Multicast routing algorithm based on Extended Simulated Annealing Algorithm

Size: px
Start display at page:

Download "Multicast routing algorithm based on Extended Simulated Annealing Algorithm"

Transcription

1 7t WSEAS Int. Con. on MATHEMATICAL METHODS n COMPUTATIONAL TECHNIQUES IN ELECTRICAL ENGINEERING, Soi, 27-29/10/05 (pp ) Multist outing lgoitm s on Extn Simult Annling Algoitm Jin-Ku Jong*, Sung-Ok Kim**, Ci-Hw Song*** * **Dpt. o Comput Eng, Hnnm Univsity 133 Ojong-ong Dok-gu, Djon , Ko ***Dpt. o Comput Sin Cungnm Ntionl Univsity 220 Gung-ong, Yusong-gu, Djon, Ko Astt: - In tis pp, w popos mto tt is l to in goo multist outing t in wi ntwok. T polm omin tt w soul solv is mol s wigt, unit gp. T gp tt psnts wi ntwok s t no typ, sv tt sns t sm t to t ipints, som nos tt o not qust t to t sv n t st tt on't n t. Ou mto uss t xtn simult nnling lgoitm[esa]. Kywo : Extn simult nnling, multist outing, optimiztion 1. Intoution W popos n lgoitm tt ss out o sning t om on sv to spii multipl lints to minimiz t totl istn in ntwoks. Tis polm is int om t polm o ining out o ll nos in ntwoks. T omplxity o ining out t is insing mtilly wn mo nos oming into tion to, it is to in t st solution (out) wit titionl lgoitm. Rntly, t outing lgoitms o multisting ing stui wily, som o wit s ollows. Simult Annling mto[1], Applying SA on Dijkst o multi-onstint outing t[2], outing lgoitm o l-tim omputing using Hopil Nul Computing[3,7] n istiut lgoitm[4]. T simult nnling mto o tmining out uss n-to-n ly n o-t wit t onsition o ntwok sous. T istiut lgoitm tmins y t gouping o t nigoing nos tt tk ti pt in multisting. Ou popos lgoitm uss ESA lgoitm[5,6] to minimiz t sum o t istn ost twn t nos. W v mpp ntwok omin into t om o t gp n in ptution sm o ESA lgoitm wit vlution omul to in st multist t. T ost untion o t popos lgoitm onsis istn ost n no ost, ut it os not onsis t num o t u o out, vg ti quntity n tns ly. Howv, ts volums n ng into igus. 2. ESA Algoitm SA lgoitm xtts possil solution st y t mtopolis smpling sm n t tmoynmi vg is lult wit nonil vg. To, t possiility o ngy wit t sttus o n si s qution 1 w, oing to sttistil ynmis igu su s ix volum ( ), ix num o ptils ( ) n un t onstnt tmptu () wit in t los systm (1) Tis is t psnttion o t los systm. Tvling Slsmn Polm(TSP) is psnttiv polm tt solution oul oun wit t usg o t SA. TSP s ix num o t ity to visit n t slsmn must in t lst ost out. W will onsi t visiting ity s t num o moluls( ) tn it will nonil nsml. Wn, t moluls lututoin unit systm is ll gn nonil nsml. Volum( ), tmptu() n mil potntil () is ix wil ngy( ) n t num o moluls( ) is opn. T poility o stt wit ngy n moluls will in qution 2. (2) To, t igst poility o stt ppn is in onn wit ngy n num o moluls. T stuy o opn systm tt not only llows ngy ut lso t num o ptils is s on gn nonil nsml. Fo xmpl, on slsmn s to visit vy ity ( ) wit t lst ost n t lst istn out wi is t polm un nonil nsml n t slsmn s to visit wit t

