A Theoretical Framework for Selecting the Cost Function for Source Routing

Size: px
Start display at page:

Download "A Theoretical Framework for Selecting the Cost Function for Source Routing"

Transcription

1 A Theoreal Framework for Seleg he Cos Fuo for Soure Roug Gag Cheg ad Nrwa Asar Seor ember IEEE Absra Fdg a feasble pah sube o mulple osras a ework s a NP-omplee problem ad has bee exesvely suded ay proposed soure roug algorhms akle hs problem by rasformg o he shores pah seleo problem whh s P-omplee wh a egraed os fuo ha maps he mul-osras of eah lk o a sgle os However how o sele a approprae os fuo s a mpora ssue ha has rarely bee addressed leraure I hs paper we provde a heoreal framework for pkg a os fuo ha a mprove he performae of soure roug erms of omplexy overgee ad probably of fdg a feasble pah Idex Terms ulple addvely osraed QoS roug os fuo NP-omplee I INTRODUCTION he remedous growh of he global Iere has gve rse T o a varey of applaos ha requre qualy-of-serve (QoS beyod wha s provded by he urre bes-effor IP pake delvery serve Oe of he hallegg ssues s o sele feasble pahs ha sasfy dffere qualy-of-serve requremes Ths problem s kow as QoS roug I geeral sae dsrbuo ad roug sraegy [] are he wo ssues relaed o QoS roug Sae dsrbuo addresses he ssue of exhagg he sae formao hroughou he ework [] Roug sraegy s used o fd a feasble pah ha mees he QoS requremes I hs paper we fous o he laer ask ad assume ha aurae ework sae formao s avalable o eah ode A umber of researh works have also addressed aurae formao [3 4] whh s however beyod he sope of hs paper QoS osras a be aegorzed o hree ypes: oave addve ad mulplave Se oave parameers se he upper lms of all he lks alog a pah suh as badwdh we a smply prue all he lks ad odes ha do o sasfy he QoS osras We a also over mulplave parameers o addve parameers by usg he logarhm fuo For sae we a ake -log(- p as he replaeme for loss rae p Thus we fous oly o addve osras hs paper I has bee proved ha mulple Auhors are wh he Advaed Neworkg Laboraory Newark NJ 7 USA (orrespodg auhor o provde phoe: ; fax: ; e-mal: asar@edu Ths work has bee suppored par by he New Jersey Commsso o Hgher Eduao va he NJ I-TOWER proe ad he New Jersey Commsso o See ad Tehology va he NJ Ceer for Wreless Teleommuaos addvely osraed QoS roug s NP-omplee [5] Hee aklg hs problem requres heurss I [6] a heurs algorhm was proposed based o a lear os fuo for wo addve osras; hs s a CP (ulple Cosraed Pah Seleo [] problem wh wo addve osras A bary searh sraegy for fdg he approprae value of k he lear os fuo w + kw or kw + w where w ( p ( are wo respeve weghs of he pah p was proposed ad a herarhal Dksra algorhm was rodued o fd he pah I was show ha he wors-ase omplexy of he algorhm s Ο (log B( m+ log where B s he upper boud of he parameer k m s he umber of lks ad s he umber of odes Smlar o [6] Lagrage Relaxao Based Aggregaed Cos (LARAC was proposed [7] for he Delay Cosraed Leas Cos pah problem (DCLC Ths algorhm s based o a lear os fuo λ + λd where deoes he os d he delay ad λ a adusable parameer I dffers from [6] o how λ s defed: λ s ompued by Lagrage Relaxao sead of he bary searh I was show ha he ompuaoal omplexy 4 of hs algorhm was Ο ( m log m However [8] for he same problem (DCLC a o-lear os fuo was proposed afer osderg he shoromg of he lear os fuo ay proposed soure roug algorhms rasform he mulple osraed QoS roug problem o a shores pah seleo problem wh a egraed os fuo ha map he mul-osras of eah lk o a sgle os However how o sele a approprae os fuo s rarely addressed leraure; hereby we wll provde a heoreal framework for hs ssue II PROBLE FORULATION We wll provde a geeral framework for seleg a os fuo whh here s o lmao o he umber of QoS osras Se oave osras a be easly addressed by prug ad mulplave osras a be geerally overed o addve osras whou loss of geeraly we oly osder addve osras ad formulae he problem as follows: Defo : ulple Addvely Cosraed Pah Seleo (ACP: Assume a ework s modeled as a dreed graph GNE ( where N s he se of all odes ad E s he se of all lks Eah lk oeed from ode u o v deoed by E s assoaed wh addve parameers: /3/$7 3 IEEE 63

2 w Gve a se of osras ( ad a par of odes s ad ACP s o fd a pah p from s o sube o W ( p w ( u v < e p for all uv Defo : Ay pah seleed by ACP s a feasble pah; ha s ay pah p from s o ha mees he requreme W ( p w ( u v e p for all s a feasble uv pah Noaos: f ( x : Cos fuo where x ( x x x C : The veor represeao of he QoS osras ( 3 W : The wegh veor of pah p e W ( W W W where W ( p w ( u v e p uv 4 C( p : The os of pah p C f( w ( e w ( e w ( e where f ( s he os fuo Noe ha C f( W for uv uv uv f( W( p f( w( uv w( uv w ( uv ( e uv However f f ( x s lear e f ( x x f ( W f( w w w ( w e uv p w ( w f ( w ( w ( w( C ( III A FRAEWOR FOR SELECTING THE COST FUNCTION ay proposed soure roug algorhms are assoaed wh os fuos whh are esseal for solvg he problem Thus desgg a approprae os fuo s a key ssue hs kd of approahes I hs paper by assumg he os fuo s ouous we prese some bas feaures of he os fuo ad he mpa of hese feaures o he performae of roug algorhms Whou loss of geeraly he os fuo for raversg a lk from ode u o v s desrable o have he followg properes: f ( ; eah varable f ( orrespods o a QoS parameer suh as delay ad er I s uve ha he os for raversg a lk wh QoS value (eg he os of raversg a lk whh does o ause ay delay should be > f x > ad f x ; e he os fuo s reasg wh respe o eah addve parameer f( x x x 3 ; e he os fuo s oave mplyg ha f w ( u v e uv he C f( W f( C I fa mos soure roug algorhms proposed he leraure possess hs propery Noe ha some oher o-ouous fuos proposed for QoS roug a also be haraerzed by fuos whh sasfy he above properes For example / f ( x x max{ x x } equals o ( x + x whh lm sasfes he above properes The followg heorems ad lemmas are derved by assumg ha he os fuo f ( x sasfes Properes -3 [Theorem ] No feasble pah exss f he leas os pah has he os larger ha f ( C Proof: By orado Assume pah p sasfes he osra C ad he leas os amog all pahs s larger ha f ( C ; ha s C > f( C p C( p > f( C (3 Also f( x x x f ( W( p C( p (4 Thus from (3 ad (4 f( W( p > f( C (5 However se ad pah p sasfes he osra C W( { } ( p f W f( C (6 whh orads (5 ad hus Theorem s proved The followg lemma wll falae he proof of Theorem [Lemma ] Le g f( xe where e s he h u veor he m-dmesoal spae ad g( ym y The: lm[ max ( ym] g ( g ( m ym dg where g( dx x Proof: Le Thus f g ( else Ly ( y y λ y λ[ g( y ] m m (7 63

