Introduction to Algorithms 6.046J/18.401J
|
|
- Adela Armstrong
- 6 years ago
- Views:
Transcription
1 3/4/28 Intoduton to Algothms 6.46J/8.4J Letue 8 - Hashng Pof. Manols Kells Hashng letue outlne Into and defnton Hashng n pate Unvesal hashng Pefet hashng Open Addessng (optonal) 3/4/28 L8.2 Data Stutues Role of data stutues: Enapsulate data Suppot etan opeatons (e.g., INSER, DELEE, SEARCH) Ou fous: effeny of the opeatons Algothms vs. data stutues Symbol-table poblem Symbol table holdng n eods: eod x key[x] Othe felds ontanng satellte data Opeatons on : INSER(, x) DELEE(, x) SEARCH(, k) How should the data stutue be oganzed? 3/4/28 L8.3 3/4/28 L8.4 Det-aess table IDEA: Suppose that the set of keys s K {,,, m }, and keys ae dstnt. Set up an aay [.. m ]: x f k K and key[x] k, [k] NIL othewse. hen, opeatons take Θ() tme. Poblem: he ange of keys an be lage. Eg.: 64-bt numbes (whh epesent 8,446,744,73,79,55,66 dffeent keys) Chaate stngs (even lage!). Hash funtons Soluton: Use a hash funton h to map the unvese U of all keys nto {,,, m }: K k 2 k k 4 k 5 U k3 When a eod to be nseted maps to an aleady ouped As eah key slot s n nseted,, a ollson h maps ous. t to a slot of. h(k ) h(k 4 ) h(k 2 ) h(k 5 ) h(k 3 ) m 3/4/28 L8.5 3/4/28 L8.6
2 3/4/28 Resolvng ollsons by hanng Reods n the same slot ae lnked nto a lst h(49) h(86) h(52) Run tme analyss eques key assumpton: 3/4/28 L8.7 Smple unfom hashng We make the assumpton of smple unfom hashng: Eah key k K of keys s equally lkely to be hashed to any slot of table, ndependent of whee othe keys ae hashed. Let n be the numbe of keys n the table, and let m be the numbe of slots. Defne the load fato of to be α n/m aveage numbe of keys pe slot. 3/4/28 L8.8 Seah ost fo hanng unde smple unfom hashng Expeted tme to seah fo a eod wth a gven key Θ( + α). apply hash funton and aess slot seah the lst Expeted seah tme Θ() f α O(), o equvalently, f n O(m). Into and defnton Hashng n pate Unvesal hashng Pefet hashng Open Addessng (optonal) 3/4/28 L8.9 3/4/28 L8. Choosng a hash funton he assumpton of smple unfom hashng s had to guaantee, but seveal ommon tehnques tend to wok well n pate as long as the defenes an be avoded. Desata: A good hash funton should dstbute the keys unfomly nto the slots of the table. Regulaty n the key dstbuton should not affet ths unfomty.. Dvson method Assume all keys ae nteges, and defne h(k) k mod m. Defeny: Don t pk an m that has a small dvso d. A pepondeane of keys that ae onguent modulo d an advesely affet unfomty. Exteme defeny: If m 2, then the hash doesn t even depend on all the bts of k: If k 2 and 6, then h(k) 2. h(k) 3/4/28 L8. 3/4/28 L8.2 2
3 3/4/28. Dvson method (ontnued) h(k) k mod m. Pk m to be a pme not too lose to a powe of 2 o and not othewse used pomnently n the omputng envonment. Annoyane: Sometmes, makng the table sze a pme s nonvenent. But, ths method s popula, although the next method we ll see s usually supeo. 2. Multplaton method Assume that all keys ae nteges, m 2, and ou ompute has w-bt wods. Defne h(k) (A k mod 2 w ) sh (w ), whee sh s the bt-wse ght-shft opeato and A s an odd ntege n the ange 2 w < A < 2 w. Don t pk A too lose to 2 w. Multplaton modulo 2 w s fast. he sh opeato s fast. 3/4/28 L8.3 3/4/28 L8.4 Suppose that m and that ou ompute has w 7-bt wods: 3A A. k h(k) A 2. Multplaton method example h(k) (A k mod 2 w ) sh (w ) Modula wheel Don t pk A too lose to 2 w. 2A 2. Multplaton method example h(k) (A k mod 2 w ) sh (w ) w k Neve omputed (mult. mod 2 w ) h(k) (exploe moe of the wheel spae) 3/4/28 L8.5 3/4/28 L8.6 A Don t pk A too lose to 2 w (have balane of s and s) 3. Dot-podut method Randomzed stategy: Eah value k < m, wth m pme. Let m be pme. Deompose k nto + k k k 2 k - k dgts, eah n {,,..m-}. Pk andom veto a, a a a 2 a - a smlaly deomposed (eah a hosen andomly m m m m m fom {,,..m-} ). Calulate dot podut k a, a k a k a 2 k 2 a k eah multplaton mod m Exellent n pate, but ha ( k) ak mod m expensve to ompute. Into and defnton Hashng n pate Unvesal hashng Pefet hashng Open Addessng (optonal) 3/4/28 L8.7 3/4/28 L8.8 3
4 3/4/28 A weakness of hashng Poblem: Fo any hash funton h, a set of keys exsts that an ause the aveage aess tme of a hash table to skyoket. Advesay an pk all keys st. {k U:h(k)} fo some slot (e.g. denal-of-seve attaks). IDEA: Choose the hash funton at andom, ndependently of the keys. Even f an advesay an see you ode, they annot fnd a bad set of keys, sne they don t know exatly whh hash funton wll be hosen. 3/4/28 L8.9 Unvesal hashng: Defnton Let U be a unvese of keys. Let H be a fnte olleton of hash funtons, eah mappng U to {,,, m }. H s unvesal f fo all x, y U, whee x y, we have {h H : h(x) h(y)} H /m,.e. only /m of hash funtons n H esult n x,y ollson. hus, f we hoose h andomly fom H, the hane of a ollson between x and y s /m {h : h(x) h(y)} H m H 3/4/28 L8.2 Unvesalty s good heoem. Let h be a hash funton hosen (unfomly) at andom fom a unvesal set H of hash funtons. Suppose h s used to hash n abtay keys nto the m slots of a table. hen, fo a gven key x, we have #ollsons wth x] < n/m. Poof of theoem Poof. Let C x be the andom vaable denotng the total numbe of ollsons of keys n wth x, and let f h(x) h(y), othewse. Note: ] /m and C. x 3/4/28 L8.2 3/4/28 L8.22 Poof (ontnued) Poof (ontnued) E [ C ] E y x ake expetaton of both sdes. C x ] E ] ake expetaton of both sdes. Lneaty of expetaton. 3/4/28 L8.23 3/4/28 L8.24 4
