEGRATION. Th indfinit intgrl In mny rspcts, th oprtion of intgrtion tht w r studying hr is th invrs oprtion of drivtion. Dfinition.. Th function F is n ntidrivtiv (or primitiv) of th function

More information

Weighted graphs -- reminder. Data Structures LECTURE 15. Shortest paths algorithms. Example: weighted graph. Two basic properties of shortest paths

Weighted graphs -- reminder. Data Structures LECTURE 15. Shortest paths algorithms. Example: weighted graph. Two basic properties of shortest paths Dt Strutur LECTURE Shortt pth lgorithm Proprti of hortt pth Bllmn-For lgorithm Dijktr lgorithm Chptr in th txtook (pp ). Wight grph -- rminr A wight grph i grph in whih g hv wight (ot) w(v i, v j ) >.

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

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

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

More information

A Low Noise and Reliable CMOS I/O Buffer for Mixed Low Voltage Applications

A Low Noise and Reliable CMOS I/O Buffer for Mixed Low Voltage Applications Proings of th 6th WSEAS Intrntionl Confrn on Miroltronis, Nnoltronis, Optoltronis, Istnul, Turky, My 27-29, 27 32 A Low Nois n Rlil CMOS I/O Buffr for Mix Low Voltg Applitions HWANG-CHERNG CHOW n YOU-GANG

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

HIGHER ORDER DIFFERENTIAL EQUATIONS

HIGHER ORDER DIFFERENTIAL EQUATIONS Prof Enriqu Mtus Nivs PhD in Mthmtis Edution IGER ORDER DIFFERENTIAL EQUATIONS omognous linr qutions with onstnt offiints of ordr two highr Appl rdution mthod to dtrmin solution of th nonhomognous qution

More information

arxiv: v1 [cs.ds] 20 Feb 2008

arxiv: v1 [cs.ds] 20 Feb 2008 Symposium on Thortil Aspts of Computr Sin 2008 (Borux), pp. 361-372 www.sts-onf.org rxiv:0802.2867v1 [s.ds] 20 F 2008 FIXED PARAMETER POLYNOMIAL TIME ALGORITHMS FOR MAXIMUM AGREEMENT AND COMPATIBLE SUPERTREES

More information

MULTIPLE-LEVEL LOGIC OPTIMIZATION II

MULTIPLE-LEVEL LOGIC OPTIMIZATION II MUTIPE-EVE OGIC OPTIMIZATION II Booln mthos Eploit Booln proprtis Giovnni D Mihli Don t r onitions Stnfor Univrsit Minimition of th lol funtions Slowr lgorithms, ttr qulit rsults Etrnl on t r onitions

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

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

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

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

More information

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

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

CSE303 - Introduction to the Theory of Computing Sample Solutions for Exercises on Finite Automata

CSE303 - Introduction to the Theory of Computing Sample Solutions for Exercises on Finite Automata CSE303 - Introduction to th Thory of Computing Smpl Solutions for Exrciss on Finit Automt Exrcis 2.1.1 A dtrministic finit utomton M ccpts th mpty string (i.., L(M)) if nd only if its initil stt is finl

More information

DUET WITH DIAMONDS COLOR SHIFTING BRACELET By Leslie Rogalski

DUET WITH DIAMONDS COLOR SHIFTING BRACELET By Leslie Rogalski Dut with Dimons Brlt DUET WITH DIAMONDS COLOR SHIFTING BRACELET By Lsli Roglski Photo y Anrw Wirth Supruo DUETS TM from BSmith rt olor shifting fft tht mks your work tk on lif of its own s you mov! This

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

Analysis for Balloon Modeling Structure based on Graph Theory

Analysis for Balloon Modeling Structure based on Graph Theory Anlysis for lloon Moling Strutur bs on Grph Thory Abstrt Mshiro Ur* Msshi Ym** Mmoru no** Shiny Miyzki** Tkmi Ysu* *Grut Shool of Informtion Sin, Ngoy Univrsity **Shool of Informtion Sin n Thnology, hukyo

More information

Chapter 9. Graphs. 9.1 Graphs

Chapter 9. Graphs. 9.1 Graphs Chptr 9 Grphs Grphs r vry gnrl lss of ojt, us to formliz wi vrity of prtil prolms in omputr sin. In this hptr, w ll s th sis of (finit) unirt grphs, inluing grph isomorphism, onntivity, n grph oloring.