3 dl( y y y λ dy λg ( y g ( y (8 λ By leg y y y g( ym (9 Also g Ly ( y y λ y Thus ym s maxmzed by (9 ( Defe I ( x as he verse fuo of g ( x So Se g max( y I ( I ( ( m g ad g ( we have di d I ad Hee max( ym reases dx dx wh Thus I ( lm[max( ] lm[ ( ] lm[ y I ] m I ( f ( x g lm g ( ( x x else [Theorem ] A pah p s a feasble pah f C m{ } Proof: Se wm( eu v m g ( w( e f( W ( e (3 So for ay pah p uv uv g( w( eu v ( The for a pah p wh C C p (4 m{ } x g( w( eu v C m{ } (5 Cosder he ase ha f ( x Lemma w ( e u v by m{ } (6 Cosder he oher ase ha { } Thus m{ } C w( e u v p(7 Thus W ( p Tha s pah p s a feasble pah w( eu v { } (8 Noe ha a pah p wh C f( C may o be a feasble pah For example osder f ( x x x+ x wh C ( ad a pah p wh W( p ( I s obvous ha C f( W < f( C bu pah p s o a feasble pah The followg wo defos a be used as he soppg rera searhg for feasble pahs Defo 3: Gve a ework G wh os fuo f ad QoS osra C o feasble pah exss from ode s o f he leas os pah p from ode s o has C > Bu ( f G C Bu ( f G C s kow as a upper boud of he os of a feasble pah Defo 4: Gve a ework G wh os fuo f ad QoS osra C f ay pah p from ode s o wh C B ( f G C s a feasble pah B ( f G C s kow as he gh boud of he os of a feasble pah From Defos 3 ad 4 he os of a feasble pah ao be larger ha Bu ( f G C ad ha of a feasble pah s larger ha B ( f G C I also follows from Theorems ad ha B ( u f G C f ( C ad B( f G C [Theorem 3] If λ pah p Proof: Se λ The m{ } w m{ } he ( uv N w λ W p for ay W w m{ } we have uv N w λ w w (9 w λ w W ( uv p ( uv p λ ( W w w ( uv p ( uv p w [Lemma ] If λ m{ } ad < λ for uv N w ay pah p wh W also mples ha W < Proof: From Theorem 3 W W λ W W λ λ < ( Lemma mples ha f < λ he h osra s me as log as he h osra s me Thus he h osra 633

4 a be omed for he ACP problem; e he -osraed For example le C ( λ λ ad he os problem a be redued o a (--osraed problem fuo f ( xy x + y k I hs ase Au ( f w [Lemma 3] Defe λ m{ } B ( f G C ad Fg shows he area overed by he gh boud reases uv N w wh k Thus r( f as k ; e a feasble wll m{ } where h ( x f ( λ x λ x λ x o be seleed h Proof: The proof s smlar o ha of Theorem However oe ha m{ h ( } does o hage wh f w [Lemma 4] If λ m{ } ad here are N odes uv N w he os fuo s lear whle m{ h ( } dereases wh f( x he ework B ( f G C m{( N h ( } where x x f > Thus we oeure N h ( x f ( λ x λ x λ x ha for ay olear os fuo here mus exs a lear Proof: The proof s smlar o ha of Theorem os fuo wh a larger r( f whe s large eough [Lemma 5] Gve a os fuo f ad QoS osra C Wh C ( λ λ Fg ad Fg 3 show he a -hop pah p sasfyg C m{ h ( } s a feasble overage raos for boh f ( x y x + y ad ( k x f ( xy ( e ( k y + e /( k k whe pah Proof: The proof s smlar o ha of Theorem ad 3 Smlar o he defo of he gh boud m{ h ( } s defed as he -hop gh boud; ha s he os of ay -hop feasble pah ao be less ha hs boud From Theorem ad Lemma 5 s kow ha a -hop pah wh os bewee he upper boud ad -hop gh boud may be a feasble pah Iuvely s desrable o have he upper boud ad gh boud lose o eah oher o mmze he possbly of seleg a wrog pah Coaeae W W W of lks of a -hop pah o a veor ( W W W Thus he area overed by he -hop gh boud s A( f dwdw dw ( f ( W m{ h ( } W C Smlarly he area overed by he upper boud s A ( f dwdw dw (3 u f ( W f ( W C Based o he above uo he overage rao defed below A ( f r( f Au ( f (4 a be used as a fgure of mer o how robus he os fuo s avodg a wrog pah seleo The larger he rao he beer he os fuo Fgure A ( f for x + y 634 Fgure Coverage Rao for Fgure 3 Coverage Rao for 3 Whe 3 he lear os fuo (e x+ y whe k yelds he larges overage rao as ompared o f ( ad f ( Furhermore he lear os fuo possesses a dsgushed propery C f( W ha makes he desg of QoS roug algorhm (seleo of os fuo parameers suh as [6] sraghforward ad easy Thus he lear os fuo s preferred for soure QoS roug algorhms Ths oluso assumes ha he os of a pah s he sum of he oss of all dvdual lks omprsg he pah; may o be feasble for algorhms whh he assumpo does o hold For example a good approah proposed [] s ha he os of pah p s f ( W e C f( W

5 where f ( s he os fuo I hs ase pah p a always be vewed as a lk oeg he soure ad he desao wh wegh W Hee f here are oly wo osras ad f ( xy x + y k he suess rao of a fdg feasble pah usg hs algorhm always reases wh k regardless he umber of he hop ou from he soure o he desao as show Fg So hs ase he lear os fuo s o opmal ad our oluso s o applable aymore IV SIULATIONS We odu our smulaos wo ework opologes: oe s he ework opology preseed [6] [] ad he oher s a 7 7 mesh ework The os fuos adoped for omparsos are f ( x y x+ y f ( x y x + y ad x y f3( x y ( e + e The QoS roug algorhms used smulaos are Dksra algorhm I boh smulaos he lk weghs are depede ad uformly dsrbued from o wo QoS osras are se o be equal ad rease from 5 o 5 wh a reme of All daa are obaed by rug requess To ruly refle algorhms apably fdg a feasble pah we propose he followg more approprae suess rao defo as our performae dex: Toal umber of suess reques of he algorhm SR (6 Toal umber of suess reques of he opmal algorhm The algorhm ha a always loae a feasble pah as log as exss s refereed o as he opmal algorhm Here s aheved smply by floodg whh s raher exhausve The smulao resuls are show Fgures 4 ad 5 SR f(xy f(xy f3(xy Cosra Fgure 4 SR he 3-ode ework I a be observed ha boh smulaos he QoS roug algorhm wh he lear os fuo aheves he hghes suess rao fdg a feasble pah whh oforms o our oeure V CONCLUSIONS Alhough soure roug has bee exesvely suded he pas mos proposed mulple osraed roug algorhms are heurs aure I hs paper we have provded a heoreal framework for seleg he os fuo for mulple addvely osraed QoS roug By deployg our proposed overage rao as he mer for evaluag he os fuo s advsable o use a lear os fuo for he approah whh he os of a pah equals he sum of he oss of all dvdual lks omprsg he pah REFERENCES [] S Che ad Nahsed A overvew of qualy of serve roug for ex-geerao hgh-speed ework: problems ad soluos IEEE Nework vol o 6 pp Deember 998 [] A Shakh J Rexford ad G Sh Evaluag he mpa of sale lk sae o qualy-of-serve roug IEEE/AC Trasaos o Neworkg vol 9 o pp 6-76 Aprl [3] R Guer ad A Orda QoS based roug eworks wh aurae formao: heory ad algorhms Proeedgs of he INFOCO 97 pp [4] J Wag W Wag J Che ad S Che A radomzed QoS roug algorhm o eworks wh aurae lk-sae formao Proeedg of WCC-ICCT vol pp 67-6 [5] Z Wag ad J Crowrof Qualy of Serve roug for supporg mulmeda applaos IEEE Joural o Seleed Areas o Commuaos vol 4 o 7 pp 8-34 Sepember 996 [6] T orkmaz ruz ad S Tragoudas A effe algorhms for fdg a pah sube o wo addve osras Proeedgs of he AC SIGETRICS pp Jue [7] A Juer B Szyaovszk I es ad Rako Lagrage releaxao based mehod for he QoS roug problem Proeedgs of IEEE INFOCO vol pp [8] L Guo ad I aa Searh spae reduo QoS roug Proeedgs of 9 h IEEE Ieraoal Coferee o Dsrbued Compug Sysems pp [9] H De Neve ad P va eghem A mulple qualy of serve roug algorhm for PNNI Proeedgs of 998 IEEE AT workshop pp [] T orkmaz ad ruz "ul-osraed opmal pah seleo" Proeedgs of he IEEE INFOCO Coferee pp Aprl 99 SR f(xy f(xy f3(xy Cosra Fgure 5 SR he 7 7 mesh ework 635

