Tries and Suffix Trees. Inge Li Gørtz

Size: px
Start display at page:

Download "Tries and Suffix Trees. Inge Li Gørtz"

Transcription

1 Tri nd Suffix Tr Ing Li Gørtz

2 String indxing prom String mtcing prom. Givn tring T (txt) nd P (pttrn) ovr n pt Σ, rport trting poition of occurrnc of P in T. Finit utomton: O(mΣ + n) tim nd pc KMP: O(m+n) tim nd pc String indxing prom. Givn tring S of crctr from n pt Σ. Prproc S into dt tructur to upport Src(P): Rturn trting poition of occurrnc of P in S. Tod: Dt tructur uing O(n) pc nd upporting Src(P) in O(m) tim. Appiction: Src ngin,.g. prfix rc. Finding common utring of mn ioogic tring Finding rpting utructur in ioogic tring Dtcting DNA contmintion

3 Outin Tri Comprd tri Suffix tr Appiction of uffix tr

4 Tri

5 Tri Txt rtriv t S 2 S 4 S 6 S 3 S 1 S 5 Tri ovr t tring:,, t,,, t.

6 Tri Txt rtriv Prfix-fr? t S 2 S 4 S 6 S 3 S 1 S 5 Tri ovr t tring:,, t,,, t,.

7 Tri Txt rtriv Prfix-fr? t S 2 S 4 S 6 S 3 S 7 S 1 S5 Tri ovr t tring:,, t,,, t,.

8 Tri Txt rtriv Src for t S 2 S 4 S 6 S 3 S 7 S 1 S5 Tri ovr t tring:,, t,,, t,.

9 Tri Txt rtriv Src for t S 2 S 4 S 6 S 3 S 7 S 1 S5 Tri ovr t tring:,, t,,, t,.

10 Tri Txt rtriv Src for t S 2 S 4 S 6 S 3 S 7 S 1 S5 Tri ovr t tring:,, t,,, t,.

11 Tri Txt rtriv Src for t S 2 S 4 S 6 S 3 S 7 S 1 S5 Tri ovr t tring:,, t,,, t,.

12 Tri Txt rtriv Src for t S 2 S 4 S 6 S 3 S 7 S 1 S5 Tri ovr t tring:,, t,,, t,.

13 Tri Txt rtriv Src for t S 2 S 4 S 6 S 3 S 7 S 1 S5 Tri ovr t tring:,, t,,, t,.

14 Tri Txt rtriv Src for t S 2 S 4 S 6 S 3 S 7 S 1 S5 Tri ovr t tring:,, t,,, t,.

15 Tri Txt rtriv Src for t S 2 S 4 S 6 S 3 S 7 S 1 S5 Tri ovr t tring:,, t,,, t,.

16 Tri Txt rtriv Src for t S 2 S 4 S 6 S 3 S 7 S 1 S5 Tri ovr t tring:,, t,,, t,.

17 Tri Txt rtriv Src for t S 2 S 4 S 6 S 3 S 7 S 1 S5 Tri ovr t tring:,, t,,, t,.

18 Tri Txt rtriv Src for t S 2 S 4 S 6 S 3 S 7 S 1 S5 Tri ovr t tring:,, t,,, t,.

19 Tri Txt rtriv Src for t S 2 S 4 S 7 S 6 S 3 S 1 S5 Tri ovr t tring:,, t,,, t,.

20 Tri Txt rtriv Src for ort t S 2 S 4 S 6 S 3 S 7 S 1 S5 Tri ovr t tring:,, t,,, t,.

21 Tri Txt rtriv Src for ort t S 2 S 4 S 6 S 3 S 7 S 1 S5 Tri ovr t tring:,, t,,, t,.

22 Tri Txt rtriv Src for ort t S 2 S 4 S 6 S 3 S 7 S 1 S5 Tri ovr t tring:,, t,,, t,.

23 Tri Txt rtriv Src for ort t S 2 S 4 S 6 S 3 S 7 S 1 S5 Tri ovr t tring:,, t,,, t,.

24 Tri Txt rtriv Src for ort t S 2 S 4 S 6 S 3 S 7 S 1 S5 Tri ovr t tring:,, t,,, t,.

25 Tri Buid tri ovr t tring:,,. S 2 S 4 S 1

26 Tri Proprti of t tri. A tri T toring coction S of tring of tot ngt n from n pt of iz d t foowing proprti: How mn cidrn cn nod v? How mn v do T v? Wt i t igt of T? Wt i t numr of nod in T?