5 3/4/28 C x Poof (ontnued) ] E / m ] ake expetaton of both sdes. Lneaty of expetaton. ] /m. Poof (ontnued) Cx ] E n. m / m ] ake expetaton of both sdes. Lneaty of expetaton. ] /m. Algeba. 3/4/28 L8.25 3/4/28 L8.26 Constutng a set of unvesal hash funtons Let m be pme. Deompose key k nto + dgts, eah wth value n the set {,,, m }. hat s, let k k, k,, k, whee k < m. Randomzed stategy w/ dot-podut method: Pk a a, a,, a whee eah a s hosen andomly fom {,,, m } ndpt of nput. Defne h ( k) a k mod m. a How bg s H {h a }? H m +. Dot podut, modulo m REMEMBER HIS! Unvesalty of dot-podut hash funtons heoem. he set H {h a } s unvesal. Poof. Suppose that x x, x,, x and y y, y,, y ae dstnt keys. hus, they dffe n at least one dgt poston, w.l.o.g. poston (and possbly moe postons). Fo how many h a H do x and y ollde? ha( x) ha( b) a x a y (mod m) 3/4/28 L8.27 3/4/28 L8.28 Poof (ontnued) Equvalently, we have o a ( x a ( x y ) (mod m) y) + a ( x y ) (mod m whh mples that ), a( x y) a ( x y ) (mod m). Multply both sdes by nvese of ( x y ). 3/4/28 L8.29 Fat fom numbe theoy heoem. Let m be pme. Fo any z Z m suh that z, thee exsts a unque z Z m suh that z z (mod m). z s known as the multplatve nvese of z. z Example: m 7. z Explanaton. gd(z,m), zx+myl, zx (mod m), whee (z,x,y) s the output of EXENDED-EUCLID(z,m). Goup theoy. (Z n *, n) s a fnte abelan goup. (See Chapte 3, Poof: 3.3 and 3.26). 3/4/28 L8.3 5
6 3/4/28 We have Bak to the poof a( x y) a ( x y ) (mod m), and sne x y, an nvese (x y ) must exst, whh mples that a ( ) ( ) a x y x y (mod m). hus, fo any hoes of a, a 2,, a, exatly one hoe of a auses x and y to ollde. Poof (ompleted) Q. How many h a s ause x and y to ollde? A. hee ae m hoes fo eah of a, a 2,, a, but one these ae hosen, exatly one hoe fo a auses x and y to ollde, namely a a ( x y ) ( x y ) mod m. hus, the numbe of h a s that ause x and y to ollde s m m H /m. 3/4/28 L8.3 3/4/28 L8.32 Poof (ompleted) Q. Why sn t the answe just a? a( x y) a ( x y ) (mod m) A. Sue, f x y fo all, then the soluton to the equaton s a. Howeve, what the theoem shows s that fo any ombnaton of <x > and <y >, thee exsts an a that satsfes: a a ( x y ) ( x y ) mod m Into and defnton Hashng n pate Unvesal hashng Pefet hashng Open Addessng (optonal) 3/4/28 L8.33 3/4/28 L8.34 Pefet hashng Gven a set of n keys, onstut a stat hash table of sze m O(n) suh that SEARCH takes Θ() tme n the wost ase. IDEA: wolevel sheme wth unvesal 2 hashng at 3 both levels No ollsons at level 2! 86 S h S 3 (4) h 3 (27) 4 S m a Collsons at level 2 heoem. Let H be a lass of unvesal hash funtons fo a table of sze m n 2. hen, f we use a andom h H to hash n keys nto the table, the expeted numbe of ollsons s at most /2. Poof. By the defnton of unvesalty, the pobablty that 2 gven keys n the table ollde n unde h s /m /n 2. Sne thee ae ( ) pas 2 of keys that an possbly ollde, the expeted numbe of ollsons s n n( n ) E [ X ] < n 2 n 2 3/4/28 L8.35 3/4/28 L8.36 6
7 3/4/28 No ollsons at level 2 Coollay. he pobablty of no ollsons s at least /2. Poof. Makov s nequalty says that fo any nonnegatve andom vaable X, we have P{X t} X]/t. Applyng ths nequalty wth t, we fnd that the pobablty of o moe ollsons s at most /2. hus, just by testng andom hash funtons n H, we ll qukly fnd one that woks. Analyss of stoage Fo level- hash table, hoose m n. Rand. va. n # of keys that hash to slot n. If we use n 2 slots fo the level-2 hash table S, expeted total stoage equed fo the two-level sheme s: m ( n 2 E Θ ) Θ( n), (the analyss s dental to the analyss of buket sot expeted unnng tme fom etaton). Fo pobablty bound, apply Makov nequalty. 3/4/28 L8.37 3/4/28 L8.38 Into and defnton Hashng n pate Unvesal hashng Pefet hashng Open addessng (optonal) Resolvng ollsons by open addessng No stoage s used outsde of the hash table tself. Inseton systematally pobes the table untl an empty slot s found. he hash funton h(k,) depends on both the key k and the pobe numbe : h : U {,,, m } {,,, m }. he pobe sequene h(k,), h(k,),, h(k,m ) should be a pemutaton of {,,, m }. he table may fll up, and deleton s dffult (but not mpossble). 3/4/28 L8.39 3/4/28 L8.4 Example of open addessng Example of open addessng Inset key k 496:. Pobe h(496,) Inset key k 496:. Pobe h(496,). Pobe h(496,) 586 ollson ollson m m 3/4/28 L8.4 3/4/28 L8.42 7
