Tackling Sequences From Prudent Self-Avoiding Walks

Size: px
Start display at page:

Download "Tackling Sequences From Prudent Self-Avoiding Walks"

Transcription

1 Taclng Sequences From Pruden Self-Avodng Wals Shanzhen Gao, Keh-Hsun Chen Deparmen of Compuer Scence, College of Compung and Informacs Unversy of Norh Carolna a Charloe, Charloe, NC 83, USA Emal: sgao3@unccedu, chen@unccedu Absrac A self-avodng wal SAW s a sequence of moves on a lace no vsng he same pon more han once A SAW on he square lace s pruden f never aes a sep owards a verex has already vsed Pruden wals dffer from mos subclasses of SAWs ha have been couned so far n ha hey can wnd around her sarng pon Some neresng problems and sequences arsng from pruden wals of one-sded and wo-sded are dscussed n hs paper A few mehods such as compuaonal, ernel, generang funcon, recurrence relaon and consrucve mehod are appled o our sudy Several open problems are posed Keywords: Self-avodng wal, pruden self-avodng wal, generang funcon, ernel mehod, neger sequence I INTRODUCTION A well-nown long sandng problem n combnaorcs and sascal mechancs s o fnd he generang funcon for self-avodng wals SAW on a wo-dmensonal lace, enumeraed by permeer A SAW s a sequence of moves on a square lace whch does no vs he same pon more han once I has been consdered by more han one hundred researchers n he pass one hundred years, ncludng George Polya, Tony Gumann, Laszlo Lovasz, Donald Knuh, Rchard Sanley, Doron Zelberger, Mrelle Bousque-Mélou, Thomas Prellberg, Neal Madras, Gordon Slade, Agnes Del, EJ Janse van Rensburg, Harry Kesen, Suar G Whngon, Lncoln Chayes, Iwan Jensen, Arhur T Benamn, and ohers More han hree hundred papers and a few volumes of boos were publshed n hs area A SAW s neresng for smulaons because s properes canno be calculaed analycally Calculang he number of self-avodng wals s a common compuaonal problem [], [], [3] In order o presen our problems and resuls clearly and effcenly, we nroduce some noaons n he followng Eas sep: E or or, 0, x-sep You can see more n he able below: 0,, 0, 0, N E NE S, 0,,, W SW NW SE : or more han consecuve seps : consecuve seps avodng : no or more han consecuve seps avodng : no consecuve seps, bu can have more han or less han consecuve seps x : he larges neger no greaer han x, floorx x : s he smalles neger no less han x, celngx [x n ]fx denoes he coeffcen of x n n he power seres expanson of a funcon fx [x m y n ]fx, y denoes he coeffcen of x m y n n he power seres expanson of a funcon fx, y n r he number of combnaons of n hngs r a a me n n! r n r!r! n n r n n r r n n r r r r In he pas few decades, many mahemacans have suded he followng wo classcal problems: Classcal Problem Wha s he number of SAWs from 0, 0 o n, n n an n n grd, ang seps from {,,, }? Donald Knuh clamed ha he number s beween and for n and he dd no beleve ha he would ever n hs lfeme now he exac answer o hs problem n 975 However, afer a few years, Rchard Schroeppel poned ou ha he exac value s, 568, 758, 030, 464, 750, 03, 4, [4], [5], [6] I s sll an unsolved problem for n > 5 Classcal Problem Wha s he number fn of n-sep SAWs, on he square lace, ang seps from {,,, }? The number fn s nown for n 7 [4], [5], [7], [8] I s clear ha n fn 4 3 n fm n fmfn There exss a consan C such ha lm n fn/n nf n [fn]/n C C 64 up o 7 seps have been couned

2 C 638 up o 9 seps have been couned fn 638 n The number of SAWs/ he number of oal wals: 00 for n 0 for n A recenly proposed model called pruden self-avodng wals PSAW was frs nroduced o he mahemacs communy n an unpublshed manuscrp of Préa, who called hem exeror wals A pruden wal s a conneced pah on square lace such ha, a each sep, he exenson of ha sep along s curren raecory wll never nersec any prevously occuped verex Such wals are clearly self-avodng [9], [0], [], [], [3] We wll al abou some sequences arsng from PSAWs n he followng Each PSAW possesses a mnmum boundng recangle, whch we call box Less obvously, he endpon of a pruden wal s always a pon on he boundary of he box Each new sep eher nflaes he box or wals prudenly along he border Afer an nflang sep, here are 3 possbles for a wal o go on Oherwse, only In a one-sded PSAW, he endpon les always on he op sde of he box The wal s parally dreced A pruden wal s wo-sded f s endpon les always on he op sde, or on he rgh sde of he box The wal n he followng fgure s a wo-sded PSAW II PRUDENT SELF-AVOIDING WALKS: DEFINITIONS AND EXAMPLES A PSAW s a proper subse of SAWs on he square lace The wal sars a 0, 0, and he empy wal s a PSAW A PSAW grows by addng a sep o he end pon of a PSAW such ha he exenson of hs sep - by any dsance - never nersecs he wal Hence he name pruden The wal s so careful o be self-avodng ha refuses o ae a sngle sep n any drecon where can see - no maer how far away - an occuped verex The followng wal s a PSAW A Properes of a PSAW Unle SAW, PSAW are usually no reversble There s such an example n he followng fgure III SOME SEQUENCES ARISING FROM ONE-SIDED PSAWS Sequence Wha s he number say fn of one-sded n- sep pruden wals, ang seps from {,, }? The generang funcon equals f n n n 0 Also, fn fn fn n n [ 0 ] n [ n ] [ 0 We oban sequence A00333 of he On-Lne Encyclopeda of Ineger Sequences[5, A00333] Sequence ]

3 The number of one-sded n-sep pruden wals, sarng from 0, 0 and endng on y-axs, ang seps from {,, } s n / mn{n,} n n For he case 3 n he above heorem, here are 6 wals as follows: We oban sequence A3609[5, A3609] Sequence 3 Consder he number of one-sded pruden wals sarng from 0, 0 o x, y, ang seps from {,, } The number of such wals wh x rgh seps, lef seps and y up seps, s mn{y,x} y x x y If and x y n, we oban sequence A9578[5, A9578] Sequence 4 The number of one-sded n-sep pruden wals, from 0, 0 o x, y, n x y s even ang seps from {,, } s mn{y, nx y } 0 y nx y nx y n x y n xy If x y 3, we oban sequence A6376[5, A6376] Sequence 5 Wha s he number of he one-sded n-sep pruden wals, avodng or more consecuve eas seps,? The generang funcon equals If, If, we oban sequence [5, A006356]:, 3, 6, 4, 3, 70, 57, 353, 793, 78, 4004, 8997, 06, I also couns he number of pahs for a ray of lgh ha eners wo layers of glass and hen s refleced exacly n mes before leavng he layers of glass If 3, we oban sequence [5, A05967]:, 3, 7, 6, 38, 89, 09, 49, 53, 708, 6360, If 4, we oban sequence [5, A90360]:, 3, 7, 7, 40, 96, 9, 547, 306, 39, 39, 7448, Sequence 6 The number of one-sded n-sep pruden wals, ang seps from {,,, } equals n n 7 We oban sequence A055099[5, A055099] Sequence 7 Wha s he number of one-sded n- sep pruden wals, ang seps from {,,,, }? The generang funcon s 4 3 We oban sequence A6473[5, A6473] Sequence 8 Wha s he number of one-sded n-sep pruden wals n he frs quadran, sarng from 0, 0 and endng on he y-axs, ang seps from {,, }? The generang funcon s 3 4 Sequence 9 Wha s he number of one-sded n-sep pruden wals exacly avodng, ang seps from {,, }? The generang funcon equals If, we oban sequence A07806[5, A07806] Sequence 0 Wha s he number of one-sded n-sep pruden wals exacly avodng and boh a he same me?

