ONLY AVAILABLE IN ELECTRONIC FORM

Size: px
Start display at page:

Download "ONLY AVAILABLE IN ELECTRONIC FORM"

Transcription

1 OPERTIONS RESERH o.287/opr.8.559c pp. c c8 -copao ONLY VILLE IN ELETRONI FORM fors 28 INFORMS Elctroc opao Optzato Mols of scrt-evt Syst yacs by Wa K (Vctor ha a L Schrub, Opratos Rsarch, o.287/opr

2 OPTIMIZTION MOELS OF ISRETE-EVENT SYSTEM YNMIS Wa K (Vctor ha * L Schrub ** * partt of cso Sccs a Egrg Systs, Rsslar Polytchc Isttut, II 55, Eght Strt, Troy, NY 28 ** partt of Iustral Egrg a Opratos Rsarch, Uvrsty of alfora, rkly, 45 Etchvrry Hall, rkly, 9472 PPENIX tals for gratg qualty costrats ERG2MP ar gv th followg. ot by X a grc tgr stat varabl th sulato talz at. Lt X(t b th valu of X at t t, a b a gv tgr ubr. X ca b crt or crt by ffrt vts; Fgur.2 lsts cass for scrt-vt systs cosr ths papr (otato ths fgur wll b f latr wh thy ar us. ass (a ( ar th four basc stuatos whr actly two vts ca chag th valu of X: (a th schulg a schul vts (vts a ; (b th schul vt (vt a o aothr vt (vt ; (c th schulg vt (vt a o aothr vt (vt ; a ( ay two vts (vts a clug th schulg a schul vts (vts a. Sytrc cass (.., th rols of crasg a crasg th valu of X ar trchag ar ot clu sc thr costrats ar rv a slar way. ass whch four vts ca chag X s valu ar pct by cass ( (h. Thy ca also b gralz to cass wth or tha four vts chagg X s valu. W oly cosr cass whr a vrt s schul by ( o cotoal arc or (2 ultpl cotoal arcs wth sa cotos (ths covrs all th systs cosr ths papr. For cass wth a vrt schul by ultpl cotoal arcs wth ffrt cotos, o ca splt th vrt to a group of vrtcs, ach of whch s schul by o cotoal arc (Yucsa a Schrub 992. tals for Stp : I Stp, sc th t lay t( s rao, th th ( vt to b schul ay ot b th th ( vt to occur; o apl s ob "ovrtakg" quug systs: for stac, a ult-srvr quug syst, th frst start ob ay ot b th frst ob to fsh. O as to tr whch vt schuls whch vt s to us bary varabls. Lt bary varabl = f th th O( vt schuls th th ( vt, a othrws. Ths assgt O (:, (: s ( O ( t (-M(- O (:, (: O ( ( - t ( ( - å O (:, (: O (:, (: å O (: I, (:.

3 HN N SHRUEN Optzato Mols of scrt-evt Syst yacs whr a M ar sutabl ral ubrs,.g., th lowr bou a uppr bou of O( ( t(, rspctvly, or sply th ubr of vts th sulato sapl path. If thr s o ovrtakg for apl, a sgl-srvr ta quu or a G//R quu, th th start ob ust b th th ob to fsh th sply rop th bary varabls a st = Stp. Th frst two costrats ar to ak sur that at ost o par of a ca achv both qualts, a hc a qualty. I so cass, havg o qualty forc woul b suffc, whch s th cas Eapl 2.2. tals for Stp 2: Spl ass For cas (a Fgur.2, aftr th occurrc of vt, f coto (X s tru, th a vt s schul to occur. t th stat ust bfor th cuto of th th vt,.., -, w ust hav Û ( X ( = ( - ( Û =, =,..., whr s th ubr of occurrcs of vt th sapl path, capturg th fact that th th occurrc of vt ust occur aftr th th occurrc of vt. Th frst qualty follows bcaus s th ubr of vts that ust occur bfor t -. Th last qualty s bcaus vt ts ar cotuous (ot to b cofus wth th tgr valu stat varabls. as (b Fgur.2 s hal slarly. ostrats wth ary Varabls O as to rvg costrats wth bary varabls s as follows. W scuss cas (c frst. If th coto o arc (, s tru rght aftr th th occurrc of vt (at t, whch s qual to, th usg (2., w hav X ( = ( - ( Û ( - Û =. - Usg a bary varabl z, :, w ca prss ths rlato as th logc: - «z =, whch s quvalt to, : - M c (- z -, : c c -, : (-, : z z 2

4 HN N SHRUEN Optzato Mols of scrt-evt Syst yacs whr M c a c ar, rspctvly, th uppr bou a lowr bou of - - (or sply th ubr of vts th sapl path of th sulato ru, a c s a postv sall ral ubr such that - c ca b cosr as - > (s Hookr 22 or Wllas 995 for th us of logc tgr prograg. So vt wll b schul f a oly f z, : =. Ths s costra by å s :, : = z, : whr s :, : s ar bary varabls forcg th logc s :, : = =, whch s quvalt to c 2 :, : c 2 :, : -M (- s. (- s. whr M 2 c a 2 c ar, rspctvly, th uppr bou a lowr bou of -. Iqualts å s :, : a å s :, : ca b a to sur that th o-to-o schulg rlatoshp btw vt a vt, v though å s :, : s ruat u to co- strat :, : = z, :. To sur that a vt wth a lowr ust b schul ar- å s å= å ca b us. = lr tha vts wth hghr s, costrats s :, : s :, : lso, f thr s o othr vt that wll schul vt, th costrats - å s pq :, : {, }( s :, : p= å ca b appl to forc th orrg q= of all vts. Th costrat rvato procur for cas ( s or coplcat a rqurs th us of four sts of bary varabls. Rght aftr th th occurrc of vt (at t, f th coto o arc (, s th tru, w hav X ( = ( - ( Thr two vt coutg fuctos valuat at t. For vt, w hav ( ( ( - - Û =. If ( = k, th k, for so k. Ths allows us to valuat th vt couts for vt as follows.