8 3/4/28 Example of open addessng Example of open addessng Inset key k 496:. Pobe h(496,). Pobe h(496,) 2. Pobe h(496,2) nseton m Seah fo key k 496:. Pobe h(496,). Pobe h(496,) 2. Pobe h(496,2) Seah uses the same pobe sequene, temnatng suessfully f t fnds the key m and unsuessfully f t enountes an empty slot. 3/4/28 L8.43 3/4/28 L8.44 Pobng stateges Pobng stateges Lnea pobng: Gven an odnay hash funton h (k), lnea pobng uses the hash funton h(k,) (h (k) + ) mod m. hs method, though smple, suffes fom pmay lusteng, whee long uns of ouped slots buld up, neasng the aveage seah tme. Moeove, the long uns of ouped slots tend to get longe. Double hashng Gven two odnay hash funtons h (k) and h 2 (k), double hashng uses the hash funton h(k,) (h (k) + h 2 (k)) mod m. hs method geneally podues exellent esults, but h 2 (k) must be elatvely pme to m (othewse yle of <m elements, not all slots ae pobed). One way s to make m a powe of 2 and desgn h 2 (k) to podue only odd numbes. 3/4/28 L8.45 3/4/28 L8.46 Analyss of open addessng We make the assumpton of unfom hashng: Eah key s equally lkely to have any one of the m! pemutatons as ts pobe sequene. heoem. Gven an open-addessed hash table wth load fato α n/m <, the expeted numbe of pobes n an unsuessful seah s at most /( α). Poof of the theoem Poof. At least one pobe s always neessay. Wth pobablty n/m, the fst pobe hts an ouped slot, and a seond pobe s neessay. Wth pobablty (n )/(m ), the seond pobe hts an ouped slot, and a thd pobe s neessay. Wth pobablty (n 2)/(m 2), the thd pobe hts an ouped slot, et. Obseve that n < n α fo, 2,, n. m m 3/4/28 L8.47 3/4/28 L8.48 8
9 3/4/28 Poof (ontnued) heefoe, the expeted numbe of pobes s + n + n + n 2 L + L m m m 2 m n + + α( + α( + α( L( + α ) L) )) + α + α 2 + α 3 + L α. α he textbook has a moe goous poof. Implatons of the theoem If α s onstant, then aessng an openaddessed hash table takes onstant tme. If the table s half full, then the expeted numbe of pobes s /(.5) 2. If the table s 9% full, then the expeted numbe of pobes s /(.9). 3/4/28 L8.49 3/4/28 L8.5 Q: What about DELEE(k) Hashng letue outlne A: Use two speal values, NIL and DEL. NIL nothng was eve hee. DEL somethng was hee but was deleted. Seah ontnues past DEL slots, but an stoe elements thee. Hene, subsequent INSER opeatons and emove DEL values. Rebuld the table when too many DEL values aumulate. Into and defnton Hashng n pate Unvesal hashng Pefet hashng Open Addessng (optonal) 3/4/28 L8.5 3/4/28 L8.52 Summay (ehash) Hashng: map lage U n a small spae {,..,m}. Content-based addessng, onstant-tme lookup! Hash funtons n pate: dv, mult, dot-pod. Unvesal hashng: and h H, pob ollson /m. Pefet hashng: n fxed keys known n advane, wost-ase O() lookup n Θ(n) spae. 2-level h. Open addessng: h(k,) pobe untl empty slot, lnea pobng, double hashng (optonal). 3/4/28 L8.53 9
Introduction to Algorithms
Introducton to Algorthms 6.046J/8.40J Lecture 7 Prof. Potr Indyk Data Structures Role of data structures: Encapsulate data Support certan operatons (e.g., INSERT, DELETE, SEARCH) Our focus: effcency of
More informationCSc545 Hashing. Symbol-table problem. Resolving collisions by chaining. Hash functions. Analysis of chaining. Search cost
CS545 Hshng hnks to Pof. Chles. Leseson Sbol-tble poble Sbol tble holdng n eods: eod ke Othe felds ontnng stellte dt Opetons on : INSR, DL, SARCH, k How should the dt stutue be ognzed? Hsh funtons Ide:
More informationIntroduction to Algorithms
Introducton to Algorthms 6.046J/18.401J Lecture 7 Prof. Potr Indyk Data Structures Role of data structures: Encapsulate data Support certan operatons (e.g., INSERT, DELETE, SEARCH) What data structures
More information8 Baire Category Theorem and Uniform Boundedness
8 Bae Categoy Theoem and Unfom Boundedness Pncple 8.1 Bae s Categoy Theoem Valdty of many esults n analyss depends on the completeness popety. Ths popety addesses the nadequacy of the system of atonal
More informationPhysics 11b Lecture #2. Electric Field Electric Flux Gauss s Law
Physcs 11b Lectue # Electc Feld Electc Flux Gauss s Law What We Dd Last Tme Electc chage = How object esponds to electc foce Comes n postve and negatve flavos Conseved Electc foce Coulomb s Law F Same
More informationClustering. Outline. Supervised vs. Unsupervised Learning. Clustering. Clustering Example. Applications of Clustering
Clusteng CS478 Mahne Leanng Spng 008 Thosten Joahms Conell Unvesty Outlne Supevsed vs. Unsupevsed Leanng Heahal Clusteng Heahal Agglomeatve Clusteng (HAC) Non-Heahal Clusteng K-means EM-Algothm Readng:
More informationClustering Techniques
Clusteng Tehnques Refeenes: Beln Chen 2003. Moden Infomaton Reteval, haptes 5, 7 2. Foundatons of Statstal Natual Language Poessng, Chapte 4 Clusteng Plae smla obets n the same goup and assgn dssmla obets
More informationA. Thicknesses and Densities
10 Lab0 The Eath s Shells A. Thcknesses and Denstes Any theoy of the nteo of the Eath must be consstent wth the fact that ts aggegate densty s 5.5 g/cm (ecall we calculated ths densty last tme). In othe
More informationSet of square-integrable function 2 L : function space F
Set of squae-ntegable functon L : functon space F Motvaton: In ou pevous dscussons we have seen that fo fee patcles wave equatons (Helmholt o Schödnge) can be expessed n tems of egenvalue equatons. H E,
More informationTest 1 phy What mass of a material with density ρ is required to make a hollow spherical shell having inner radius r i and outer radius r o?