27 Tri Src tim: O(d) in c nod => O(dm). O(m) if d contnt. d not contnt: u dictionr Hing O(1) Bncd BST: O(og d) Tim nd pc for tri (for m/contnt d): O(m) for rcing for tring of ngt m. O(n) pc. Prprocing: O(n)

28 Tri Prfix rc: rturn word in t tri trting wit t S 2 S 4 S 6 S 3 S 7 S 1 S5

29 Tri Prfix rc: rturn word in t tri trting wit t S 2 S 4 S 6 S 3 S 7 S 1 S5

30 Tri Prfix rc: rturn word in t tri trting wit t S 2 S 4 S 6 S 3 S 7 S 1 S5

31 Tri Tim for prfix rc: O(m) + tim to rport occurrnc. Coud rg!! Soution: compct tri.

32 Compct tri

33 Tri Compct tri: Cin of nod wit ing cid i mrgd into ing nod. t S 2 S 4 S 6 S 3 S 7 S 1 S5

34 Tri Compct tri: Cin of nod wit ing cid i mrgd into ing nod. t t S 2 S6 S3 S4 S1 S5 S7

35 Tri Proprti of t compct tri. A compct tri T toring coction S of tring of tot ngt n from n pt of iz d t foowing proprti: Evr intrn nod of T t t 2 nd t mot d cidrn. T v T numr of nod in T i < 2. Tim nd pc for compct tri (contnt d): O(m) for rcing for tring of ngt m. O(m + occ) for prfix rc, wr occ = #occurrnc O() pc. Prprocing: O(n)

36 Suffix tr

37 Suffix tr String indxing prom. Givn tring S of crctr from n pt Σ. Prproc S into dt tructur to upport Src(P): Rturn trting poition of occurrnc of P in S. Buid comprd tri ovr uffix of S (uffix tr). L v wit indx of uffix. Orvtion: An occurrnc of P i prfix of uffix of S. occurrnc of P Suffix of S

38 Suffix tr String indxing prom. Givn tring S of crctr from n pt Σ. Prproc S into dt tructur to upport Src(P): Rturn trting poition of occurrnc of P in S. Buid comprd tri ovr uffix of S (uffix tr). L v wit indx of uffix. Orvtion: An occurrnc of P i prfix of uffix of S. occurrnc of P Suffix of S Exmp: P = n. n n t r i n g d Suffix of S Suffix of S

39 Suffix Tr Suffix tr: ovr t tring nn n n 6 n n 1 n n

40 Suffix Tr Suffix tr: ovr t tring nn n n 6 n n 1 n n Src for P. Rport of v ow fin nod

41 Suffix Tr Suffix tr: ovr t tring nn Find occurrnc of P= n n n 6 n n 1 n n Src for P. Rport of v ow fin nod

42 Suffix Tr Suffix tr: ovr t tring nn n n 6 n n 1 n n Stor S nd tor nod rfrnc to S n n

43 Suffix Tr Suffix tr: ovr t tring nn [2,2] [3,4] [7,7] [1,7] [3,4] [7,7] 7 6 [5,7] [7,7] [5,7] [7,7] Stor S nd tor nod rfrnc to S n n

44 Suffix tr nd common utring

45 Suffix tr Suffix tr of tring S: Compct tri ovr uffix of S. Spc nd tim: Spc: O(n) Src tim: O(m) + tim to rport occurrnc = O(m+occ) Prprocing: Cn don in O(ort(n, Σ )) tim, wr ort(n, Σ ) i t tim it tk to ort n crctr from n pt Σ. Suffix tr cn ud to ov t String indxing prom in: Spc: O(n) Src tim: O(m+occ) Prprocing: O(ort(n, Σ )) tim

46 Appiction of uffix tr

47 Longt common utring Find ongt common utring of tring S1 nd S2. Contruct t uffix tr ovr S11S22. Exmp: Find ongt common utring of pipi nd pipi: Contruct uffix tr of pipi1pipi2.

48 Gnrizd uffix tr Suffix tr of pipi1pipi i p i p i 2 2 p i 2 2 p i p i p i p i p i

49 Gnrizd uffix tr Suffix tr of pipi1pipi2. Mrk f wit if 1 uffix trt in S

50 Gnrizd uffix tr Suffix tr of pipi1pipi2. Mrk f wit if 1 uffix trt in S

51 Gnrizd uffix tr Suffix tr of pipi1pipi2. Mrk f wit if 1 uffix trt in S1. Add tring-dpt. [18,18] [9,18] [15,15] [14,15] [13,15] [17,17] [16,18] [17,17] [13,18] [16,18] [8,8] [13,18] [16,18] [8,8] [13,18] [18,18] [5,18] [9,18] [18,18] [9,18] [5,18] [9,18] [5,18] [9,18] [5,18]

52 Gnrizd uffix tr Suffix tr of pipi1pipi2. Mrk f wit if 1 uffix trt in S1. Add tring-dpt. [18,18] [9,18] [15,15] [14,15] [13,15] [17,17] [16,18] [17,17] [13,18] [16,18] [8,8] [13,18] [16,18] [8,8] [13,18] [18,18] [5,18] [9,18] [18,18] [9,18] [5,18] [9,18] [5,18] [9,18] [5,18] S[13,15] = pi i t ongt common utring.