5 HN N SHRUEN Optzato Mols of scrt-evt Syst yacs ( ( - ( k - k Û k - Û > Û =. whr 2 s a postv sall ral ubr such that - 2 ca b cosr ( as ( - >. ostrats k a k - 2 sply tst whthr th coto o arc (, s tru urg th t trval btw k a k -. If th th vt occurs urg ths t pro, th so vt, say th th vt, wll b schul to occur. To tst what k s satsfy such coto, w us two bary varabls, z k :, : a h k :, : -, a plt th logc: k «zk :, : = a k - «h - = usg th followg qualts, k :, : k - (- k :, : k k :, : (- k :, : k k :, : - M z z z M ( h k - 2 k :, : - h. whr M a ar, rspctvly, th uppr bou a lowr bou of k -, a M 2 a ar, rspctvly, th uppr bou a lowr bou of k - -, a s a 2 postv sall ral ubr such that wthout whch th corrspog qualty k ca b rgar as k >. If both z k :, :, a h k :, : - ar qual to, th th coto o arc (, s tru a th th vt wll b schul to occur. y usg bary varabls g k :, : a s :, :, ths ca b suarz as zk :, : = & hk :, : - = «gk :, : = a å g «å s =, gvg qualts k k :, : :, : gk :, : -( zk :, : h k :, : - 2 gk -( zk h k - :, : :, : :, : å å å s å g, gk :, : - s :, : k :, : - k :, : k a th followg bouary costrats for k =,,, 4

6 HN N SHRUEN Optzato Mols of scrt-evt Syst yacs g g k :, : - zk :, : k :, : zk :, : -. To schul th th vt, w us th logc s :, : = =, whch gv costrats -M (- s. :, : (- s. :, : whr M a ar, rspctvly, th uppr bou a lowr bou of -, Slar to cas (c, th followg qualts ca b a to sur th thr rqurts: th o-too rlatoshp, 2 lowr- vts shoul b schul frst, a th orrg of vts. å s :, : å s :, : å å s :, : s = = :, : - å s pq :, : {, }( s :, : p= å q= ovoluto ostrats ass ( (h volv or tha two vts that coul chag th valu of X. W rly o covolutos to grat costrats for all th volvg vts. W scuss cas (. ass (f (h ca b hal by cobg th covoluto costrats rv cas ( a th costrats rv cass (b (. Rght aftr th th occurrc of vt (at t, f th coto o arc (, s th tru, w hav X ( = ( ( -( - ( Û ( - ( whr W = È s a coutr vt, for vts a, that occurs wh thr vt or vt occurs,.., W (t = (t (t, " t. Thrfor, by fg such a coutr vt, cass whch th valu of a varabl ca b chag by or tha two vts ca b hal usg costrats vlop cas (. Nt, w scuss th costrats grato procur for vts,, a W. Th rlato ( = ( ( s a covoluto rlatoshp. For apl, th W st W vt coul occur wh th st vt occurs or wh th st vt occurs, but ot both; usg th laguag of ath prograg a troucg cotuous varabls P,, a P,, (th frst, sco, a thr subscrpts ar th cs for th th W vt, th vt, a th 2 vt, rspctvly, such that = 2, ths as P,, =, P,, =, a o of th co- W 5

7 HN N SHRUEN Optzato Mols of scrt-evt Syst yacs strats, W P,, a W P,,, s bg (.., W = {P,,, P,, }. For th 2 W vt (wth w cotuous varabls P 2,,, 2 2 =, ths covoluto rlatoshps ar P2,2, = 2, P2,,2 = 2, o of th costrats, P2,, a P2,,, s bg (.., P 2,, = a{, }, a o of th costrats, W2 P2,2,, W2 P2,,, a W2 P2,,2, s bg (.., W 2 = {P 2,2,, P 2,,, P 2,,2 }. Rpatg ths costructo utl Z wll grat å ( costrats. Th bg costrats ca b fou by usg bary varabls th = sa way as bfor. For apl, to sur P 2,, = a{, }, lt bary varabl a 2,, = «a apply th followg costrats: -M (- a2,,, a2,, (- a2,,, P2,, -M2(- a2,,, P2,, 2( - a2,,, P2,, - Ma2,,, P2,, a2,,, whr M, M 2, M,, 2, a, ar th uppr bou a lowr bou of th corrspog costrats rspctvly, a s a postv sall ral ubr such that wthout whch th corrspog qualty ca b rgar as >. ll th w cotuous varabls ca b put to th obctv fucto to b z. For a cas wth thr vts that coul crt X,.., lt {,, E} b ths thr vts (ot that ths cas s ot pct Fgur.2, ot by Z = È È E th coutr vt for vts,, a E. To rprst th rlatoshps btw Z a th thr vts that vt Z couts (,, a E, w ot that thr ar ((2/2 possbl prutatos that coul caus Z to happ (th ubr of tgr pots o a hyprpla crat by th cs of ths thr vts, pct Fgur.. For apl, cosr Z Fgur., P,,, rprsts th au of,, a E ; all th tgr pots o th tragl-pla ar th possbl prutatos of Z ; a th u of th s th t for Z. Lt L Z ( b th st of all possbl prutatos of Z a P,, 2, b o of ths prutatos. For ach =,...,, w hav: P,, 2, P -, -, 2, P,, 2, P -,, 2-, P P Z P, (,, L (.,, 2, -,, 2, -,,, " 2 Î Z 2 (. Th bouary qualts ar: P,,, = P,, = P,,, = E, =,...,. ga, o of th costrats govrg Z a P,, 2, ust b bg, whch ca b forc by usg bary varabls. Rug th sulato to solv th rla athatcal progra wthout ths bary varabl wll sply tr th bg costrat, but w wll forc t hr wth th bary varabl for copltss. 6

8 HN N SHRUEN Optzato Mols of scrt-evt Syst yacs Now, for a gral cas wth vts crtg X(t (.., {,,..., } å å å - 2 = = = bco: =, thr ar prutatos of ach. Th abov two sts of costrats wll th P,,..., P -, -,..., P P Z P, (,..., L (,,..., -,,..., -,,..., " Î Z P,,,..., P,,...,,, =,...,. W ot that th ubr of such costrats grows potally th ubr of vts. Howvr, w ar ot trst solvg th athatcal progras ffctly sc rug th sulato ca prov solutos to th rla ath progras. Th bfts of stablshg th act athatcal rprstatos for ERGs ar suarz Scto. a applcatos ar llustrat Scto. P,,, E Fgur. : Th Gotrc Itrprtato of ostrat (. 7

9 HN N SHRUEN Optzato Mols of scrt-evt Syst yacs (a ( (b (f E (c (g E ( (h E F Fgur.2: Gratg ostrats ur ffrt Stuatos 8

Correlation in tree The (ferromagnetic) Ising model

Correlation in tree The (ferromagnetic) Ising model 5/3/00 :\ jh\slf\nots.oc\7 Chaptr 7 Blf propagato corrlato tr Corrlato tr Th (frromagtc) Isg mol Th Isg mol s a graphcal mol or par ws raom Markov fl cosstg of a urct graph wth varabls assocat wth th vrtcs.

More information

Department of Mathematics and Statistics Indian Institute of Technology Kanpur MSO202A/MSO202 Assignment 3 Solutions Introduction To Complex Analysis

Department of Mathematics and Statistics Indian Institute of Technology Kanpur MSO202A/MSO202 Assignment 3 Solutions Introduction To Complex Analysis Dpartmt of Mathmatcs ad Statstcs Ida Isttut of Tchology Kapur MSOA/MSO Assgmt 3 Solutos Itroducto To omplx Aalyss Th problms markd (T) d a xplct dscusso th tutoral class. Othr problms ar for hacd practc..