4 also, The generang funcon s For, fn n n/ n/ /5, fn fn fn fn 3 wh f, f 3, f3 7 Ths s sequence A007909[5, A007909] IV SOME SEQUENCES ARISING FROM TWO-SIDED PSAWS Wha s he number of wo-sded, n-sep pruden wals endng on he op sde of her box avodng boh paerns, boh a he same me, ang seps from {,,, }? Theorem The generang funcon say T, u of he above wo-sded pruden wals endng on he op sde of her box sasfes u T, u u T,, where u couns he dsance beween he endpon and he norh-eas NE corner of he box For nsance, n he followng fgure, a wal aes 5 seps, and he dsance beween he endpon and he norh-eas corner s 3 So we can use 5 u 3 o coun hs wal Oulne of he proof of he heorem: Case : Neher he op nor he rgh sde has ever moved; he wal s only a wes sep Ths case conrbues o he generang funcon Case : The las nflang sep goes eas Ths mples ha he endpon of he wal was on he rgh sde of he box before ha sep Afer ha eas sep, he wal has made a sequence of norh seps o reach he op sde of he box Observe ha, by symmery, he seres T, u also couns wals endng on he rgh sde of he box by he lengh and he dsance beween he endpon and he norh-eas corner These wo observaons gve he generang funcon for hs class as T, Case 3: The las nflang sep goes norh Afer hs sep, here s eher a wes sep or a bounded sequence of Eas seps Ths gves he generaon funcon for hs class as u u T, T, Pung he hree cases ogeher, we ge he generang funcon for T, u Solve hs generang funcon for T, u usng he Kernel Mehod: From u we can ge T, u u T,, u u 3 u T, u u u T, u u Se u u 3 u 0, hen here s only one power seres soluon for u u Le U be hs soluon, U U Se u u T, u 0, and replace u by U: ge From T, U u u 3 u T, u U U 3 u u T, u u u u T, u u u 3 u T, u u u u 3 u Replace T, by 3 Now u T, u u 3 u U U u U u u 3 u where U has been defned n Sequence Noce ha T, s he generang funcon of he number of wo-sded n-sep pruden wals endng on he op sde of her box avodng boh paerns,, ang seps from {,,, }, hus T,

5 Sequence Noe ha T, 0 s he generang funcon of he number of wo-sded n-sep pruden wals endng a he norh-eas corner of her box avodng boh paerns,, ang seps from {,,, }, so T, Sequence 3 Furhermore, T, T, 0 s he generang funcon of he number of wo-sded n-sep pruden wals endng on he op sde or rgh sde of her box avodng boh paerns,, ang seps from {,,, }, hus T, T, Open Problem Wha s he number of wo-sded n-sep pruden wals, endng on he op sde of her box, avodng boh, and > ang seps from {,,, }? The generang funcon sasfes: u u u T, u u u u T,, where u couns he dsance beween he endpon and he norh-eas corner of he box For 3, u 3 3 u 3 4 u 4 u 4 5 u 3 u T, u u u 3 u 3 T, e, 3 u 4 u 5 3 u 3 4 u 4 T, u u u 3 u 3 T, Se 3 u 4 u 5 3 u 3 4 u 4 0, and solve for u, as a power seres of We obaned he frs one hundred erms for u, begnnng wh u Usng hs u, we can ge many examples for he sequence Open Problem Wha s he number of wo-sded n-sep pruden wals, endng on he op sde of her box, exacly avodng boh,, ang seps from {,,, }? The generang funcon s u u u 3 T, u u u T, I seems o us s no rval o solve hs generang funcon V SOME THEOREMS AND PROOFS Theorem The generang funcon of he number, say fn,, of he one-sded n-sep pruden wals, ang seps from {,, }, avodng or more consecuve eas seps, sasfes, and for, fn, n 0 0 n 0 0 n 0 0 n n n fn, n n n n Proof: Le F denoe he lengh generang funcon of he number of one-sded pruden wals, avodng or more consecuve eas seps We have he followng hree cases For he wals whch do no conan Norh seps, hey can be empy wal, wals wh only wes seps, wals wh only eas seps wh lengh a leas one and a mos, he conrbuons are,, respecvely For he wals obaned by concaenang a one-sded wal, a Norh sep, and hen a Wes wal, he conrbuon s F 3 For he wals obaned by concaenang a one-sded wal, a Norh sep, and hen a Eas wal wh a leas sep and a mos seps, he conrbuon s F Addng hese hree conrbuons gve he equaon F F F

6 Thus, F Now, le [ n ]F denoe he coeffcen of n n he power seres expanson of F [ n ] [ n ] 0 [ n ] 0 0 [ n ] 0 0 [ n ] 0 0 l0 n 0 0 n 0 0 n 0 0 l n n n l0 l l l l l ll n n n I s also he number of posve neger soluons o he equaon r x n Whou loss of generaly, we assume ha here are Eas seps, Wes seps and n Norh seps n a one-sded n-sep pruden wals, sarng from 0, 0 and endng on he y-axs We also assume ha > 0 snce here s only one such wal for 0 I s easy o see ha n / The n Norh seps provde n posons we can say n dfferen cells for Eas seps and Wes seps o be nsered Suppose ha we pu Eas seps no mn{n, } cells wh no empy cell Then here are ways of pung Eas seps no cells and n ways of choosng cells Now we dsrbue Wes seps no he remanng n cells, whch gve us n Therefore, we ge he number: n / mn{n,} n n Example: For n 4 n he above heorem, we have 7 such wals as follows: Theorem 3 The number of one-sded n-sep pruden wals, sarng from 0, 0 and endng on he y-axs, ang seps from {,, } s n / mn{n,} n n Proof: In our proof, we wll use he followng wo resuls whch could be found n some mahemacs boos such as [6]: The number of ways of pung n le obecs no r dfferen cells s n r n r n r I s also he number of nonnegave neger soluons o he equaon r x n The number of ways of pung n le obecs no r dfferen cells wh no empy cell s n r Theorem 4 The number, say fn, of generalzed one-sded n-sep pruden wals, ang seps from {,,, } equals n n n 3 n n 3 n n n wh generang funcon 0 n n,