53 Longt common utring Uing uffix tr w cn ov t ongt common utring prom in inr tim (for contnt iz pt).

Algorithms. Algorithms 5.2 TRIES. R-way tries ternary search tries character-based operations ROBERT SEDGEWICK KEVIN WAYNE

Algorithms. Algorithms 5.2 TRIES. R-way tries ternary search tries character-based operations ROBERT SEDGEWICK KEVIN WAYNE Agoritm ROBERT SEDGEWICK KEVIN WAYNE 5.2 TRIES Agoritm F O U R T H E D I T I O N R-wy tri trnry rc tri crctr-bd oprtion ROBERT SEDGEWICK KEVIN WAYNE ttp://g4.c.princton.du Lt updtd on 11/12/15 7:45 AM

More information

Indexed Search Tree (Trie)

Indexed Search Tree (Trie) Indxd Sarch Tr (Tri) Fawzi Emad Chau-Wn Tsng Dpartmnt of Computr Scinc Univrsity of Maryand, Cog Park Indxd Sarch Tr (Tri) Spcia cas of tr Appicab whn Ky C can b dcomposd into a squnc of subkys C 1, C

More information

CPE702 Algorithm Analysis and Design Week 11 String Processing

CPE702 Algorithm Analysis and Design Week 11 String Processing CPE702 Agorithm Anaysis and Dsign Wk 11 String Procssing Prut Boonma prut@ng.cmu.ac.th Dpartmnt of Computr Enginring Facuty of Enginring, Chiang Mai Univrsity Basd on Sids by M.T. Goodrich and R. Tamassia

More information

Algorithms. Algorithms 5.2 TRIES. R-way tries ternary search tries character-based operations ROBERT SEDGEWICK KEVIN WAYNE

Algorithms. Algorithms 5.2 TRIES. R-way tries ternary search tries character-based operations ROBERT SEDGEWICK KEVIN WAYNE Agoritm ROBERT SEDGEWICK KEVIN WAYNE 5.2 TRIES Agoritm F O U R T H E D I T I O N R-way tri trnary arc tri caractr-bad opration ROBERT SEDGEWICK KEVIN WAYNE ttp://ag4.c.princton.du Lat updatd on 12/4/18

More information

Algorithms. Algorithms 5.2 TRIES. R-way tries ternary search tries character-based operations ROBERT SEDGEWICK KEVIN WAYNE

Algorithms. Algorithms 5.2 TRIES. R-way tries ternary search tries character-based operations ROBERT SEDGEWICK KEVIN WAYNE Agoritm ROBERT SEDGEWICK KEVIN WAYNE 5.2 TRIES Agoritm F O U R T H E D I T I O N R-way tri trnary arc tri caractr-bad opration ROBERT SEDGEWICK KEVIN WAYNE ttp://ag4.c.princton.du Summary of t prformanc

More information

Winter 2016 COMP-250: Introduction to Computer Science. Lecture 23, April 5, 2016

Winter 2016 COMP-250: Introduction to Computer Science. Lecture 23, April 5, 2016 Wintr 2016 COMP-250: Introduction to Computr Scinc Lctur 23, April 5, 2016 Commnt out input siz 2) Writ ny lgorithm tht runs in tim Θ(n 2 log 2 n) in wors cs. Explin why this is its running tim. I don

More information

Multiple patterns. Why? Algorithms. Aho Corasick (AC) Text Algorithms (4AP) Lecture 3.2: Multiple pattern matching. Jaak Vilo 2008 fall

Multiple patterns. Why? Algorithms. Aho Corasick (AC) Text Algorithms (4AP) Lecture 3.2: Multiple pattern matching. Jaak Vilo 2008 fall Multipl pattrn Txt Algoritm (AP) Lctur.: Multipl pattrn matcing Jaak Vilo 8 fall S {P} Jaak Vilo MTAT..9 Txt Algoritm Wy? Multipl pattrn Higligt multipl diffrnt arc word on t pag Viru dtction filtr for

More information

Some Terminologies. Some Terminologies. Trees. Example: UNIX Directory. Trees. Binary Trees, Binary Search Trees 1/9/2014

Some Terminologies. Some Terminologies. Trees. Example: UNIX Directory. Trees. Binary Trees, Binary Search Trees 1/9/2014 // Som Trminoogis Binry Trs, Binry Srch Trs www.cs.ust.hk/~humin/comp /st.ppt Chid nd prnt Evry nod xcpt th root hs on prnt A nod cn hv n ritrry numr of chidrn Lvs Nods with no chidrn Siing nods with sm