More information

Introduction to logistic regression

Introduction to logistic regression Itroducto to logstc rgrsso Gv: datast D { 2 2... } whr s a k-dmsoal vctor of ral-valud faturs or attrbuts ad s a bar class labl or targt. hus w ca sa that R k ad {0 }. For ampl f k 4 a datast of 3 data

More information

Chapter 4 NUMERICAL METHODS FOR SOLVING BOUNDARY-VALUE PROBLEMS

Chapter 4 NUMERICAL METHODS FOR SOLVING BOUNDARY-VALUE PROBLEMS Chaptr 4 NUMERICL METHODS FOR SOLVING BOUNDRY-VLUE PROBLEMS 00 4. Varatoal formulato two-msoal magtostatcs Lt th followg magtostatc bouar-valu problm b cosr ( ) J (4..) 0 alog ΓD (4..) 0 alog ΓN (4..)

More information

Counting the compositions of a positive integer n using Generating Functions Start with, 1. x = 3 ), the number of compositions of 4.

Counting the compositions of a positive integer n using Generating Functions Start with, 1. x = 3 ), the number of compositions of 4. Coutg th compostos of a postv tgr usg Gratg Fuctos Start wth,... - Whr, for ampl, th co-ff of s, for o summad composto of aml,. To obta umbr of compostos of, w d th co-ff of (...) ( ) ( ) Hr for stac w

More information

Independent Domination in Line Graphs

Independent Domination in Line Graphs Itratoal Joural of Sctfc & Egrg Rsarch Volum 3 Issu 6 Ju-1 1 ISSN 9-5518 Iddt Domato L Grahs M H Muddbhal ad D Basavarajaa Abstract - For ay grah G th l grah L G H s th trscto grah Thus th vrtcs of LG

More information

Total Prime Graph. Abstract: We introduce a new type of labeling known as Total Prime Labeling. Graphs which admit a Total Prime labeling are

Total Prime Graph. Abstract: We introduce a new type of labeling known as Total Prime Labeling. Graphs which admit a Total Prime labeling are Itratoal Joural Of Computatoal Egrg Rsarch (crol.com) Vol. Issu. 5 Total Prm Graph M.Rav (a) Ramasubramaa 1, R.Kala 1 Dpt.of Mathmatcs, Sr Shakth Isttut of Egrg & Tchology, Combator 641 06. Dpt. of Mathmatcs,

More information

LECTURE 6 TRANSFORMATION OF RANDOM VARIABLES

LECTURE 6 TRANSFORMATION OF RANDOM VARIABLES LECTURE 6 TRANSFORMATION OF RANDOM VARIABLES TRANSFORMATION OF FUNCTION OF A RANDOM VARIABLE UNIVARIATE TRANSFORMATIONS TRANSFORMATION OF RANDOM VARIABLES If s a rv wth cdf F th Y=g s also a rv. If w wrt

More information

Estimating the Variance in a Simulation Study of Balanced Two Stage Predictors of Realized Random Cluster Means Ed Stanek

Estimating the Variance in a Simulation Study of Balanced Two Stage Predictors of Realized Random Cluster Means Ed Stanek Etatg th Varac a Sulato Study of Balacd Two Stag Prdctor of Ralzd Rado Clutr Ma Ed Stak Itroducto W dcrb a pla to tat th varac copot a ulato tudy N ( µ µ W df th varac of th clutr paratr a ug th N ulatd

More information

Weights Interpreting W and lnw What is β? Some Endnotes = n!ω if we neglect the zero point energy then ( )

Weights Interpreting W and lnw What is β? Some Endnotes = n!ω if we neglect the zero point energy then ( ) Sprg Ch 35: Statstcal chacs ad Chcal Ktcs Wghts... 9 Itrprtg W ad lw... 3 What s?... 33 Lt s loo at... 34 So Edots... 35 Chaptr 3: Fudatal Prcpls of Stat ch fro a spl odl (drvato of oltza dstrbuto, also

More information

COMPLEX NUMBERS AND ELEMENTARY FUNCTIONS OF COMPLEX VARIABLES

COMPLEX NUMBERS AND ELEMENTARY FUNCTIONS OF COMPLEX VARIABLES COMPLEX NUMBERS AND ELEMENTARY FUNCTIONS OF COMPLEX VARIABLES DEFINITION OF A COMPLEX NUMBER: A umbr of th form, whr = (, ad & ar ral umbrs s calld a compl umbr Th ral umbr, s calld ral part of whl s calld

More information

Power Spectrum Estimation of Stochastic Stationary Signals

Power Spectrum Estimation of Stochastic Stationary Signals ag of 6 or Spctru stato of Stochastc Statoary Sgas Lt s cosr a obsrvato of a stochastc procss (). Ay obsrvato s a ft rcor of th ra procss. Thrfor, ca say:

More information

3.4 Properties of the Stress Tensor

3.4 Properties of the Stress Tensor cto.4.4 Proprts of th trss sor.4. trss rasformato Lt th compots of th Cauchy strss tsor a coordat systm wth bas vctors b. h compots a scod coordat systm wth bas vctors j,, ar gv by th tsor trasformato

More information

A study of stochastic programming having some continuous random variables

A study of stochastic programming having some continuous random variables Itratoal Joural of Egrg Trds ad Tchology (IJETT) Volu 7 Nur 5 - July 06 A study of stochastc prograg havg so cotuous rado varals Mr.Hr S. Dosh, Dr.Chrag J. Trvd, Assocat Profssor, H Collg of Corc Navragura,

More information

Reliability of time dependent stress-strength system for various distributions

Reliability of time dependent stress-strength system for various distributions IOS Joural of Mathmatcs (IOS-JM ISSN: 78-578. Volum 3, Issu 6 (Sp-Oct., PP -7 www.osrjourals.org lablty of tm dpdt strss-strgth systm for varous dstrbutos N.Swath, T.S.Uma Mahswar,, Dpartmt of Mathmatcs,

More information

The probability of Riemann's hypothesis being true is. equal to 1. Yuyang Zhu 1

The probability of Riemann's hypothesis being true is. equal to 1. Yuyang Zhu 1 Th robablty of Ra's hyothss bg tru s ual to Yuyag Zhu Abstract Lt P b th st of all r ubrs P b th -th ( ) lt of P ascdg ordr of sz b ostv tgrs ad s a rutato of wth Th followg rsults ar gv ths ar: () Th

More information

In 1991 Fermat s Last Theorem Has Been Proved

In 1991 Fermat s Last Theorem Has Been Proved I 99 Frmat s Last Thorm Has B Provd Chu-Xua Jag P.O.Box 94Bg 00854Cha Jcxua00@s.com;cxxxx@6.com bstract I 67 Frmat wrot: It s mpossbl to sparat a cub to two cubs or a bquadrat to two bquadrats or gral

More information

minimize c'x subject to subject to subject to

minimize c'x subject to subject to subject to z ' sut to ' M ' M N uostrd N z ' sut to ' z ' sut to ' sl vrls vtor of : vrls surplus vtor of : uostrd s s s s s s z sut to whr : ut ost of :out of : out of ( ' gr of h food ( utrt : rqurt for h utrt

More information

Comparisons of the Variance of Predictors with PPS sampling (update of c04ed26.doc) Ed Stanek