7 Proof: Le P denoe he lengh generang funcon of generalzed one-sded pruden wals The conrbuon n P of wals ha do no conan Norh seps or Norheas seps horzonal wals s The conrbuon of wals obaned by concaenang a generalzed one-sded wal, a Norh sep or Norheas sep, hen a horzonal wal s P Addng hese wo conrbuons gves a lnear equaon for P : Therefore, P P P fn [ n ]P n n 3 n n 0 n n 3 n n 0 The second formula of fn can be easly derved from he lengh generang funcon Example: For n n he above heorem, we have 4 such wals: EN,NE, W N,NW,NNE,NEN,ENE,NEE, NEW, W NE, NN, W W, EE, NENE Theorem 5 The generang funcon of he number, say fn, of generalzed one-sded n-sep pruden wals, ang seps from {,,,, } s fn [ n ] [ n ] 4 m 3 m m m 0 m0 n [ ] 3 4 n 3 n n n 0 Proof: Le P denoe he lengh generang funcon of generalzed one-sded pruden wals The conrbuon n P of wals ha do no conan Norh seps or Norheas seps, or Norhwes sep horzonal wals s The conrbuon of wals obaned by concaenang a generalzed one-sded wal, a Norh sep or Norheas sep or a Norhwes sep, hen a horzonal wal s 3 P Addng hese wo conrbuons gves a lnear equaon for P, from whch we can ge P REFERENCES [] M Neal and S Gordon, The Self-Avodng Wal Brhäuser 996 [] LF Gregory, Inersecons of Random Wals Brhäuser 996 [3] AJ Gumann Ed, Polygons, Polyomnoes and Polycubes, Sprnger 009 [4] M Bousque-Mélou, AJ Gumann and I Jensen, Self-avodng wals crossng a square, J Phys A [5] J Iwan, Enumeraon of self-avodng wals on he square lace J Phys A [6] KE Donald, Scence [7] EJ Janse van Rensburg, Mone Carlo mehods for he self-avodng wal, Topcal Revew J Phys A 4 009, 3300 [8] AR Conway and AJ Gumann, Square lace self-avodng wals and correcons o scalng, Phys Rev Le , [9] E Duch, On some classes of pruden wals In Proceedngs of he FPSAC 05, Taormna, Ialy, 005 [0] P Préa, Exeror self-avodng wals on he square lace, 997 manuscrp [] JC Dehrdge and AJ Gumann, Pruden self-avodng wals, Enropy 0 008, [] TM Garon, AJ Gumann, I Jensen and JC Dehrdge, Pruden wals and polygons, J Phys A 4 009, [3] M Bousque-Mélou, Famles of pruden self-avodng wals, J of Combnaoral Theory, Seres A [4] R P Sanley, Enumerave Combnaorcs I, Wadsworh & Broos/Cole 986 [5] The Onlne Encyclopeda of Ineger Sequences 04, publshed elecroncally a hp://oesorg [6] John Rordan, An Inroducon o Combnaoral Analyss orgnally publshed: New Yor: John Wley 958, Dover Publcaons 00

Sequences Arising From Prudent Self-Avoiding Walks Shanzhen Gao, Heinrich Niederhausen Florida Atlantic University, Boca Raton, Florida 33431

Sequences Arising From Prudent Self-Avoiding Walks Shanzhen Gao, Heinrich Niederhausen Florida Atlantic University, Boca Raton, Florida 33431 Seqences Arising From Prden Self-Avoiding Walks Shanzhen Gao, Heinrich Niederhasen Florida Alanic Universiy, Boca Raon, Florida 33431 Absrac A self-avoiding walk (SAW) is a seqence of moves on a laice

More information

( ) () we define the interaction representation by the unitary transformation () = ()

( ) () we define the interaction representation by the unitary transformation () = () Hgher Order Perurbaon Theory Mchael Fowler 3/7/6 The neracon Represenaon Recall ha n he frs par of hs course sequence, we dscussed he chrödnger and Hesenberg represenaons of quanum mechancs here n he chrödnger

More information

Solution in semi infinite diffusion couples (error function analysis)

Solution in semi infinite diffusion couples (error function analysis) Soluon n sem nfne dffuson couples (error funcon analyss) Le us consder now he sem nfne dffuson couple of wo blocks wh concenraon of and I means ha, n a A- bnary sysem, s bondng beween wo blocks made of

More information

V.Abramov - FURTHER ANALYSIS OF CONFIDENCE INTERVALS FOR LARGE CLIENT/SERVER COMPUTER NETWORKS

V.Abramov - FURTHER ANALYSIS OF CONFIDENCE INTERVALS FOR LARGE CLIENT/SERVER COMPUTER NETWORKS R&RATA # Vol.) 8, March FURTHER AALYSIS OF COFIDECE ITERVALS FOR LARGE CLIET/SERVER COMPUTER ETWORKS Vyacheslav Abramov School of Mahemacal Scences, Monash Unversy, Buldng 8, Level 4, Clayon Campus, Wellngon

More information

In the complete model, these slopes are ANALYSIS OF VARIANCE FOR THE COMPLETE TWO-WAY MODEL. (! i+1 -! i ) + [(!") i+1,q - [(!

In the complete model, these slopes are ANALYSIS OF VARIANCE FOR THE COMPLETE TWO-WAY MODEL. (! i+1 -! i ) + [(!) i+1,q - [(! ANALYSIS OF VARIANCE FOR THE COMPLETE TWO-WAY MODEL The frs hng o es n wo-way ANOVA: Is here neracon? "No neracon" means: The man effecs model would f. Ths n urn means: In he neracon plo (wh A on he horzonal

More information

GENERATING CERTAIN QUINTIC IRREDUCIBLE POLYNOMIALS OVER FINITE FIELDS. Youngwoo Ahn and Kitae Kim

GENERATING CERTAIN QUINTIC IRREDUCIBLE POLYNOMIALS OVER FINITE FIELDS. Youngwoo Ahn and Kitae Kim Korean J. Mah. 19 (2011), No. 3, pp. 263 272 GENERATING CERTAIN QUINTIC IRREDUCIBLE POLYNOMIALS OVER FINITE FIELDS Youngwoo Ahn and Kae Km Absrac. In he paper [1], an explc correspondence beween ceran

More information

[ ] 2. [ ]3 + (Δx i + Δx i 1 ) / 2. Δx i-1 Δx i Δx i+1. TPG4160 Reservoir Simulation 2018 Lecture note 3. page 1 of 5

[ ] 2. [ ]3 + (Δx i + Δx i 1 ) / 2. Δx i-1 Δx i Δx i+1. TPG4160 Reservoir Simulation 2018 Lecture note 3. page 1 of 5 TPG460 Reservor Smulaon 08 page of 5 DISCRETIZATIO OF THE FOW EQUATIOS As we already have seen, fne dfference appromaons of he paral dervaves appearng n he flow equaons may be obaned from Taylor seres

More information

Cubic Bezier Homotopy Function for Solving Exponential Equations

Cubic Bezier Homotopy Function for Solving Exponential Equations Penerb Journal of Advanced Research n Compung and Applcaons ISSN (onlne: 46-97 Vol. 4, No.. Pages -8, 6 omoopy Funcon for Solvng Eponenal Equaons S. S. Raml *,,. Mohamad Nor,a, N. S. Saharzan,b and M.