Cyclically Interval Total Colorings of Cycles and Middle Graphs of Cycles

Cyclically Interval Total Colorings of Cycles and Middle Graphs of Cycles Ope Joural of Dsree Mahemas 2017 7 200-217 hp://wwwsrporg/joural/ojdm ISSN Ole: 2161-7643 ISSN Pr: 2161-7635 Cylally Ierval Toal Colorgs of Cyles Mddle Graphs of Cyles Yogqag Zhao 1 Shju Su 2 1 Shool of

More information

On Metric Dimension of Two Constructed Families from Antiprism Graph

On Metric Dimension of Two Constructed Families from Antiprism Graph Mah S Le 2, No, -7 203) Mahemaal Sees Leers A Ieraoal Joural @ 203 NSP Naural Sees Publhg Cor O Mer Dmeso of Two Cosrued Famles from Aprm Graph M Al,2, G Al,2 ad M T Rahm 2 Cere for Mahemaal Imagg Tehques

More information

The Poisson Process Properties of the Poisson Process

The Poisson Process Properties of the Poisson Process Posso Processes Summary The Posso Process Properes of he Posso Process Ierarrval mes Memoryless propery ad he resdual lfeme paradox Superposo of Posso processes Radom seleco of Posso Pos Bulk Arrvals ad

More information

Solution. The straightforward approach is surprisingly difficult because one has to be careful about the limits.

Solution. The straightforward approach is surprisingly difficult because one has to be careful about the limits. ose ad Varably Homewor # (8), aswers Q: Power spera of some smple oses A Posso ose A Posso ose () s a sequee of dela-fuo pulses, eah ourrg depedely, a some rae r (More formally, s a sum of pulses of wdh

More information

Chebyshev Polynomials for Solving a Class of Singular Integral Equations

Chebyshev Polynomials for Solving a Class of Singular Integral Equations Appled Mahemas, 4, 5, 75-764 Publshed Ole Marh 4 SRes. hp://www.srp.org/joural/am hp://d.do.org/.46/am.4.547 Chebyshev Polyomals for Solvg a Class of Sgular Iegral Equaos Samah M. Dardery, Mohamed M. Alla

More information

Midterm Exam. Tuesday, September hour, 15 minutes

Midterm Exam. Tuesday, September hour, 15 minutes Ecoomcs of Growh, ECON560 Sa Fracsco Sae Uvers Mchael Bar Fall 203 Mderm Exam Tuesda, Sepember 24 hour, 5 mues Name: Isrucos. Ths s closed boo, closed oes exam. 2. No calculaors of a d are allowed. 3.

More information

Learning of Graphical Models Parameter Estimation and Structure Learning

Learning of Graphical Models Parameter Estimation and Structure Learning Learg of Grahal Models Parameer Esmao ad Sruure Learg e Fukumzu he Isue of Sasal Mahemas Comuaoal Mehodology Sasal Iferee II Work wh Grahal Models Deermg sruure Sruure gve by modelg d e.g. Mxure model

More information

The algebraic immunity of a class of correlation immune H Boolean functions

The algebraic immunity of a class of correlation immune H Boolean functions Ieraoal Coferece o Advaced Elecroc Scece ad Techology (AEST 06) The algebrac mmuy of a class of correlao mmue H Boolea fucos a Jgla Huag ad Zhuo Wag School of Elecrcal Egeerg Norhwes Uversy for Naoales

More information

ANSWERS TO ODD NUMBERED EXERCISES IN CHAPTER 2

ANSWERS TO ODD NUMBERED EXERCISES IN CHAPTER 2 Joh Rley Novembe ANSWERS O ODD NUMBERED EXERCISES IN CHAPER Seo Eese -: asvy (a) Se y ad y z follows fom asvy ha z Ehe z o z We suppose he lae ad seek a oado he z Se y follows by asvy ha z y Bu hs oads

More information

Real-Time Systems. Example: scheduling using EDF. Feasibility analysis for EDF. Example: scheduling using EDF

Real-Time Systems. Example: scheduling using EDF. Feasibility analysis for EDF. Example: scheduling using EDF EDA/DIT6 Real-Tme Sysems, Chalmers/GU, 0/0 ecure # Updaed February, 0 Real-Tme Sysems Specfcao Problem: Assume a sysem wh asks accordg o he fgure below The mg properes of he asks are gve he able Ivesgae

More information

Determination of Antoine Equation Parameters. December 4, 2012 PreFEED Corporation Yoshio Kumagae. Introduction

Determination of Antoine Equation Parameters. December 4, 2012 PreFEED Corporation Yoshio Kumagae. Introduction refeed Soluos for R&D o Desg Deermao of oe Equao arameers Soluos for R&D o Desg December 4, 0 refeed orporao Yosho Kumagae refeed Iroduco hyscal propery daa s exremely mpora for performg process desg ad

More information

Key words: Fractional difference equation, oscillatory solutions,

Key words: Fractional difference equation, oscillatory solutions, OSCILLATION PROPERTIES OF SOLUTIONS OF FRACTIONAL DIFFERENCE EQUATIONS Musafa BAYRAM * ad Ayd SECER * Deparme of Compuer Egeerg, Isabul Gelsm Uversy Deparme of Mahemacal Egeerg, Yldz Techcal Uversy * Correspodg

More information

8. Queueing systems lect08.ppt S Introduction to Teletraffic Theory - Fall

8. Queueing systems lect08.ppt S Introduction to Teletraffic Theory - Fall 8. Queueg sysems lec8. S-38.45 - Iroduco o Teleraffc Theory - Fall 8. Queueg sysems Coes Refresher: Smle eleraffc model M/M/ server wag laces M/M/ servers wag laces 8. Queueg sysems Smle eleraffc model

More information

Solution set Stat 471/Spring 06. Homework 2

Solution set Stat 471/Spring 06. Homework 2 oluo se a 47/prg 06 Homework a Whe he upper ragular elemes are suppressed due o smmer b Le Y Y Y Y A weep o he frs colum o oba: A ˆ b chagg he oao eg ad ec YY weep o he secod colum o oba: Aˆ YY weep o

More information

FALL HOMEWORK NO. 6 - SOLUTION Problem 1.: Use the Storage-Indication Method to route the Input hydrograph tabulated below.