Test 1 phy 0 1. a) What s the pupose of measuement? b) Wte all fou condtons, whch must be satsfed by a scala poduct. (Use dffeent symbols to dstngush opeatons on ectos fom opeatons on numbes.) c) What
More informationData Structures. Element Uniqueness Problem. Hash Tables. Example. Hash Tables. Dana Shapira. 19 x 1. ) h(x 4. ) h(x 2. ) h(x 3. h(x 1. x 4. x 2.
Element Uniqueness Poblem Dt Stuctues Let x,..., xn < m Detemine whethe thee exist i j such tht x i =x j Sot Algoithm Bucket Sot Dn Shpi Hsh Tbles fo (i=;i
More informationIf there are k binding constraints at x then re-label these constraints so that they are the first k constraints.
Mathematcal Foundatons -1- Constaned Optmzaton Constaned Optmzaton Ma{ f ( ) X} whee X {, h ( ), 1,, m} Necessay condtons fo to be a soluton to ths mamzaton poblem Mathematcally, f ag Ma{ f ( ) X}, then
More informationTian Zheng Department of Statistics Columbia University
Haplotype Tansmsson Assocaton (HTA) An "Impotance" Measue fo Selectng Genetc Makes Tan Zheng Depatment of Statstcs Columba Unvesty Ths s a jont wok wth Pofesso Shaw-Hwa Lo n the Depatment of Statstcs at
More informationReview for the previous lecture
Review fo the pevious letue Definition: sample spae, event, opeations (union, intesetion, omplementay), disjoint, paiwise disjoint Theoem: ommutatitivity, assoiativity, distibution law, DeMogan s law Pobability
More informationLearning the structure of Bayesian belief networks
Lectue 17 Leanng the stuctue of Bayesan belef netwoks Mlos Hauskecht mlos@cs.ptt.edu 5329 Sennott Squae Leanng of BBN Leanng. Leanng of paametes of condtonal pobabltes Leanng of the netwok stuctue Vaables:
More informationEnergy in Closed Systems
Enegy n Closed Systems Anamta Palt palt.anamta@gmal.com Abstact The wtng ndcates a beakdown of the classcal laws. We consde consevaton of enegy wth a many body system n elaton to the nvese squae law and
More informationMultistage Median Ranked Set Sampling for Estimating the Population Median
Jounal of Mathematcs and Statstcs 3 (: 58-64 007 ISSN 549-3644 007 Scence Publcatons Multstage Medan Ranked Set Samplng fo Estmatng the Populaton Medan Abdul Azz Jeman Ame Al-Oma and Kamaulzaman Ibahm
More informationProfessor Wei Zhu. 1. Sampling from the Normal Population
AMS570 Pofesso We Zhu. Samplg fom the Nomal Populato *Example: We wsh to estmate the dstbuto of heghts of adult US male. It s beleved that the heght of adult US male follows a omal dstbuto N(, ) Def. Smple
More informationChapter 3: Theory of Modular Arithmetic 38
Chapte 3: Theoy of Modula Aithmetic 38 Section D Chinese Remainde Theoem By the end of this section you will be able to pove the Chinese Remainde Theoem apply this theoem to solve simultaneous linea conguences
More informationCorrespondence Analysis & Related Methods
Coespondene Analysis & Related Methods Oveview of CA and basi geometi onepts espondents, all eades of a etain newspape, osstabulated aoding to thei eduation goup and level of eading of the newspape Mihael
More information9.1 The multiplicative group of a finite field. Theorem 9.1. The multiplicative group F of a finite field is cyclic.
Chapte 9 Pimitive Roots 9.1 The multiplicative goup of a finite fld Theoem 9.1. The multiplicative goup F of a finite fld is cyclic. Remak: In paticula, if p is a pime then (Z/p) is cyclic. In fact, this
More informationMath 301: The Erdős-Stone-Simonovitz Theorem and Extremal Numbers for Bipartite Graphs
Math 30: The Edős-Stone-Simonovitz Theoem and Extemal Numbes fo Bipatite Gaphs May Radcliffe The Edős-Stone-Simonovitz Theoem Recall, in class we poved Tuán s Gaph Theoem, namely Theoem Tuán s Theoem Let
More informationPart V: Velocity and Acceleration Analysis of Mechanisms
Pat V: Velocty an Acceleaton Analyss of Mechansms Ths secton wll evew the most common an cuently pactce methos fo completng the knematcs analyss of mechansms; escbng moton though velocty an acceleaton.
More informationPhysics 2A Chapter 11 - Universal Gravitation Fall 2017
Physcs A Chapte - Unvesal Gavtaton Fall 07 hese notes ae ve pages. A quck summay: he text boxes n the notes contan the esults that wll compse the toolbox o Chapte. hee ae thee sectons: the law o gavtaton,
More informationKhintchine-Type Inequalities and Their Applications in Optimization
Khntchne-Type Inequaltes and The Applcatons n Optmzaton Anthony Man-Cho So Depatment of Systems Engneeng & Engneeng Management The Chnese Unvesty of Hong Kong ISDS-Kolloquum Unvestaet Wen 29 June 2009
More informationHashing. Alexandra Stefan
Hashng Alexandra Stefan 1 Hash tables Tables Drect access table (or key-ndex table): key => ndex Hash table: key => hash value => ndex Man components Hash functon Collson resoluton Dfferent keys mapped
More informationLecture 5 Single factor design and analysis
Lectue 5 Sngle fcto desgn nd nlss Completel ndomzed desgn (CRD Completel ndomzed desgn In the desgn of expements, completel ndomzed desgns e fo studng the effects of one pm fcto wthout the need to tke
More informationSymbol-table problem. Hashing. Direct-access table. Hash functions. CS Spring Symbol table T holding n records: record.
CS 5633 -- Spring 25 Symbol-table problem Hashing Carola Wenk Slides courtesy of Charles Leiserson with small changes by Carola Wenk CS 5633 Analysis of Algorithms 1 Symbol table holding n records: record
More informationChapter I Matrices, Vectors, & Vector Calculus 1-1, 1-9, 1-10, 1-11, 1-17, 1-18, 1-25, 1-27, 1-36, 1-37, 1-41.