More information

Computational Biology, Phylogenetic Trees. Consensus methods

Computational Biology, Phylogenetic Trees. Consensus methods Computtionl Biology, Phylognti Trs Consnsus mthos Asgr Bruun & Bo Simonsn Th 16th of Jnury 2008 Dprtmnt of Computr Sin Th univrsity of Copnhgn 0 Motivtion Givn olltion of Trs Τ = { T 0,..., T n } W wnt

More information

CS61B Lecture #33. Administrivia: Autograder will run this evening. Today s Readings: Graph Structures: DSIJ, Chapter 12

CS61B Lecture #33. Administrivia: Autograder will run this evening. Today s Readings: Graph Structures: DSIJ, Chapter 12 Aministrivi: CS61B Ltur #33 Autogrr will run this vning. Toy s Rings: Grph Struturs: DSIJ, Chptr 12 Lst moifi: W Nov 8 00:39:28 2017 CS61B: Ltur #33 1 Why Grphs? For xprssing non-hirrhilly rlt itms Exmpls:

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

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

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

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

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

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

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

Journal of Solid Mechanics and Materials Engineering

Journal of Solid Mechanics and Materials Engineering n Mtrils Enginring Strss ntnsit tor of n ntrf Crk in Bon Plt unr Uni-Axil Tnsion No-Aki NODA, Yu ZHANG, Xin LAN, Ysushi TAKASE n Kzuhiro ODA Dprtmnt of Mhnil n Control Enginring, Kushu nstitut of Thnolog,

More information

SOLVED EXAMPLES. be the foci of an ellipse with eccentricity e. For any point P on the ellipse, prove that. tan

SOLVED EXAMPLES. be the foci of an ellipse with eccentricity e. For any point P on the ellipse, prove that. tan LOCUS 58 SOLVED EXAMPLES Empl Lt F n F th foci of n llips with ccntricit. For n point P on th llips, prov tht tn PF F tn PF F Assum th llips to, n lt P th point (, sin ). P(, sin ) F F F = (-, 0) F = (,

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

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

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

More information

This chapter covers special properties of planar graphs.

This chapter covers special properties of planar graphs. Chptr 21 Plnr Grphs This hptr ovrs spil proprtis of plnr grphs. 21.1 Plnr grphs A plnr grph is grph whih n b rwn in th pln without ny gs rossing. Som piturs of plnr grph might hv rossing gs, but it s possibl

More information

Section 3: Antiderivatives of Formulas

Section 3: Antiderivatives of Formulas Chptr Th Intgrl Appli Clculus 96 Sction : Antirivtivs of Formuls Now w cn put th is of rs n ntirivtivs togthr to gt wy of vluting finit intgrls tht is ct n oftn sy. To vlut finit intgrl f(t) t, w cn fin

More information

(a) v 1. v a. v i. v s. (b)

(a) v 1. v a. v i. v s. (b) Outlin RETIMING Struturl optimiztion mthods. Gionni D Mihli Stnford Unirsity Rtiming. { Modling. { Rtiming for minimum dly. { Rtiming for minimum r. Synhronous Logi Ntwork Synhronous Logi Ntwork Synhronous

More information

Mid-Term Examination - Spring 2014 Mathematical Programming with Applications to Economics Total Score: 45; Time: 3 hours

Mid-Term Examination - Spring 2014 Mathematical Programming with Applications to Economics Total Score: 45; Time: 3 hours Mi-Term Exmintion - Spring 0 Mthemtil Progrmming with Applitions to Eonomis Totl Sore: 5; Time: hours. Let G = (N, E) e irete grph. Define the inegree of vertex i N s the numer of eges tht re oming into