Comparisons of the Variance of Predictors with PPS sampling (update of c04ed26.doc) Ed Stanek Coparo o th Varac o Prdctor wth PPS aplg (updat o c04d6doc Ed Sta troducto W copar prdctor o a PSU a or total bad o PPS aplg Th tratgy to ollow that o Sta ad Sgr (JASA, 004 whr w xpr th prdctor a a lar

More information

Aotomorphic Functions And Fermat s Last Theorem(4)

Aotomorphic Functions And Fermat s Last Theorem(4) otomorphc Fuctos d Frmat s Last Thorm(4) Chu-Xua Jag P. O. Box 94 Bg 00854 P. R. Cha agchuxua@sohu.com bsract 67 Frmat wrot: It s mpossbl to sparat a cub to two cubs or a bquadrat to two bquadrats or gral

More information

Homework 1: Solutions

Homework 1: Solutions Howo : Solutos No-a Fals supposto tst but passs scal tst lthouh -f th ta as slowss [S /V] vs t th appaac of laty alty th path alo whch slowss s to b tat to obta tavl ts ps o th ol paat S o V as a cosquc

More information

Lecture 1: Empirical economic relations

Lecture 1: Empirical economic relations Ecoomcs 53 Lctur : Emprcal coomc rlatos What s coomtrcs? Ecoomtrcs s masurmt of coomc rlatos. W d to kow What s a coomc rlato? How do w masur such a rlato? Dfto: A coomc rlato s a rlato btw coomc varabls.

More information

Chiang Mai J. Sci. 2014; 41(2) 457 ( 2) ( ) ( ) forms a simply periodic Proof. Let q be a positive integer. Since

Chiang Mai J. Sci. 2014; 41(2) 457 ( 2) ( ) ( ) forms a simply periodic Proof. Let q be a positive integer. Since 56 Chag Ma J Sc 0; () Chag Ma J Sc 0; () : 56-6 http://pgscccmuacth/joural/ Cotrbutd Papr Th Padova Sucs Ft Groups Sat Taș* ad Erdal Karaduma Dpartmt of Mathmatcs, Faculty of Scc, Atatürk Uvrsty, 50 Erzurum,

More information

Numerical Method: Finite difference scheme

Numerical Method: Finite difference scheme Numrcal Mthod: Ft dffrc schm Taylor s srs f(x 3 f(x f '(x f ''(x f '''(x...(1! 3! f(x 3 f(x f '(x f ''(x f '''(x...(! 3! whr > 0 from (1, f(x f(x f '(x R Droppg R, f(x f(x f '(x Forward dffrcg O ( x from

More information

pn Junction Under Reverse-Bias Conditions 3.3 Physical Operation of Diodes

pn Junction Under Reverse-Bias Conditions 3.3 Physical Operation of Diodes 3.3 Physcal Orato of os Jucto Ur vrs-bas Cotos rft Currt S : ato to th ffuso Currt comot u to majorty carrr ffuso, caus by thrmally grat morty carrrs, thr ar two currt comots lctros mov by rft from to

More information

C.11 Bang-bang Control

C.11 Bang-bang Control Itroucto to Cotrol heory Iclug Optmal Cotrol Nguye a e -.5 C. Bag-bag Cotrol. Itroucto hs chapter eals wth the cotrol wth restrctos: s boue a mght well be possble to have scotutes. o llustrate some of

More information

Second Handout: The Measurement of Income Inequality: Basic Concepts

Second Handout: The Measurement of Income Inequality: Basic Concepts Scod Hadout: Th Masurmt of Icom Iqualty: Basc Cocpts O th ormatv approach to qualty masurmt ad th cocpt of "qually dstrbutd quvalt lvl of com" Suppos that that thr ar oly two dvduals socty, Rachl ad Mart

More information

( x) min. Nonlinear optimization problem without constraints NPP: then. Global minimum of the function f(x)

( x) min. Nonlinear optimization problem without constraints NPP: then. Global minimum of the function f(x) Objectve fucto f() : he optzato proble cossts of fg a vector of ecso varables belogg to the feasble set of solutos R such that It s eote as: Nolear optzato proble wthout costrats NPP: R f ( ) : R R f f

More information

PURE MATHEMATICS A-LEVEL PAPER 1

PURE MATHEMATICS A-LEVEL PAPER 1 -AL P MATH PAPER HONG KONG EXAMINATIONS AUTHORITY HONG KONG ADVANCED LEVEL EXAMINATION PURE MATHEMATICS A-LEVEL PAPER 8 am am ( hours) This papr must b aswrd i Eglish This papr cosists of Sctio A ad Sctio

More information

GRAPHS IN SCIENCE. drawn correctly, the. other is not. Which. Best Fit Line # one is which?

GRAPHS IN SCIENCE. drawn correctly, the. other is not. Which. Best Fit Line # one is which? 5 9 Bt Ft L # 8 7 6 5 GRAPH IN CIENCE O of th thg ot oft a rto of a xrt a grah of o k. A grah a vual rrtato of ural ata ollt fro a xrt. o of th ty of grah you ll f ar bar a grah. Th o u ot oft a l grah,

More information

On the Possible Coding Principles of DNA & I Ching

On the Possible Coding Principles of DNA & I Ching Sctfc GOD Joural May 015 Volum 6 Issu 4 pp. 161-166 Hu, H. & Wu, M., O th Possbl Codg Prcpls of DNA & I Chg 161 O th Possbl Codg Prcpls of DNA & I Chg Hupg Hu * & Maox Wu Rvw Artcl ABSTRACT I ths rvw artcl,

More information

Priority Search Trees - Part I

Priority Search Trees - Part I .S. 252 Pro. Rorto Taassa oputatoal otry S., 1992 1993 Ltur 9 at: ar 8, 1993 Sr: a Q ol aro Prorty Sar Trs - Part 1 trouto t last ltur, w loo at trval trs. or trval pot losur prols, ty us lar spa a optal

More information

Introduction to logistic regression

Introduction to logistic regression Itroducto to logstc rgrsso Gv: datast D {... } whr s a k-dmsoal vctor of ral-valud faturs or attrbuts ad s a bar class labl or targt. hus w ca sa that R k ad {0 }. For ampl f k 4 a datast of 3 data pots

More information

Inner Product Spaces INNER PRODUCTS

Inner Product Spaces INNER PRODUCTS MA4Hcdoc Ir Product Spcs INNER PRODCS Dto A r product o vctor spc V s ucto tht ssgs ubr spc V such wy tht th ollowg xos holds: P : w s rl ubr P : P : P 4 : P 5 : v, w = w, v v + w, u = u + w, u rv, w =

More information

Unbalanced Panel Data Models

Unbalanced Panel Data Models Ubalacd Pal Data odls Chaptr 9 from Baltag: Ecoomtrc Aalyss of Pal Data 5 by Adrás alascs 4448 troducto balacd or complt pals: a pal data st whr data/obsrvatos ar avalabl for all crosssctoal uts th tr

More information

Binary Choice. Multiple Choice. LPM logit logistic regresion probit. Multinomial Logit