More information

HEAT CONDUCTION PROBLEM IN A TWO-LAYERED HOLLOW CYLINDER BY USING THE GREEN S FUNCTION METHOD

HEAT CONDUCTION PROBLEM IN A TWO-LAYERED HOLLOW CYLINDER BY USING THE GREEN S FUNCTION METHOD Journal of Appled Mahemacs and Compuaonal Mechancs 3, (), 45-5 HEAT CONDUCTION PROBLEM IN A TWO-LAYERED HOLLOW CYLINDER BY USING THE GREEN S FUNCTION METHOD Sansław Kukla, Urszula Sedlecka Insue of Mahemacs,

More information

Testing a new idea to solve the P = NP problem with mathematical induction

Testing a new idea to solve the P = NP problem with mathematical induction Tesng a new dea o solve he P = NP problem wh mahemacal nducon Bacground P and NP are wo classes (ses) of languages n Compuer Scence An open problem s wheher P = NP Ths paper ess a new dea o compare he

More information

Comparison of Differences between Power Means 1

Comparison of Differences between Power Means 1 In. Journal of Mah. Analyss, Vol. 7, 203, no., 5-55 Comparson of Dfferences beween Power Means Chang-An Tan, Guanghua Sh and Fe Zuo College of Mahemacs and Informaon Scence Henan Normal Unversy, 453007,

More information

Tight results for Next Fit and Worst Fit with resource augmentation

Tight results for Next Fit and Worst Fit with resource augmentation Tgh resuls for Nex F and Wors F wh resource augmenaon Joan Boyar Leah Epsen Asaf Levn Asrac I s well known ha he wo smple algorhms for he classc n packng prolem, NF and WF oh have an approxmaon rao of

More information

Linear Response Theory: The connection between QFT and experiments

Linear Response Theory: The connection between QFT and experiments Phys540.nb 39 3 Lnear Response Theory: The connecon beween QFT and expermens 3.1. Basc conceps and deas Q: ow do we measure he conducvy of a meal? A: we frs nroduce a weak elecrc feld E, and hen measure

More information

On One Analytic Method of. Constructing Program Controls

On One Analytic Method of. Constructing Program Controls Appled Mahemacal Scences, Vol. 9, 05, no. 8, 409-407 HIKARI Ld, www.m-hkar.com hp://dx.do.org/0.988/ams.05.54349 On One Analyc Mehod of Consrucng Program Conrols A. N. Kvko, S. V. Chsyakov and Yu. E. Balyna

More information

Approximate Analytic Solution of (2+1) - Dimensional Zakharov-Kuznetsov(Zk) Equations Using Homotopy

Approximate Analytic Solution of (2+1) - Dimensional Zakharov-Kuznetsov(Zk) Equations Using Homotopy Arcle Inernaonal Journal of Modern Mahemacal Scences, 4, (): - Inernaonal Journal of Modern Mahemacal Scences Journal homepage: www.modernscenfcpress.com/journals/jmms.aspx ISSN: 66-86X Florda, USA Approxmae

More information

Lecture 18: The Laplace Transform (See Sections and 14.7 in Boas)

Lecture 18: The Laplace Transform (See Sections and 14.7 in Boas) Lecure 8: The Lalace Transform (See Secons 88- and 47 n Boas) Recall ha our bg-cure goal s he analyss of he dfferenal equaon, ax bx cx F, where we emloy varous exansons for he drvng funcon F deendng on

More information

Variants of Pegasos. December 11, 2009

Variants of Pegasos. December 11, 2009 Inroducon Varans of Pegasos SooWoong Ryu bshboy@sanford.edu December, 009 Youngsoo Cho yc344@sanford.edu Developng a new SVM algorhm s ongong research opc. Among many exng SVM algorhms, we wll focus on

More information

CS434a/541a: Pattern Recognition Prof. Olga Veksler. Lecture 4

CS434a/541a: Pattern Recognition Prof. Olga Veksler. Lecture 4 CS434a/54a: Paern Recognon Prof. Olga Veksler Lecure 4 Oulne Normal Random Varable Properes Dscrmnan funcons Why Normal Random Varables? Analycally racable Works well when observaon comes form a corruped

More information

Existence and Uniqueness Results for Random Impulsive Integro-Differential Equation

Existence and Uniqueness Results for Random Impulsive Integro-Differential Equation Global Journal of Pure and Appled Mahemacs. ISSN 973-768 Volume 4, Number 6 (8), pp. 89-87 Research Inda Publcaons hp://www.rpublcaon.com Exsence and Unqueness Resuls for Random Impulsve Inegro-Dfferenal

More information

Performance Analysis for a Network having Standby Redundant Unit with Waiting in Repair

Performance Analysis for a Network having Standby Redundant Unit with Waiting in Repair TECHNI Inernaonal Journal of Compung Scence Communcaon Technologes VOL.5 NO. July 22 (ISSN 974-3375 erformance nalyss for a Nework havng Sby edundan Un wh ang n epar Jendra Sngh 2 abns orwal 2 Deparmen

More information

FI 3103 Quantum Physics

FI 3103 Quantum Physics /9/4 FI 33 Quanum Physcs Aleander A. Iskandar Physcs of Magnesm and Phooncs Research Grou Insu Teknolog Bandung Basc Conces n Quanum Physcs Probably and Eecaon Value Hesenberg Uncerany Prncle Wave Funcon

More information

Notes on the stability of dynamic systems and the use of Eigen Values.

Notes on the stability of dynamic systems and the use of Eigen Values. Noes on he sabl of dnamc ssems and he use of Egen Values. Source: Macro II course noes, Dr. Davd Bessler s Tme Seres course noes, zarads (999) Ineremporal Macroeconomcs chaper 4 & Techncal ppend, and Hamlon

More information

II. Light is a Ray (Geometrical Optics)

II. Light is a Ray (Geometrical Optics) II Lgh s a Ray (Geomercal Opcs) IIB Reflecon and Refracon Hero s Prncple of Leas Dsance Law of Reflecon Hero of Aleandra, who lved n he 2 nd cenury BC, posulaed he followng prncple: Prncple of Leas Dsance:

More information

Scattering at an Interface: Oblique Incidence

Scattering at an Interface: Oblique Incidence Course Insrucor Dr. Raymond C. Rumpf Offce: A 337 Phone: (915) 747 6958 E Mal: rcrumpf@uep.edu EE 4347 Appled Elecromagnecs Topc 3g Scaerng a an Inerface: Oblque Incdence Scaerng These Oblque noes may

More information

M. Y. Adamu Mathematical Sciences Programme, AbubakarTafawaBalewa University, Bauchi, Nigeria

M. Y. Adamu Mathematical Sciences Programme, AbubakarTafawaBalewa University, Bauchi, Nigeria IOSR Journal of Mahemacs (IOSR-JM e-issn: 78-578, p-issn: 9-765X. Volume 0, Issue 4 Ver. IV (Jul-Aug. 04, PP 40-44 Mulple SolonSoluons for a (+-dmensonalhroa-sasuma shallow waer wave equaon UsngPanlevé-Bӓclund

More information

On computing differential transform of nonlinear non-autonomous functions and its applications

On computing differential transform of nonlinear non-autonomous functions and its applications On compung dfferenal ransform of nonlnear non-auonomous funcons and s applcaons Essam. R. El-Zahar, and Abdelhalm Ebad Deparmen of Mahemacs, Faculy of Scences and Humanes, Prnce Saam Bn Abdulazz Unversy,

More information

Chapter Lagrangian Interpolation

Chapter Lagrangian Interpolation Chaper 5.4 agrangan Inerpolaon Afer readng hs chaper you should be able o:. dere agrangan mehod of nerpolaon. sole problems usng agrangan mehod of nerpolaon and. use agrangan nerpolans o fnd deraes and

More information

Ordinary Differential Equations in Neuroscience with Matlab examples. Aim 1- Gain understanding of how to set up and solve ODE s

Ordinary Differential Equations in Neuroscience with Matlab examples. Aim 1- Gain understanding of how to set up and solve ODE s Ordnary Dfferenal Equaons n Neuroscence wh Malab eamples. Am - Gan undersandng of how o se up and solve ODE s Am Undersand how o se up an solve a smple eample of he Hebb rule n D Our goal a end of class

More information

Mechanics Physics 151

Mechanics Physics 151 Mechancs Physcs 5 Lecure 0 Canoncal Transformaons (Chaper 9) Wha We Dd Las Tme Hamlon s Prncple n he Hamlonan formalsm Dervaon was smple δi δ Addonal end-pon consrans pq H( q, p, ) d 0 δ q ( ) δq ( ) δ

More information

Survival Analysis and Reliability. A Note on the Mean Residual Life Function of a Parallel System

Survival Analysis and Reliability. A Note on the Mean Residual Life Function of a Parallel System Communcaons n Sascs Theory and Mehods, 34: 475 484, 2005 Copyrgh Taylor & Francs, Inc. ISSN: 0361-0926 prn/1532-415x onlne DOI: 10.1081/STA-200047430 Survval Analyss and Relably A Noe on he Mean Resdual

More information

PHYS 1443 Section 001 Lecture #4

PHYS 1443 Section 001 Lecture #4 PHYS 1443 Secon 001 Lecure #4 Monda, June 5, 006 Moon n Two Dmensons Moon under consan acceleraon Projecle Moon Mamum ranges and heghs Reerence Frames and relae moon Newon s Laws o Moon Force Newon s Law

More information

. The geometric multiplicity is dim[ker( λi. number of linearly independent eigenvectors associated with this eigenvalue.