2 7t WSEAS Int. Con. on MATHEMATICAL METHODS n COMPUTATIONAL TECHNIQUES IN ELECTRICAL ENGINEERING, Soi, 27-29/10/05 (pp ) limittion o t ost s to visit itis ( n, ) tis onition it is gn nonil nsml. 3. Dinition o polm o ESA 3.1 psnttion o ntwok In ug ntwok on sv must st t sm t to multipl lints so it s to i t st out o vy no o t som no ( ). To in multisting out w psnt ntwok in gp. Evy no in ntwok inluing t sv is psnt s vtis. T pysil onntion o no is psnt s gs. E g is onnt to two ot nos s. Figu 1. Gpi Rpsnttion o Ntwok To psnt igu 1 s omul will s qution 3. (3) To slt t st out it is not nssy to ivi into sv n lint ut o t s o t tinking lt us onsi ist no s sv. No is no tt i not qust o svi ut in t wol tns out t to onnt to t nigoing no it oul inst to tns out. Wn no is not inlu in tnsing out it is not l to iv no n no, to, it must in t out. 3.2 Rpsnttion o tnsing out To solv t polm lik igu 1 in slt som o t no n g n o t no (sv) s oot n outing t. To st t tns out o vy no in ntwok systm it is gnl to us spnning t lgoitm. St som o t no o tns out. g T = ((()),g()) R = ((),(),(),()) Figu 2. Tns out t T t in igu 2 psnts tns out n psnts t nos tt is not inlu in outing t. T oot no in is onnt to t silings tt is xlu in. T sum o no in n is lwys t sm s t num o nos in. 3.3 ptution sm In gp itily slts on tns out t. W woul us s n initil solution o ESA lgoitm n tmin wt it is qutnss y vlution untion tt w popos. W us t vlution untion to viy. Until w in tt solution t systm ptu. T sis o ptu solution v to om Mkov in so t mtos n si s ollowing 3 typs. (1) Mto o movl on no (2) Mto o no ng in num o no (3) Mto o ing on no In t mto o (1) w oul mov ny no xpt oot no in t. T mov no is inst in vitul t. In mto o (2) t is two wys to ng. Fist, mov on no in t to t ot lotion. Son, intng on no in n on no in. In t mto o (3) slt on no ity in n inst no to. Lt s onsi t t o stt tnsition s igu 3. At tis point is no o t, n is t no o xtnl t. T=(()(()(()))) R=(g()()) Figu 3. Initil t T To mov on no in T, w onsi two typs on is to lt l no n t ot is to lt no o t mil on. In to mov mil no lik w must onsi w to sn il no's lotion. Fo xmpl, in wn no is lt t silings lotion will tmin y t ltion o itsl wn t ltion o t pnt is tking pl (igu 4-) o t onnt silings to t pnt o t pnt lik in igu 4-. Wn t ltion o vy no in su-t t ngy ng is so pi pning on t lotion o t no n oing to t spiition o its ntu it is to su optiml solution. An in t onition o igu 4- t oot no is onnt to t no g

3 7t WSEAS Int. Con. on MATHEMATICAL METHODS n COMPUTATIONAL TECHNIQUES IN ELECTRICAL ENGINEERING, Soi, 27-29/10/05 (pp ) vi. In tis sitution witout onsition o ltion o no t oot must sn its t itly to n no is iving its t om no witout onsition o ltion o no. g () ltion o totl sut g () lt no t moving Figu 4. Dltion o t no To pou witout ng o t num o t no it xtts on ity no n ng its lotion n gins nw tns out in t. Figu 5, igu 6 n igu 7 sow t mov o t no. T two ss o moving no; igu 5 psnts t mov o t l no, n igu 6 n 7 psnt t mov o t mil no. T onition o ng o t mil no is tgoiz y w to mov its il no. Tis onition is psnt in igu 6 n igu 7. () () () mov l no to nonl no () mov l no to l no Figu 5. Mov o t l no It is mning lss tt t siling nos lt o igt. Figu 6- psnts mov o t l no to l no n no is onnt itly to t oot, ut in t pt o no is ins to, t t tns out is ws t t must tvl two mo no to gt ss to t oot. I t no os not v onntion g in = n in nw out t xists g w = t tns ost o is smll t s ost. In igu 7- it illustts mov o t non l no to l no. An igu 7- illustts non l no to non l no. T inl stt tnsition mto is to on mo no to t. In vitul t sltion o ity no ut o t oot no n inst into t o no it is illustt in igu 8. () mov to () mov to Figu 6. Mov o t su-t () l no moving () non-l no moving Figu 7. Mov o t no () inst s l () inst s non l Figu 8. A o t no 3.4 Cost untion In igu 1 g s its own wigt. Wigt psnts istn twn t nos. T tns ost in t onsi ntwok ly o possing tim o t out o nwit. W iv t om igu 1 s ollows. Figu 9. Tns t Tns ost o tis stt will t sum o t