More information

TRIES BBM ALGORITHMS DEPT. OF COMPUTER ENGINEERING ERKUT ERDEM. Apr. 21, 2015

TRIES BBM ALGORITHMS DEPT. OF COMPUTER ENGINEERING ERKUT ERDEM. Apr. 21, 2015 BBM 202 - ALGORITHMS DEPT. OF COMPUTER ENGINEERING ERKUT ERDEM TRIES Apr. 21, 2015 Acknowdgmnt: T cour id ar adaptd from t id prpard by R. Sdgwick and K. Wayn of Princton Univrity. Tri R-way tri Trnary

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

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

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

Overview. Splay trees. Balanced binary search trees. Inge Li Gørtz. Self-adjusting BST (Sleator-Tarjan 1983).

Overview. Splay trees. Balanced binary search trees. Inge Li Gørtz. Self-adjusting BST (Sleator-Tarjan 1983). Ovrvw B r rh r: R-k r -3-4 r 00 Ig L Gør Amor Dm rogrmmg Nwork fow Srg mhg Srg g Comuo gomr Irouo o NP-om Rom gorhm B r rh r -3-4 r Aow,, or 3 k r o Prf Evr h from roo o f h m gh mr h E w E R E R rgr h

More information

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

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

More information

Bayesian belief networks: Inference

Bayesian belief networks: Inference C 740 Knowd rprntton ctur 0 n f ntwork: nfrnc o ukrcht o@c.ptt.du 539 nnott qur C 750 chn rnn n f ntwork. 1. Drctd ccc rph Nod rndo vr nk n nk ncod ndpndnc. urr rthquk r ohnc rc C 750 chn rnn n f ntwork.

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

Graphs Depth First Search

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

More information

Minimum Spanning Trees

Minimum Spanning Trees Minimum Spnning Trs Minimum Spnning Trs Problm A town hs st of houss nd st of rods A rod conncts nd only houss A rod conncting houss u nd v hs rpir cost w(u, v) Gol: Rpir nough (nd no mor) rods such tht:

More information

The second condition says that a node α of the tree has exactly n children if the arity of its label is n.

The second condition says that a node α of the tree has exactly n children if the arity of its label is n. CS 6110 S14 Hanout 2 Proof of Conflunc 27 January 2014 In this supplmntary lctur w prov that th λ-calculus is conflunt. This is rsult is u to lonzo Church (1903 1995) an J. arkly Rossr (1907 1989) an is

More information

Graphs Breadth First Search

Graphs Breadth First Search Grp Brdt Frt Sr SFO ORD LAX DFW - 1 - Outo Ø By undrtndn t tur, you oud to: q L rp ordn to t ordr n w vrt r dovrd n rdt-rt r. q Idnty t urrnt tt o rdt-rt r n tr o vrt tt r prvouy dovrd, ut dovrd or undovrd.

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

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

Who is this Great Team? Nickname. Strangest Gift/Friend. Hometown. Best Teacher. Hobby. Travel Destination. 8 G People, Places & Possibilities

Who is this Great Team? Nickname. Strangest Gift/Friend. Hometown. Best Teacher. Hobby. Travel Destination. 8 G People, Places & Possibilities Who i thi Gt Tm? Exi Sh th foowing i of infomtion bot of with o tb o tm mt. Yo o not hv to wit n of it own. Yo wi b givn on 5 mint to omih thi tk. Stngt Gift/Fin Niknm Homtown Bt Th Hobb Tv Dtintion Robt

More information

(1) Then we could wave our hands over this and it would become:

(1) Then we could wave our hands over this and it would become: MAT* K285 Spring 28 Anthony Bnoit 4/17/28 Wk 12: Laplac Tranform Rading: Kohlr & Johnon, Chaptr 5 to p. 35 HW: 5.1: 3, 7, 1*, 19 5.2: 1, 5*, 13*, 19, 45* 5.3: 1, 11*, 19 * Pla writ-up th problm natly and

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

Pattern Matching. a b a c a a b. a b a c a b. a b a c a b. Pattern Matching 1

Pattern Matching. a b a c a a b. a b a c a b. a b a c a b. Pattern Matching 1 Pattern Matching a b a c a a b 1 4 3 2 Pattern Matching 1 Outline and Reading Strings ( 9.1.1) Pattern matching algorithms Brute-force algorithm ( 9.1.2) Boyer-Moore algorithm ( 9.1.3) Knuth-Morris-Pratt

More information

Solutions Problem Set 2. Problem (a) Let M denote the DFA constructed by swapping the accept and non-accepting state in M.