Chapte I Matces, Vectos, & Vecto Calculus -, -9, -0, -, -7, -8, -5, -7, -36, -37, -4. . Concept of a Scala Consde the aa of patcles shown n the fgue. he mass of the patcle at (,) can be epessed as. M (,
More informationAdvanced Topics of Artificial Intelligence
Adaned Tops of Atfal Intellgene - Intoduton and Motaton - Tehnshe Unestät Gaz Unestät Klagenfut Motatng Eample to put the at befoe the hose 2 Motaton the ths yeas letue has two majo goals. to show moe
More information24-2: Electric Potential Energy. 24-1: What is physics
D. Iyad SAADEDDIN Chapte 4: Electc Potental Electc potental Enegy and Electc potental Calculatng the E-potental fom E-feld fo dffeent chage dstbutons Calculatng the E-feld fom E-potental Potental of a
More informationUNIT10 PLANE OF REGRESSION
UIT0 PLAE OF REGRESSIO Plane of Regesson Stuctue 0. Intoducton Ojectves 0. Yule s otaton 0. Plane of Regesson fo thee Vaales 0.4 Popetes of Resduals 0.5 Vaance of the Resduals 0.6 Summay 0.7 Solutons /
More informationGenerating Functions, Weighted and Non-Weighted Sums for Powers of Second-Order Recurrence Sequences
Geneatng Functons, Weghted and Non-Weghted Sums fo Powes of Second-Ode Recuence Sequences Pantelmon Stăncă Aubun Unvesty Montgomey, Depatment of Mathematcs Montgomey, AL 3614-403, USA e-mal: stanca@studel.aum.edu
More informationExperiment 1 Electric field and electric potential
Expeiment 1 Eleti field and eleti potential Pupose Map eleti equipotential lines and eleti field lines fo two-dimensional hage onfiguations. Equipment Thee sheets of ondutive papes with ondutive-ink eletodes,
More informationP 365. r r r )...(1 365
SCIENCE WORLD JOURNAL VOL (NO4) 008 www.scecncewoldounal.og ISSN 597-64 SHORT COMMUNICATION ANALYSING THE APPROXIMATION MODEL TO BIRTHDAY PROBLEM *CHOJI, D.N. & DEME, A.C. Depatment of Mathematcs Unvesty
More informationPHYSICS 212 MIDTERM II 19 February 2003
PHYSICS 1 MIDERM II 19 Feruary 003 Exam s losed ook, losed notes. Use only your formula sheet. Wrte all work and answers n exam ooklets. he aks of pages wll not e graded unless you so request on the front
More informationLecture 3 January 31, 2017
CS 224: Advanced Algorthms Sprng 207 Prof. Jelan Nelson Lecture 3 January 3, 207 Scrbe: Saketh Rama Overvew In the last lecture we covered Y-fast tres and Fuson Trees. In ths lecture we start our dscusson
More informationON THE FRESNEL SINE INTEGRAL AND THE CONVOLUTION
IJMMS 3:37, 37 333 PII. S16117131151 http://jmms.hndaw.com Hndaw Publshng Cop. ON THE FRESNEL SINE INTEGRAL AND THE CONVOLUTION ADEM KILIÇMAN Receved 19 Novembe and n evsed fom 7 Mach 3 The Fesnel sne
More informationMATHEMATICS II PUC VECTOR ALGEBRA QUESTIONS & ANSWER
MATHEMATICS II PUC VECTOR ALGEBRA QUESTIONS & ANSWER I One M Queston Fnd the unt veto n the deton of Let ˆ ˆ 9 Let & If Ae the vetos & equl? But vetos e not equl sne the oespondng omponents e dstnt e detons
More informationEvent Shape Update. T. Doyle S. Hanlon I. Skillicorn. A. Everett A. Savin. Event Shapes, A. Everett, U. Wisconsin ZEUS Meeting, October 15,
Event Shape Update A. Eveett A. Savn T. Doyle S. Hanlon I. Skllcon Event Shapes, A. Eveett, U. Wsconsn ZEUS Meetng, Octobe 15, 2003-1 Outlne Pogess of Event Shapes n DIS Smla to publshed pape: Powe Coecton
More information(8) Gain Stage and Simple Output Stage
EEEB23 Electoncs Analyss & Desgn (8) Gan Stage and Smple Output Stage Leanng Outcome Able to: Analyze an example of a gan stage and output stage of a multstage amplfe. efeence: Neamen, Chapte 11 8.0) ntoducton
More informationAlgorithms Design & Analysis. Hash Tables
Algorthms Desg & Aalyss Hash Tables Recap Lower boud Order statstcs 2 Today s topcs Drect-accessble table Hash tables Hash fuctos Uversal hashg Perfect Hashg Ope addressg 3 Symbol-table problem Symbol
More informationSOME NEW SELF-DUAL [96, 48, 16] CODES WITH AN AUTOMORPHISM OF ORDER 15. KEYWORDS: automorphisms, construction, self-dual codes
Факултет по математика и информатика, том ХVІ С, 014 SOME NEW SELF-DUAL [96, 48, 16] CODES WITH AN AUTOMORPHISM OF ORDER 15 NIKOLAY I. YANKOV ABSTRACT: A new method fo constuctng bnay self-dual codes wth
More informationDistinct 8-QAM+ Perfect Arrays Fanxin Zeng 1, a, Zhenyu Zhang 2,1, b, Linjie Qian 1, c
nd Intenatonal Confeence on Electcal Compute Engneeng and Electoncs (ICECEE 15) Dstnct 8-QAM+ Pefect Aays Fanxn Zeng 1 a Zhenyu Zhang 1 b Lnje Qan 1 c 1 Chongqng Key Laboatoy of Emegency Communcaton Chongqng
More informationThe Substring Search Problem
The Substing Seach Poblem One algoithm which is used in a vaiety of applications is the family of substing seach algoithms. These algoithms allow a use to detemine if, given two chaacte stings, one is
More informationSuppose you have a bank account that earns interest at rate r, and you have made an initial deposit of X 0