Binary Choice. Multiple Choice. LPM logit logistic regresion probit. Multinomial Logit (c Pogsa Porchawssul, Faculty of Ecoomcs, Chulalogor Uvrsty (c Pogsa Porchawssul, Faculty of Ecoomcs, Chulalogor Uvrsty 3 Bary Choc LPM logt logstc rgrso probt Multpl Choc Multomal Logt (c Pogsa Porchawssul,

More information

Extension Formulas of Lauricella s Functions by Applications of Dixon s Summation Theorem

Extension Formulas of Lauricella s Functions by Applications of Dixon s Summation Theorem Avll t http:pvu.u Appl. Appl. Mth. ISSN: 9-9466 Vol. 0 Issu Dr 05 pp. 007-08 Appltos Appl Mthts: A Itrtol Jourl AAM Etso oruls of Lurll s utos Appltos of Do s Suto Thor Ah Al Atsh Dprtt of Mthts A Uvrst

More information

Almost all Cayley Graphs Are Hamiltonian

Almost all Cayley Graphs Are Hamiltonian Acta Mathmatca Sca, Nw Srs 199, Vol1, No, pp 151 155 Almost all Cayly Graphs Ar Hamltoa Mg Jxag & Huag Qogxag Abstract It has b cocturd that thr s a hamltoa cycl vry ft coctd Cayly graph I spt of th dffculty

More information

Washington State University

Washington State University he 3 Ktics ad Ractor Dsig Sprg, 00 Washgto Stat Uivrsity Dpartmt of hmical Egrg Richard L. Zollars Exam # You will hav o hour (60 muts) to complt this xam which cosists of four (4) problms. You may us

More information

Chapter 2 Infinite Series Page 1 of 11. Chapter 2 : Infinite Series

Chapter 2 Infinite Series Page 1 of 11. Chapter 2 : Infinite Series Chatr Ifiit Sris Pag of Sctio F Itgral Tst Chatr : Ifiit Sris By th d of this sctio you will b abl to valuat imror itgrals tst a sris for covrgc by alyig th itgral tst aly th itgral tst to rov th -sris

More information

Inference on Stress-Strength Reliability for Weighted Weibull Distribution

Inference on Stress-Strength Reliability for Weighted Weibull Distribution Arca Joural of Mathatcs a Statstcs 03, 3(4: 0-6 DOI: 0.593/j.ajs.030304.06 Ifrc o Strss-Strgth Rlablty for Wght Wbull Dstrbuto Hay M. Sal Dpartt of Statstcs, Faculty of Corc, Al-Azhr Uvrsty, Egypt & Qass

More information

CS 491 G Combinatorial Optimization

CS 491 G Combinatorial Optimization CS 49 G Cobinatorial Optiization Lctur Nots Junhui Jia. Maiu Flow Probls Now lt us iscuss or tails on aiu low probls. Thor. A asibl low is aiu i an only i thr is no -augnting path. Proo: Lt P = A asibl

More information

1 Onto functions and bijections Applications to Counting

1 Onto functions and bijections Applications to Counting 1 Oto fuctos ad bectos Applcatos to Coutg Now we move o to a ew topc. Defto 1.1 (Surecto. A fucto f : A B s sad to be surectve or oto f for each b B there s some a A so that f(a B. What are examples of

More information

Chapter 5 Special Discrete Distributions. Wen-Guey Tzeng Computer Science Department National Chiao University

Chapter 5 Special Discrete Distributions. Wen-Guey Tzeng Computer Science Department National Chiao University Chatr 5 Scal Dscrt Dstrbutos W-Guy Tzg Comutr Scc Dartmt Natoal Chao Uvrsty Why study scal radom varabls Thy aar frqutly thory, alcatos, statstcs, scc, grg, fac, tc. For aml, Th umbr of customrs a rod

More information

Math Tricks. Basic Probability. x k. (Combination - number of ways to group r of n objects, order not important) (a is constant, 0 < r < 1)

Math Tricks. Basic Probability. x k. (Combination - number of ways to group r of n objects, order not important) (a is constant, 0 < r < 1) Math Trcks r! Combato - umbr o was to group r o objcts, ordr ot mportat r! r! ar 0 a r a s costat, 0 < r < k k! k 0 EX E[XX-] + EX Basc Probablt 0 or d Pr[X > ] - Pr[X ] Pr[ X ] Pr[X ] - Pr[X ] Proprts

More information

A New Method for Solving Fuzzy Linear. Programming by Solving Linear Programming

A New Method for Solving Fuzzy Linear. Programming by Solving Linear Programming ppled Matheatcal Sceces Vol 008 o 50 7-80 New Method for Solvg Fuzzy Lear Prograg by Solvg Lear Prograg S H Nasser a Departet of Matheatcs Faculty of Basc Sceces Mazadara Uversty Babolsar Ira b The Research

More information

Ideal multigrades with trigonometric coefficients

Ideal multigrades with trigonometric coefficients Ideal multgrades wth trgoometrc coeffcets Zarathustra Brady December 13, 010 1 The problem A (, k) multgrade s defed as a par of dstct sets of tegers such that (a 1,..., a ; b 1,..., b ) a j = =1 for all

More information

A Penalty Function Algorithm with Objective Parameters and Constraint Penalty Parameter for Multi-Objective Programming

A Penalty Function Algorithm with Objective Parameters and Constraint Penalty Parameter for Multi-Objective Programming Aerca Joural of Operatos Research, 4, 4, 33-339 Publshed Ole Noveber 4 ScRes http://wwwscrporg/oural/aor http://ddoorg/436/aor4463 A Pealty Fucto Algorth wth Obectve Paraeters ad Costrat Pealty Paraeter

More information

ERDOS-SMARANDACHE NUMBERS. Sabin Tabirca* Tatiana Tabirca**

ERDOS-SMARANDACHE NUMBERS. Sabin Tabirca* Tatiana Tabirca** ERDO-MARANDACHE NUMBER b Tbrc* Tt Tbrc** *Trslv Uvrsty of Brsov, Computr cc Dprtmt **Uvrsty of Mchstr, Computr cc Dprtmt Th strtg pot of ths rtcl s rprstd by rct work of Fch []. Bsd o two symptotc rsults

More information

Worksheet: Taylor Series, Lagrange Error Bound ilearnmath.net

Worksheet: Taylor Series, Lagrange Error Bound ilearnmath.net Taylor s Thorm & Lagrag Error Bouds Actual Error This is th ral amout o rror, ot th rror boud (worst cas scario). It is th dirc btw th actual () ad th polyomial. Stps:. Plug -valu ito () to gt a valu.