4 7t WSEAS Int. Con. on MATHEMATICAL METHODS n COMPUTATIONAL TECHNIQUES IN ELECTRICAL ENGINEERING, Soi, 27-29/10/05 (pp ) g s wigt o. To, it will. In t oiginl gp t is no g w. To, to lult totl ngy ost w must i t ost o g lik n it must gt tn ny ot vlus o g psnt in. So w us t vlu o g tt is not xist s In t onition o igu 1 t mximum g vlu is 2.0. Wn vil t g vlu tt is not psnt in is 3.0 to, t tns t in igu 10 will 8.2 Aoing to igu 1 t no must iv t t om t sv. But tns t in igu 10 o igu 11 no is nglt om tns t. To, t minimum ost wit t sm no lik igu 11 it will solution tt nnot pt. In t poss to lult optimiz tns t t nos n tgoiz into 4 tgois. Cs 1: It must povi wit svi n tt lis wit in t tns out. Cs 2: It must povi wit svi ut it is in vitul t. Cs 3: It os not n to svi ut witin t out o tns. Cs 4: It os not n to svi n it is not in t out o tns. In t popos lgoitm w us ooln typ inx to t no to know wt no is n to onnt t sv. T ost untion is psnt in t omul s ollows (4) 3.5 Algoitm To o t onvgn to t optiml vlu w us t stt tnsition untions. W slt vlu nomly mong 0,1,2 wn 0 is slt t untion will us n. In t onition o 2 t untion is us n In t onition o 3 untion is us. Ts untions stt tnsition untions n t ist untion is us t ution stt tnsition, n t ollowing is witout t ng o t tns no ut ng its lotion n t ltt is o t ng wit insting xtnl no in. W iv nw tns t T om xisting tns t T. T tns ost ontin t gin o no(t gin vlu o no in Gn nonil nsml psnts t mil potntil ). W tmin wit t ng o t ost wt t nw iv tns out t is pt o not. Ts posss onut until t tmptu is stiliz. Wn it oms into stil sitution in givn tmptu w u tmptu littl it n o t smpling. Wn t tmptu is ig t systm pt t most stt tt is ptu ut wn t tmptu is los to 0.0 it stts to pt t low ngy stt only wit non zo possiility. T systm will vok t sult o t psnt tns out t until it oms to low tmptu ( ) tt is not 0.0 possiilitis. Initil tmptu n α vil. n psnts tns out t n n psnts t ost o n. T systm uss n to gt nw stt tt is tmpol tns t n tns ost. I t nw solutions v lss ngy it will t nw solution. Wn is igg tn tt o wit t possiility o n will pl to n. Di initil pmt vlus : : gt nom pt t s initil solution, = initil tmptu, Stp 1 : slt vlu witin 0,1,2 nomly s 1 : s 2 : s 3 : Stp 2 : Stp 3 : i OR tn i (stt is in quiliium) tn s tmptu Rpt stp1~stp3 Until tmptu 0.0 Figu 10. ESA Algoitm 4. Expimnt n sults In igu 11 it psnts t t tt ws us in t xpimnt. T il psnts ntwok

5 7t WSEAS Int. Con. on MATHEMATICAL METHODS n COMPUTATIONAL TECHNIQUES IN ELECTRICAL ENGINEERING, Soi, 27-29/10/05 (pp ) no n t nums insi o t il stns o its inxs o t no. T inx os not ts o o t siz. T onntion twn t nos psnts pysilly onnt no n t wigt o g psnts istn n o t xpimntl it is us o t tns ost. Num 0 no is sv n t ots lints. ost is Figu 13. T ost o tmptu lvl 5. Conlusion As in qution 4 it onigus no ost wit n it pvnt t typ s 3 nos om t inl out. Wn t vlu o is too ig o too smll w oul not gt goo solution us it istu tnsition to positiv stt us it ks t ln o no ost n istn ost. Ts gnl polm o Simult Annling Algoitm. In tis xpimnt w us t gn nonil nsml o ESA lgoitm. An w sow tns out polm o vy no n sltion o ity no n oul ooto t ining o t optimiz tns out. Figu 11. Expimntl Dt Figu 12. Finl onlusion o ntwok out Figu 12 is t inl output. In igu 12 t nos tt wnts to svi s olo o gy n t ot tt s wit il nos will t nos tt is not ing svi. No 1, 8, 28 psnt in tns outing t so tt lys t t to snnt nos. Figu 13 illustts t ng o tns ost uing possing tim. T tmptu stt om n ool own to 0.01 n t initil tns ost is n t inl tns Rns : [1] Xingwi Wng, Hui Cng, Jinnong Co, Linwi Zng, Ming Hung, A Simultnnling s QoS Multisting Algoitm, Poings o ICCT2003 [2] Yong Cui, K Xu, Jinping Wu, Zongo Yu, Youjin Zo, Multi onstin Routing Bs on Simult Annling, ieee 2003 [3] Cotipt Ponvli, Goutm Ckoty, Noio Sitoi, A Nul Ntwok Appo to Multist Routing in Rl Tim Communition Ntwoks, 1995,IEEE [4] F Bu, Anujn Vm, Distiut Algoitms o Multist Pt Stup in Dt Ntwoks, IEEE/ACM Tnstion on Ntwoking, Vol. 4, No. 2, Apil [5] C.H.Song, K.H.L, W.D.L, An Algoitm o Augmnt Multipl Vil Routing Polm, WSEAS TRANSACTIONS ON SYSTEMS Issu 4, Vol. 2, pp , Ot [6] Jong Suk Coi, Won Don L, Sukoon L, N Hyun Pk, Sng Il Hwng, Yo Duk Youn, "Extn Simult Annling s on Gn Cnonil Ensml", Jounl o t Ko Inomtion Sin Soity, Vol 17, No. 4, July 1990 [7] Pllp Vnktm, Suip Gosl, B.P. Vijy Kum, "Nul ntwok s optiml outing lgoitm o ommunition ntwoks", Nul Ntwoks 15, 2002, pp