. The geometric multiplicity is dim[ker( λi. number of linearly independent eigenvectors associated with this eigenvalue. Lnear Algebra Lecure # Noes We connue wh he dscusson of egenvalues, egenvecors, and dagonalzably of marces We wan o know, n parcular wha condons wll assure ha a marx can be dagonalzed and wha he obsrucons

More information

Advanced Machine Learning & Perception

Advanced Machine Learning & Perception Advanced Machne Learnng & Percepon Insrucor: Tony Jebara SVM Feaure & Kernel Selecon SVM Eensons Feaure Selecon (Flerng and Wrappng) SVM Feaure Selecon SVM Kernel Selecon SVM Eensons Classfcaon Feaure/Kernel

More information

P R = P 0. The system is shown on the next figure:

P R = P 0. The system is shown on the next figure: TPG460 Reservor Smulaon 08 page of INTRODUCTION TO RESERVOIR SIMULATION Analycal and numercal soluons of smple one-dmensonal, one-phase flow equaons As an nroducon o reservor smulaon, we wll revew he smples

More information

Comb Filters. Comb Filters

Comb Filters. Comb Filters The smple flers dscussed so far are characered eher by a sngle passband and/or a sngle sopband There are applcaons where flers wh mulple passbands and sopbands are requred Thecomb fler s an example of

More information

PHYS 705: Classical Mechanics. Canonical Transformation

PHYS 705: Classical Mechanics. Canonical Transformation PHYS 705: Classcal Mechancs Canoncal Transformaon Canoncal Varables and Hamlonan Formalsm As we have seen, n he Hamlonan Formulaon of Mechancs,, are ndeenden varables n hase sace on eual foong The Hamlon

More information

SOME NOISELESS CODING THEOREMS OF INACCURACY MEASURE OF ORDER α AND TYPE β

SOME NOISELESS CODING THEOREMS OF INACCURACY MEASURE OF ORDER α AND TYPE β SARAJEVO JOURNAL OF MATHEMATICS Vol.3 (15) (2007), 137 143 SOME NOISELESS CODING THEOREMS OF INACCURACY MEASURE OF ORDER α AND TYPE β M. A. K. BAIG AND RAYEES AHMAD DAR Absrac. In hs paper, we propose

More information

CS286.2 Lecture 14: Quantum de Finetti Theorems II

CS286.2 Lecture 14: Quantum de Finetti Theorems II CS286.2 Lecure 14: Quanum de Fne Theorems II Scrbe: Mara Okounkova 1 Saemen of he heorem Recall he las saemen of he quanum de Fne heorem from he prevous lecure. Theorem 1 Quanum de Fne). Le ρ Dens C 2

More information

Outline. Probabilistic Model Learning. Probabilistic Model Learning. Probabilistic Model for Time-series Data: Hidden Markov Model

Outline. Probabilistic Model Learning. Probabilistic Model Learning. Probabilistic Model for Time-series Data: Hidden Markov Model Probablsc Model for Tme-seres Daa: Hdden Markov Model Hrosh Mamsuka Bonformacs Cener Kyoo Unversy Oulne Three Problems for probablsc models n machne learnng. Compung lkelhood 2. Learnng 3. Parsng (predcon

More information

Chapters 2 Kinematics. Position, Distance, Displacement

Chapters 2 Kinematics. Position, Distance, Displacement Chapers Knemacs Poson, Dsance, Dsplacemen Mechancs: Knemacs and Dynamcs. Knemacs deals wh moon, bu s no concerned wh he cause o moon. Dynamcs deals wh he relaonshp beween orce and moon. The word dsplacemen

More information

Appendix H: Rarefaction and extrapolation of Hill numbers for incidence data

Appendix H: Rarefaction and extrapolation of Hill numbers for incidence data Anne Chao Ncholas J Goell C seh lzabeh L ander K Ma Rober K Colwell and Aaron M llson 03 Rarefacon and erapolaon wh ll numbers: a framewor for samplng and esmaon n speces dversy sudes cology Monographs

More information

A Deza Frankl type theorem for set partitions

A Deza Frankl type theorem for set partitions A Deza Frankl ype heorem for se parons Cheng Yeaw Ku Deparmen of Mahemacs Naonal Unversy of Sngapore Sngapore 117543 makcy@nus.edu.sg Kok Bn Wong Insue of Mahemacal Scences Unversy of Malaya 50603 Kuala

More information

Implementation of Quantized State Systems in MATLAB/Simulink

Implementation of Quantized State Systems in MATLAB/Simulink SNE T ECHNICAL N OTE Implemenaon of Quanzed Sae Sysems n MATLAB/Smulnk Parck Grabher, Mahas Rößler 2*, Bernhard Henzl 3 Ins. of Analyss and Scenfc Compung, Venna Unversy of Technology, Wedner Haupsraße

More information

Mechanics Physics 151

Mechanics Physics 151 Mechancs Physcs 5 Lecure 9 Hamlonan Equaons of Moon (Chaper 8) Wha We Dd Las Tme Consruced Hamlonan formalsm H ( q, p, ) = q p L( q, q, ) H p = q H q = p H = L Equvalen o Lagrangan formalsm Smpler, bu

More information

NATIONAL UNIVERSITY OF SINGAPORE PC5202 ADVANCED STATISTICAL MECHANICS. (Semester II: AY ) Time Allowed: 2 Hours

NATIONAL UNIVERSITY OF SINGAPORE PC5202 ADVANCED STATISTICAL MECHANICS. (Semester II: AY ) Time Allowed: 2 Hours NATONAL UNVERSTY OF SNGAPORE PC5 ADVANCED STATSTCAL MECHANCS (Semeser : AY 1-13) Tme Allowed: Hours NSTRUCTONS TO CANDDATES 1. Ths examnaon paper conans 5 quesons and comprses 4 prned pages.. Answer all

More information

John Geweke a and Gianni Amisano b a Departments of Economics and Statistics, University of Iowa, USA b European Central Bank, Frankfurt, Germany

John Geweke a and Gianni Amisano b a Departments of Economics and Statistics, University of Iowa, USA b European Central Bank, Frankfurt, Germany Herarchcal Markov Normal Mxure models wh Applcaons o Fnancal Asse Reurns Appendx: Proofs of Theorems and Condonal Poseror Dsrbuons John Geweke a and Gann Amsano b a Deparmens of Economcs and Sascs, Unversy

More information

Response of MDOF systems

Response of MDOF systems Response of MDOF syses Degree of freedo DOF: he nu nuber of ndependen coordnaes requred o deerne copleely he posons of all pars of a syse a any nsan of e. wo DOF syses hree DOF syses he noral ode analyss

More information

Mechanics Physics 151

Mechanics Physics 151 Mechancs Physcs 5 Lecure 9 Hamlonan Equaons of Moon (Chaper 8) Wha We Dd Las Tme Consruced Hamlonan formalsm Hqp (,,) = qp Lqq (,,) H p = q H q = p H L = Equvalen o Lagrangan formalsm Smpler, bu wce as

More information

J i-1 i. J i i+1. Numerical integration of the diffusion equation (I) Finite difference method. Spatial Discretization. Internal nodes.