Solutions Problem Set 2. Problem (a) Let M denote the DFA constructed by swapping the accept and non-accepting state in M. Solution Prolem Set 2 Prolem.4 () Let M denote the DFA contructed y wpping the ccept nd non-ccepting tte in M. For ny tring w B, w will e ccepted y M, tht i, fter conuming the tring w, M will e in n ccepting

More information

Minimum Spanning Trees

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

More information

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

Provider Satisfaction

Provider Satisfaction Prider Satisfaction Prider Satisfaction [1] NOTE: if you nd to navigate away from this page, please click the "Save Draft" page at the bottom (visible to ONLY logged in users). Otherwise, your rpons will

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

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

Examples and applications on SSSP and MST

Examples and applications on SSSP and MST Exampls an applications on SSSP an MST Dan (Doris) H & Junhao Gan ITEE Univrsity of Qunslan COMP3506/7505, Uni of Qunslan Exampls an applications on SSSP an MST Dijkstra s Algorithm Th algorithm solvs

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

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

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

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

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

Pattern Matching. a b a c a a b. a b a c a b. a b a c a b. Pattern Matching Goodrich, Tamassia

Pattern Matching. a b a c a a b. a b a c a b. a b a c a b. Pattern Matching Goodrich, Tamassia Pattern Matching a b a c a a b 1 4 3 2 Pattern Matching 1 Brute-Force Pattern Matching ( 11.2.1) The brute-force pattern matching algorithm compares the pattern P with the text T for each possible shift

More information

David Eigen. MA112 Final Paper. May 10, 2002

David Eigen. MA112 Final Paper. May 10, 2002 David Eigen MA112 Fina Paper May 1, 22 The Schrodinger equation describes the position of an eectron as a wave. The wave function Ψ(t, x is interpreted as a probabiity density for the position of the eectron.

More information

Improved Approximate String Matching and Regular Expression Matching on Ziv-Lempel Compressed Texts

Improved Approximate String Matching and Regular Expression Matching on Ziv-Lempel Compressed Texts Improved Approximate String Matching and Regular Expression Matching on Ziv-Lempel Compressed Texts Philip Bille IT University of Copenhagen Rolf Fagerberg University of Southern Denmark Inge Li Gørtz

More information

Case Study 1 PHA 5127 Fall 2006 Revised 9/19/06

Case Study 1 PHA 5127 Fall 2006 Revised 9/19/06 Cas Study Qustion. A 3 yar old, 5 kg patint was brougt in for surgry and was givn a /kg iv bolus injction of a muscl rlaxant. T plasma concntrations wr masurd post injction and notd in t tabl blow: Tim

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

Succinct 2D Dictionary Matching with No Slowdown

Succinct 2D Dictionary Matching with No Slowdown Succinct 2D Dictionary Matching with No Slowdown Shoshana Neuburger and Dina Sokol City University of New York Problem Definition Dictionary Matching Input: Dictionary D = P 1,P 2,...,P d containing d

More information

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

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

More information

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

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

Lecture contents. Bloch theorem k-vector Brillouin zone Almost free-electron model Bands Effective mass Holes. NNSE 508 EM Lecture #9

Lecture contents. Bloch theorem k-vector Brillouin zone Almost free-electron model Bands Effective mass Holes. NNSE 508 EM Lecture #9 Lctur contnts Bloch thorm -vctor Brillouin zon Almost fr-lctron modl Bnds ffctiv mss Hols Trnsltionl symmtry: Bloch thorm On-lctron Schrödingr qution ch stt cn ccommo up to lctrons: If Vr is priodic function:

More information

Balanced binary search trees

Balanced binary search trees 02110 Inge Li Gørtz Overview Blnced binry serch trees: Red-blck trees nd 2-3-4 trees Amortized nlysis Dynmic progrmming Network flows String mtching String indexing Computtionl geometry Introduction to

More information

Last time: introduced our first computational model the DFA.

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

More information

I M P O R T A N T S A F E T Y I N S T R U C T I O N S W h e n u s i n g t h i s e l e c t r o n i c d e v i c e, b a s i c p r e c a u t i o n s s h o

I M P O R T A N T S A F E T Y I N S T R U C T I O N S W h e n u s i n g t h i s e l e c t r o n i c d e v i c e, b a s i c p r e c a u t i o n s s h o I M P O R T A N T S A F E T Y I N S T R U C T I O N S W h e n u s i n g t h i s e l e c t r o n i c d e v i c e, b a s i c p r e c a u t i o n s s h o u l d a l w a y s b e t a k e n, i n c l u d f o l

More information

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

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

More information

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

INC 693, 481 Dynamics System and Modelling: Linear Graph Modeling II Dr.-Ing. Sudchai Boonto Assistant Professor

