Linked-List Implementation. Linked-lists for two sets. Multiple Operations. UNION Implementation. An Application of Disjoint-Set 1/9/2014

Size: px
Start display at page:

Download "Linked-List Implementation. Linked-lists for two sets. Multiple Operations. UNION Implementation. An Application of Disjoint-Set 1/9/2014"

Transcription

1 Disjoint Sts Data Strutur (Chap. 21) A disjoint-st is a olltion ={S 1, S 2,, S k } o distint dynami sts. Eah st is idntiid by a mmbr o th st, alld rprsntativ. Disjoint st oprations: MAKE-SET(x): rat a nw st with only x. assum x is not alrady in som othr st. UNION(x,y): ombin th two sts ontaining x and y into on nw st. A nw rprsntativ is sltd. FIND-SET(x): rturn th rprsntativ o th st ontaining x. Linkd-List Implmntation Eah st as a linkd-list, with had and tail, and ah nod ontains valu, nxt nod pointr and bak-to-rprsntativ pointr. Exampl: MAKE-SET osts O(1): just rat a singl lmnt list. FIND-SET osts O(1): just rturn bak-torprsntativ pointr. Multipl Oprations Linkd-lists or two sts Suppos multipl oprations: n: #MAKE-SET oprations (xutd at bginning). m: #MAKE-SET, UNION, FIND-SET oprations. m n, #UNION opration is at most n-1. St {,h,} had tail St {, g} had tail h g UNION o two Sts had g h tail An Appliation o Disjoint-St Dtrmin th onntd omponnts o an undirtd graph. CONNECTED-COMPONENTS(G) 1. or ah vrtx v V[G] 2. do MAKE-SET(v) 3. or ah dg (u,v) E[G] 4. do i FIND-SET(u) FIND-SET(v) 5. thn UNION(u,v) SAME-COMPONENT(u,v) 1. i FIND-SET(u)=FIND-SET(v) 2. thn rturn TRUE 3. ls rturn FALSE UNION Implmntation A simpl implmntation: UNION(x,y) just appnds x to th nd o y, updats all bak-to-rprsntativ pointrs in x to th had o y. Eah UNION taks tim linar in th x s lngth. Suppos n MAKE-SET(x i ) oprations (O(1) ah) ollowd by n-1 UNION UNION(x 1, x 2 ), O(1), UNION(x 2, x 3 ), O(2),.. UNION(x n-1, x n ), O(n-1) Th UNIONs ost n-1=θ(n 2 ) So 2n-1 oprations ost Θ(n 2 ), avrag Θ(n) ah. Not good!! How to solv it??? 1

2 Wightd-Union Huristi Instad appnding x to y, appnding th shortr list to th longr list. Assoiatd a lngth with ah list, whih indiats how many lmnts in th list. Rsult: a squn o m MAKE-SET, UNION, FIND-SET oprations, n o whih ar MAKE-SET oprations, th running tim is O(m+nlg n). Why??? Hints: Count th numbr o updats to bak-to-rprsntativ pointr or any x in a st o n lmnts. Considr that ah tim, th UNION will at last doubl th lngth o unitd st, it will tak at most lg n UNIONS to unit n lmnts. So ah x s bak-to-rprsntativ pointr an b updatd at most lg n tims. Union by Rank & Path Comprssion Union by Rank: Eah nod is assoiatd with a rank, whih is th uppr bound on th hight o th nod (i.., th hight o subtr rootd at th nod), thn whn UNION, lt th root with smallr rank point to th root with largr rank. Path Comprssion: usd in FIND-SET(x) opration, mak ah nod in th path rom x to th root dirtly point to th root. Thus rdu th tr hight. Disjoint-st Implmntation: Forsts Path Comprssion Rootd trs, ah tr is a st, root is th rprsntativ. Eah nod points to its parnt. Root points to itsl. d d h d St {,h,} St {,d} h d UNION Straightorward Solution Thr oprations MAKE-SET(x): rat a tr ontaining x. O(1) FIND-SET(x): ollow th hain o parnt pointrs until to th root. O(hight o x s tr) UNION(x,y): lt th root o on tr point to th root o th othr. O(1) It is possibl that n-1 UNIONs rsults in a tr o hight n-1. (just a linar hain o n nods). So n FIND-SET oprations will ost O(n 2 ). Algorithm or Disjoint-St Forst UNION(x,y) 1. LINK(FIND-SET(x),FIND-SET(y)) MAKE-SET(x) 1. p[x] x 2. rank[x] 0 LINK(x,y) 1. i rank[x]>rank[y] 2. thn p[y] x 3. ls p[x] y 4. i rank[x]=rank[y] 5. thn rank[y]++ FIND-SET(x) 1. i x p[x] 2. thn p[x] FIND-SET(p[x]) 3. rturn p[x] Worst as running tim or m MAKE-SET, UNION, FIND-SET oprations is: O(mα(n)) whr α(n) 4. So narly linar in m. 2

3 Analysis o Union by Rank with Path Comprssion (by amortizd analysis) Disuss th ollowing: A vry quikly growing untion and its vry slowly growing invrs Proprtis o Ranks Proving tim bound o O(mα(n)) whr α(n) is a vry slowly growing untion. How Quik A k (j) Inras Lt us s A k (1): or k=0,1,2,3,4. A 0 (1)=1+1=2 A 1 (1)=2.1+1=3 A 2 (1)=2 1+1 (1+1)-1=7 A 3 (1)=A 2 (1+1) (1)=A 2 (2) (1)=A 2 (A 2 (1))=A 2 (7)=2 7+1 (7+1)- 1= =2047 A 4 (1)=A 32 (1)=A 3 (A 3 (1)) =A 3 (2047)=A 2 (2048) (2047) >> A 2 (2047) = > =(2 4 ) 512 =(16) 512 >> (stimatd numbr o atoms in univrs) A vry quikly growing untion and its invrs For intgrs k 0 and j 1, din A k (j): A k (j)= j+1 i k=0 A k-1 (j+1) (j) i k 1 Whr A k-10 (j)=j, A k-1 (i) (j)= A k-1 (A k-1 (i-1) (j)) or i 1. k is alld th lvl o th untion and i in th abov is alld itrations. A k (j) stritly inras with both j and k. Lt us s how quik th inras is!! Invrs o A k (n):α(n) α(n)=min{k: A k (1) n} (so, A α(n) (1) n ) α(n)= 0 or 0 n 2 1 n =3 2 or 4 n 7 3 or 8 n or 2048 n A 4 (1). Extrmly slow inrasing untion. α(n) 4 or all pratial purposs. Quiknss o Funtion A k (j) s Inras Lmma 21.2 (Pag 510): For any intgr j, A 1 (j) =2j+1. Proo: By indution on i, prov A 0i (j) =j+i. So A 1 (j)= A 0 (j+1) (j) =j+(j+1)=2j+1. Lmma 21.3 (Pag 510): For any intgr j, A 2 (j) =2 j+1 (j+1)-1. Proo: By indution on i, prov A 1i (j) =2 i (j+1)-1 A 2 (j)= A 1 (j+1) (j) = 2 j+1 (j+1)-1. O(mα(n)) bound: Proprty o Ranks Lmma 21.4 (pag 511): For all nods x, rank[x] rank[p[x]], with strit inquality i x p[x]. Corollary 21.5 (pag 511): As w ollow th path rom any nod to th root, th nod ranks stritly inras. Lmma 21.6 (pag 512): Evry nod had rank at most n-1. Proo: rank bgins with 0, inras possibly with only LINK oprations, whih is at most n-1 tim. In at, at most log(n). 3