Self-Adjusting Top Trees

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

More information

Outline. CSE 473: Artificial Intelligence Spring Types of Agents

Outline. CSE 473: Artificial Intelligence Spring Types of Agents 9/9/7 CE 7: Atiiil Intllign ing 07 Polms Outlin Polm s & Dit Fox Uninom Mtos Dt-Fist Bt-Fist Uniom-Cost Wit slis om Dn Wl, Pit Al, Dn Klin, tut Russll, Anw Moo, Luk Zttlmoy Agnt vs. Envionmnt Tys o Agnts

More information

CS 188: Artificial Intelligence Spring Announcements

CS 188: Artificial Intelligence Spring Announcements C 188: Atiiil Intllign ing 2006 Ltu 2: Quu-Bs 8/31/2006 Dn Klin UC Bkly Mny slis om it tut Russll o Anw Moo Announmnts L Fiy 1-5m in o 275 Ln Pyton tt on Pojt 1.1: Mzwol Com o wtv tims you lik No stions

More information

Today. CS 232: Ar)ficial Intelligence. Search. Agents that Plan. September 3 rd, 2015 Search Problems. Uninformed Search Methods

Today. CS 232: Ar)ficial Intelligence. Search. Agents that Plan. September 3 rd, 2015 Search Problems. Uninformed Search Methods 1 C 232: A)iil Intllign Toy tm 3, 2015 Agnts tt Pln A Polms Uninom Mtos Dt- Fist Bt- Fist Uniom- Cost [Ts slis w t y Dn Klin n Pit Al o C188 Into to AI t UC Bkly. All C188 mtils vill t M://i.kly.u.] Agnts

More information

Announcements. CS 188: Artificial Intelligence Fall Today. Reflex Agents. Goal Based Agents. Search Problems

Announcements. CS 188: Artificial Intelligence Fall Today. Reflex Agents. Goal Based Agents. Search Problems C 88: Atiiil Intllign Fll 009 Ltu : Quu-Bs 9//009 Dn Klin UC Bkly Multil slis om tut Russll, Anw Moo Announmnts Pojt 0: Pyton Tutoil Du tomoow! T is l tomoow om m-3m in o 75 T l tim is otionl, ut P0 itsl

More information

CS 188: Artificial Intelligence Fall Announcements

CS 188: Artificial Intelligence Fall Announcements C 188: Atiiil Intllign Fll 2009 Ltu 2: Quu-Bs 9/1/2009 Dn Klin UC Bkly Multil slis om tut Russll, Anw Moo Announmnts Pojt 0: Pyton Tutoil Du tomoow! T is l tomoow om 1m-3m in o 275 T l tim is otionl, ut

More information

CS 188: Artificial Intelligence

CS 188: Artificial Intelligence C 188: Atiiil Intllign Toy Agnts tt Pln A Polms Instuto: Mo Alvz Univsity o Ro Isln (Ts slis w t/moii y Dn Klin, Pit Al, An Dgn o C188 t UC Bkly) Uninom Mtos Dt-Fist Bt-Fist Uniom-Cost Agnts tt Pln Rlx

More information

CS 188: Artificial Intelligence Fall Announcements

CS 188: Artificial Intelligence Fall Announcements C 188: Atiiil Intllign Fll 007 Ltu : Quu-Bs 8/31/007 Dn Klin UC Bkly Mny slis om it tut Russll o Anw Moo Announmnts Nxt wk Nw oom is 105 Not Gt, stts Tusy Ck w g o stions (nw oming) L Fiy 10m to 5m in