IOECONOMIC MODEL OF A FISHERY (ontinued) Dynami Maximum Eonomi Yield In ou deivation of maximum eonomi yield (MEY) we examined a system at equilibium and ou analysis made no distintion between pofits in
More informationValue Distribution of L-Functions with Rational Moving Targets
Advanes in Pue Mathematis 3 3 79-73 Pulished Online Deeme 3 (http://wwwsipog/jounal/apm) http://dxdoiog/436/apm33998 Value Distiution of -Funtions wh ational Moving agets Matthew Cadwell Zhuan Ye * ntelligent
More informationPHY126 Summer Session I, 2008
PHY6 Summe Sesson I, 8 Most of nfomaton s avalable at: http://nngoup.phscs.sunsb.edu/~chak/phy6-8 ncludng the sllabus and lectue sldes. Read sllabus and watch fo mpotant announcements. Homewok assgnment
More informationChapter Fifiteen. Surfaces Revisited
Chapte Ffteen ufaces Revsted 15.1 Vecto Descpton of ufaces We look now at the vey specal case of functons : D R 3, whee D R s a nce subset of the plane. We suppose s a nce functon. As the pont ( s, t)
More informationSome Consequences. Example of Extended Euclidean Algorithm. The Fundamental Theorem of Arithmetic, II. Characterizing the GCD and LCM
Example of Extended Eucldean Algorthm Recall that gcd(84, 33) = gcd(33, 18) = gcd(18, 15) = gcd(15, 3) = gcd(3, 0) = 3 We work backwards to wrte 3 as a lnear combnaton of 84 and 33: 3 = 18 15 [Now 3 s
More informationHashing, Hash Functions. Lecture 7
Hashing, Hash Functions Lecture 7 Symbol-table problem Symbol table T holding n records: x record key[x] Other fields containing satellite data Operations on T: INSERT(T, x) DELETE(T, x) SEARCH(T, k) How
More informationGoodness-of-fit for composite hypotheses.
Section 11 Goodness-of-fit fo composite hypotheses. Example. Let us conside a Matlab example. Let us geneate 50 obsevations fom N(1, 2): X=nomnd(1,2,50,1); Then, unning a chi-squaed goodness-of-fit test
More informationCSJM University Class: B.Sc.-II Sub:Physics Paper-II Title: Electromagnetics Unit-1: Electrostatics Lecture: 1 to 4
CSJM Unvesty Class: B.Sc.-II Sub:Physcs Pape-II Ttle: Electomagnetcs Unt-: Electostatcs Lectue: to 4 Electostatcs: It deals the study of behavo of statc o statonay Chages. Electc Chage: It s popety by
More informationAPPLICATIONS OF SEMIGENERALIZED -CLOSED SETS
Intenatonal Jounal of Mathematcal Engneeng Scence ISSN : 22776982 Volume Issue 4 (Apl 202) http://www.mes.com/ https://stes.google.com/ste/mesounal/ APPLICATIONS OF SEMIGENERALIZED CLOSED SETS G.SHANMUGAM,
More informationAuchmuty High School Mathematics Department Advanced Higher Notes Teacher Version
The Binomial Theoem Factoials Auchmuty High School Mathematics Depatment The calculations,, 6 etc. often appea in mathematics. They ae called factoials and have been given the notation n!. e.g. 6! 6!!!!!
More informationReview of Vector Algebra and Vector Calculus Operations
Revew of Vecto Algeba and Vecto Calculus Opeatons Tpes of vaables n Flud Mechancs Repesentaton of vectos Dffeent coodnate sstems Base vecto elatons Scala and vecto poducts Stess Newton s law of vscost
More information4 SingularValue Decomposition (SVD)
/6/00 Z:\ jeh\self\boo Kannan\Jan-5-00\4 SVD 4 SngulaValue Decomposton (SVD) Chapte 4 Pat SVD he sngula value decomposton of a matx s the factozaton of nto the poduct of thee matces = UDV whee the columns
More informationChapter 23: Electric Potential
Chapte 23: Electc Potental Electc Potental Enegy It tuns out (won t show ths) that the tostatc foce, qq 1 2 F ˆ = k, s consevatve. 2 Recall, fo any consevatve foce, t s always possble to wte the wok done
More informationLinks in edge-colored graphs
Lnks n edge-coloed gaphs J.M. Becu, M. Dah, Y. Manoussaks, G. Mendy LRI, Bât. 490, Unvesté Pas-Sud 11, 91405 Osay Cedex, Fance Astact A gaph s k-lnked (k-edge-lnked), k 1, f fo each k pas of vetces x 1,
More informationGroupoid and Topological Quotient Group
lobal Jounal of Pue and Appled Mathematcs SSN 0973-768 Volume 3 Numbe 7 07 pp 373-39 Reseach nda Publcatons http://wwwpublcatoncom oupod and Topolocal Quotent oup Mohammad Qasm Manna Depatment of Mathematcs
More informationIntegral Vector Operations and Related Theorems Applications in Mechanics and E&M
Dola Bagayoko (0) Integal Vecto Opeatons and elated Theoems Applcatons n Mechancs and E&M Ι Basc Defnton Please efe to you calculus evewed below. Ι, ΙΙ, andιιι notes and textbooks fo detals on the concepts
More informationSolutions to Problem Set 8
Massachusetts Institute of Technology 6.042J/18.062J, Fall 05: Mathematics fo Compute Science Novembe 21 Pof. Albet R. Meye and Pof. Ronitt Rubinfeld evised Novembe 27, 2005, 858 minutes Solutions to Poblem
More informationDilations and Commutant Lifting for Jointly Isometric OperatorsA Geometric Approach
jounal of functonal analyss 140, 300311 (1996) atcle no. 0109 Dlatons and Commutant Lftng fo Jontly Isometc OpeatosA Geometc Appoach K. R. M. Attele and A. R. Lubn Depatment of Mathematcs, Illnos Insttute
More informationPhysics 207 Lecture 16