4 O(mα(n)) bound proo Using amortizd analysis (Chap. 17) Using LINK instad UNION (vry UNION is don by two FIND-SETs and on LINK) Lmma 21.7 (pag 512): Suppos onvrting a squn S' o m' MAKE-SET, UNION, and FIND-SET oprations into a squn S o m MAKE-SET, LINK, FIND-SET by turning UNION to two FIND-SETs and on LINK, thn i S runs in O(mα(n)), thn S' runs in O(m'α(n)). Proo: baus o m' m 3m', thus m=o(m'). Rlations among rank[p[x]], lvl(x) and itr(x) Sin rank[p[x]] monotonially inras ovr tim, in ordr or itr(x) to dras, lvl(x) must inras. Or say anothr way, as long as lvl(x) rmains unhangd, itr(x) must ithr inras or rmains unhangd. Potntial Funtion For ah nod x, assign a potntial untion φ q (x) atr q oprations. Thn potntial or ntir orst, Φ q = x φ q (x) Φ 0 =0 at th bginning. Φ q will nvr b ngativ. φ q (x)= α(n) rank[x] i x is a root or rank[x]=0. [α(n)-lvl(x)] rank[x]-itr(x) othrwis. Proprtis or Potntial Funtion φ q (x) Lmma 21.8 (pag 514): For vry nod x, and or all q, 0 φ q (x) α(n) rank[x] Proo: i x is a root or rank[x]=0, thn orrt by dinition. Suppos x is not a root and rank[x]>0, φ q (x)= [α(n)-lvl(x)] rank[x]-itr(x) (α(n)-(α(n)-1)) rank[x]-rank[x] =rank[x]-rank[x]=0. φ q (x)= [α(n)-lvl(x)] rank[x]-itr(x) [α(n)-0] rank[x]-1= α(n) rank[x]-1<α(n) rank[x] lvl(x) and itr(x) lvl(x)=max{k: rank[p[x]] A k (rank[x])} 0 Lvl(x)<α(n), sin rank[p[x]] rank[x]+1=a 0 (rank[x]) and A α(n) (rank[x]) A α(n) (1) n > rank[p[x]]. itr(x)=max{i: rank[p[x]] A lvl(x) (i) (rank[x])} 1 itr(x) rank[x], sin rank[p[x]] A lvl(x) (rank[x])=a lvl(x) (1) (rank[x]) and A lvl(x) (rank[x]+1) (rank[x])=a lvl(x)+1 (rank[x])>rank[p[x]]. Potntial Changs o Oprations Lmma 21.9 (pag 515): Lt x b a nod that is not a root, and suppos qth opration is ithr LINK or FIND-SET. Thn atr th qth opration, φ q (x) φ q-1 (x). Morovr, i rank[x] 1 and ithr lvl[x] or itr(x) hangs du to th qth opration, thn φ q (x) φ q-1 (x)-1. Proo: x not root rank[x] not hang n not hang α(n) not hang. I rank[x]=0, thn φ q (x) = φ q-1 (x)=0. suppos rank[x]>0. I lvl(x) not hang, I itr(x) not hang, φ q (x) = φ q-1 (x), sin all kp sam I itr(x) inras, thn at las by 1, φ q (x) will dras at last 1. I lvl(x) inrass (at last by 1), thn (α(n)-lvl(x)) rank[x] drops at last by rank[x]. Suppos itr(x) drops, thn, th drop is at most rank[x]-1. so φ q (x) will drop at last rank[x]-(rank[x]-1)=1. Thus φ q (x) φ q-1 (x)-1. 4

5 Amortizd Costs o Oprations Lmma (pag 515): Th amortizd ost o ah MAKE-SET opration is O(1). Proo: rat a singl nod x with rank 0, so φ q (x) =0. no othr hang to th orst, so Φ q= Φ q-1. Th lt is th atual ost, whih is O(1). Proo o Lmma (ont.) Proo that at las max(0,s-(α(n)+2)) nods on th ind path hav thir potntial dras by at last 1. Lt x b a nod on th ind path: rank[x]>0, ollowd somwhr by y that is not a root, and lvl(y)=lvl(x) just bor FIND-SET. At most α(n)+2 nods do not satisy: 1th nod, root nod, th last nod w or whih lvl(w)=0,1,, α(n)-1. Thus at last max(0,s-(α(n)+2)) nods satisy. Lt us ix x, show x s potntial drass by at last 1. Amortizd Costs o Oprations (ont.) Lmma (pag 515): Th amortizd ost o ah LINK opration is O(α(n)). Proo: (LINK(x,y) maks y th parnt o x). Atual ost or LINK opration is O(1). Considring potntial hang: Thr kinds o nods: x, y, and th old hildrn o y. By Lmma 21.9, th potntial o y s old hildrn not inras. For x (hangd to non-root rom a root), φ q (x)= [α(n)-lvl(x)] rank[x]- itr(x) [α(n)-0] rank[x]-1= α(n) rank[x]-1<α(n) rank[x]=φ q-1 (x). For y, rank[y] may stay sam or inras by 1, so φ q (y)=α(n) rank[y]=φ q- 1(y) or φ q-1 (y)+α(n). Thus th potntial inras du to th LINK opration is at most α(n). Thus th amortizd ost is O(1)+O(α(n))=O(α(n)) Proo o Lmma (ont.) Lt k=lvl(x)=lvl(y), Just prior to path omprssion ausd by FIND-SET, w hav rank[p[x]] A k (itr(x)) (rank[x]) (by itr(x) s d.) rank[p[y]] A k (rank[y]) (by lvl(y) s d.) rank[y] rank[p[x]] (sin y ollows x somwhr). Lt i=itr(x) bor path omprssion, w hav rank[p[y]] A k (rank[y]) A k (rank[p[x]]) (sin A k (j) is stritly inrasing) A k (A k (itr(x)) (rank[x])) =A k (i+1) (rank[x]) Atr path omprssion, rank[p[x]]=rank[p[y]], whih not dras, and rank[x] not hang, so rank[p[x]] A k (i+1) (rank[x]). Whih mans that ithr itr(x) inrass (to at last i+1), or lvl(x) to inras. Thus by Lmma 21.9, φ q (x) φ q-1 (x)-1. that is x s potntial drass by at last 1. As a rsult, w prov th lmma Amortizd Costs o Oprations (ont.) Lmma (pag 516): Th amortizd ost o ah FIND-SET opration is O(α(n)). Proo: suppos thr ar s nods in th ind path. Th atual ost o FIND-SET is O(s). Root s potntial dos not hang and no othr nod s potntial inrass (by Lmma 21.9). At last max(0,s-(α(n)+2)) nods on th ind path hav thir potntial dras by at last 1. Thus th amortizd ost is at most O(s)-(s-(α(n)+2)) =O(α(n)). Uppr bound or Disjoint-sts Thorm (pag 517): A squn o m MAKE-SET, UNION, FIND- SET oprations, n o whih ar MAKE-SET oprations, an b prormd on a disjoint-st orst with union by rank and path omprssion in worst ast tim O(mα(n)). 5