More information

Announcements. CS 188: Artificial Intelligence Fall Reflex Agents. Today. Goal Based Agents. Search Problems

Announcements. CS 188: Artificial Intelligence Fall Reflex Agents. Today. Goal Based Agents. Search Problems Announmnts Pojt 0: Pyton Tutoil Du tomoow! T is l Wnsy om 3m-5m in o 75 T l tim is otionl, ut P0 itsl is not On sumit, you soul gt mil om t utog Pojt : On t w toy tt ly n sk ustions. It s long tn most!

More information

CS 188: Artificial Intelligence Fall 2011

CS 188: Artificial Intelligence Fall 2011 Announmnts Pojt 0: Pyton Tutoil Du tomoow! T is l Wnsy om 3m-5m in o 275 T l tim is otionl, ut P0 itsl is not On sumit, you soul gt mil om t utog Pojt 1: On t w toy tt ly n sk ustions. It s long tn most!

More information

Announcements. CS 188: Artificial Intelligence Spring More Announcements. Today. From Last Time: Reflex Agents.

Announcements. CS 188: Artificial Intelligence Spring More Announcements. Today. From Last Time: Reflex Agents. C 88: Atiiil Intllign ing 009 Ltu : Quu-Bs //008 Jon DNo UC Bkly Mny slis om Dn Klin, tut Russll o Anw Moo Announmnts Pojt 0: Pyton Tutoil Post onlin now Du nxt Wnsy, Jn 8 T is l toy om m-3m in o 75 T

More information

CS 188: Artificial Intelligence Spring 2009

CS 188: Artificial Intelligence Spring 2009 C 188: Atiiil Intllign ing 009 Ltu : Quu-Bs 1//008 Jon DNo UC Bkly Mny slis om Dn Klin, tut Russll o Anw Moo Announmnts Pojt 0: Pyton Tutoil Post onlin now Du nxt Wnsy, Jn 8 T is l toy om 1m-3m in o 75

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

CS 188: Artificial Intelligence Spring Announcements

CS 188: Artificial Intelligence Spring Announcements CS 188: Atiiil Intllign Sing 2011 Ltu 2: Quu-Bs S 1/24/2010 Pit Al UC Bkly Mny slis om Dn Klin Announmnts Pojt 0: Pyton Tutoil Du Fiy 5m. L sssion Wnsy 3-5m in 271 So T l tim is otionl, ut P0 itsl is not

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

A search problem. Formalizing a search problem. Our Search Problem. Our Search Problem. Overview

A search problem. Formalizing a search problem. Our Search Problem. Our Search Problem. Overview Sing: Dtministi singl-gnt Atully, tis is otimiztion ov tim wit ist vils Anw W. Moo Posso Sool o Comut Sin Cngi Mllon Univsity www.s.mu.u/~wm wm@s.mu.u -6-7 ot to ot ts n uss o ts slis. Anw woul ligt i

More information

CS 188: Artificial Intelligence Spring Announcements

CS 188: Artificial Intelligence Spring Announcements C 188: Atiiil Intllign ing 2010 Ltu 2: Quu-Bs 1/21/2010 Pit Al UC Bkly Mny slis om Dn Klin Announmnts Pojt 0: Pyton Tutoil Out toy. Du nxt wk Tusy. L sssions in 271 o: Mony 2-3m Wnsy 4-5m T l tim is otionl,

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

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

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

C-Curves. An alternative to the use of hyperbolic decline curves S E R A F I M. Prepared by: Serafim Ltd. P. +44 (0)

C-Curves. An alternative to the use of hyperbolic decline curves S E R A F I M. Prepared by: Serafim Ltd. P. +44 (0) An ltntiv to th us of hypolic dclin cuvs Ppd y: Sfim Ltd S E R A F I M info@sfimltd.com P. +44 (02890 4206 www.sfimltd.com Contnts Contnts... i Intoduction... Initil ssumptions... Solving fo cumultiv...

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

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

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

Searching: Deterministic single-agent

Searching: Deterministic single-agent Sing: Dtministi singl-gnt Anw W. Moo Posso Sool o Comut Sin Cngi Mllon Univsity www.s.mu.u/~wm wm@s.mu.u -68-7 ot to ot ts n uss o ts slis. Anw woul ligt i you oun tis sou mtil usul in giving you own ltus.