J i-1 i. J i i+1. Numerical integration of the diffusion equation (I) Finite difference method. Spatial Discretization. Internal nodes. umercal negraon of he dffuson equaon (I) Fne dfference mehod. Spaal screaon. Inernal nodes. R L V For hermal conducon le s dscree he spaal doman no small fne spans, =,,: Balance of parcles for an nernal

More information

Example: MOSFET Amplifier Distortion

Example: MOSFET Amplifier Distortion 4/25/2011 Example MSFET Amplfer Dsoron 1/9 Example: MSFET Amplfer Dsoron Recall hs crcu from a prevous handou: ( ) = I ( ) D D d 15.0 V RD = 5K v ( ) = V v ( ) D o v( ) - K = 2 0.25 ma/v V = 2.0 V 40V.

More information

New M-Estimator Objective Function. in Simultaneous Equations Model. (A Comparative Study)

New M-Estimator Objective Function. in Simultaneous Equations Model. (A Comparative Study) Inernaonal Mahemacal Forum, Vol. 8, 3, no., 7 - HIKARI Ld, www.m-hkar.com hp://dx.do.org/.988/mf.3.3488 New M-Esmaor Objecve Funcon n Smulaneous Equaons Model (A Comparave Sudy) Ahmed H. Youssef Professor

More information

. The geometric multiplicity is dim[ker( λi. A )], i.e. the number of linearly independent eigenvectors associated with this eigenvalue.

. The geometric multiplicity is dim[ker( λi. A )], i.e. the number of linearly independent eigenvectors associated with this eigenvalue. Mah E-b Lecure #0 Noes We connue wh he dscusson of egenvalues, egenvecors, and dagonalzably of marces We wan o know, n parcular wha condons wll assure ha a marx can be dagonalzed and wha he obsrucons are

More information

A NEW TECHNIQUE FOR SOLVING THE 1-D BURGERS EQUATION

A NEW TECHNIQUE FOR SOLVING THE 1-D BURGERS EQUATION S19 A NEW TECHNIQUE FOR SOLVING THE 1-D BURGERS EQUATION by Xaojun YANG a,b, Yugu YANG a*, Carlo CATTANI c, and Mngzheng ZHU b a Sae Key Laboraory for Geomechancs and Deep Underground Engneerng, Chna Unversy

More information

Method of upper lower solutions for nonlinear system of fractional differential equations and applications

Method of upper lower solutions for nonlinear system of fractional differential equations and applications Malaya Journal of Maemak, Vol. 6, No. 3, 467-472, 218 hps://do.org/1.26637/mjm63/1 Mehod of upper lower soluons for nonlnear sysem of fraconal dfferenal equaons and applcaons D.B. Dhagude1 *, N.B. Jadhav2

More information

FTCS Solution to the Heat Equation

FTCS Solution to the Heat Equation FTCS Soluon o he Hea Equaon ME 448/548 Noes Gerald Reckenwald Porland Sae Unversy Deparmen of Mechancal Engneerng gerry@pdxedu ME 448/548: FTCS Soluon o he Hea Equaon Overvew Use he forward fne d erence

More information

Dynamic Team Decision Theory. EECS 558 Project Shrutivandana Sharma and David Shuman December 10, 2005

Dynamic Team Decision Theory. EECS 558 Project Shrutivandana Sharma and David Shuman December 10, 2005 Dynamc Team Decson Theory EECS 558 Proec Shruvandana Sharma and Davd Shuman December 0, 005 Oulne Inroducon o Team Decson Theory Decomposon of he Dynamc Team Decson Problem Equvalence of Sac and Dynamc

More information

Graduate Macroeconomics 2 Problem set 5. - Solutions

Graduate Macroeconomics 2 Problem set 5. - Solutions Graduae Macroeconomcs 2 Problem se. - Soluons Queson 1 To answer hs queson we need he frms frs order condons and he equaon ha deermnes he number of frms n equlbrum. The frms frs order condons are: F K

More information

Robust and Accurate Cancer Classification with Gene Expression Profiling

Robust and Accurate Cancer Classification with Gene Expression Profiling Robus and Accurae Cancer Classfcaon wh Gene Expresson Proflng (Compuaonal ysems Bology, 2005) Auhor: Hafeng L, Keshu Zhang, ao Jang Oulne Background LDA (lnear dscrmnan analyss) and small sample sze problem

More information

Online Appendix for. Strategic safety stocks in supply chains with evolving forecasts

Online Appendix for. Strategic safety stocks in supply chains with evolving forecasts Onlne Appendx for Sraegc safey socs n supply chans wh evolvng forecass Tor Schoenmeyr Sephen C. Graves Opsolar, Inc. 332 Hunwood Avenue Hayward, CA 94544 A. P. Sloan School of Managemen Massachuses Insue

More information

Density Matrix Description of NMR BCMB/CHEM 8190

Density Matrix Description of NMR BCMB/CHEM 8190 Densy Marx Descrpon of NMR BCMBCHEM 89 Operaors n Marx Noaon If we say wh one bass se, properes vary only because of changes n he coeffcens weghng each bass se funcon x = h< Ix > - hs s how we calculae

More information

ON THE ADDITION OF UNITS AND NON-UNITS IN FINITE COMMUTATIVE RINGS

ON THE ADDITION OF UNITS AND NON-UNITS IN FINITE COMMUTATIVE RINGS ROCKY MOUNTAIN JOURNAL OF MATHEMATICS Volume 45, Number 6, 2015 ON THE ADDITION OF UNITS AND NON-UNITS IN FINITE COMMUTATIVE RINGS DARIUSH KIANI AND MOHSEN MOLLAHAJIAGHAEI ABSTRACT. Le R be a fne commuave

More information

Including the ordinary differential of distance with time as velocity makes a system of ordinary differential equations.