6 Summary Disjoint st Thr oprations Dirnt implmntations and dirnt osts Forst implmntation: Union by rank and path omprssion Proprtis: rank, lvl, itr. Amortizd analysis o th oprations: Potntial untion. A k (j) union: A k (j)= j+1 i k=0 A (j+1) k-1 (j) i k 1 Whr A k-10 (j)=j, A (i) k-1 (j)= A k-1 (A (i-1) k-1 (j)) or i 1. k is alld th lvl o th untion and i in th abov is alld itrations. α(n)=min{k: A k (1) n} A typial xampl using Disjoint St Kruskal's algorithm (Minimum Spanning Tr) sort th dgs o G in inrasing ordr by lngth kp a subgraph S o G, initially mpty or ah dg in sortd ordr i th ndpoints o ar disonntd in S add to S rturn S Not: grdy algorithm Analysis: Th tsting whthr two ndpoints ar disonntd looks lik it should b slow (linar tim pr itration, or O(mn) total). in at, onstant tim. 6

Computing Connected Components Given a graph G = (V; E) compute the connected components of G. Connected-Components(G) 1 for each vertex v 2 V [G] 2 d

Computing Connected Components Given a graph G = (V; E) compute the connected components of G. Connected-Components(G) 1 for each vertex v 2 V [G] 2 d Data Structures for Disjoint Sets Maintain a Dynamic collection of disjoint sets. Each set has a unique representative (an arbitrary member of the set). x. Make-Set(x) - Create a new set with one member

More information

Week 3: Connected Subgraphs

Week 3: Connected Subgraphs Wk 3: Connctd Subgraphs Sptmbr 19, 2016 1 Connctd Graphs Path, Distanc: A path from a vrtx x to a vrtx y in a graph G is rfrrd to an xy-path. Lt X, Y V (G). An (X, Y )-path is an xy-path with x X and y

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

Basic Polyhedral theory

Basic Polyhedral theory Basic Polyhdral thory Th st P = { A b} is calld a polyhdron. Lmma 1. Eithr th systm A = b, b 0, 0 has a solution or thr is a vctorπ such that π A 0, πb < 0 Thr cass, if solution in top row dos not ist

More information

Junction Tree Algorithm 1. David Barber

Junction Tree Algorithm 1. David Barber Juntion Tr Algorithm 1 David Barbr Univrsity Collg London 1 Ths slids aompany th book Baysian Rasoning and Mahin Larning. Th book and dmos an b downloadd from www.s.ul.a.uk/staff/d.barbr/brml. Fdbak and

More information

Searching Linked Lists. Perfect Skip List. Building a Skip List. Skip List Analysis (1) Assume the list is sorted, but is stored in a linked list.

Searching Linked Lists. Perfect Skip List. Building a Skip List. Skip List Analysis (1) Assume the list is sorted, but is stored in a linked list. 3 3 4 8 6 3 3 4 8 6 3 3 4 8 6 () (d) 3 Sarching Linkd Lists Sarching Linkd Lists Sarching Linkd Lists ssum th list is sortd, but is stord in a linkd list. an w us binary sarch? omparisons? Work? What if

More information

Strongly Connected Components

Strongly Connected Components Strongly Connctd Componnts Lt G = (V, E) b a dirctd graph Writ if thr is a path from to in G Writ if and is an quivalnc rlation: implis and implis s quivalnc classs ar calld th strongly connctd componnts

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

Homework #3. 1 x. dx. It therefore follows that a sum of the

Homework #3. 1 x. dx. It therefore follows that a sum of the Danil Cannon CS 62 / Luan March 5, 2009 Homwork # 1. Th natural logarithm is dfind by ln n = n 1 dx. It thrfor follows that a sum of th 1 x sam addnd ovr th sam intrval should b both asymptotically uppr-

More information

Section 6.1. Question: 2. Let H be a subgroup of a group G. Then H operates on G by left multiplication. Describe the orbits for this operation.

Section 6.1. Question: 2. Let H be a subgroup of a group G. Then H operates on G by left multiplication. Describe the orbits for this operation. MAT 444 H Barclo Spring 004 Homwork 6 Solutions Sction 6 Lt H b a subgroup of a group G Thn H oprats on G by lft multiplication Dscrib th orbits for this opration Th orbits of G ar th right costs of H

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

The Equitable Dominating Graph

The Equitable Dominating Graph Intrnational Journal of Enginring Rsarch and Tchnology. ISSN 0974-3154 Volum 8, Numbr 1 (015), pp. 35-4 Intrnational Rsarch Publication Hous http://www.irphous.com Th Equitabl Dominating Graph P.N. Vinay

More information

Random Process Part 1

Random Process Part 1 Random Procss Part A random procss t (, ζ is a signal or wavform in tim. t : tim ζ : outcom in th sampl spac Each tim w rapat th xprimnt, a nw wavform is gnratd. ( W will adopt t for short. Tim sampls