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

(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

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

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

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

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

INFLUENCE OF ANTICLIMBING DEVICE ON THE VARIATION OF LOADS ON WHEELS IN DIESEL ELECTRIC 4000 HP

INFLUENCE OF ANTICLIMBING DEVICE ON THE VARIATION OF LOADS ON WHEELS IN DIESEL ELECTRIC 4000 HP U..B. Si. Bull., Si D, Vol.,., SS 454-5 UEE O AMBG DEVE O HE VARAO O OADS O WHEES DESE EER 4 H onl ătălin OESU Dipozitiul nt intodu ini uplimnt p oţil oiilo loomotilo. n lu pzint iti to ini, unţi d dtl

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

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

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

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

Problem 1. Solution: = show that for a constant number of particles: c and V. a) Using the definitions of P

Problem 1. Solution: = show that for a constant number of particles: c and V. a) Using the definitions of P rol. Using t dfinitions of nd nd t first lw of trodynis nd t driv t gnrl rltion: wr nd r t sifi t itis t onstnt rssur nd volu rstivly nd nd r t intrnl nrgy nd volu of ol. first lw rlts d dq d t onstnt

More information

Appendix. In the absence of default risk, the benefit of the tax shield due to debt financing by the firm is 1 C E C

Appendix. In the absence of default risk, the benefit of the tax shield due to debt financing by the firm is 1 C E C nx. Dvon o h n wh In h sn o ul sk h n o h x shl u o nnng y h m s s h ol ouon s h num o ssus s h oo nom x s h sonl nom x n s h v x on quy whh s wgh vg o vn n l gns x s. In hs s h o sonl nom xs on h x shl

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

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

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

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

Classical Theory of Fourier Series : Demystified and Generalised VIVEK V. RANE. The Institute of Science, 15, Madam Cama Road, Mumbai

Classical Theory of Fourier Series : Demystified and Generalised VIVEK V. RANE. The Institute of Science, 15, Madam Cama Road, Mumbai Clssil Thoy o Foi Sis : Dmystii Glis VIVEK V RANE Th Istitt o Si 5 Mm Cm Ro Mmbi-4 3 -mil ss : v_v_@yhoooi Abstt : Fo Rim itgbl tio o itvl o poit thi w i Foi Sis t th poit o th itvl big ot how wh th tio

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

Trade Patterns, Production networks, and Trade and employment in the Asia-US region

Trade Patterns, Production networks, and Trade and employment in the Asia-US region Trade Patterns, Production networks, and Trade and employment in the Asia-U region atoshi Inomata Institute of Developing Economies ETRO Development of cross-national production linkages, 1985-2005 1985

More information

CSE 573: Artificial Intelligence Autumn Search thru a. Goal Based Agents 9/28/2012. Agent vs. Environment. Example: N Queens

CSE 573: Artificial Intelligence Autumn Search thru a. Goal Based Agents 9/28/2012. Agent vs. Environment. Example: N Queens CE 573: Atiiil Intllign Autumn 0 Intoution & Dn Wl Wit slis om Dn Klin, tut Russll, Anw Moo, Luk Zttlmoy Agnt vs. Envionmnt An gnt is n ntity tt ivs n ts. A tionl gnt slts tions tt mximiz its utility untion.

More information

Lecture 20: Minimum Spanning Trees (CLRS 23)

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

More information

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

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

More information

Stable Matching for Spectrum Market with Guaranteed Minimum Requirement

Stable Matching for Spectrum Market with Guaranteed Minimum Requirement Sl g Spum Gun mum Rqumn Yno n T S Ky Sw ngg ompu Sool Wun Uny nyno@wuun Yuxun Xong T S Ky Sw ngg ompu Sool Wun Uny xongyx@mlluun Qn Wng ompu Sool Wun Uny qnwng@wuun STRT Xoyn Y Sool mon Tlogy ow Uny X

More information

10.3 The Quadratic Formula

10.3 The Quadratic Formula . Te Qudti Fomul We mentioned in te lst setion tt ompleting te sque n e used to solve ny qudti eqution. So we n use it to solve 0. We poeed s follows 0 0 Te lst line of tis we ll te qudti fomul. Te Qudti

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

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

P a g e 5 1 of R e p o r t P B 4 / 0 9

P a g e 5 1 of R e p o r t P B 4 / 0 9 P a g e 5 1 of R e p o r t P B 4 / 0 9 J A R T a l s o c o n c l u d e d t h a t a l t h o u g h t h e i n t e n t o f N e l s o n s r e h a b i l i t a t i o n p l a n i s t o e n h a n c e c o n n e