INC 693, 481 Dynamics System and Modelling: Linear Graph Modeling II Dr.-Ing. Sudchai Boonto Assistant Professor INC 69, 48 Dynamics Systm and Modlling: Linar Graph Modling II Dr.-Ing. Sudchai Boonto Assistant Profssor Dpartmnt of Control Systm and Instrumntation Enginring King Mongkut s Unnivrsity of Tchnology Thonuri

More information

Lecture Models for heavy-ion collisions (Part III): transport models. SS2016: Dynamical models for relativistic heavy-ion collisions

Lecture Models for heavy-ion collisions (Part III): transport models. SS2016: Dynamical models for relativistic heavy-ion collisions Lecture Models for heavy-ion collisions (Part III: transport models SS06: Dynamical models for relativistic heavy-ion collisions Quantum mechanical description of the many-body system Dynamics of heavy-ion

More information

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

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

More information

6-8, 7-0 & 7-2 Heights with 3-1/2 Hinges

6-8, 7-0 & 7-2 Heights with 3-1/2 Hinges TDRD RDWR LOTO FOR DOOR FRM TL DT T O. 509, & eights with 3-1/2 inges DOOR O T * 6 3/4 6 3/4 6 3/4 6 3/4" 33 3/4" 32 3/4" 30 3/4" 61 1/2" 33 3/4" 32 3/4" 30 3/4" TRK The hardware locations shown are for

More information

RF circuits design Grzegorz Beziuk. Introduction. Basic definitions and parameters. References

RF circuits design Grzegorz Beziuk. Introduction. Basic definitions and parameters. References RF cicuit dign Gzgoz Bziuk ntoduction. Bic dinition nd pmt Rnc [] Titz., Schnk C., Ectonic cicuit : hndook o dign nd ppiction, Sping 8 [] Goio M., RF nd micowv piv nd ctiv tchnoogi in: RF nd Micowv hndook,

More information

Aim To manage files and directories using Linux commands. 1. file Examines the type of the given file or directory

Aim To manage files and directories using Linux commands. 1. file Examines the type of the given file or directory m E x. N o. 3 F I L E M A N A G E M E N T Aim To manag ils and dirctoris using Linux commands. I. F i l M a n a g m n t 1. il Examins th typ o th givn il or dirctory i l i l n a m > ( o r ) < d i r c t

More information

SEASHORE LEARNING CENTER

SEASHORE LEARNING CENTER 0 PR- 0 V-R T-R R PR- 0 V-R R V-R INR 0 V-R T-R STOR 0. VSTIUL 0 R IRLS 0. R OYS 0. ORRIOR 0 OYS 0. IRLS 0. R VSTIUL 0 STOR 0. OYS 0. R VSTIUL 0 IRLS 0. V-R INR 0 STOR 0 R PR- 0 V-R T-R PR- 0 V-R RZWY

More information

(Minimum) Spanning Trees

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

More information

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

CPS 616 W2017 MIDTERM SOLUTIONS 1

CPS 616 W2017 MIDTERM SOLUTIONS 1 CPS 616 W2017 MIDTERM SOLUTIONS 1 PART 1 20 MARKS - MULTIPLE CHOICE Instructions Plas ntr your answrs on t bubbl st wit your nam unlss you ar writin tis xam at t Tst Cntr, in wic cas you sould just circl

More information

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

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

More information

12 - M G P L Z - M9BW. Port type. Bore size ø12, ø16 20/25/32/40/50/ MPa 10 C to 60 C (With no condensation) 50 to 400 mm/s +1.

12 - M G P L Z - M9BW. Port type. Bore size ø12, ø16 20/25/32/40/50/ MPa 10 C to 60 C (With no condensation) 50 to 400 mm/s +1. ris - MP - Compt gui ylinr ø, ø, ø, ø, ø, ø, ø, ø ow to Orr Cln sris lif typ (with spilly trt sliing prts) Vuum sution typ (with spilly trt sliing prts) ir ylinr otry tutor - M P - - MW ll ushing ring

More information

priority queue ADT heaps 1

priority queue ADT heaps 1 COMP 250 Lctur 23 priority quu ADT haps 1 Nov. 1/2, 2017 1 Priority Quu Li a quu, but now w hav a mor gnral dinition o which lmnt to rmov nxt, namly th on with highst priority..g. hospital mrgncy room

More information

shhgs@wgqqh.com chinapub 2002 7 Bruc Eckl 1000 7 Bruc Eckl 1000 Th gnsis of th computr rvolution was in a machin. Th gnsis of our programming languags thus tnds to look lik that Bruc machin. 10 7 www.wgqqh.com/shhgs/tij.html

More information

Algorithms in Computational. Biology. More on BWT

Algorithms in Computational. Biology. More on BWT Algorithms in Computtionl Biology More on BWT tody Plese Lst clss! don't forget to submit And by next (vi emil, repo ) implementtion week or shre prgectfltw get Not I would like reding overview! Discuss

More information

Overview. Splay trees. Balanced binary search trees. Inge Li Gørtz. Self-adjusting BST (Sleator-Tarjan 1983).