More information

Estimation of Population Variance Using a Generalized Double Sampling Estimator

Estimation of Population Variance Using a Generalized Double Sampling Estimator r Laka Joural o Appl tatstcs Vol 5-3 stmato o Populato Varac Us a Gralz Doubl ampl stmator Push Msra * a R. Kara h Dpartmt o tatstcs D.A.V.P.G. Coll Dhrau- 8 Uttarakha Ia. Dpartmt o tatstcs Luckow Uvrst

More information

LINEARLY CONSTRAINED MINIMIZATION BY USING NEWTON S METHOD

LINEARLY CONSTRAINED MINIMIZATION BY USING NEWTON S METHOD Jural Karya Asl Loreka Ahl Matematk Vol 8 o 205 Page 084-088 Jural Karya Asl Loreka Ahl Matematk LIEARLY COSTRAIED MIIMIZATIO BY USIG EWTO S METHOD Yosza B Dasrl, a Ismal B Moh 2 Faculty Electrocs a Computer

More information

Linear Prediction Analysis of

Linear Prediction Analysis of Lr Prdcto Alyss of Sch Souds Brl Ch Drtt of Coutr Scc & Iforto grg Ntol Tw Norl Uvrsty frcs: X Hug t l So Lgug g Procssg Chtrs 5 6 J Dllr t l Dscrt-T Procssg of Sch Sgls Chtrs 4-6 3 J W Pco Sgl odlg tchqus

More information

Repeated Trials: We perform both experiments. Our space now is: Hence: We now can define a Cartesian Product Space.

Repeated Trials: We perform both experiments. Our space now is: Hence: We now can define a Cartesian Product Space. Rpatd Trals: As w hav lood at t, th thory of probablty dals wth outcoms of sgl xprmts. I th applcatos o s usually trstd two or mor xprmts or rpatd prformac or th sam xprmt. I ordr to aalyz such problms

More information

Chapter 6. pn-junction diode: I-V characteristics

Chapter 6. pn-junction diode: I-V characteristics Chatr 6. -jucto dod: -V charactrstcs Tocs: stady stat rsos of th jucto dod udr ald d.c. voltag. ucto udr bas qualtatv dscusso dal dod quato Dvatos from th dal dod Charg-cotrol aroach Prof. Yo-S M Elctroc

More information

Complex Numbers. Prepared by: Prof. Sunil Department of Mathematics NIT Hamirpur (HP)

Complex Numbers. Prepared by: Prof. Sunil Department of Mathematics NIT Hamirpur (HP) th Topc Compl Nmbrs Hyprbolc fctos ad Ivrs hyprbolc fctos, Rlato btw hyprbolc ad crclar fctos, Formla of hyprbolc fctos, Ivrs hyprbolc fctos Prpard by: Prof Sl Dpartmt of Mathmatcs NIT Hamrpr (HP) Hyprbolc

More information

MODEL QUESTION. Statistics (Theory) (New Syllabus) dx OR, If M is the mode of a discrete probability distribution with mass function f

MODEL QUESTION. Statistics (Theory) (New Syllabus) dx OR, If M is the mode of a discrete probability distribution with mass function f MODEL QUESTION Statstcs (Thory) (Nw Syllabus) GROUP A d θ. ) Wrt dow th rsult of ( ) ) d OR, If M s th mod of a dscrt robablty dstrbuto wth mass fucto f th f().. at M. d d ( θ ) θ θ OR, f() mamum valu

More information

Algorithms behind the Correlation Setting Window

Algorithms behind the Correlation Setting Window Algorths behd the Correlato Settg Wdow Itroducto I ths report detaled forato about the correlato settg pop up wdow s gve. See Fgure. Ths wdow s obtaed b clckg o the rado butto labelled Kow dep the a scree

More information

7.0 Equality Contraints: Lagrange Multipliers

7.0 Equality Contraints: Lagrange Multipliers Systes Optzato 7.0 Equalty Cotrats: Lagrage Multplers Cosder the zato of a o-lear fucto subject to equalty costrats: g f() R ( ) 0 ( ) (7.) where the g ( ) are possbly also olear fuctos, ad < otherwse

More information

New families of p-ary sequences with low correlation and large linear span

New families of p-ary sequences with low correlation and large linear span THE JOURNAL OF CHINA UNIVERSITIES OF POSTS AND TELECOMMUNICATIONS Volu 4 Issu 4 Dcbr 7 TONG X WEN Qao-ya Nw fals of -ary sucs wth low corrlato ad larg lar sa CLC ubr TN98 Docut A Artcl ID 5-8885 (7 4-53-4

More information

Linear Prediction Analysis of Speech Sounds

Linear Prediction Analysis of Speech Sounds Lr Prdcto Alyss of Sch Souds Brl Ch 4 frcs: X Hug t l So Lgug Procssg Chtrs 5 6 J Dllr t l Dscrt-T Procssg of Sch Sgls Chtrs 4-6 3 J W Pco Sgl odlg tchqus sch rcogto rocdgs of th I Stbr 993 5-47 Lr Prdctv

More information

ME 501A Seminar in Engineering Analysis Page 1

ME 501A Seminar in Engineering Analysis Page 1 St Ssts o Ordar Drtal Equatos Novbr 7 St Ssts o Ordar Drtal Equatos Larr Cartto Mcacal Er 5A Sar Er Aalss Novbr 7 Outl Mr Rsults Rvw last class Stablt o urcal solutos Stp sz varato or rror cotrol Multstp

More information

1985 AP Calculus BC: Section I

1985 AP Calculus BC: Section I 985 AP Calculus BC: Sctio I 9 Miuts No Calculator Nots: () I this amiatio, l dots th atural logarithm of (that is, logarithm to th bas ). () Ulss othrwis spcifid, th domai of a fuctio f is assumd to b

More information

Option 3. b) xe dx = and therefore the series is convergent. 12 a) Divergent b) Convergent Proof 15 For. p = 1 1so the series diverges.

Option 3. b) xe dx = and therefore the series is convergent. 12 a) Divergent b) Convergent Proof 15 For. p = 1 1so the series diverges. Optio Chaptr Ercis. Covrgs to Covrgs to Covrgs to Divrgs Covrgs to Covrgs to Divrgs 8 Divrgs Covrgs to Covrgs to Divrgs Covrgs to Covrgs to Covrgs to Covrgs to 8 Proof Covrgs to π l 8 l a b Divrgt π Divrgt

More information

The real E-k diagram of Si is more complicated (indirect semiconductor). The bottom of E C and top of E V appear for different values of k.

The real E-k diagram of Si is more complicated (indirect semiconductor). The bottom of E C and top of E V appear for different values of k. Modr Smcoductor Dvcs for Itgratd rcuts haptr. lctros ad Hols Smcoductors or a bad ctrd at k=0, th -k rlatoshp ar th mmum s usually parabolc: m = k * m* d / dk d / dk gatv gatv ffctv mass Wdr small d /

More information

Optimal Design of Two-Channel Recursive Parallelogram Quadrature Mirror Filter Banks Ju-Hong Lee, Yi-Lin Shieh

Optimal Design of Two-Channel Recursive Parallelogram Quadrature Mirror Filter Banks Ju-Hong Lee, Yi-Lin Shieh Worl cay of cc Er a choloy Itratoal Joural of oputr a Iato Er Vol:8 o:7 4 Optal s of wo-hal Rcursv aralllora Quaratur rror Fltr Baks Ju-o L Y-L hh Itratoal cc Ix oputr a Iato Er Vol:8 o:7 4 wast.orublcato99989

More information

In the name of Allah Proton Electromagnetic Form Factors

In the name of Allah Proton Electromagnetic Form Factors I th a of Allah Poto Elctoagtc o actos By : Maj Hazav Pof A.A.Rajab Shahoo Uvsty of Tchology Atoc o acto: W cos th tactos of lcto bas wth atos assu to b th gou stats. Th ct lcto ay gt scatt lastcally wth

More information

Debabrata Dey and Atanu Lahiri

Debabrata Dey and Atanu Lahiri RESEARCH ARTICLE QUALITY COMPETITION AND MARKET SEGMENTATION IN THE SECURITY SOFTWARE MARKET Debabrata Dey ad Atau Lahr Mchael G. Foster School of Busess, Uersty of Washgto, Seattle, Seattle, WA 9895 U.S.A.