FALL HOMEWORK NO. 6 - SOLUTION Problem 1.: Use the Storage-Indication Method to route the Input hydrograph tabulated below. Jorge A. Ramírez HOMEWORK NO. 6 - SOLUTION Problem 1.: Use he Sorage-Idcao Mehod o roue he Ipu hydrograph abulaed below. Tme (h) Ipu Hydrograph (m 3 /s) Tme (h) Ipu Hydrograph (m 3 /s) 0 0 90 450 6 50

More information

14. Poisson Processes

14. Poisson Processes 4. Posso Processes I Lecure 4 we roduced Posso arrvals as he lmg behavor of Bomal radom varables. Refer o Posso approxmao of Bomal radom varables. From he dscusso here see 4-6-4-8 Lecure 4 " arrvals occur

More information

(1) Cov(, ) E[( E( ))( E( ))]

(1) Cov(, ) E[( E( ))( E( ))] Impac of Auocorrelao o OLS Esmaes ECON 3033/Evas Cosder a smple bvarae me-seres model of he form: y 0 x The four key assumpos abou ε hs model are ) E(ε ) = E[ε x ]=0 ) Var(ε ) =Var(ε x ) = ) Cov(ε, ε )

More information

The Linear Regression Of Weighted Segments

The Linear Regression Of Weighted Segments The Lear Regresso Of Weghed Segmes George Dael Maeescu Absrac. We proposed a regresso model where he depede varable s made o up of pos bu segmes. Ths suao correspods o he markes hroughou he da are observed

More information

ASYMPTOTIC BEHAVIOR OF SOLUTIONS OF DISCRETE EQUATIONS ON DISCRETE REAL TIME SCALES

ASYMPTOTIC BEHAVIOR OF SOLUTIONS OF DISCRETE EQUATIONS ON DISCRETE REAL TIME SCALES ASYPTOTI BEHAVIOR OF SOLUTIONS OF DISRETE EQUATIONS ON DISRETE REAL TIE SALES J. Dlí B. Válvíová 2 Bro Uversy of Tehology Bro zeh Repul 2 Deprme of heml Alyss d Appled hems Fuly of See Uversy of Zl Žl

More information

QR factorization. Let P 1, P 2, P n-1, be matrices such that Pn 1Pn 2... PPA

QR factorization. Let P 1, P 2, P n-1, be matrices such that Pn 1Pn 2... PPA QR facorzao Ay x real marx ca be wre as AQR, where Q s orhogoal ad R s upper ragular. To oba Q ad R, we use he Householder rasformao as follows: Le P, P, P -, be marces such ha P P... PPA ( R s upper ragular.

More information

VARIATIONAL ITERATION METHOD FOR DELAY DIFFERENTIAL-ALGEBRAIC EQUATIONS. Hunan , China,

VARIATIONAL ITERATION METHOD FOR DELAY DIFFERENTIAL-ALGEBRAIC EQUATIONS. Hunan , China, Mahemacal ad Compuaoal Applcaos Vol. 5 No. 5 pp. 834-839. Assocao for Scefc Research VARIATIONAL ITERATION METHOD FOR DELAY DIFFERENTIAL-ALGEBRAIC EQUATIONS Hoglag Lu Aguo Xao Yogxag Zhao School of Mahemacs

More information

θ = θ Π Π Parametric counting process models θ θ θ Log-likelihood: Consider counting processes: Score functions:

θ = θ Π Π Parametric counting process models θ θ θ Log-likelihood: Consider counting processes: Score functions: Paramerc coug process models Cosder coug processes: N,,..., ha cou he occurreces of a eve of eres for dvduals Iesy processes: Lelhood λ ( ;,,..., N { } λ < Log-lelhood: l( log L( Score fucos: U ( l( log

More information

Design and Optimization for Energy-Efficient Cooperative MIMO Transmission in Ad Hoc Networks

Design and Optimization for Energy-Efficient Cooperative MIMO Transmission in Ad Hoc Networks Ths arle has bee aeped for publao a fuure ssue of hs joural, bu has o bee fully eded. Coe may hage pror o fal publao. Cao formao: DOI 0.09/TVT.06.536803, IEEE Trasaos o Vehular Tehology Desg ad Opmzao

More information

4. THE DENSITY MATRIX

4. THE DENSITY MATRIX 4. THE DENSTY MATRX The desy marx or desy operaor s a alerae represeao of he sae of a quaum sysem for whch we have prevously used he wavefuco. Alhough descrbg a quaum sysem wh he desy marx s equvale o

More information

Fault Tolerant Computing. Fault Tolerant Computing CS 530 Probabilistic methods: overview

Fault Tolerant Computing. Fault Tolerant Computing CS 530 Probabilistic methods: overview Probably 1/19/ CS 53 Probablsc mehods: overvew Yashwa K. Malaya Colorado Sae Uversy 1 Probablsc Mehods: Overvew Cocree umbers presece of uceray Probably Dsjo eves Sascal depedece Radom varables ad dsrbuos

More information

Real-time Classification of Large Data Sets using Binary Knapsack

Real-time Classification of Large Data Sets using Binary Knapsack Real-me Classfcao of Large Daa Ses usg Bary Kapsack Reao Bru bru@ds.uroma. Uversy of Roma La Sapeza AIRO 004-35h ANNUAL CONFERENCE OF THE ITALIAN OPERATIONS RESEARCH Sepember 7-0, 004, Lecce, Ialy Oule

More information

Solving fuzzy linear programming problems with piecewise linear membership functions by the determination of a crisp maximizing decision

Solving fuzzy linear programming problems with piecewise linear membership functions by the determination of a crisp maximizing decision Frs Jo Cogress o Fuzzy ad Iellge Sysems Ferdows Uversy of Mashhad Ira 9-3 Aug 7 Iellge Sysems Scefc Socey of Ira Solvg fuzzy lear programmg problems wh pecewse lear membershp fucos by he deermao of a crsp

More information

Cyclone. Anti-cyclone

Cyclone. Anti-cyclone Adveco Cycloe A-cycloe Lorez (963) Low dmesoal aracors. Uclear f hey are a good aalogy o he rue clmae sysem, bu hey have some appealg characerscs. Dscusso Is he al codo balaced? Is here a al adjusme

More information

Least Squares Fitting (LSQF) with a complicated function Theexampleswehavelookedatsofarhavebeenlinearintheparameters

Least Squares Fitting (LSQF) with a complicated function Theexampleswehavelookedatsofarhavebeenlinearintheparameters Leas Squares Fg LSQF wh a complcaed fuco Theeampleswehavelookedasofarhavebeelearheparameers ha we have bee rg o deerme e.g. slope, ercep. For he case where he fuco s lear he parameers we ca fd a aalc soluo

More information

Ensemble Of Image Segmentation With Generalized Entropy Based Fuzzy Clustering

Ensemble Of Image Segmentation With Generalized Entropy Based Fuzzy Clustering Ieraoal Joural of Copuer ad Iforao Tehology (ISSN: 79 0764) Volue 03 Issue 05, Sepeber 014 Eseble Of Iage Segeao Wh Geeralzed Eropy Based Fuzzy Cluserg Ka L *, Zhx Guo Hebe Uversy College of Maheas ad

More information

Analyzing Control Structures

Analyzing Control Structures Aalyzg Cotrol Strutures sequeg P, P : two fragmets of a algo. t, t : the tme they tae the tme requred to ompute P ;P s t t Θmaxt,t For loops for to m do P t: the tme requred to ompute P total tme requred

More information

Collocation Method for Nonlinear Volterra-Fredholm Integral Equations

Collocation Method for Nonlinear Volterra-Fredholm Integral Equations Ope Joural of Appled Sees 5- do:436/oapps6 Publshed Ole Jue (hp://wwwsrporg/oural/oapps) Colloao Mehod for olear Volerra-Fredhol Iegral Equaos Jafar Ahad Shal Parvz Daraa Al Asgar Jodayree Akbarfa Depare

More information

Continuous Time Markov Chains

Continuous Time Markov Chains Couous me Markov chas have seay sae probably soluos f a oly f hey are ergoc, us lke scree me Markov chas. Fg he seay sae probably vecor for a couous me Markov cha s o more ffcul ha s he scree me case,

More information

The ray paths and travel times for multiple layers can be computed using ray-tracing, as demonstrated in Lab 3.