More information

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

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

More information

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

ON STOICHIOMETRY FOR THE ASSEMBLY OF FLEXIBLE TILE DNA COMPLEXES

ON STOICHIOMETRY FOR THE ASSEMBLY OF FLEXIBLE TILE DNA COMPLEXES ON STOICHIOMETRY FOR THE ASSEMBLY OF FLEXIBLE TILE DNA COMPLEXES N. JONOSKA, G. L. MCCOLM, AND A. STANINSKA Astrt. Givn st of flxil rnh juntion DNA moluls with stiky-ns (uiling loks), ll hr tils, w onsir

More information

Chem 104A, Fall 2016, Midterm 1 Key

Chem 104A, Fall 2016, Midterm 1 Key hm 104A, ll 2016, Mitrm 1 Ky 1) onstruct microstt tl for p 4 configurtion. Pls numrt th ms n ml for ch lctron in ch microstt in th tl. (Us th formt ml m s. Tht is spin -½ lctron in n s oritl woul writtn

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

Multipoint Alternate Marking method for passive and hybrid performance monitoring

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

More information

Can transitive orientation make sandwich problems easier?

Can transitive orientation make sandwich problems easier? Disrt Mthmtis 07 (007) 00 04 www.lsvir.om/lot/is Cn trnsitiv orinttion mk snwih prolms sir? Mihl Hi, Dvi Klly, Emmnull Lhr,, Christoph Pul,, CNRS, LIRMM, Univrsité Montpllir II, 6 ru A, 4 9 Montpllir C,

More information

Instructions for Section 1

Instructions for Section 1 Instructions for Sction 1 Choos th rspons tht is corrct for th qustion. A corrct nswr scors 1, n incorrct nswr scors 0. Mrks will not b dductd for incorrct nswrs. You should ttmpt vry qustion. No mrks

More information

Properties of Hexagonal Tile local and XYZ-local Series

Properties of Hexagonal Tile local and XYZ-local Series 1 Proprtis o Hxgonl Til lol n XYZ-lol Sris Jy Arhm 1, Anith P. 2, Drsnmik K. S. 3 1 Dprtmnt o Bsi Sin n Humnitis, Rjgiri Shool o Enginring n, Thnology, Kkkn, Ernkulm, Krl, Ini. jyjos1977@gmil.om 2 Dprtmnt

More information

Module 2 Motion Instructions

Module 2 Motion Instructions Moul 2 Motion Instrutions CAUTION: Bor you strt this xprimnt, unrstn tht you r xpt to ollow irtions EXPLICITLY! Tk your tim n r th irtions or h stp n or h prt o th xprimnt. You will rquir to ntr t in prtiulr

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

New challenges on Independent Gate FinFET Transistor Network Generation

New challenges on Independent Gate FinFET Transistor Network Generation Nw hllngs on Inpnnt Gt FinFET Trnsistor Ntwork Gnrtion Viniius N. Possni, Anré I. Ris, Rnto P. Ris, Flip S. Mrqus, Lomr S. Ros Junior Thnology Dvlopmnt Cntr, Frl Univrsity o Plots, Plots, Brzil Institut

More information

On the Protection of Multicast Trees in All Optical Networks Using the NEPC Strategy

On the Protection of Multicast Trees in All Optical Networks Using the NEPC Strategy On th rottion of Multist Trs in All Optil Ntworks Using th NEC Strtgy Miklós Molnár To it this vrsion: Miklós Molnár. On th rottion of Multist Trs in All Optil Ntworks Using th NEC Strtgy. RR-11022, 2011.