Overview. Splay trees. Balanced binary search trees. Inge Li Gørtz. Self-adjusting BST (Sleator-Tarjan 1983). Ovrvw Bn nr rh r: R-k r n -- r 00 Ing L Gør Amor n Dnm rogrmmng Nwork fow Srng mhng Srng nng Comuon gomr Inrouon o NP-omn Rnom gorhm Bn nr rh r -- r. Aow,, or k r no Prf n. Evr h from roo o f h m ngh.

More information

Fingerprint idea. Assume:

Fingerprint idea. Assume: Fingerprint ide Assume: We cn compute fingerprint f(p) of P in O(m) time. If f(p) f(t[s.. s+m 1]), then P T[s.. s+m 1] We cn compre fingerprints in O(1) We cn compute f = f(t[s+1.. s+m]) from f(t[s.. s+m

More information

Knuth-Morris-Pratt Algorithm

Knuth-Morris-Pratt Algorithm Knuth-Morris-Pratt Algorithm Jayadev Misra June 5, 2017 The Knuth-Morris-Pratt string matching algorithm (KMP) locates all occurrences of a pattern string in a text string in linear time (in the combined

More information

CS 541 Algorithms and Programs. Exam 2 Solutions. Jonathan Turner 11/8/01

CS 541 Algorithms and Programs. Exam 2 Solutions. Jonathan Turner 11/8/01 CS 1 Algorim nd Progrm Exm Soluion Jonn Turnr 11/8/01 B n nd oni, u ompl. 1. (10 poin). Conidr vrion of or p prolm wi mulipliiv o. In i form of prolm, lng of p i produ of dg lng, rr n um. Explin ow or

More information

Planar convex hulls (I)

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

More information

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

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

More information

a b [^ab] ^a [^ab] [^ab]

a b [^ab] ^a [^ab] [^ab] Genertion of Pttern-Mtcing Algoritm y Extended Regulr Expreion Ikuo NAKATA 3 Summry. It i dicult to expre te denition of te comment of C lnguge in regulr expreion. However, te denition cn e expreed y imple

More information

7 ACM FOR FRAME 2SET 6 FRAME 2SET 5 ACM FOR MAIN FRAME 2SET 4 MAIN FRAME 2SET 3 POLE ASSLY 1 2 CROWN STRUCTURE ASSLY 1 1 CROWN ASSLY 1

7 ACM FOR FRAME 2SET 6 FRAME 2SET 5 ACM FOR MAIN FRAME 2SET 4 MAIN FRAME 2SET 3 POLE ASSLY 1 2 CROWN STRUCTURE ASSLY 1 1 CROWN ASSLY 1 7 M OR RM 2ST 6 RM 2ST 5 M OR MIN RM 2ST 4 MIN RM 2ST 3 POL SSLY 1 2 ROWN STRUTUR SSLY 1 1 ROWN SSLY 1 SR.NO. SRIPTION QTY. a LL IMNSIONS R IN mm I N MT Pi IOLMI 1'NTION LT. Tm: XPLO VIW OR POL MOUNT MLM

More information

CSI35 Chapter 11 Review

CSI35 Chapter 11 Review 1. Which of th grphs r trs? c f c g f c x y f z p q r 1 1. Which of th grphs r trs? c f c g f c x y f z p q r . Answr th qustions out th following tr 1) Which vrtx is th root of c th tr? ) wht is th hight

More information

EE 6882 Statistical Methods for Video Indexing and Analysis

EE 6882 Statistical Methods for Video Indexing and Analysis EE 6882 Statistical Mthods for Vido Indxing and Analysis Fall 2004 Prof. Shih-Fu Chang http://www..colubia.du/~sfchang Lctur 3 Part B (9/5/04) Exapl of E-M: Machin Translation Brown t al 993 A translation

More information

Allowable bearing capacity and settlement Vertical stress increase in soil

Allowable bearing capacity and settlement Vertical stress increase in soil 5 Allwabl barg aaity and ttlmnt Vrtial tr ra il - du t nntratd lad: 3 5 r r x y - du t irularly ladd ara lad:. G t tabl 5..6 Fd / by dtrmg th trm: r/(/) /(/) 3- blw rtangular ladd ara: th t i at th rnr

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

Data Structures and Algorithm. Xiaoqing Zheng

Data Structures and Algorithm. Xiaoqing Zheng Dt Strutures nd Algorithm Xioqing Zheng zhengxq@fudn.edu.n String mthing prolem Pttern P ours with shift s in text T (or, equivlently, tht pttern P ours eginning t position s + in text T) if T[s +... s