Including the ordinary differential of distance with time as velocity makes a system of ordinary differential equations. Soluons o Ordnary Derenal Equaons An ordnary derenal equaon has only one ndependen varable. A sysem o ordnary derenal equaons consss o several derenal equaons each wh he same ndependen varable. An eample

More information

Lecture 6: Learning for Control (Generalised Linear Regression)

Lecture 6: Learning for Control (Generalised Linear Regression) Lecure 6: Learnng for Conrol (Generalsed Lnear Regresson) Conens: Lnear Mehods for Regresson Leas Squares, Gauss Markov heorem Recursve Leas Squares Lecure 6: RLSC - Prof. Sehu Vjayakumar Lnear Regresson

More information

2/20/2013. EE 101 Midterm 2 Review

2/20/2013. EE 101 Midterm 2 Review //3 EE Mderm eew //3 Volage-mplfer Model The npu ressance s he equalen ressance see when lookng no he npu ermnals of he amplfer. o s he oupu ressance. I causes he oupu olage o decrease as he load ressance

More information

Density Matrix Description of NMR BCMB/CHEM 8190

Density Matrix Description of NMR BCMB/CHEM 8190 Densy Marx Descrpon of NMR BCMBCHEM 89 Operaors n Marx Noaon Alernae approach o second order specra: ask abou x magnezaon nsead of energes and ranson probables. If we say wh one bass se, properes vary

More information

THEORETICAL AUTOCORRELATIONS. ) if often denoted by γ. Note that

THEORETICAL AUTOCORRELATIONS. ) if often denoted by γ. Note that THEORETICAL AUTOCORRELATIONS Cov( y, y ) E( y E( y))( y E( y)) ρ = = Var( y) E( y E( y)) =,, L ρ = and Cov( y, y ) s ofen denoed by whle Var( y ) f ofen denoed by γ. Noe ha γ = γ and ρ = ρ and because

More information

THE PREDICTION OF COMPETITIVE ENVIRONMENT IN BUSINESS

THE PREDICTION OF COMPETITIVE ENVIRONMENT IN BUSINESS THE PREICTION OF COMPETITIVE ENVIRONMENT IN BUSINESS INTROUCTION The wo dmensonal paral dfferenal equaons of second order can be used for he smulaon of compeve envronmen n busness The arcle presens he

More information

Advanced time-series analysis (University of Lund, Economic History Department)

Advanced time-series analysis (University of Lund, Economic History Department) Advanced me-seres analss (Unvers of Lund, Economc Hsor Dearmen) 3 Jan-3 Februar and 6-3 March Lecure 4 Economerc echnues for saonar seres : Unvarae sochasc models wh Box- Jenns mehodolog, smle forecasng

More information

Sampling Procedure of the Sum of two Binary Markov Process Realizations

Sampling Procedure of the Sum of two Binary Markov Process Realizations Samplng Procedure of he Sum of wo Bnary Markov Process Realzaons YURY GORITSKIY Dep. of Mahemacal Modelng of Moscow Power Insue (Techncal Unversy), Moscow, RUSSIA, E-mal: gorsky@yandex.ru VLADIMIR KAZAKOV

More information

Relative controllability of nonlinear systems with delays in control

Relative controllability of nonlinear systems with delays in control Relave conrollably o nonlnear sysems wh delays n conrol Jerzy Klamka Insue o Conrol Engneerng, Slesan Techncal Unversy, 44- Glwce, Poland. phone/ax : 48 32 37227, {jklamka}@a.polsl.glwce.pl Keywor: Conrollably.

More information

ON THE WEAK LIMITS OF SMOOTH MAPS FOR THE DIRICHLET ENERGY BETWEEN MANIFOLDS

ON THE WEAK LIMITS OF SMOOTH MAPS FOR THE DIRICHLET ENERGY BETWEEN MANIFOLDS ON THE WEA LIMITS OF SMOOTH MAPS FOR THE DIRICHLET ENERGY BETWEEN MANIFOLDS FENGBO HANG Absrac. We denfy all he weak sequenal lms of smooh maps n W (M N). In parcular, hs mples a necessary su cen opologcal

More information

Track Properities of Normal Chain

Track Properities of Normal Chain In. J. Conemp. Mah. Scences, Vol. 8, 213, no. 4, 163-171 HIKARI Ld, www.m-har.com rac Propes of Normal Chan L Chen School of Mahemacs and Sascs, Zhengzhou Normal Unversy Zhengzhou Cy, Hennan Provnce, 4544,

More information

Normal Random Variable and its discriminant functions

Normal Random Variable and its discriminant functions Noral Rando Varable and s dscrnan funcons Oulne Noral Rando Varable Properes Dscrnan funcons Why Noral Rando Varables? Analycally racable Works well when observaon coes for a corruped snle prooype 3 The

More information

@FMI c Kyung Moon Sa Co.

@FMI c Kyung Moon Sa Co. Annals of Fuzzy Mahemacs and Informacs Volume 8, No. 2, (Augus 2014), pp. 245 257 ISSN: 2093 9310 (prn verson) ISSN: 2287 6235 (elecronc verson) hp://www.afm.or.kr @FMI c Kyung Moon Sa Co. hp://www.kyungmoon.com

More information

CH.3. COMPATIBILITY EQUATIONS. Continuum Mechanics Course (MMC) - ETSECCPB - UPC

CH.3. COMPATIBILITY EQUATIONS. Continuum Mechanics Course (MMC) - ETSECCPB - UPC CH.3. COMPATIBILITY EQUATIONS Connuum Mechancs Course (MMC) - ETSECCPB - UPC Overvew Compably Condons Compably Equaons of a Poenal Vecor Feld Compably Condons for Infnesmal Srans Inegraon of he Infnesmal

More information

Bernoulli process with 282 ky periodicity is detected in the R-N reversals of the earth s magnetic field

Bernoulli process with 282 ky periodicity is detected in the R-N reversals of the earth s magnetic field Submed o: Suden Essay Awards n Magnecs Bernoull process wh 8 ky perodcy s deeced n he R-N reversals of he earh s magnec feld Jozsef Gara Deparmen of Earh Scences Florda Inernaonal Unversy Unversy Park,

More information

Homework 8: Rigid Body Dynamics Due Friday April 21, 2017

Homework 8: Rigid Body Dynamics Due Friday April 21, 2017 EN40: Dynacs and Vbraons Hoework 8: gd Body Dynacs Due Frday Aprl 1, 017 School of Engneerng Brown Unversy 1. The earh s roaon rae has been esaed o decrease so as o ncrease he lengh of a day a a rae of

More information

Efficient Asynchronous Channel Hopping Design for Cognitive Radio Networks

Efficient Asynchronous Channel Hopping Design for Cognitive Radio Networks Effcen Asynchronous Channel Hoppng Desgn for Cognve Rado Neworks Chh-Mn Chao, Chen-Yu Hsu, and Yun-ng Lng Absrac In a cognve rado nework (CRN), a necessary condon for nodes o communcae wh each oher s ha

More information

TSS = SST + SSE An orthogonal partition of the total SS

TSS = SST + SSE An orthogonal partition of the total SS ANOVA: Topc 4. Orhogonal conrass [ST&D p. 183] H 0 : µ 1 = µ =... = µ H 1 : The mean of a leas one reamen group s dfferen To es hs hypohess, a basc ANOVA allocaes he varaon among reamen means (SST) equally

More information

Online Supplement for Dynamic Multi-Technology. Production-Inventory Problem with Emissions Trading

Online Supplement for Dynamic Multi-Technology. Production-Inventory Problem with Emissions Trading Onlne Supplemen for Dynamc Mul-Technology Producon-Invenory Problem wh Emssons Tradng by We Zhang Zhongsheng Hua Yu Xa and Baofeng Huo Proof of Lemma For any ( qr ) Θ s easy o verfy ha he lnear programmng

More information

Math 128b Project. Jude Yuen

Math 128b Project. Jude Yuen Mah 8b Proec Jude Yuen . Inroducon Le { Z } be a sequence of observed ndependen vecor varables. If he elemens of Z have a on normal dsrbuon hen { Z } has a mean vecor Z and a varancecovarance marx z. Geomercally

More information

Introduction to Boosting

Introduction to Boosting Inroducon o Boosng Cynha Rudn PACM, Prnceon Unversy Advsors Ingrd Daubeches and Rober Schapre Say you have a daabase of news arcles, +, +, -, -, +, +, -, -, +, +, -, -, +, +, -, + where arcles are labeled

More information

, t 1. Transitions - this one was easy, but in general the hardest part is choosing the which variables are state and control variables

, t 1. Transitions - this one was easy, but in general the hardest part is choosing the which variables are state and control variables Opmal Conrol Why Use I - verss calcls of varaons, opmal conrol More generaly More convenen wh consrans (e.g., can p consrans on he dervaves More nsghs no problem (a leas more apparen han hrogh calcls of

More information

A Combinatorial Analysis of Tree-Like Sentences

A Combinatorial Analysis of Tree-Like Sentences Open Journal of Dscree Mahemacs 05 5 3-53 Publshed Onlne July 05 n Sces hp://wwwscrporg/journal/ojdm hp://dxdoorg/0436/ojdm0553004 A Combnaoral Analyss of Tree-Le Senences Glber Labelle Louse Lafores Déparemen

More information

How about the more general "linear" scalar functions of scalars (i.e., a 1st degree polynomial of the following form with a constant term )?