More information

FSA. CmSc 365 Theory of Computation. Finite State Automata and Regular Expressions (Chapter 2, Section 2.3) ALPHABET operations: U, concatenation, *

FSA. CmSc 365 Theory of Computation. Finite State Automata and Regular Expressions (Chapter 2, Section 2.3) ALPHABET operations: U, concatenation, * CmSc 365 Thory of Computtion Finit Stt Automt nd Rgulr Exprssions (Chptr 2, Sction 2.3) ALPHABET oprtions: U, conctntion, * otin otin Strings Form Rgulr xprssions dscri Closd undr U, conctntion nd * (if

More information

Discovering Pairwise Compatibility Graphs

Discovering Pairwise Compatibility Graphs Disovring Pirwis Comptiility Grphs Muhmm Nur Ynhon, M. Shmsuzzoh Byzi, n M. Siur Rhmn Dprtmnt of Computr Sin n Enginring Bnglsh Univrsity of Enginring n Thnology nur.ynhon@gmil.om, shms.yzi@gmil.om, siurrhmn@s.ut..

More information

Greedy Algorithms, Activity Selection, Minimum Spanning Trees Scribes: Logan Short (2015), Virginia Date: May 18, 2016

Greedy Algorithms, Activity Selection, Minimum Spanning Trees Scribes: Logan Short (2015), Virginia Date: May 18, 2016 Ltur 4 Gry Algorithms, Ativity Sltion, Minimum Spnning Trs Sris: Logn Short (5), Virgini Dt: My, Gry Algorithms Suppos w wnt to solv prolm, n w r l to om up with som rursiv ormultion o th prolm tht woul

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

Multi-Section Coupled Line Couplers

Multi-Section Coupled Line Couplers /0/009 MultiSction Coupld Lin Couplrs /8 Multi-Sction Coupld Lin Couplrs W cn dd multipl coupld lins in sris to incrs couplr ndwidth. Figur 7.5 (p. 6) An N-sction coupld lin l W typiclly dsign th couplr

More information

Page 1. Question 19.1b Electric Charge II Question 19.2a Conductors I. ConcepTest Clicker Questions Chapter 19. Physics, 4 th Edition James S.

Page 1. Question 19.1b Electric Charge II Question 19.2a Conductors I. ConcepTest Clicker Questions Chapter 19. Physics, 4 th Edition James S. ConTst Clikr ustions Chtr 19 Physis, 4 th Eition Jms S. Wlkr ustion 19.1 Two hrg blls r rlling h othr s thy hng from th iling. Wht n you sy bout thir hrgs? Eltri Chrg I on is ositiv, th othr is ngtiv both

More information

Layout Decomposition for Quadruple Patterning Lithography and Beyond

Layout Decomposition for Quadruple Patterning Lithography and Beyond Lyout Domposition for Qurupl Pttrning Lithogrphy n Byon Bi Yu ECE Dprtmnt Univrsity of Txs t Austin, Austin, TX i@r.utxs.u Dvi Z. Pn ECE Dprtmnt Univrsity of Txs t Austin, Austin, TX pn@.utxs.u ABSTRACT

More information

Announcements. These are Graphs. This is not a Graph. Graph Definitions. Applications of Graphs. Graphs & Graph Algorithms

Announcements. These are Graphs. This is not a Graph. Graph Definitions. Applications of Graphs. Graphs & Graph Algorithms Grphs & Grph Algorithms Ltur CS Fll 5 Announmnts Upoming tlk h Mny Crrs o Computr Sintist Or how Computr Sin gr mpowrs you to o muh mor thn o Dn Huttnlohr, Prossor in th Dprtmnt o Computr Sin n Johnson

More information

Discovering Frequent Graph Patterns Using Disjoint Paths

Discovering Frequent Graph Patterns Using Disjoint Paths Disovring Frqunt Grph Pttrns Using Disjoint Pths E. Gus, S. E. Shimony, N. Vntik {hu,shimony,orlovn}@s.gu..il Dpt. of Computr Sin, Bn-Gurion Univrsity of th Ngv, Br-Shv, Isrl Astrt Whrs t-mining in strutur

More information