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

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

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

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

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

An action with positive kinetic energy term for general relativity. T. Mei

An action with positive kinetic energy term for general relativity. T. Mei An ton wt post nt ny t fo n tty T (Dptnt of Jon Cnt Cn o Unsty Wn H PRO Pop s Rp of Cn E-: to@nn tow@pwn ) Astt: At fst w stt so sts n X: 7769 n tn sn post nt ny oont onton n y X: 7769 w psnt n ton wt

More information

BACKFILLED 6" MIN TRENCH BOTT OF CONT FTG PROVIDE SLEEVE 1" CLR AROUND PIPE - TYP BOTTOM OF TRENCH PIPE SHALL NOT EXTEND BELOW THIS LINE

BACKFILLED 6 MIN TRENCH BOTT OF CONT FTG PROVIDE SLEEVE 1 CLR AROUND PIPE - TYP BOTTOM OF TRENCH PIPE SHALL NOT EXTEND BELOW THIS LINE TT T I I. VTS T T, SPIS. 0 MSY ITS MS /" = '-0" +' - " T /" d ' - 0" I SIGTI d d w/ " M I S IS M I d 0 T d T d w/ å" 0 K W TT I IS ITPT SM SIZ x 0'-0" I T T W PSSI d /" TY I SIGTI Y ' - 0" '-0" '-0" P

More information

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

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

More information

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

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

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

More information

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

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

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

Physics 222 Midterm, Form: A

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

More information

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

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

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

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

BACKFILLED 6" MIN TRENCH BOTT OF CONT FTG PROVIDE SLEEVE 1" CLR AROUND PIPE - TYP BOTTOM OF TRENCH PIPE SHALL NOT EXTEND BELOW THIS LINE

BACKFILLED 6 MIN TRENCH BOTT OF CONT FTG PROVIDE SLEEVE 1 CLR AROUND PIPE - TYP BOTTOM OF TRENCH PIPE SHALL NOT EXTEND BELOW THIS LINE TT T I I. VTS T T, SPIS. 0 MSY ITS MS /" = '-0" +' - " T /" d ' - 0" I SIGTI d d w/ " M I S IS M I d 0 MI T d T d w/ å" 0 K W TT I IS ITPT SM SIZ x 0'-0" MI I T T W PSSI d /" TY I SIGTI Y ' - 0" MI '-0"

More information

Easy Steps to build a part number... Tri-Start Series III CF P

Easy Steps to build a part number... Tri-Start Series III CF P ulti-l i Oti iul ( oto) ow to O ol os sy ts to uil t u... i-tt is 1. 2 3 4. 5. 6. oto y til iis ll tyl ll iz- st t ott y & y/ ywy ositio 50 9 0 17-08 ol ulti-l i oti otos o us wit ulti-o sil o tii o y

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

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

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

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

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

PRELIMINARY ONLY GENERAL NOTES CITY OF CHARLOTTETOWN TRAFFIC SIGNAL UPGRADES 2018 OVERALL SITE PLAN D-C01

PRELIMINARY ONLY GENERAL NOTES CITY OF CHARLOTTETOWN TRAFFIC SIGNAL UPGRADES 2018 OVERALL SITE PLAN D-C01 NOT POJT LOTION (UTON T. / PONL T.) PIN PK. UTON TT UTON TT OHFO TT KNT TT PONL TT QUN TT FITZOY TT POJT LOTION (KNT T. / T O T..) KNT TT T O TT POJT LOTION (KNT T. / PIN T.) PIN TT KNT TT HILLOOUH TT

More information

A L A BA M A L A W R E V IE W

A L A BA M A L A W R E V IE W A L A BA M A L A W R E V IE W Volume 52 Fall 2000 Number 1 B E F O R E D I S A B I L I T Y C I V I L R I G HT S : C I V I L W A R P E N S I O N S A N D TH E P O L I T I C S O F D I S A B I L I T Y I N

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

Outline. Binary Tree

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

More information

A simple 2-D interpolation model for analysis of nonlinear data

A simple 2-D interpolation model for analysis of nonlinear data Vol No - p://oog//n Nl Sn A mpl -D npolon mol o nl o nonln M Zmn Dpmn o Cvl Engnng Fl o nolog n Engnng Yo Unv Yo In; m@ml Rv M ; v Apl ; p M ABSRAC o mnon volm n wg o nonnom o n o po vlon o mnng n o ng

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

Reminder. CS 188: Artificial Intelligence. A reflex agent for pacman. Reflex Agent. A reflex agent for pacman (3) A reflex agent for pacman (2)

Reminder. CS 188: Artificial Intelligence. A reflex agent for pacman. Reflex Agent. A reflex agent for pacman (3) A reflex agent for pacman (2) C 88: Atiiil Intllign Ltus n : Rmin Only vy smll tion o AI is out mking omuts ly gms intlligntly Rll: omut vision, ntul lngug, ootis, min lning, omuttionl iology, t. Pit Al UC Bkly Mny slis om Dn Klin

More information

Equations from The Relativistic Transverse Doppler Effect at Distances from One to Zero Wavelengths. Copyright 2006 Joseph A.