Physcs 07 Lectue 6 Goals: Lectue 6 Chapte Extend the patcle odel to gd-bodes Undestand the equlbu of an extended object. Analyze ollng oton Undestand otaton about a fxed axs. Eploy consevaton of angula
More informationCOMPARING MORE THAN TWO POPULATION MEANS: AN ANALYSIS OF VARIANCE
COMPARING MORE THAN TWO POPULATION MEANS: AN ANALYSIS OF VARIANCE To see how the piniple behind the analysis of vaiane method woks, let us onside the following simple expeiment. The means ( 1 and ) of
More informationMachine Learning 4771
Machne Leanng 4771 Instucto: Tony Jebaa Topc 6 Revew: Suppot Vecto Machnes Pmal & Dual Soluton Non-sepaable SVMs Kenels SVM Demo Revew: SVM Suppot vecto machnes ae (n the smplest case) lnea classfes that
More information3. A Review of Some Existing AW (BT, CT) Algorithms
3. A Revew of Some Exstng AW (BT, CT) Algothms In ths secton, some typcal ant-wndp algothms wll be descbed. As the soltons fo bmpless and condtoned tansfe ae smla to those fo ant-wndp, the pesented algothms
More informationRECAPITULATION & CONDITIONAL PROBABILITY. Number of favourable events n E Total number of elementary events n S
Fomulae Fo u Pobablty By OP Gupta [Ida Awad We, +91-9650 350 480] Impotat Tems, Deftos & Fomulae 01 Bascs Of Pobablty: Let S ad E be the sample space ad a evet a expemet espectvely Numbe of favouable evets
More information2/24/2014. The point mass. Impulse for a single collision The impulse of a force is a vector. The Center of Mass. System of particles
/4/04 Chapte 7 Lnea oentu Lnea oentu of a Sngle Patcle Lnea oentu: p υ It s a easue of the patcle s oton It s a vecto, sla to the veloct p υ p υ p υ z z p It also depends on the ass of the object, sla
More informationA Brief Guide to Recognizing and Coping With Failures of the Classical Regression Assumptions
A Bef Gude to Recognzng and Copng Wth Falues of the Classcal Regesson Assumptons Model: Y 1 k X 1 X fxed n epeated samples IID 0, I. Specfcaton Poblems A. Unnecessay explanatoy vaables 1. OLS s no longe
More informationOptimal System for Warm Standby Components in the Presence of Standby Switching Failures, Two Types of Failures and General Repair Time
Intenatonal Jounal of ompute Applcatons (5 ) Volume 44 No, Apl Optmal System fo Wam Standby omponents n the esence of Standby Swtchng Falues, Two Types of Falues and Geneal Repa Tme Mohamed Salah EL-Shebeny
More informationScalars and Vectors Scalar
Scalas and ectos Scala A phscal quantt that s completel chaacteed b a eal numbe (o b ts numecal value) s called a scala. In othe wods a scala possesses onl a magntude. Mass denst volume tempeatue tme eneg
More informationMechanics Physics 151
Mechancs Physcs 151 Lectue 18 Hamltonan Equatons of Moton (Chapte 8) What s Ahead We ae statng Hamltonan fomalsm Hamltonan equaton Today and 11/6 Canoncal tansfomaton 1/3, 1/5, 1/10 Close lnk to non-elatvstc
More informationALL QUESTIONS ARE WORTH 20 POINTS. WORK OUT FIVE PROBLEMS.
GNRAL PHYSICS PH -3A (D. S. Mov) Test (/3/) key STUDNT NAM: STUDNT d #: -------------------------------------------------------------------------------------------------------------------------------------------
More information1. A body will remain in a state of rest, or of uniform motion in a straight line unless it
Pncples of Dnamcs: Newton's Laws of moton. : Foce Analss 1. A bod wll eman n a state of est, o of unfom moton n a staght lne unless t s acted b etenal foces to change ts state.. The ate of change of momentum
More informationCSCE 478/878 Lecture 4: Experimental Design and Analysis. Stephen Scott. 3 Building a tree on the training set Introduction. Outline.
In Homewok, you ae (supposedly) Choosing a data set 2 Extacting a test set of size > 3 3 Building a tee on the taining set 4 Testing on the test set 5 Repoting the accuacy (Adapted fom Ethem Alpaydin and
More informationMACHINE LEARNING. Mistake and Loss Bound Models of Learning
Iowa State Unvesty MACHINE LEARNING Vasant Honava Bonfomatcs and Computatonal Bology Pogam Cente fo Computatonal Intellgence, Leanng, & Dscovey Iowa State Unvesty honava@cs.astate.edu www.cs.astate.edu/~honava/
More informationRigid Bodies: Equivalent Systems of Forces
Engneeng Statcs, ENGR 2301 Chapte 3 Rgd Bodes: Equvalent Sstems of oces Intoducton Teatment of a bod as a sngle patcle s not alwas possble. In geneal, the se of the bod and the specfc ponts of applcaton
More information9/12/2013. Microelectronics Circuit Analysis and Design. Modes of Operation. Cross Section of Integrated Circuit npn Transistor
Mcoelectoncs Ccut Analyss and Desgn Donald A. Neamen Chapte 5 The pola Juncton Tanssto In ths chapte, we wll: Dscuss the physcal stuctue and opeaton of the bpola juncton tanssto. Undestand the dc analyss
More informationiclicker Quiz a) True b) False Theoretical physics: the eternal quest for a missing minus sign and/or a factor of two. Which will be an issue today?