More information

Section 5.1/5.2: Areas and Distances the Definite Integral

Section 5.1/5.2: Areas and Distances the Definite Integral Scto./.: Ars d Dstcs th Dt Itgrl Sgm Notto Prctc HW rom Stwrt Ttook ot to hd p. #,, 9 p. 6 #,, 9- odd, - odd Th sum o trms,,, s wrtt s, whr th d o summto Empl : Fd th sum. Soluto: Th Dt Itgrl Suppos w

More information

A Conventional Approach for the Solution of the Fifth Order Boundary Value Problems Using Sixth Degree Spline Functions

A Conventional Approach for the Solution of the Fifth Order Boundary Value Problems Using Sixth Degree Spline Functions Appled Matheatcs, 1, 4, 8-88 http://d.do.org/1.4/a.1.448 Publshed Ole Aprl 1 (http://www.scrp.org/joural/a) A Covetoal Approach for the Soluto of the Ffth Order Boudary Value Probles Usg Sth Degree Sple

More information

Time : 1 hr. Test Paper 08 Date 04/01/15 Batch - R Marks : 120

Time : 1 hr. Test Paper 08 Date 04/01/15 Batch - R Marks : 120 Tim : hr. Tst Papr 8 D 4//5 Bch - R Marks : SINGLE CORRECT CHOICE TYPE [4, ]. If th compl umbr z sisfis th coditio z 3, th th last valu of z is qual to : z (A) 5/3 (B) 8/3 (C) /3 (D) o of ths 5 4. Th itgral,

More information

Motivation. We talk today for a more flexible approach for modeling the conditional probabilities.

Motivation. We talk today for a more flexible approach for modeling the conditional probabilities. Baysia Ntworks Motivatio Th coditioal idpdc assuptio ad by aïv Bays classifirs ay s too rigid spcially for classificatio probls i which th attributs ar sowhat corrlatd. W talk today for a or flibl approach

More information

A COMPARISON OF SEVERAL TESTS FOR EQUALITY OF COEFFICIENTS IN QUADRATIC REGRESSION MODELS UNDER HETEROSCEDASTICITY

A COMPARISON OF SEVERAL TESTS FOR EQUALITY OF COEFFICIENTS IN QUADRATIC REGRESSION MODELS UNDER HETEROSCEDASTICITY Colloquum Bomtrcum 44 04 09 7 COMPISON OF SEVEL ESS FO EQULIY OF COEFFICIENS IN QUDIC EGESSION MODELS UNDE HEEOSCEDSICIY Małgorzata Szczpa Dorota Domagała Dpartmt of ppld Mathmatcs ad Computr Scc Uvrsty

More information

Chapter Five. More Dimensions. is simply the set of all ordered n-tuples of real numbers x = ( x 1

Chapter Five. More Dimensions. is simply the set of all ordered n-tuples of real numbers x = ( x 1 Chatr Fiv Mor Dimsios 51 Th Sac R W ar ow rard to mov o to sacs of dimsio gratr tha thr Ths sacs ar a straightforward gralizatio of our Euclida sac of thr dimsios Lt b a ositiv itgr Th -dimsioal Euclida

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

ON THE RELATION BETWEEN THE CAUSAL BESSEL DERIVATIVE AND THE MARCEL RIESZ ELLIPTIC AND HYPERBOLIC KERNELS

ON THE RELATION BETWEEN THE CAUSAL BESSEL DERIVATIVE AND THE MARCEL RIESZ ELLIPTIC AND HYPERBOLIC KERNELS ACENA Vo.. 03-08 005 03 ON THE RELATION BETWEEN THE CAUSAL BESSEL DERIVATIVE AND THE MARCEL RIESZ ELLIPTIC AND HYPERBOLIC KERNELS Rub A. CERUTTI RESUMEN: Cosrao os úcos Rsz coo casos artcuars úco causa

More information

Generalized Linear Regression with Regularization

Generalized Linear Regression with Regularization Geeralze Lear Regresso wth Regularzato Zoya Bylsk March 3, 05 BASIC REGRESSION PROBLEM Note: I the followg otes I wll make explct what s a vector a what s a scalar usg vec t or otato, to avo cofuso betwee

More information

Convergence Theorems for Two Iterative Methods. A stationary iterative method for solving the linear system: (1.1)

Convergence Theorems for Two Iterative Methods. A stationary iterative method for solving the linear system: (1.1) Conrgnc Thors for Two Itrt Mthods A sttonry trt thod for solng th lnr syst: Ax = b (.) ploys n trton trx B nd constnt ctor c so tht for gn strtng stt x of x for = 2... x Bx c + = +. (.2) For such n trton

More information

The Hyperelastic material is examined in this section.

The Hyperelastic material is examined in this section. 4. Hyprlastcty h Hyprlastc matral s xad n ths scton. 4..1 Consttutv Equatons h rat of chang of ntrnal nrgy W pr unt rfrnc volum s gvn by th strss powr, whch can b xprssd n a numbr of dffrnt ways (s 3.7.6):

More information

å 1 13 Practice Final Examination Solutions - = CS109 Dec 5, 2018

å 1 13 Practice Final Examination Solutions - = CS109 Dec 5, 2018 Chrs Pech Fal Practce CS09 Dec 5, 08 Practce Fal Examato Solutos. Aswer: 4/5 8/7. There are multle ways to obta ths aswer; here are two: The frst commo method s to sum over all ossbltes for the rak of

More information

Series of New Information Divergences, Properties and Corresponding Series of Metric Spaces

Series of New Information Divergences, Properties and Corresponding Series of Metric Spaces Srs of Nw Iforao Dvrgcs, Proprs ad Corrspodg Srs of Mrc Spacs K.C.Ja, Praphull Chhabra Profssor, Dpar of Mahacs, Malavya Naoal Isu of Tchology, Japur (Rajasha), Ida Ph.d Scholar, Dpar of Mahacs, Malavya

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

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

Nuclear Chemistry -- ANSWERS

Nuclear Chemistry -- ANSWERS Hoor Chstry Mr. Motro 5-6 Probl St Nuclar Chstry -- ANSWERS Clarly wrt aswrs o sparat shts. Show all work ad uts.. Wrt all th uclar quatos or th radoactv dcay srs o Urau-38 all th way to Lad-6. Th dcay