Equations from The Relativistic Transverse Doppler Effect at Distances from One to Zero Wavelengths. Copyright 2006 Joseph A. Equtins m Th Rltiisti Tnss ppl Et t istns m On t Z Wlngths Cpyight 006 Jsph A. Rybzyk Psntd is mplt list ll th qutin usd in did in Th Rltiisti Tnss ppl Et t istns m On t Z Wlngths pp. Als inludd ll th

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

SHT 1 OF 3 SHT 2 AND 3 ARE -A- SIZE

SHT 1 OF 3 SHT 2 AND 3 ARE -A- SIZE 0 SH NO TYP O MOL NXT SSMLY QTY PT NUM SIPTION O MTIL ITM 00 MIN ION SOL SI K-O ION SOL SI X X 0 Y U T 0 U OM O SSY 0-0-0 V SHM 0-0-00 U U 0 O J X U 0 0 X S TIL V T 0 V U U J L U L MH U U V U0 0 U U U

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

RUTH. land_of_israel: the *country *which God gave to his people in the *Old_Testament. [*map # 2]

RUTH. land_of_israel: the *country *which God gave to his people in the *Old_Testament. [*map # 2] RUTH 1 Elimlk g ln M 1-2 I in im n ln Irl i n *king. Tr r lr rul ln. Ty r ug. Tr n r l in Ju u r g min. Elimlk mn y in n Blm in Ju. H i nm Nmi. S n Elimlk 2 *n. Tir nm r Mln n Kilin. Ty r ll rm Er mily.

More information

SYMMETRICAL COMPONENTS

SYMMETRICAL COMPONENTS SYMMETRCA COMPONENTS Syl oponn llow ph un of volg n un o pl y h p ln yl oponn Con h ph ln oponn wh Engy Convon o 4 o o wh o, 4 o, 6 o Engy Convon SYMMETRCA COMPONENTS Dfn h opo wh o Th o of pho : pov ph

More information

The Similar Construction Method of Non-Homogeneous Boundary Value Problems for Second-Order Homogeneous Linear Differential Equations

The Similar Construction Method of Non-Homogeneous Boundary Value Problems for Second-Order Homogeneous Linear Differential Equations Intntionl Jounl o Sintii n Innovtiv Mthmtil Rsh IJSIMR Volum 3 Issu Novm 5 PP 9-5 ISSN 347-37X Pint & ISSN 347-34 Onlin www.jounls.og Th Simil Constution Mtho o Non-Homognous Boun Vlu Polms o Son-O Homognous

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

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

Why CEHCH? Completion of this program provides participants direct access to sit for the NAB HCBS exam.

Why CEHCH? Completion of this program provides participants direct access to sit for the NAB HCBS exam. Wy? T fus f s pns n us s p ssn pnns f w-bn & uny bs ss pg. Ts us s p n n ff s s bs p wn & uny bs ss pfssn. W uny n s f O s n n ns qu f sp u D, sussfu pn f s n us w nb yu ns n knwg bs. T O un f & sp n O

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

176 5 t h Fl oo r. 337 P o ly me r Ma te ri al s

176 5 t h Fl oo r. 337 P o ly me r Ma te ri al s A g la di ou s F. L. 462 E l ec tr on ic D ev el op me nt A i ng er A.W.S. 371 C. A. M. A l ex an de r 236 A d mi ni st ra ti on R. H. (M rs ) A n dr ew s P. V. 326 O p ti ca l Tr an sm is si on A p ps

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

P a g e 3 6 of R e p o r t P B 4 / 0 9

P a g e 3 6 of R e p o r t P B 4 / 0 9 P a g e 3 6 of R e p o r t P B 4 / 0 9 p r o t e c t h um a n h e a l t h a n d p r o p e r t y fr om t h e d a n g e rs i n h e r e n t i n m i n i n g o p e r a t i o n s s u c h a s a q u a r r y. J

More information