The ray paths and travel times for multiple layers can be computed using ray-tracing, as demonstrated in Lab 3. C. Trael me cures for mulple reflecors The ray pahs ad rael mes for mulple layers ca be compued usg ray-racg, as demosraed Lab. MATLAB scrp reflec_layers_.m performs smple ray racg. (m) ref(ms) ref(ms)

More information

Fundamentals of Speech Recognition Suggested Project The Hidden Markov Model

Fundamentals of Speech Recognition Suggested Project The Hidden Markov Model . Projec Iroduco Fudameals of Speech Recogo Suggesed Projec The Hdde Markov Model For hs projec, s proposed ha you desg ad mpleme a hdde Markov model (HMM) ha opmally maches he behavor of a se of rag sequeces

More information

For the plane motion of a rigid body, an additional equation is needed to specify the state of rotation of the body.

For the plane motion of a rigid body, an additional equation is needed to specify the state of rotation of the body. The kecs of rgd bodes reas he relaoshps bewee he exeral forces acg o a body ad he correspodg raslaoal ad roaoal moos of he body. he kecs of he parcle, we foud ha wo force equaos of moo were requred o defe

More information

A Mean- maximum Deviation Portfolio Optimization Model

A Mean- maximum Deviation Portfolio Optimization Model A Mea- mamum Devato Portfolo Optmzato Model Wu Jwe Shool of Eoom ad Maagemet, South Cha Normal Uversty Guagzhou 56, Cha Tel: 86-8-99-6 E-mal: wujwe@9om Abstrat The essay maes a thorough ad systemat study

More information

AML710 CAD LECTURE 12 CUBIC SPLINE CURVES. Cubic Splines Matrix formulation Normalised cubic splines Alternate end conditions Parabolic blending

AML710 CAD LECTURE 12 CUBIC SPLINE CURVES. Cubic Splines Matrix formulation Normalised cubic splines Alternate end conditions Parabolic blending CUIC SLINE CURVES Cubc Sples Marx formulao Normalsed cubc sples Alerae ed codos arabolc bledg AML7 CAD LECTURE CUIC SLINE The ame sple comes from he physcal srume sple drafsme use o produce curves A geeral

More information

Continuous Indexed Variable Systems

Continuous Indexed Variable Systems Ieraoal Joural o Compuaoal cece ad Mahemacs. IN 0974-389 Volume 3, Number 4 (20), pp. 40-409 Ieraoal Research Publcao House hp://www.rphouse.com Couous Idexed Varable ysems. Pouhassa ad F. Mohammad ghjeh

More information

IMPROVED PORTFOLIO OPTIMIZATION MODEL WITH TRANSACTION COST AND MINIMAL TRANSACTION LOTS

IMPROVED PORTFOLIO OPTIMIZATION MODEL WITH TRANSACTION COST AND MINIMAL TRANSACTION LOTS Vol.7 No.4 (200) p73-78 Joural of Maageme Scece & Sascal Decso IMPROVED PORTFOLIO OPTIMIZATION MODEL WITH TRANSACTION COST AND MINIMAL TRANSACTION LOTS TIANXIANG YAO AND ZAIWU GONG College of Ecoomcs &

More information

An AGV-Routing Algorithm in the Mesh Topology with Random Partial Permutation

An AGV-Routing Algorithm in the Mesh Topology with Random Partial Permutation A AGV-Rou Alorhm he Mesh Topoloy wh Radom aral ermuao Ze Jaya, Hsu We-J ad Vee Voo Yee ere for Advaed Iformao Sysems, Shool of ompuer eer Naya Teholoal Uversy, Sapore 69798 {p8589, hsu, ASVYV}@uedus Absra

More information

The Mean Residual Lifetime of (n k + 1)-out-of-n Systems in Discrete Setting

The Mean Residual Lifetime of (n k + 1)-out-of-n Systems in Discrete Setting Appled Mahemacs 4 5 466-477 Publshed Ole February 4 (hp//wwwscrporg/oural/am hp//dxdoorg/436/am45346 The Mea Resdual Lfeme of ( + -ou-of- Sysems Dscree Seg Maryam Torab Sahboom Deparme of Sascs Scece ad

More information

FORCED VIBRATION of MDOF SYSTEMS

FORCED VIBRATION of MDOF SYSTEMS FORCED VIBRAION of DOF SSES he respose of a N DOF sysem s govered by he marx equao of moo: ] u C] u K] u 1 h al codos u u0 ad u u 0. hs marx equao of moo represes a sysem of N smulaeous equaos u ad s me

More information

Fully Fuzzy Linear Systems Solving Using MOLP

Fully Fuzzy Linear Systems Solving Using MOLP World Appled Sceces Joural 12 (12): 2268-2273, 2011 ISSN 1818-4952 IDOSI Publcaos, 2011 Fully Fuzzy Lear Sysems Solvg Usg MOLP Tofgh Allahvraloo ad Nasser Mkaelvad Deparme of Mahemacs, Islamc Azad Uversy,

More information

Partial Molar Properties of solutions

Partial Molar Properties of solutions Paral Molar Properes of soluos A soluo s a homogeeous mxure; ha s, a soluo s a oephase sysem wh more ha oe compoe. A homogeeous mxures of wo or more compoes he gas, lqud or sold phase The properes of a

More information

B. Maddah INDE 504 Simulation 09/02/17

B. Maddah INDE 504 Simulation 09/02/17 B. Maddah INDE 54 Simulaio 9/2/7 Queueig Primer Wha is a queueig sysem? A queueig sysem cosiss of servers (resources) ha provide service o cusomers (eiies). A Cusomer requesig service will sar service

More information

1 Notes on Little s Law (l = λw)

1 Notes on Little s Law (l = λw) Copyrigh c 26 by Karl Sigma Noes o Lile s Law (l λw) We cosider here a famous ad very useful law i queueig heory called Lile s Law, also kow as l λw, which assers ha he ime average umber of cusomers i

More information

arxiv: v2 [cs.lg] 19 Dec 2016

arxiv: v2 [cs.lg] 19 Dec 2016 1 Sasfcg mul-armed bad problems Paul Reverdy, Vabhav Srvasava, ad Naom Ehrch Leoard arxv:1512.07638v2 [cs.lg] 19 Dec 2016 Absrac Sasfcg s a relaxao of maxmzg ad allows for less rsky decso makg he face

More information

Some Probability Inequalities for Quadratic Forms of Negatively Dependent Subgaussian Random Variables

Some Probability Inequalities for Quadratic Forms of Negatively Dependent Subgaussian Random Variables Joural of Sceces Islamc epublc of Ira 6(: 63-67 (005 Uvers of ehra ISSN 06-04 hp://scecesuacr Some Probabl Iequales for Quadrac Forms of Negavel Depede Subgaussa adom Varables M Am A ozorga ad H Zare 3

More information

Optimal Eye Movement Strategies in Visual Search (Supplement)

Optimal Eye Movement Strategies in Visual Search (Supplement) Opmal Eye Moveme Sraeges Vsual Search (Suppleme) Jr Naemk ad Wlso S. Gesler Ceer for Percepual Sysems ad Deparme of Psychology, Uversy of exas a Aus, Aus X 787 Here we derve he deal searcher for he case

More information

ECON 8105 FALL 2017 ANSWERS TO MIDTERM EXAMINATION

ECON 8105 FALL 2017 ANSWERS TO MIDTERM EXAMINATION MACROECONOMIC THEORY T. J. KEHOE ECON 85 FALL 7 ANSWERS TO MIDTERM EXAMINATION. (a) Wh an Arrow-Debreu markes sruure fuures markes for goods are open n perod. Consumers rade fuures onras among hemselves.