More information

1 Minimum Cut Problem

1 Minimum Cut Problem CS 6 Lctur 6 Min Cut and argr s Algorithm Scribs: Png Hui How (05), Virginia Dat: May 4, 06 Minimum Cut Problm Today, w introduc th minimum cut problm. This problm has many motivations, on of which coms

More information

First derivative analysis

First derivative analysis Robrto s Nots on Dirntial Calculus Chaptr 8: Graphical analysis Sction First drivativ analysis What you nd to know alrady: How to us drivativs to idntiy th critical valus o a unction and its trm points

More information

Abstract Interpretation: concrete and abstract semantics

Abstract Interpretation: concrete and abstract semantics Abstract Intrprtation: concrt and abstract smantics Concrt smantics W considr a vry tiny languag that manags arithmtic oprations on intgrs valus. Th (concrt) smantics of th languags cab b dfind by th funzcion

More information

Solutions to Homework 5

Solutions to Homework 5 Solutions to Homwork 5 Pro. Silvia Frnánz Disrt Mathmatis Math 53A, Fall 2008. [3.4 #] (a) Thr ar x olor hois or vrtx an x or ah o th othr thr vrtis. So th hromati polynomial is P (G, x) =x (x ) 3. ()

More information

Hydrogen Atom and One Electron Ions

Hydrogen Atom and One Electron Ions Hydrogn Atom and On Elctron Ions Th Schrödingr quation for this two-body problm starts out th sam as th gnral two-body Schrödingr quation. First w sparat out th motion of th cntr of mass. Th intrnal potntial

More information

cycle that does not cross any edges (including its own), then it has at least

cycle that does not cross any edges (including its own), then it has at least W prov th following thorm: Thorm If a K n is drawn in th plan in such a way that it has a hamiltonian cycl that dos not cross any dgs (including its own, thn it has at last n ( 4 48 π + O(n crossings Th

More information

Section 11.6: Directional Derivatives and the Gradient Vector

Section 11.6: Directional Derivatives and the Gradient Vector Sction.6: Dirctional Drivativs and th Gradint Vctor Practic HW rom Stwart Ttbook not to hand in p. 778 # -4 p. 799 # 4-5 7 9 9 35 37 odd Th Dirctional Drivativ Rcall that a b Slop o th tangnt lin to th

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

Supplementary Materials

Supplementary Materials 6 Supplmntary Matrials APPENDIX A PHYSICAL INTERPRETATION OF FUEL-RATE-SPEED FUNCTION A truck running on a road with grad/slop θ positiv if moving up and ngativ if moving down facs thr rsistancs: arodynamic

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

10. EXTENDING TRACTABILITY

10. EXTENDING TRACTABILITY Coping with NP-compltnss 0. EXTENDING TRACTABILITY ining small vrtx covrs solving NP-har problms on trs circular arc covrings vrtx covr in bipartit graphs Q. Suppos I n to solv an NP-complt problm. What

More information

Propositional Logic. Combinatorial Problem Solving (CPS) Albert Oliveras Enric Rodríguez-Carbonell. May 17, 2018

Propositional Logic. Combinatorial Problem Solving (CPS) Albert Oliveras Enric Rodríguez-Carbonell. May 17, 2018 Propositional Logic Combinatorial Problm Solving (CPS) Albrt Olivras Enric Rodríguz-Carbonll May 17, 2018 Ovrviw of th sssion Dfinition of Propositional Logic Gnral Concpts in Logic Rduction to SAT CNFs

More information

SECTION where P (cos θ, sin θ) and Q(cos θ, sin θ) are polynomials in cos θ and sin θ, provided Q is never equal to zero.

SECTION where P (cos θ, sin θ) and Q(cos θ, sin θ) are polynomials in cos θ and sin θ, provided Q is never equal to zero. SETION 6. 57 6. Evaluation of Dfinit Intgrals Exampl 6.6 W hav usd dfinit intgrals to valuat contour intgrals. It may com as a surpris to larn that contour intgrals and rsidus can b usd to valuat crtain

More information

CPSC 665 : An Algorithmist s Toolkit Lecture 4 : 21 Jan Linear Programming

CPSC 665 : An Algorithmist s Toolkit Lecture 4 : 21 Jan Linear Programming CPSC 665 : An Algorithmist s Toolkit Lctur 4 : 21 Jan 2015 Lcturr: Sushant Sachdva Linar Programming Scrib: Rasmus Kyng 1. Introduction An optimization problm rquirs us to find th minimum or maximum) of

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

NEW APPLICATIONS OF THE ABEL-LIOUVILLE FORMULA

NEW APPLICATIONS OF THE ABEL-LIOUVILLE FORMULA NE APPLICATIONS OF THE ABEL-LIOUVILLE FORMULA Mirca I CÎRNU Ph Dp o Mathmatics III Faculty o Applid Scincs Univrsity Polithnica o Bucharst Cirnumirca @yahoocom Abstract In a rcnt papr [] 5 th indinit intgrals

More information

MA1506 Tutorial 2 Solutions. Question 1. (1a) 1 ) y x. e x. 1 exp (in general, Integrating factor is. ye dx. So ) (1b) e e. e c.

MA1506 Tutorial 2 Solutions. Question 1. (1a) 1 ) y x. e x. 1 exp (in general, Integrating factor is. ye dx. So ) (1b) e e. e c. MA56 utorial Solutions Qustion a Intgrating fator is ln p p in gnral, multipl b p So b ln p p sin his kin is all a Brnoulli quation -- st Sin w fin Y, Y Y, Y Y p Qustion Dfin v / hn our quation is v μ

More information

COHORT MBA. Exponential function. MATH review (part2) by Lucian Mitroiu. The LOG and EXP functions. Properties: e e. lim.

COHORT MBA. Exponential function. MATH review (part2) by Lucian Mitroiu. The LOG and EXP functions. Properties: e e. lim. MTH rviw part b Lucian Mitroiu Th LOG and EXP functions Th ponntial function p : R, dfind as Proprtis: lim > lim p Eponntial function Y 8 6 - -8-6 - - X Th natural logarithm function ln in US- log: function

More information

AP Calculus BC Problem Drill 16: Indeterminate Forms, L Hopital s Rule, & Improper Intergals

AP Calculus BC Problem Drill 16: Indeterminate Forms, L Hopital s Rule, & Improper Intergals AP Calulus BC Problm Drill 6: Indtrminat Forms, L Hopital s Rul, & Impropr Intrgals Qustion No. of Instrutions: () Rad th problm and answr hois arfully () Work th problms on papr as ndd () Pik th answr

More information

u x v x dx u x v x v x u x dx d u x v x u x v x dx u x v x dx Integration by Parts Formula

u x v x dx u x v x v x u x dx d u x v x u x v x dx u x v x dx Integration by Parts Formula 7. Intgration by Parts Each drivativ formula givs ris to a corrsponding intgral formula, as w v sn many tims. Th drivativ product rul yilds a vry usful intgration tchniqu calld intgration by parts. Starting

More information

Where k is either given or determined from the data and c is an arbitrary constant.