Clce Quz I egsteed my quz tansmtte va the couse webste (not on the clce.com webste. I ealze that untl I do so, my quz scoes wll not be ecoded. a Tue b False Theoetcal hyscs: the etenal quest fo a mssng
More informationNew problems in universal algebraic geometry illustrated by boolean equations
New poblems in univesal algebaic geomety illustated by boolean equations axiv:1611.00152v2 [math.ra] 25 Nov 2016 Atem N. Shevlyakov Novembe 28, 2016 Abstact We discuss new poblems in univesal algebaic
More informationRanks of quotients, remainders and p-adic digits of matrices
axv:1401.6667v2 [math.nt] 31 Jan 2014 Ranks of quotents, emandes and p-adc dgts of matces Mustafa Elshekh Andy Novocn Mak Gesbecht Abstact Fo a pme p and a matx A Z n n, wte A as A = p(a quo p)+ (A em
More informationForce and Work: Reminder
Electic Potential Foce and Wok: Reminde Displacement d a: initial point b: final point Reminde fom Mechanics: Foce F if thee is a foce acting on an object (e.g. electic foce), this foce may do some wok
More informationLecture 9 Multiple Class Models
Lectue 9 Multple Class Models Multclass MVA Appoxmate MVA 8.4.2002 Copyght Teemu Keola 2002 1 Aval Theoem fo Multple Classes Wth jobs the system, a job class avg to ay seve sees the seve as equlbum wth
More informationarxiv: v1 [math.nt] 12 May 2017
SEQUENCES OF CONSECUTIVE HAPPY NUMBERS IN NEGATIVE BASES HELEN G. GRUNDMAN AND PAMELA E. HARRIS axiv:1705.04648v1 [math.nt] 12 May 2017 ABSTRACT. Fo b 2 and e 2, let S e,b : Z Z 0 be the function taking
More informationA Bijective Approach to the Permutational Power of a Priority Queue
A Bijective Appoach to the Pemutational Powe of a Pioity Queue Ia M. Gessel Kuang-Yeh Wang Depatment of Mathematics Bandeis Univesity Waltham, MA 02254-9110 Abstact A pioity queue tansfoms an input pemutation
More informationSound Radiation of Circularly Oscillating Spherical and Cylindrical Shells. John Wang and Hongan Xu Volvo Group 4/30/2013
Sound Radaton of Culaly Osllatng Spheal and Cylndal Shells John Wang and Hongan Xu Volvo Goup /0/0 Abstat Closed-fom expesson fo sound adaton of ulaly osllatng spheal shells s deved. Sound adaton of ulaly
More informationExtra Examples for Chapter 1
Exta Examples fo Chapte 1 Example 1: Conenti ylinde visomete is a devie used to measue the visosity of liquids. A liquid of unknown visosity is filling the small gap between two onenti ylindes, one is
More informationMachine Learning. Spectral Clustering. Lecture 23, April 14, Reading: Eric Xing 1
Machne Leanng -7/5 7/5-78, 78, Spng 8 Spectal Clusteng Ec Xng Lectue 3, pl 4, 8 Readng: Ec Xng Data Clusteng wo dffeent ctea Compactness, e.g., k-means, mxtue models Connectvty, e.g., spectal clusteng
More informationOptimization Methods: Linear Programming- Revised Simplex Method. Module 3 Lecture Notes 5. Revised Simplex Method, Duality and Sensitivity analysis
Optmzaton Meods: Lnea Pogammng- Revsed Smple Meod Module Lectue Notes Revsed Smple Meod, Dualty and Senstvty analyss Intoducton In e pevous class, e smple meod was dscussed whee e smple tableau at each
More informationSingle-Carrier Frequency Domain Adaptive Antenna Array for Distributed Antenna Network
Sngle-Cae Fequeny Doman Aaptve Antenna Aay fo Dstbute Antenna etwok We Peng Depatment of Eletal an Communaton ohoku Unvesty Sena, Japan peng@moble.ee.tohoku.a.jp Fumyuk Aah Depatment of Eletal an Communaton
More informationTutorial Chemical Reaction Engineering:
Dpl.-Ing. ndeas Jöke Tutoal Chemal eaton Engneeng:. eal eatos, esdene tme dstbuton and seletvty / yeld fo eaton netwoks Insttute of Poess Engneeng, G5-7, andeas.joeke@ovgu.de 8-Jun-6 Tutoal CE: esdene
More informationLecture: Analysis of Algorithms (CS )
Lecture: Analysis of Algorithms (CS483-001) Amarda Shehu Spring 2017 1 Outline of Today s Class 2 Choosing Hash Functions Universal Universality Theorem Constructing a Set of Universal Hash Functions Perfect
More informationOnline-routing on the butterfly network: probabilistic analysis
Online-outing on the buttefly netwok: obabilistic analysis Andey Gubichev 19.09.008 Contents 1 Intoduction: definitions 1 Aveage case behavio of the geedy algoithm 3.1 Bounds on congestion................................
More informationAQI: Advanced Quantum Information Lecture 2 (Module 4): Order finding and factoring algorithms February 20, 2013
AQI: Advanced Quantum Infomation Lectue 2 (Module 4): Ode finding and factoing algoithms Febuay 20, 203 Lectue: D. Mak Tame (email: m.tame@impeial.ac.uk) Intoduction In the last lectue we looked at the
More informationDensity Functional Theory I
Densty Functonal Theoy I cholas M. Hason Depatment of Chemsty Impeal College Lonon & Computatonal Mateals Scence Daesbuy Laboatoy ncholas.hason@c.ac.uk Densty Functonal Theoy I The Many Electon Schönge
More informationChapter 8. Linear Momentum, Impulse, and Collisions
Chapte 8 Lnea oentu, Ipulse, and Collsons 8. Lnea oentu and Ipulse The lnea oentu p of a patcle of ass ovng wth velocty v s defned as: p " v ote that p s a vecto that ponts n the sae decton as the velocty
More informationNumerical Integration
MCEN 473/573 Chapte 0 Numeical Integation Fall, 2006 Textbook, 0.4 and 0.5 Isopaametic Fomula Numeical Integation [] e [ ] T k = h B [ D][ B] e B Jdsdt In pactice, the element stiffness is calculated numeically.
More informationTHE ISOMORPHISM PROBLEM FOR CAYLEY GRAPHS ON THE GENERALIZED DICYCLIC GROUP
IJAMM 4:1 (016) 19-30 Mach 016 ISSN: 394-58 Avalale at http://scentfcadvances.co.n DOI: http://dx.do.og/10.1864/amml_710011617 THE ISOMORPHISM PROBEM FOR CAYEY RAPHS ON THE ENERAIZED DICYCIC ROUP Pedo
More informationAPPENDIX A Some Linear Algebra
APPENDIX A Some Lnear Algebra The collecton of m, n matrces A.1 Matrces a 1,1,..., a 1,n A = a m,1,..., a m,n wth real elements a,j s denoted by R m,n. If n = 1 then A s called a column vector. Smlarly,
More information