More information

PTAS for Bin-Packing

PTAS for Bin-Packing CS 663: Patter Matchg Algorthms Scrbe: Che Jag /9/00. Itroducto PTAS for B-Packg The B-Packg problem s NP-hard. If we use approxmato algorthms, the B-Packg problem could be solved polyomal tme. For example,

More information

Quantum Mechanics II Lecture 11 Time-dependent perturbation theory. Time-dependent perturbation theory (degenerate or non-degenerate starting state)

Quantum Mechanics II Lecture 11 Time-dependent perturbation theory. Time-dependent perturbation theory (degenerate or non-degenerate starting state) Pro. O. B. Wrgh, Auum Quaum Mechacs II Lecure Tme-depede perurbao heory Tme-depede perurbao heory (degeerae or o-degeerae sarg sae) Cosder a sgle parcle whch, s uperurbed codo wh Hamloa H, ca exs a superposo

More information

UNIVERSITAT AUTÒNOMA DE BARCELONA MARCH 2017 EXAMINATION

UNIVERSITAT AUTÒNOMA DE BARCELONA MARCH 2017 EXAMINATION INTERNATIONAL TRADE T. J. KEHOE UNIVERSITAT AUTÒNOMA DE BARCELONA MARCH 27 EXAMINATION Please answer wo of he hree quesons. You can consul class noes, workng papers, and arcles whle you are workng on he

More information

RATIO ESTIMATORS USING CHARACTERISTICS OF POISSON DISTRIBUTION WITH APPLICATION TO EARTHQUAKE DATA

RATIO ESTIMATORS USING CHARACTERISTICS OF POISSON DISTRIBUTION WITH APPLICATION TO EARTHQUAKE DATA The 7 h Ieraoal as of Sascs ad Ecoomcs Prague Sepember 9-0 Absrac RATIO ESTIMATORS USING HARATERISTIS OF POISSON ISTRIBUTION WITH APPLIATION TO EARTHQUAKE ATA Gamze Özel Naural pulaos bolog geecs educao

More information

Lecture 3 Topic 2: Distributions, hypothesis testing, and sample size determination

Lecture 3 Topic 2: Distributions, hypothesis testing, and sample size determination Lecure 3 Topc : Drbuo, hypohe eg, ad ample ze deermao The Sude - drbuo Coder a repeaed drawg of ample of ze from a ormal drbuo of mea. For each ample, compue,,, ad aoher ac,, where: The ac he devao of

More information

Quantitative Portfolio Theory & Performance Analysis

Quantitative Portfolio Theory & Performance Analysis 550.447 Quaave Porfolo heory & Performace Aalyss Week February 4 203 Coceps. Assgme For February 4 (hs Week) ead: A&L Chaper Iroduco & Chaper (PF Maageme Evrome) Chaper 2 ( Coceps) Seco (Basc eur Calculaos)

More information

Asymptotic Behavior of Solutions of Nonlinear Delay Differential Equations With Impulse

Asymptotic Behavior of Solutions of Nonlinear Delay Differential Equations With Impulse P a g e Vol Issue7Ver,oveber Global Joural of Scece Froer Research Asypoc Behavor of Soluos of olear Delay Dffereal Equaos Wh Ipulse Zhag xog GJSFR Classfcao - F FOR 3 Absrac Ths paper sudes he asypoc

More information

ENGINEERING solutions to decision-making problems are

ENGINEERING solutions to decision-making problems are 3788 IEEE TRANSACTIONS ON AUTOMATIC CONTROL, VOL. 62, NO. 8, AUGUST 2017 Sasfcg Mul-Armed Bad Problems Paul Reverdy, Member, IEEE, Vabhav Srvasava, ad Naom Ehrch Leoard, Fellow, IEEE Absrac Sasfcg s a

More information

COMPUTER SCIENCE 349A SAMPLE EXAM QUESTIONS WITH SOLUTIONS PARTS 1, 2

COMPUTER SCIENCE 349A SAMPLE EXAM QUESTIONS WITH SOLUTIONS PARTS 1, 2 COMPUTE SCIENCE 49A SAMPLE EXAM QUESTIONS WITH SOLUTIONS PATS, PAT.. a Dene he erm ll-ondoned problem. b Gve an eample o a polynomal ha has ll-ondoned zeros.. Consder evaluaon o anh, where e e anh. e e

More information

Pendulum Dynamics. = Ft tangential direction (2) radial direction (1)

Pendulum Dynamics. = Ft tangential direction (2) radial direction (1) Pendulum Dynams Consder a smple pendulum wh a massless arm of lengh L and a pon mass, m, a he end of he arm. Assumng ha he fron n he sysem s proporonal o he negave of he angenal veloy, Newon s seond law

More information

International Journal Of Engineering And Computer Science ISSN: Volume 5 Issue 12 Dec. 2016, Page No.

International Journal Of Engineering And Computer Science ISSN: Volume 5 Issue 12 Dec. 2016, Page No. www.jecs. Ieraoal Joural Of Egeerg Ad Compuer Scece ISSN: 19-74 Volume 5 Issue 1 Dec. 16, Page No. 196-1974 Sofware Relably Model whe mulple errors occur a a me cludg a faul correco process K. Harshchadra

More information

Complementary Tree Paired Domination in Graphs

Complementary Tree Paired Domination in Graphs IOSR Joural of Mahemacs (IOSR-JM) e-issn: 2278-5728, p-issn: 239-765X Volume 2, Issue 6 Ver II (Nov - Dec206), PP 26-3 wwwosrjouralsorg Complemeary Tree Pared Domao Graphs A Meeaksh, J Baskar Babujee 2

More information

The Cell Transmission Model, Newell s Cumulative Curves and Min-Plus Algebra

The Cell Transmission Model, Newell s Cumulative Curves and Min-Plus Algebra The Cell Trasmsso Model eell s Cumulae Cures ad M-Plus lgebra Takash kamasu Deember 003. Prelmares Dagazo s Cell Trasmsso Model Suppose ha he relaoshp beee raff flo q ad desy k a homogeeous road seo s

More information

Efficient Estimators for Population Variance using Auxiliary Information

Efficient Estimators for Population Variance using Auxiliary Information Global Joural of Mahemacal cece: Theor ad Praccal. IN 97-3 Volume 3, Number (), pp. 39-37 Ieraoal Reearch Publcao Houe hp://www.rphoue.com Effce Emaor for Populao Varace ug Aular Iformao ubhah Kumar Yadav

More information

EE 6885 Statistical Pattern Recognition

EE 6885 Statistical Pattern Recognition EE 6885 Sascal Paer Recogo Fall 005 Prof. Shh-Fu Chag hp://.ee.columba.edu/~sfchag Lecure 8 (/8/05 8- Readg Feaure Dmeso Reduco PCA, ICA, LDA, Chaper 3.8, 0.3 ICA Tuoral: Fal Exam Aapo Hyväre ad Erkk Oja,