Where k is either given or determined from the data and c is an arbitrary constant. Exponntial growth and dcay applications W wish to solv an quation that has a drivativ. dy ky k > dx This quation says that th rat of chang of th function is proportional to th function. Th solution is

More information

Calculus II (MAC )

Calculus II (MAC ) Calculus II (MAC232-2) Tst 2 (25/6/25) Nam (PRINT): Plas show your work. An answr with no work rcivs no crdit. You may us th back of a pag if you nd mor spac for a problm. You may not us any calculators.

More information

EXST Regression Techniques Page 1

EXST Regression Techniques Page 1 EXST704 - Rgrssion Tchniqus Pag 1 Masurmnt rrors in X W hav assumd that all variation is in Y. Masurmnt rror in this variabl will not ffct th rsults, as long as thy ar uncorrlatd and unbiasd, sinc thy

More information

UNTYPED LAMBDA CALCULUS (II)

UNTYPED LAMBDA CALCULUS (II) 1 UNTYPED LAMBDA CALCULUS (II) RECALL: CALL-BY-VALUE O.S. Basic rul Sarch ruls: (\x.) v [v/x] 1 1 1 1 v v CALL-BY-VALUE EVALUATION EXAMPLE (\x. x x) (\y. y) x x [\y. y / x] = (\y. y) (\y. y) y [\y. y /

More information

EEO 401 Digital Signal Processing Prof. Mark Fowler

EEO 401 Digital Signal Processing Prof. Mark Fowler EEO 401 Digital Signal Procssing Prof. Mark Fowlr Dtails of th ot St #19 Rading Assignmnt: Sct. 7.1.2, 7.1.3, & 7.2 of Proakis & Manolakis Dfinition of th So Givn signal data points x[n] for n = 0,, -1

More information

Combinatorial Networks Week 1, March 11-12

Combinatorial Networks Week 1, March 11-12 1 Nots on March 11 Combinatorial Ntwors W 1, March 11-1 11 Th Pigonhol Principl Th Pigonhol Principl If n objcts ar placd in hols, whr n >, thr xists a box with mor than on objcts 11 Thorm Givn a simpl

More information

Chapter 6 Folding. Folding

Chapter 6 Folding. Folding Chaptr 6 Folding Wintr 1 Mokhtar Abolaz Folding Th folding transformation is usd to systmatically dtrmin th control circuits in DSP architctur whr multipl algorithm oprations ar tim-multiplxd to a singl

More information

Chapter Finding Small Vertex Covers. Extending the Limits of Tractability. Coping With NP-Completeness. Vertex Cover

Chapter Finding Small Vertex Covers. Extending the Limits of Tractability. Coping With NP-Completeness. Vertex Cover Coping With NP-Compltnss Chaptr 0 Extning th Limits o Tractability Q. Suppos I n to solv an NP-complt problm. What shoul I o? A. Thory says you'r unlikly to in poly-tim algorithm. Must sacriic on o thr

More information

That is, we start with a general matrix: And end with a simpler matrix:

That is, we start with a general matrix: And end with a simpler matrix: DIAGON ALIZATION OF THE STR ESS TEN SOR INTRO DUCTIO N By th us of Cauchy s thorm w ar abl to rduc th numbr of strss componnts in th strss tnsor to only nin valus. An additional simplification of th strss

More information

On spanning trees and cycles of multicolored point sets with few intersections

On spanning trees and cycles of multicolored point sets with few intersections On spanning trs and cycls of multicolord point sts with fw intrsctions M. Kano, C. Mrino, and J. Urrutia April, 00 Abstract Lt P 1,..., P k b a collction of disjoint point sts in R in gnral position. W

More information

A Propagating Wave Packet Group Velocity Dispersion

A Propagating Wave Packet Group Velocity Dispersion Lctur 8 Phys 375 A Propagating Wav Packt Group Vlocity Disprsion Ovrviw and Motivation: In th last lctur w lookd at a localizd solution t) to th 1D fr-particl Schrödingr quation (SE) that corrsponds to

More information

Evans, Lipson, Wallace, Greenwood

Evans, Lipson, Wallace, Greenwood Camrig Snior Mathmatial Mthos AC/VCE Units 1& Chaptr Quaratis: Skillsht C 1 Solv ah o th ollowing or x: a (x )(x + 1) = 0 x(5x 1) = 0 x(1 x) = 0 x = 9x Solv ah o th ollowing or x: a x + x 10 = 0 x 8x +

More information

From Elimination to Belief Propagation

From Elimination to Belief Propagation School of omputr Scinc Th lif Propagation (Sum-Product lgorithm Probabilistic Graphical Modls (10-708 Lctur 5, Sp 31, 2007 Rcptor Kinas Rcptor Kinas Kinas X 5 ric Xing Gn G T X 6 X 7 Gn H X 8 Rading: J-hap

More information

The Matrix Exponential

The Matrix Exponential Th Matrix Exponntial (with xrciss) by D. Klain Vrsion 207.0.05 Corrctions and commnts ar wlcom. Th Matrix Exponntial For ach n n complx matrix A, dfin th xponntial of A to b th matrix A A k I + A + k!

More information

Codes on Planar Graphs

Codes on Planar Graphs Cods on Planar Graphs Srimathy Srinivasan, Andrw Thangaraj Dpartmnt of Eltrial Enginring Indian Institut of Thnology, Madras Email: andrw@iitm.a.in arxiv:090.0768v [s.it] 5 May 009 Astrat Cods dfind on

More information

Steinberg s Conjecture is false

Steinberg s Conjecture is false Stinrg s Conjtur is als arxiv:1604.05108v2 [math.co] 19 Apr 2016 Vinnt Cohn-Aa Mihal Hig Danil Král Zhntao Li Estan Salgao Astrat Stinrg onjtur in 1976 that vry planar graph with no yls o lngth our or

More information

Roadmap. XML Indexing. DataGuide example. DataGuides. Strong DataGuides. Multiple DataGuides for same data. CPS Topics in Database Systems

Roadmap. XML Indexing. DataGuide example. DataGuides. Strong DataGuides. Multiple DataGuides for same data. CPS Topics in Database Systems Roadmap XML Indxing CPS 296.1 Topics in Databas Systms Indx fabric Coopr t al. A Fast Indx for Smistructurd Data. VLDB, 2001 DataGuid Goldman and Widom. DataGuids: Enabling Qury Formulation and Optimization

More information

Lecture 16: Bipolar Junction Transistors. Large Signal Models.