More information

Approximation of functions by piecewise defined trial functions

Approximation of functions by piecewise defined trial functions Approiation of functions by picwis fin tria functions In a prvious chaptr was approit by a continuous function fin ovr th who oain R with ˆ ψ + { ;,2,3, L} ψ Γ n Γ Γ R a n its bounary such that anψ fin

More information

1 Finite Automata and Regular Expressions

1 Finite Automata and Regular Expressions 1 Fini Auom nd Rgulr Exprion Moivion: Givn prn (rgulr xprion) for ring rching, w migh wn o convr i ino drminiic fini uomon or nondrminiic fini uomon o mk ring rching mor fficin; drminiic uomon only h o

More information

Gold s algorithm. Acknowledgements. Why would this be true? Gold's Algorithm. 1 Key ideas. Strings as states

Gold s algorithm. Acknowledgements. Why would this be true? Gold's Algorithm. 1 Key ideas. Strings as states Acknowledgements Gold s lgorithm Lurent Miclet, Jose Oncin nd Tim Otes for previous versions of these slides. Rfel Crrsco, Pco Cscuert, Rémi Eyrud, Philippe Ezequel, Henning Fernu, Thierry Murgue, Frnck

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

The Plan. Honey, I Shrunk the Data. Why Compress. Data Compression Concepts. Braille Example. Braille. x y xˆ

The Plan. Honey, I Shrunk the Data. Why Compress. Data Compression Concepts. Braille Example. Braille. x y xˆ h ln ony, hrunk th t ihr nr omputr in n nginring nivrsity of shington t omprssion onpts ossy t omprssion osslss t omprssion rfix os uffmn os th y 24 2 t omprssion onpts originl omprss o x y xˆ nor or omprss

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

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

perm4 A cnt 0 for for if A i 1 A i cnt cnt 1 cnt i j. j k. k l. i k. j l. i l

perm4 A cnt 0 for for if A i 1 A i cnt cnt 1 cnt i j. j k. k l. i k. j l. i l h 4D, 4th Rank, Antisytric nsor and th 4D Equivalnt to th Cross Product or Mor Fun with nsors!!! Richard R Shiffan Digital Graphics Assoc 8 Dunkirk Av LA, Ca 95 rrs@isidu his docunt dscribs th four dinsional

More information

Final Exam Solutions

Final Exam Solutions CS 2 Advancd Data Structurs and Algorithms Final Exam Solutions Jonathan Turnr /8/20. (0 points) Suppos that r is a root of som tr in a Fionacci hap. Assum that just for a dltmin opration, r has no childrn

More information

The Z transform techniques

The Z transform techniques h Z trnfor tchniqu h Z trnfor h th rol in dicrt yt tht th Lplc trnfor h in nlyi of continuou yt. h Z trnfor i th principl nlyticl tool for ingl-loop dicrt-ti yt. h Z trnfor h Z trnfor i to dicrt-ti yt

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

Steady-state tracking & sys. types

Steady-state tracking & sys. types Sty-tt trcking & y. ty Unity fck control: um CL tl lnt r C y - r - o.l. y y r ol ol o.l. m m n n n N N N N N, N,, ut N N, m, ol.. clo-loo: y r ol.. trcking rror: r y r ty-tt trcking: t r ol.. ol.. For

More information

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

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

More information

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

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

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

More information

NORTHLAKE APARTMENTS

NORTHLAKE APARTMENTS PRT RW RVTO VTY P PROT RPTO TR # UR P PROPRTY.. /..T..... T. R. V..W.P.. '. OT......... R...U..O. O. O. OT. OOR. PT..T..Y...... P. V.. R... W....... V.. Q. QUP..W.. XT.....O.................. OR OT R OTO

More information

Binomials and Pascal s Triangle

Binomials and Pascal s Triangle Binomils n Psl s Tringl Binomils n Psl s Tringl Curriulum R AC: 0, 0, 08 ACS: 00 www.mthltis.om Binomils n Psl s Tringl Bsis 0. Intif th prts of th polnomil: 8. (i) Th gr. Th gr is. (Sin is th highst

More information

OH BOY! Story. N a r r a t iv e a n d o bj e c t s th ea t e r Fo r a l l a g e s, fr o m th e a ge of 9

OH BOY! Story. N a r r a t iv e a n d o bj e c t s th ea t e r Fo r a l l a g e s, fr o m th e a ge of 9 OH BOY! O h Boy!, was or igin a lly cr eat ed in F r en ch an d was a m a jor s u cc ess on t h e Fr en ch st a ge f or young au di enc es. It h a s b een s een by ap pr ox i ma t ely 175,000 sp ect at

More information

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

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

More information