More information

Bianchi Type II Stiff Fluid Tilted Cosmological Model in General Relativity

Bianchi Type II Stiff Fluid Tilted Cosmological Model in General Relativity Ieraoal Joural of Mahemacs esearch. IN 0976-50 Volume 6, Number (0), pp. 6-7 Ieraoal esearch Publcao House hp://www.rphouse.com Bach ype II ff Flud led Cosmologcal Model Geeral elay B. L. Meea Deparme

More information

Nonsmooth Optimization Algorithms in Some Problems of Fracture Dynamics

Nonsmooth Optimization Algorithms in Some Problems of Fracture Dynamics Iellge Iformao Maageme, 2, 2, 637-646 do:.4236/m.2.273 Publshed Ole November 2 (hp://www.srp.org/joural/m) Nosmooh Opmzao Algorhms Some Problems of Fraure Dyams Absra V. V. Zozulya Cero de Ivesgao Cefa

More information

Least squares and motion. Nuno Vasconcelos ECE Department, UCSD

Least squares and motion. Nuno Vasconcelos ECE Department, UCSD Leas squares ad moo uo Vascocelos ECE Deparme UCSD Pla for oda oda we wll dscuss moo esmao hs s eresg wo was moo s ver useful as a cue for recogo segmeao compresso ec. s a grea eample of leas squares problem

More information

Optimal Transform: The Karhunen-Loeve Transform (KLT)

Optimal Transform: The Karhunen-Loeve Transform (KLT) Opimal ransform: he Karhunen-Loeve ransform (KL) Reall: We are ineresed in uniary ransforms beause of heir nie properies: energy onservaion, energy ompaion, deorrelaion oivaion: τ (D ransform; assume separable)

More information

Redundancy System Fault Sampling Under Imperfect Maintenance

Redundancy System Fault Sampling Under Imperfect Maintenance A publcao of CHEMICAL EGIEERIG TRASACTIOS VOL. 33, 03 Gues Edors: Erco Zo, Pero Barald Copyrgh 03, AIDIC Servz S.r.l., ISB 978-88-95608-4-; ISS 974-979 The Iala Assocao of Chemcal Egeerg Ole a: www.adc./ce

More information

Average Consensus in Networks of Multi-Agent with Multiple Time-Varying Delays

Average Consensus in Networks of Multi-Agent with Multiple Time-Varying Delays I. J. Commucaos ewor ad Sysem Sceces 3 96-3 do:.436/jcs..38 Publshed Ole February (hp://www.scrp.org/joural/jcs/). Average Cosesus ewors of Mul-Age wh Mulple me-varyg Delays echeg ZHAG Hu YU Isue of olear

More information

Pricing of CDO s Based on the Multivariate Wang Transform*

Pricing of CDO s Based on the Multivariate Wang Transform* Prcg of DO s Based o he Mulvarae Wag Trasform* ASTIN 2009 olloquum @ Helsk 02 Jue 2009 Masaak Kma Tokyo Meropola versy/ Kyoo versy Emal: kma@mu.ac.p hp://www.comp.mu.ac.p/kmam * Jo Work wh Sh-ch Moomya

More information

Probability Bracket Notation and Probability Modeling. Xing M. Wang Sherman Visual Lab, Sunnyvale, CA 94087, USA. Abstract

Probability Bracket Notation and Probability Modeling. Xing M. Wang Sherman Visual Lab, Sunnyvale, CA 94087, USA. Abstract Probably Bracke Noao ad Probably Modelg Xg M. Wag Sherma Vsual Lab, Suyvale, CA 94087, USA Absrac Ispred by he Drac oao, a ew se of symbols, he Probably Bracke Noao (PBN) s proposed for probably modelg.

More information

Survival Prediction Based on Compound Covariate under Cox Proportional Hazard Models

Survival Prediction Based on Compound Covariate under Cox Proportional Hazard Models Ieraoal Bomerc Coferece 22/8/3, Kobe JAPAN Survval Predco Based o Compoud Covarae uder Co Proporoal Hazard Models PLoS ONE 7. do:.37/oural.poe.47627. hp://d.plos.org/.37/oural.poe.47627 Takesh Emura Graduae

More information

Final Exam Applied Econometrics

Final Exam Applied Econometrics Fal Eam Appled Ecoomercs. 0 Sppose we have he followg regresso resl: Depede Varable: SAT Sample: 437 Iclded observaos: 437 Whe heeroskedasc-cosse sadard errors & covarace Varable Coeffce Sd. Error -Sasc

More information

FROM THE BCS EQUATIONS TO THE ANISOTROPIC SUPERCONDUCTIVITY EQUATIONS. Luis A. PérezP. Chumin Wang

FROM THE BCS EQUATIONS TO THE ANISOTROPIC SUPERCONDUCTIVITY EQUATIONS. Luis A. PérezP. Chumin Wang FROM THE BCS EQUATIONS TO THE ANISOTROPIC SUPERCONDUCTIVITY EQUATIONS J. Samuel Mllá Faulad de Igeería Uversdad Auóoma del Carme Méxo. M Lus A. PérezP Isuo de Físa F UNAM MéxoM xo. Chum Wag Isuo de Ivesgaoes

More information

Ruin Probability-Based Initial Capital of the Discrete-Time Surplus Process

Ruin Probability-Based Initial Capital of the Discrete-Time Surplus Process Ru Probablty-Based Ital Captal of the Dsrete-Tme Surplus Proess by Parote Sattayatham, Kat Sagaroo, ad Wathar Klogdee AbSTRACT Ths paper studes a surae model uder the regulato that the surae ompay has

More information

Comparison of Four Methods for Estimating. the Weibull Distribution Parameters

Comparison of Four Methods for Estimating. the Weibull Distribution Parameters Appled Mathematal Sees, Vol. 8, 14, o. 83, 4137-4149 HIKARI Ltd, www.m-hkar.om http://dx.do.org/1.1988/ams.14.45389 Comparso of Four Methods for Estmatg the Webull Dstrbuto Parameters Ivaa Pobočíková ad

More information

As evident from the full-sample-model, we continue to assume that individual errors are identically and

As evident from the full-sample-model, we continue to assume that individual errors are identically and Maxmum Lkelhood smao Greee Ch.4; App. R scrp modsa, modsb If we feel safe makg assumpos o he sascal dsrbuo of he error erm, Maxmum Lkelhood smao (ML) s a aracve alerave o Leas Squares for lear regresso

More information

Moments of Order Statistics from Nonidentically Distributed Three Parameters Beta typei and Erlang Truncated Exponential Variables

Moments of Order Statistics from Nonidentically Distributed Three Parameters Beta typei and Erlang Truncated Exponential Variables Joural of Mahemacs ad Sascs 6 (4): 442-448, 200 SSN 549-3644 200 Scece Publcaos Momes of Order Sascs from Nodecally Dsrbued Three Parameers Bea ype ad Erlag Trucaed Expoeal Varables A.A. Jamoom ad Z.A.