Lecture 16: Bipolar Junction Transistors. Large Signal Models. Whits, EE 322 Ltur 16 Pag 1 of 8 Ltur 16: Bipolar Juntion Transistors. Larg Signal Modls. Transistors prform ky funtions in most ltroni iruits. This is rtainly tru in RF iruits, inluding th NorCal 40A.

More information

The Matrix Exponential

The Matrix Exponential Th Matrix Exponntial (with xrciss) by Dan Klain Vrsion 28928 Corrctions and commnts ar wlcom Th Matrix Exponntial For ach n n complx matrix A, dfin th xponntial of A to b th matrix () A A k I + A + k!

More information

CS 361 Meeting 12 10/3/18

CS 361 Meeting 12 10/3/18 CS 36 Mting 2 /3/8 Announcmnts. Homwork 4 is du Friday. If Friday is Mountain Day, homwork should b turnd in at my offic or th dpartmnt offic bfor 4. 2. Homwork 5 will b availabl ovr th wknd. 3. Our midtrm

More information

Search sequence databases 3 10/25/2016

Search sequence databases 3 10/25/2016 Sarch squnc databass 3 10/25/2016 Etrm valu distribution Ø Suppos X is a random variabl with probability dnsity function p(, w sampl a larg numbr S of indpndnt valus of X from this distribution for an

More information

y = 2xe x + x 2 e x at (0, 3). solution: Since y is implicitly related to x we have to use implicit differentiation: 3 6y = 0 y = 1 2 x ln(b) ln(b)

y = 2xe x + x 2 e x at (0, 3). solution: Since y is implicitly related to x we have to use implicit differentiation: 3 6y = 0 y = 1 2 x ln(b) ln(b) 4. y = y = + 5. Find th quation of th tangnt lin for th function y = ( + ) 3 whn = 0. solution: First not that whn = 0, y = (1 + 1) 3 = 8, so th lin gos through (0, 8) and thrfor its y-intrcpt is 8. y

More information

Higher order derivatives

Higher order derivatives Robrto s Nots on Diffrntial Calculus Chaptr 4: Basic diffrntiation ruls Sction 7 Highr ordr drivativs What you nd to know alrady: Basic diffrntiation ruls. What you can larn hr: How to rpat th procss of

More information

COUNTING TAMELY RAMIFIED EXTENSIONS OF LOCAL FIELDS UP TO ISOMORPHISM

COUNTING TAMELY RAMIFIED EXTENSIONS OF LOCAL FIELDS UP TO ISOMORPHISM COUNTING TAMELY RAMIFIED EXTENSIONS OF LOCAL FIELDS UP TO ISOMORPHISM Jim Brown Dpartmnt of Mathmatical Scincs, Clmson Univrsity, Clmson, SC 9634, USA jimlb@g.clmson.du Robrt Cass Dpartmnt of Mathmatics,

More information

Brief Introduction to Statistical Mechanics

Brief Introduction to Statistical Mechanics Brif Introduction to Statistical Mchanics. Purpos: Ths nots ar intndd to provid a vry quick introduction to Statistical Mchanics. Th fild is of cours far mor vast than could b containd in ths fw pags.

More information

Thus, because if either [G : H] or [H : K] is infinite, then [G : K] is infinite, then [G : K] = [G : H][H : K] for all infinite cases.

Thus, because if either [G : H] or [H : K] is infinite, then [G : K] is infinite, then [G : K] = [G : H][H : K] for all infinite cases. Homwork 5 M 373K Solutions Mark Lindbrg and Travis Schdlr 1. Prov that th ring Z/mZ (for m 0) is a fild if and only if m is prim. ( ) Proof by Contrapositiv: Hr, thr ar thr cass for m not prim. m 0: Whn

More information

Chapter 13 GMM for Linear Factor Models in Discount Factor form. GMM on the pricing errors gives a crosssectional

Chapter 13 GMM for Linear Factor Models in Discount Factor form. GMM on the pricing errors gives a crosssectional Chaptr 13 GMM for Linar Factor Modls in Discount Factor form GMM on th pricing rrors givs a crosssctional rgrssion h cas of xcss rturns Hors rac sting for charactristic sting for pricd factors: lambdas

More information

Block Crossings in Storyline Visualizations

Block Crossings in Storyline Visualizations Journal of Graph Algorithms and Appliations http://jgaa.info/ vol. 21, no. 5, pp. 873 913 (2017) DOI: 10.7155/jgaa.00443 Blok Crossings in Storylin Visualizations Thomas C. van Dijk 1 Martin Fink 2 Norbrt

More information

Lecture 14 (Oct. 30, 2017)

Lecture 14 (Oct. 30, 2017) Ltur 14 8.31 Quantum Thory I, Fall 017 69 Ltur 14 (Ot. 30, 017) 14.1 Magnti Monopols Last tim, w onsidrd a magnti fild with a magnti monopol onfiguration, and bgan to approah dsribing th quantum mhanis

More information

BINOMIAL COEFFICIENTS INVOLVING INFINITE POWERS OF PRIMES

BINOMIAL COEFFICIENTS INVOLVING INFINITE POWERS OF PRIMES BINOMIAL COEFFICIENTS INVOLVING INFINITE POWERS OF PRIMES DONALD M. DAVIS Abstract. If p is a prim (implicit in notation and n a positiv intgr, lt ν(n dnot th xponnt of p in n, and U(n n/p ν(n, th unit

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

Square of Hamilton cycle in a random graph

Square of Hamilton cycle in a random graph Squar of Hamilton cycl in a random graph Andrzj Dudk Alan Friz Jun 28, 2016 Abstract W show that p = n is a sharp thrshold for th random graph G n,p to contain th squar of a Hamilton cycl. This improvs

More information

September 23, Honors Chem Atomic structure.notebook. Atomic Structure

September 23, Honors Chem Atomic structure.notebook. Atomic Structure Atomic Structur Topics covrd Atomic structur Subatomic particls Atomic numbr Mass numbr Charg Cations Anions Isotops Avrag atomic mass Practic qustions atomic structur Sp 27 8:16 PM 1 Powr Standards/ Larning