More information

Estimators for Finite Population Variance Using Mean and Variance of Auxiliary Variable

Estimators for Finite Population Variance Using Mean and Variance of Auxiliary Variable Itratoal Jal o Probablt a tattc 5 : - DOI:.59/j.jp.5. tmat Ft Poplato Varac U Ma a Varac o Alar Varabl Ph Mra * R. Kara h Dpartmt o tattc Lcow Urt Lcow Ia Abtract F tmat t poplato arac mato o l alar arabl

More information

The Selection Problem - Variable Size Decrease/Conquer (Practice with algorithm analysis)

The Selection Problem - Variable Size Decrease/Conquer (Practice with algorithm analysis) We have covered: Selecto, Iserto, Mergesort, Bubblesort, Heapsort Next: Selecto the Qucksort The Selecto Problem - Varable Sze Decrease/Coquer (Practce wth algorthm aalyss) Cosder the problem of fdg the

More information

Shortest Paths in Graphs. Paths in graphs. Shortest paths CS 445. Alon Efrat Slides courtesy of Erik Demaine and Carola Wenk

Shortest Paths in Graphs. Paths in graphs. Shortest paths CS 445. Alon Efrat Slides courtesy of Erik Demaine and Carola Wenk S 445 Shortst Paths n Graphs lon frat Sls courtsy of rk man an arola Wnk Paths n raphs onsr a raph G = (V, ) wth -wht functon w : R. Th wht of path p = v v v k s fn to xampl: k = w ( p) = w( v, v + ).

More information

NP!= P. By Liu Ran. Table of Contents. The P versus NP problem is a major unsolved problem in computer

NP!= P. By Liu Ran. Table of Contents. The P versus NP problem is a major unsolved problem in computer NP!= P By Lu Ra Table of Cotets. Itroduce 2. Prelmary theorem 3. Proof 4. Expla 5. Cocluso. Itroduce The P versus NP problem s a major usolved problem computer scece. Iformally, t asks whether a computer

More information

Heisenberg Model. Sayed Mohammad Mahdi Sadrnezhaad. Supervisor: Prof. Abdollah Langari

Heisenberg Model. Sayed Mohammad Mahdi Sadrnezhaad. Supervisor: Prof. Abdollah Langari snbrg Modl Sad Mohammad Mahd Sadrnhaad Survsor: Prof. bdollah Langar bstract: n ths rsarch w tr to calculat analtcall gnvalus and gnvctors of fnt chan wth ½-sn artcls snbrg modl. W drov gnfuctons for closd

More information

Exercises for Square-Congruence Modulo n ver 11

Exercises for Square-Congruence Modulo n ver 11 Exercses for Square-Cogruece Modulo ver Let ad ab,.. Mark True or False. a. 3S 30 b. 3S 90 c. 3S 3 d. 3S 4 e. 4S f. 5S g. 0S 55 h. 8S 57. 9S 58 j. S 76 k. 6S 304 l. 47S 5347. Fd the equvalece classes duced

More information

APPENDIX: STATISTICAL TOOLS

APPENDIX: STATISTICAL TOOLS I. Nots o radom samplig Why do you d to sampl radomly? APPENDI: STATISTICAL TOOLS I ordr to masur som valu o a populatio of orgaisms, you usually caot masur all orgaisms, so you sampl a subst of th populatio.

More information

Chapter Discrete Fourier Transform

Chapter Discrete Fourier Transform haptr.4 Dscrt Fourr Trasform Itroducto Rcad th xpota form of Fourr srs s Equatos 8 ad from haptr., wt f t 8, h.. T w t f t dt T Wh th abov tgra ca b usd to comput, h.., t s mor prfrab to hav a dscrtzd

More information

On Approximation Lower Bounds for TSP with Bounded Metrics

On Approximation Lower Bounds for TSP with Bounded Metrics O Approxmato Lowr Bouds for TSP wth Boudd Mtrcs Mark Karpsk Rchard Schmd Abstract W dvlop a w mthod for provg xplct approxmato lowr bouds for TSP problms wth boudd mtrcs mprovg o th bst up to ow kow bouds.

More information

KURODA S METHOD FOR CONSTRUCTING CONSISTENT INPUT-OUTPUT DATA SETS. Peter J. Wilcoxen. Impact Research Centre, University of Melbourne.

KURODA S METHOD FOR CONSTRUCTING CONSISTENT INPUT-OUTPUT DATA SETS. Peter J. Wilcoxen. Impact Research Centre, University of Melbourne. KURODA S METHOD FOR CONSTRUCTING CONSISTENT INPUT-OUTPUT DATA SETS by Peter J. Wlcoxe Ipact Research Cetre, Uversty of Melboure Aprl 1989 Ths paper descrbes a ethod that ca be used to resolve cossteces

More information

Multi-Machine Systems with Constant Impedance Loads

Multi-Machine Systems with Constant Impedance Loads Mult-Mach Systms wth Costat Impac Loas Th parts of th txt whch w hav yt to covr clu: Chaptr 3: Systm rspos to small sturbacs Chaptr 6: Lar mols of sychroous machs Chaptrs 7-8: Exctato systms a Effct of

More information

' 1.00, has the form of a rhomb with

' 1.00, has the form of a rhomb with Problm I Rflcto ad rfracto of lght A A trstg prsm Th ma scto of a glass prsm stuatd ar ' has th form of a rhomb wth A th yllow bam of moochromatc lght propagatg towards th prsm paralll wth th dagoal AC

More information

Packing of graphs with small product of sizes

Packing of graphs with small product of sizes Joural of Combatoral Theory, Seres B 98 (008) 4 45 www.elsever.com/locate/jctb Note Packg of graphs wth small product of szes Alexadr V. Kostochka a,b,,gexyu c, a Departmet of Mathematcs, Uversty of Illos,

More information

How many neutrino species?

How many neutrino species? ow may utrio scis? Two mthods for dtrmii it lium abudac i uivrs At a collidr umbr of utrio scis Exasio of th uivrs is ovrd by th Fridma quatio R R 8G tot Kc R Whr: :ubblcostat G :Gravitatioal costat 6.

More information

For combinatorial problems we might need to generate all permutations, combinations, or subsets of a set.

For combinatorial problems we might need to generate all permutations, combinations, or subsets of a set. Addtoal Decrease ad Coquer Algorthms For combatoral problems we mght eed to geerate all permutatos, combatos, or subsets of a set. Geeratg Permutatos If we have a set f elemets: { a 1, a 2, a 3, a } the

More information

Study on 2-tuple Linguistic Assessment Method based on Grey Cluster with Incomplete Attribute Weight Information

Study on 2-tuple Linguistic Assessment Method based on Grey Cluster with Incomplete Attribute Weight Information Procgs of 2009 IEEE Itratoal Cofrc o Systs, Ma, a Cybrtcs Sa Atoo, TX, USA - Octobr 2009 Sty o 2-tpl Lgstc Asssst Mo bas o Gry Clstr w Icoplt Attrbt Wght Iforato Cha M IEEE Mbr, Sfg L, Yaogo Dag, Jaglg

More information