More information

General Complex Fuzzy Transformation Semigroups in Automata

General Complex Fuzzy Transformation Semigroups in Automata Joural of Advaces Compuer Research Quarerly pissn: 345-606x eissn: 345-6078 Sar Brach Islamc Azad Uversy Sar IRIra Vol 7 No May 06 Pages: 7-37 wwwacrausaracr Geeral Complex uzzy Trasformao Semgroups Auomaa

More information

Reliability Analysis. Basic Reliability Measures

Reliability Analysis. Basic Reliability Measures elably /6/ elably Aaly Perae faul Πelably decay Teporary faul ΠOfe Seady ae characerzao Deg faul Πelably growh durg eg & debuggg A pace hule Challeger Lauch, 986 Ocober 6, Bac elably Meaure elably:

More information

The Properties of Probability of Normal Chain

The Properties of Probability of Normal Chain I. J. Coep. Mah. Sceces Vol. 8 23 o. 9 433-439 HIKARI Ld www.-hkar.co The Properes of Proaly of Noral Cha L Che School of Maheacs ad Sascs Zheghou Noral Uversy Zheghou Cy Hea Provce 4544 Cha cluu6697@sa.co

More information

Feature Selection: Part 2. 1 Greedy Algorithms (continued from the last lecture)

Feature Selection: Part 2. 1 Greedy Algorithms (continued from the last lecture) CSE 546: Mache Learg Lecture 6 Feature Selecto: Part 2 Istructor: Sham Kakade Greedy Algorthms (cotued from the last lecture) There are varety of greedy algorthms ad umerous amg covetos for these algorthms.

More information

Extremal graph theory II: K t and K t,t

Extremal graph theory II: K t and K t,t Exremal graph heory II: K ad K, Lecure Graph Theory 06 EPFL Frak de Zeeuw I his lecure, we geeralize he wo mai heorems from he las lecure, from riagles K 3 o complee graphs K, ad from squares K, o complee

More information

Synchronization of Complex Network System with Time-Varying Delay Via Periodically Intermittent Control

Synchronization of Complex Network System with Time-Varying Delay Via Periodically Intermittent Control Sychrozao of Complex ework Sysem wh me-varyg Delay Va Perodcally Ierme Corol JIAG Ya Deparme of Elecrcal ad Iformao Egeerg Hua Elecrcal College of echology Xaga 4, Cha Absrac he sychrozao corol problem

More information

Multiple Choice Test. Chapter Adequacy of Models for Regression

Multiple Choice Test. Chapter Adequacy of Models for Regression Multple Choce Test Chapter 06.0 Adequac of Models for Regresso. For a lear regresso model to be cosdered adequate, the percetage of scaled resduals that eed to be the rage [-,] s greater tha or equal to

More information

Big O Notation for Time Complexity of Algorithms

Big O Notation for Time Complexity of Algorithms BRONX COMMUNITY COLLEGE of he Ciy Uiversiy of New York DEPARTMENT OF MATHEMATICS AND COMPUTER SCIENCE CSI 33 Secio E01 Hadou 1 Fall 2014 Sepember 3, 2014 Big O Noaio for Time Complexiy of Algorihms Time

More information

Solution of Impulsive Differential Equations with Boundary Conditions in Terms of Integral Equations

Solution of Impulsive Differential Equations with Boundary Conditions in Terms of Integral Equations Joural of aheacs ad copuer Scece (4 39-38 Soluo of Ipulsve Dffereal Equaos wh Boudary Codos Ters of Iegral Equaos Arcle hsory: Receved Ocober 3 Acceped February 4 Avalable ole July 4 ohse Rabba Depare

More information

Linear Quadratic Regulator (LQR) - State Feedback Design

Linear Quadratic Regulator (LQR) - State Feedback Design Linear Quadrai Regulaor (LQR) - Sae Feedbak Design A sysem is expressed in sae variable form as x = Ax + Bu n m wih x( ) R, u( ) R and he iniial ondiion x() = x A he sabilizaion problem using sae variable

More information

Chapter Simpson s 1/3 Rule of Integration. ( x)

Chapter Simpson s 1/3 Rule of Integration. ( x) Cper 7. Smpso s / Rule o Iegro Aer redg s per, you sould e le o. derve e ormul or Smpso s / rule o egro,. use Smpso s / rule o solve egrls,. develop e ormul or mulple-segme Smpso s / rule o egro,. use

More information

New Guaranteed H Performance State Estimation for Delayed Neural Networks

New Guaranteed H Performance State Estimation for Delayed Neural Networks Ieraoal Joural of Iformao ad Elecrocs Egeerg Vol. o. 6 ovember ew Guaraeed H Performace ae Esmao for Delayed eural eworks Wo Il Lee ad PooGyeo Park Absrac I hs paper a ew guaraeed performace sae esmao

More information

A note on Turán number Tk ( 1, kn, )

A note on Turán number Tk ( 1, kn, ) A oe o Turá umber T (,, ) L A-Pg Beg 00085, P.R. Cha apl000@sa.com Absrac: Turá umber s oe of prmary opcs he combaorcs of fe ses, hs paper, we wll prese a ew upper boud for Turá umber T (,, ). . Iroduco

More information

An Efficient Dual to Ratio and Product Estimator of Population Variance in Sample Surveys

An Efficient Dual to Ratio and Product Estimator of Population Variance in Sample Surveys "cece as True Here" Joural of Mahemacs ad ascal cece, Volume 06, 78-88 cece gpos Publshg A Effce Dual o Rao ad Produc Esmaor of Populao Varace ample urves ubhash Kumar Yadav Deparme of Mahemacs ad ascs

More information

Queuing Theory: Memory Buffer Limits on Superscalar Processing

Queuing Theory: Memory Buffer Limits on Superscalar Processing Cle/ Model of I/O Queug Theory: Memory Buffer Lms o Superscalar Processg Cle reques respose Devce Fas CPU s cle for slower I/O servces Buffer sores cle requess ad s a slower server respose rae Laecy Tme

More information

Chapter 3: Maximum-Likelihood & Bayesian Parameter Estimation (part 1)

Chapter 3: Maximum-Likelihood & Bayesian Parameter Estimation (part 1) Aoucemes Reags o E-reserves Proec roosal ue oay Parameer Esmao Bomercs CSE 9-a Lecure 6 CSE9a Fall 6 CSE9a Fall 6 Paer Classfcao Chaer 3: Mamum-Lelhoo & Bayesa Parameer Esmao ar All maerals hese sles were

More information

The textbook expresses the stock price as the present discounted value of the dividend paid and the price of the stock next period.

The textbook expresses the stock price as the present discounted value of the dividend paid and the price of the stock next period. ublc Affars 974 Meze D. Ch Fall Socal Sceces 748 Uversy of Wscos-Madso Sock rces, News ad he Effce Markes Hypohess (rev d //) The rese Value Model Approach o Asse rcg The exbook expresses he sock prce

More information

Multivariate Transformation of Variables and Maximum Likelihood Estimation

Multivariate Transformation of Variables and Maximum Likelihood Estimation Marquette Uversty Multvarate Trasformato of Varables ad Maxmum Lkelhood Estmato Dael B. Rowe, Ph.D. Assocate Professor Departmet of Mathematcs, Statstcs, ad Computer Scece Copyrght 03 by Marquette Uversty

More information

Fourth Order Runge-Kutta Method Based On Geometric Mean for Hybrid Fuzzy Initial Value Problems

Fourth Order Runge-Kutta Method Based On Geometric Mean for Hybrid Fuzzy Initial Value Problems IOSR Joural of Mahemacs (IOSR-JM) e-issn: 2278-5728, p-issn: 29-765X. Volume, Issue 2 Ver. II (Mar. - Apr. 27), PP 4-5 www.osrjourals.org Fourh Order Ruge-Kua Mehod Based O Geomerc Mea for Hybrd Fuzzy

More information