More information

Derangements and Applications

Derangements and Applications 2 3 47 6 23 Journal of Intgr Squncs, Vol. 6 (2003), Articl 03..2 Drangmnts and Applications Mhdi Hassani Dpartmnt of Mathmatics Institut for Advancd Studis in Basic Scincs Zanjan, Iran mhassani@iasbs.ac.ir

More information

Engineering 323 Beautiful HW #13 Page 1 of 6 Brown Problem 5-12

Engineering 323 Beautiful HW #13 Page 1 of 6 Brown Problem 5-12 Enginring Bautiful HW #1 Pag 1 of 6 5.1 Two componnts of a minicomputr hav th following joint pdf for thir usful liftims X and Y: = x(1+ x and y othrwis a. What is th probability that th liftim X of th

More information

SCHUR S THEOREM REU SUMMER 2005

SCHUR S THEOREM REU SUMMER 2005 SCHUR S THEOREM REU SUMMER 2005 1. Combinatorial aroach Prhas th first rsult in th subjct blongs to I. Schur and dats back to 1916. On of his motivation was to study th local vrsion of th famous quation

More information

Figure 1: Closed surface, surface with boundary, or not a surface?

Figure 1: Closed surface, surface with boundary, or not a surface? QUESTION 1 (10 marks) Two o th topological spacs shown in Figur 1 ar closd suracs, two ar suracs with boundary, and two ar not suracs. Dtrmin which is which. You ar not rquird to justiy your answr, but,

More information

Approximate Maximum Flow in Undirected Networks by Christiano, Kelner, Madry, Spielmann, Teng (STOC 2011)

Approximate Maximum Flow in Undirected Networks by Christiano, Kelner, Madry, Spielmann, Teng (STOC 2011) Approximat Maximum Flow in Undirctd Ntworks by Christiano, Klnr, Madry, Spilmann, Tng (STOC 2011) Kurt Mhlhorn Max Planck Institut for Informatics and Saarland Univrsity Sptmbr 28, 2011 Th Rsult High-Lvl

More information

Hardy-Littlewood Conjecture and Exceptional real Zero. JinHua Fei. ChangLing Company of Electronic Technology Baoji Shannxi P.R.

Hardy-Littlewood Conjecture and Exceptional real Zero. JinHua Fei. ChangLing Company of Electronic Technology Baoji Shannxi P.R. Hardy-Littlwood Conjctur and Excptional ral Zro JinHua Fi ChangLing Company of Elctronic Tchnology Baoji Shannxi P.R.China E-mail: fijinhuayoujian@msn.com Abstract. In this papr, w assum that Hardy-Littlwood

More information

MIT Sloan School of Management

MIT Sloan School of Management MIT Sloan School of Managmnt Working Papr 4465-03 Rvisd: Jun 2003 Original: April 2001 SENSITIVITY ANALYSIS FOR SHORTEST PATH PROBLEMS AND MAXIMUM CAPACITY PATH PROBLEMS IN UNDIRECTED GRAPHS Ramkumar Ramaswamy,

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

Addition of angular momentum

Addition of angular momentum Addition of angular momntum April, 0 Oftn w nd to combin diffrnt sourcs of angular momntum to charactriz th total angular momntum of a systm, or to divid th total angular momntum into parts to valuat th

More information

11/13/17. directed graphs. CS 220: Discrete Structures and their Applications. relations and directed graphs; transitive closure zybooks

11/13/17. directed graphs. CS 220: Discrete Structures and their Applications. relations and directed graphs; transitive closure zybooks dirctd graphs CS 220: Discrt Strctrs and thir Applications rlations and dirctd graphs; transiti closr zybooks 9.3-9.6 G=(V, E) rtics dgs dgs rtics/ nods Edg (, ) gos from rtx to rtx. in-dgr of a rtx: th

More information

Function Spaces. a x 3. (Letting x = 1 =)) a(0) + b + c (1) = 0. Row reducing the matrix. b 1. e 4 3. e 9. >: (x = 1 =)) a(0) + b + c (1) = 0

Function Spaces. a x 3. (Letting x = 1 =)) a(0) + b + c (1) = 0. Row reducing the matrix. b 1. e 4 3. e 9. >: (x = 1 =)) a(0) + b + c (1) = 0 unction Spacs Prrquisit: Sction 4.7, Coordinatization n this sction, w apply th tchniqus of Chaptr 4 to vctor spacs whos lmnts ar functions. Th vctor spacs P n and P ar familiar xampls of such spacs. Othr

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

Limiting value of higher Mahler measure

Limiting value of higher Mahler measure Limiting valu of highr Mahlr masur Arunabha Biswas a, Chris Monico a, a Dpartmnt of Mathmatics & Statistics, Txas Tch Univrsity, Lubbock, TX 7949, USA Abstract W considr th k-highr Mahlr masur m k P )

More information

Computing and Communications -- Network Coding

Computing and Communications -- Network Coding 89 90 98 00 Computing and Communications -- Ntwork Coding Dr. Zhiyong Chn Institut of Wirlss Communications Tchnology Shanghai Jiao Tong Univrsity China Lctur 5- Nov. 05 0 Classical Information Thory Sourc

More information

Image Filtering: Noise Removal, Sharpening, Deblurring. Yao Wang Polytechnic University, Brooklyn, NY11201

Image Filtering: Noise Removal, Sharpening, Deblurring. Yao Wang Polytechnic University, Brooklyn, NY11201 Imag Filtring: Nois Rmoval, Sharpning, Dblurring Yao Wang Polytchnic Univrsity, Brooklyn, NY http://wb.poly.du/~yao Outlin Nois rmoval by avraging iltr Nois rmoval by mdian iltr Sharpning Edg nhancmnt

More information

5. B To determine all the holes and asymptotes of the equation: y = bdc dced f gbd

5. B To determine all the holes and asymptotes of the equation: y = bdc dced f gbd 1. First you chck th domain of g x. For this function, x cannot qual zro. Thn w find th D domain of f g x D 3; D 3 0; x Q x x 1 3, x 0 2. Any cosin graph is going to b symmtric with th y-axis as long as

More information

Recall that by Theorems 10.3 and 10.4 together provide us the estimate o(n2 ), S(q) q 9, q=1

Recall that by Theorems 10.3 and 10.4 together provide us the estimate o(n2 ), S(q) q 9, q=1 Chaptr 11 Th singular sris Rcall that by Thorms 10 and 104 togthr provid us th stimat 9 4 n 2 111 Rn = SnΓ 2 + on2, whr th singular sris Sn was dfind in Chaptr 10 as Sn = q=1 Sq q 9, with Sq = 1 a q gcda,q=1

More information

Construction of asymmetric orthogonal arrays of strength three via a replacement method