How about the more general linear scalar functions of scalars (i.e., a 1st degree polynomial of the following form with a constant term )? lmcd Lnear ransformaon of a vecor he deas presened here are que general hey go beyond he radonal mar-vecor ype seen n lnear algebra Furhermore, hey do no deal wh bass and are equally vald for any se of

More information

Lecture VI Regression

Lecture VI Regression Lecure VI Regresson (Lnear Mehods for Regresson) Conens: Lnear Mehods for Regresson Leas Squares, Gauss Markov heorem Recursve Leas Squares Lecure VI: MLSC - Dr. Sehu Vjayakumar Lnear Regresson Model M

More information

A New Generalized Gronwall-Bellman Type Inequality

A New Generalized Gronwall-Bellman Type Inequality 22 Inernaonal Conference on Image, Vson and Comung (ICIVC 22) IPCSIT vol. 5 (22) (22) IACSIT Press, Sngaore DOI:.7763/IPCSIT.22.V5.46 A New Generalzed Gronwall-Bellman Tye Ineualy Qnghua Feng School of

More information

THERMODYNAMICS 1. The First Law and Other Basic Concepts (part 2)

THERMODYNAMICS 1. The First Law and Other Basic Concepts (part 2) Company LOGO THERMODYNAMICS The Frs Law and Oher Basc Conceps (par ) Deparmen of Chemcal Engneerng, Semarang Sae Unversy Dhon Harano S.T., M.T., M.Sc. Have you ever cooked? Equlbrum Equlbrum (con.) Equlbrum

More information

Attribute Reduction Algorithm Based on Discernibility Matrix with Algebraic Method GAO Jing1,a, Ma Hui1, Han Zhidong2,b

Attribute Reduction Algorithm Based on Discernibility Matrix with Algebraic Method GAO Jing1,a, Ma Hui1, Han Zhidong2,b Inernaonal Indusral Informacs and Compuer Engneerng Conference (IIICEC 05) Arbue educon Algorhm Based on Dscernbly Marx wh Algebrac Mehod GAO Jng,a, Ma Hu, Han Zhdong,b Informaon School, Capal Unversy

More information

Two Coupled Oscillators / Normal Modes

Two Coupled Oscillators / Normal Modes Lecure 3 Phys 3750 Two Coupled Oscillaors / Normal Modes Overview and Moivaion: Today we ake a small, bu significan, sep owards wave moion. We will no ye observe waves, bu his sep is imporan in is own

More information

Epistemic Game Theory: Online Appendix

Epistemic Game Theory: Online Appendix Epsemc Game Theory: Onlne Appendx Edde Dekel Lucano Pomao Marcano Snscalch July 18, 2014 Prelmnares Fx a fne ype srucure T I, S, T, β I and a probably µ S T. Le T µ I, S, T µ, βµ I be a ype srucure ha

More information

TitleA random walk analogue of Levy's th. Studia scientiarum mathematicarum H Citation

TitleA random walk analogue of Levy's th. Studia scientiarum mathematicarum H Citation TleA random walk analogue of Levy's h Auhor(s) Fuja, Takahko Suda scenarum mahemacarum H Caon 3-33 Issue 008-06 Dae Type Journal Arcle Tex Verson auhor URL hp://hdlhandlene/10086/15876 Ths s an auhor's

More information

Motion in Two Dimensions

Motion in Two Dimensions Phys 1 Chaper 4 Moon n Two Dmensons adzyubenko@csub.edu hp://www.csub.edu/~adzyubenko 005, 014 A. Dzyubenko 004 Brooks/Cole 1 Dsplacemen as a Vecor The poson of an objec s descrbed by s poson ecor, r The

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

12d Model. Civil and Surveying Software. Drainage Analysis Module Detention/Retention Basins. Owen Thornton BE (Mech), 12d Model Programmer

12d Model. Civil and Surveying Software. Drainage Analysis Module Detention/Retention Basins. Owen Thornton BE (Mech), 12d Model Programmer d Model Cvl and Surveyng Soware Dranage Analyss Module Deenon/Reenon Basns Owen Thornon BE (Mech), d Model Programmer owen.hornon@d.com 4 January 007 Revsed: 04 Aprl 007 9 February 008 (8Cp) Ths documen

More information

F-Tests and Analysis of Variance (ANOVA) in the Simple Linear Regression Model. 1. Introduction

F-Tests and Analysis of Variance (ANOVA) in the Simple Linear Regression Model. 1. Introduction ECOOMICS 35* -- OTE 9 ECO 35* -- OTE 9 F-Tess and Analyss of Varance (AOVA n he Smple Lnear Regresson Model Inroducon The smple lnear regresson model s gven by he followng populaon regresson equaon, or

More information

Lecture 2 M/G/1 queues. M/G/1-queue

Lecture 2 M/G/1 queues. M/G/1-queue Lecure M/G/ queues M/G/-queue Posson arrval process Arbrary servce me dsrbuon Sngle server To deermne he sae of he sysem a me, we mus now The number of cusomers n he sysems N() Tme ha he cusomer currenly

More information

Robustness Experiments with Two Variance Components

Robustness Experiments with Two Variance Components Naonal Insue of Sandards and Technology (NIST) Informaon Technology Laboraory (ITL) Sascal Engneerng Dvson (SED) Robusness Expermens wh Two Varance Componens by Ana Ivelsse Avlés avles@ns.gov Conference

More information

Should Exact Index Numbers have Standard Errors? Theory and Application to Asian Growth

Should Exact Index Numbers have Standard Errors? Theory and Application to Asian Growth Should Exac Index umbers have Sandard Errors? Theory and Applcaon o Asan Growh Rober C. Feensra Marshall B. Rensdorf ovember 003 Proof of Proposon APPEDIX () Frs, we wll derve he convenonal Sao-Vara prce

More information

A DECOMPOSITION METHOD FOR SOLVING DIFFUSION EQUATIONS VIA LOCAL FRACTIONAL TIME DERIVATIVE

A DECOMPOSITION METHOD FOR SOLVING DIFFUSION EQUATIONS VIA LOCAL FRACTIONAL TIME DERIVATIVE S13 A DECOMPOSITION METHOD FOR SOLVING DIFFUSION EQUATIONS VIA LOCAL FRACTIONAL TIME DERIVATIVE by Hossen JAFARI a,b, Haleh TAJADODI c, and Sarah Jane JOHNSTON a a Deparen of Maheacal Scences, Unversy

More information