Construction of asymmetric orthogonal arrays of strength three via a replacement method isid/ms/26/2 Fbruary, 26 http://www.isid.ac.in/ statmath/indx.php?modul=prprint Construction of asymmtric orthogonal arrays of strngth thr via a rplacmnt mthod Tian-fang Zhang, Qiaoling Dng and Alok Dy

More information

u r du = ur+1 r + 1 du = ln u + C u sin u du = cos u + C cos u du = sin u + C sec u tan u du = sec u + C e u du = e u + C

u r du = ur+1 r + 1 du = ln u + C u sin u du = cos u + C cos u du = sin u + C sec u tan u du = sec u + C e u du = e u + C Tchniqus of Intgration c Donald Kridr and Dwight Lahr In this sction w ar going to introduc th first approachs to valuating an indfinit intgral whos intgrand dos not hav an immdiat antidrivativ. W bgin

More information

10. The Discrete-Time Fourier Transform (DTFT)

10. The Discrete-Time Fourier Transform (DTFT) Th Discrt-Tim Fourir Transform (DTFT Dfinition of th discrt-tim Fourir transform Th Fourir rprsntation of signals plays an important rol in both continuous and discrt signal procssing In this sction w

More information

ENGR 323 BHW 15 Van Bonn 1/7

ENGR 323 BHW 15 Van Bonn 1/7 ENGR 33 BHW 5 Van Bonn /7 4.4 In Eriss and 3 as wll as man othr situations on has th PDF o X and wishs th PDF o Yh. Assum that h is an invrtibl untion so that h an b solvd or to ild. Thn it an b shown

More information

BINOMIAL COEFFICIENTS INVOLVING INFINITE POWERS OF PRIMES. 1. Statement of results

BINOMIAL COEFFICIENTS INVOLVING INFINITE POWERS OF PRIMES. 1. Statement of results BINOMIAL COEFFICIENTS INVOLVING INFINITE POWERS OF PRIMES DONALD M. DAVIS Abstract. If p is a prim and n a positiv intgr, lt ν p (n dnot th xponnt of p in n, and u p (n n/p νp(n th unit part of n. If α

More information

COMPUTER GENERATED HOLOGRAMS Optical Sciences 627 W.J. Dallas (Monday, April 04, 2005, 8:35 AM) PART I: CHAPTER TWO COMB MATH.

COMPUTER GENERATED HOLOGRAMS Optical Sciences 627 W.J. Dallas (Monday, April 04, 2005, 8:35 AM) PART I: CHAPTER TWO COMB MATH. C:\Dallas\0_Courss\03A_OpSci_67\0 Cgh_Book\0_athmaticalPrliminaris\0_0 Combath.doc of 8 COPUTER GENERATED HOLOGRAS Optical Scincs 67 W.J. Dallas (onday, April 04, 005, 8:35 A) PART I: CHAPTER TWO COB ATH

More information

ECE602 Exam 1 April 5, You must show ALL of your work for full credit.

ECE602 Exam 1 April 5, You must show ALL of your work for full credit. ECE62 Exam April 5, 27 Nam: Solution Scor: / This xam is closd-book. You must show ALL of your work for full crdit. Plas rad th qustions carfully. Plas chck your answrs carfully. Calculators may NOT b

More information

Collisions between electrons and ions

Collisions between electrons and ions DRAFT 1 Collisions btwn lctrons and ions Flix I. Parra Rudolf Pirls Cntr for Thortical Physics, Unirsity of Oxford, Oxford OX1 NP, UK This rsion is of 8 May 217 1. Introduction Th Fokkr-Planck collision

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

Problem Set 6 Solutions

Problem Set 6 Solutions 6.04/18.06J Mathmatics for Computr Scinc March 15, 005 Srini Dvadas and Eric Lhman Problm St 6 Solutions Du: Monday, March 8 at 9 PM in Room 3-044 Problm 1. Sammy th Shark is a financial srvic providr

More information

Addition of angular momentum

Addition of angular momentum Addition of angular momntum April, 07 Oftn w nd to combin diffrnt sourcs of angular momntum to charactriz th total angular momntum of a systm, or to divid th total angular momntum into parts to valuat

More information

Finding low cost TSP and 2-matching solutions using certain half integer subtour vertices

Finding low cost TSP and 2-matching solutions using certain half integer subtour vertices Finding low cost TSP and 2-matching solutions using crtain half intgr subtour vrtics Sylvia Boyd and Robrt Carr Novmbr 996 Introduction Givn th complt graph K n = (V, E) on n nods with dg costs c R E,

More information

Introduction to Arithmetic Geometry Fall 2013 Lecture #20 11/14/2013

Introduction to Arithmetic Geometry Fall 2013 Lecture #20 11/14/2013 18.782 Introduction to Arithmtic Gomtry Fall 2013 Lctur #20 11/14/2013 20.1 Dgr thorm for morphisms of curvs Lt us rstat th thorm givn at th nd of th last lctur, which w will now prov. Thorm 20.1. Lt φ:

More information

Coupled Pendulums. Two normal modes.

Coupled Pendulums. Two normal modes. Tim Dpndnt Two Stat Problm Coupld Pndulums Wak spring Two normal mods. No friction. No air rsistanc. Prfct Spring Start Swinging Som tim latr - swings with full amplitud. stationary M +n L M +m Elctron

More information

Chapter 10. The singular integral Introducing S(n) and J(n)

Chapter 10. The singular integral Introducing S(n) and J(n) Chaptr Th singular intgral Our aim in this chaptr is to rplac th functions S (n) and J (n) by mor convnint xprssions; ths will b calld th singular sris S(n) and th singular intgral J(n). This will b don

More information

CLONES IN 3-CONNECTED FRAME MATROIDS

CLONES IN 3-CONNECTED FRAME MATROIDS CLONES IN 3-CONNECTED FRAME MATROIDS JAKAYLA ROBBINS, DANIEL SLILATY, AND XIANGQIAN ZHOU Abstract. W dtrmin th structur o clonal classs o 3-connctd ram matroids in trms o th structur o biasd graphs. Robbins

More information

Utilizing exact and Monte Carlo methods to investigate properties of the Blume Capel Model applied to a nine site lattice.

Utilizing exact and Monte Carlo methods to investigate properties of the Blume Capel Model applied to a nine site lattice. Utilizing xat and Mont Carlo mthods to invstigat proprtis of th Blum Capl Modl applid to a nin sit latti Nik Franios Writing various xat and Mont Carlo omputr algorithms in C languag, I usd th Blum Capl

More information