THE PUBLISHING HOUSE PROCEEDINGS OF THE ROMANIAN ACADEMY, Series A, OF THE ROMANIAN ACADEMY Volume 9, Number 1/2008, pp
|
|
- Hugo Flowers
- 5 years ago
- Views:
Transcription
1 THE PUBLISHING HOUSE PROCEEDINGS OF THE ROMNIN CDEMY, Seres, OF THE ROMNIN CDEMY Volue 9, Nuber /008, pp ON CIMMINO'S REFLECTION LGORITHM Consann POP Ovdus Unversy of Consana, Roana, E-al: Ganfranco Cno presened for he frs e hs faous reflecon algorh n 938. He proved ha, f he ran of he proble arx s greaer han one hen, for any nal approxaon he sequence generaed by hs algorh converges o a soluon of he noral equaon assocaed o a perurbed (dagonally scaled) leas-squares proble. Sared fro hs resul we consruc a frs exenson of Cno's ehod whch generaes sequences of approxaons of leas-squares soluons for general nconssen probles. Usng soe resuls of H. Keller (965) and D. Young (97), we show ha he se of l pons of hs exenson copleely characerzes he se of leassquares soluons. The second exenson of Cno's algorh s obaned by sarng fro a prevous resul of he auhor, concernng Jacob's sulaneous proecons algorh. In hs sense, we prove ha a parcular case of Cno's ehod can be consdered as a parcular case of Jacob's ehod and ha s l pons also copleely characerze he leas-squares soluons of he nal proble. Key words: Cno s reflecon algorh, Jacob s proecons algorh, nconssen leas-squares probles. THE ORIGINL CIMMINO S LGORITHM One year afer S. Kaczarz presened for he frs e hs faous successve proecons algorh n [6], G. Cno proposed n [3] hs sulaneous reflecons ehod. Ths uses nsead of he orhogonal proecons P on he hyperplanes generaed by he syse equaons, orhogonal reflecons S, gven by x, a b P = x a, () a x, a b S ( x) = x a, () a (we denoed by a, b he -h row of he n syse arx and he -h coponen of he rgh hand sde b R and by,,, he Eucldean scalar produc and he assocaed nor on soe space R q ). Cno's reflecons S are sulaneously appled o an approxaon x R and, a convex cobnaon of he defnes he nex one x by x = ωs ( x ) = x + ω ( S( x ) x ) = ω ω (3) x, a b x ω a, ω a where
2 Consann POP ω > 0, ω = ω. (4) In [3] Cno proved he followng an convergence resul concernng he algorh (3)-(4). Theore. Le and b be such ha and he syse a 0, =,,, ran( ), (5) x = b. (6) s conssen. Then, f he weghs ω are as n (4), for any x 0 R n he sequence (x ) 0 generaed by (3) converges o a soluon of (6). Rear. The fac ha he l pons of Cno's sequence (x) 0 fro (4) (wh respec o he nal approxaon x0) "cover" he se S(; b) of all soluons of (6) wll be proved n he nex secon. The "unpleasan" aspec of he above Cno's algorh s ha he sequence (x) 0 approxaes a soluon x only n he conssen case for (6). Bu unforunaely, "real world probles" usually gve rse o nconssen syses of he for (6), (see e.g. [5], [8], [9]), whch us be reforulaed as "lnear leassquares probles": fnd x Rn such ha n x b = n{ z b, z R } (7) In hs general case, Cno's algorh or soe of s exensons sll converge, bu o soluons of "weghed" forulaons of (7) (see e.g. [], []). Ths s why we decded o analyse n hs paper soe possbles o "exend" or "adap" Cno's orgnal ehod (3)-(4) o he ore general proble (7), such ha he sequence of approxaons generaed n hs way, sll converges o one of s soluons (slar wh he resuls obaned by one of he auhors n [0], bu wh respec o Kaczarz-le proecons algorhs). Moreover, we were also neresed n he possbly of characerzng wh hese l pons, he se of all soluons of (7), denoed by LSS(;b) (see also [] and Rear before). These versons of Cno's algorh wll be descrbed n he nex wo secons of he paper.. THE FIRST EXTENSION For he consrucon of our frs verson of he algorh (3), we sared fro a rear of G. Cno, ade n he orgnal paper [3]. Ths resul (based on soe seps fro he proof of Theore ) can be brefly descrbed as follows. Corollary. If he syse (6) s no conssen and verfes (5), for any x 0 R n, he sequence (x ) 0 generaed by (3) converges o a soluon x of he noral equaon where ( ) = ( ) x b, (8) (,,, ) = ω ω ω R, (9) = D and D s he dagonal arx gven by ; b = D b (0) ω ω D dag a a = (,, ) ()
3 3 On Cno s reflecon algorh Rear. If he syse (6) s conssen, he above Corollary does no conradc he resul n Theore. Indeed, n hs case and because D s nverble (ω > 0), he (conssen) syse (6) s equvalen wh he (conssen) syse D x= D b, () whch s equvalen wh he noral equaon (8). Sarng fro he above Corollary, we oban he followng verson of Cno's algorh (3) convergen o soluons of he general leas-squares forulaon (7). Proposon. Le be as n (5), x 0 R n an arbrary nal approxaon and he weghs ω fro (4) gven by a, ω =, =,. (3) Then, he sequence (x ) 0 generaed by (3)-(4) converges o an eleen fro LSS(;b). Proof For ω as n (3) he arx D fro () s he deny, hus he noral equaon (8) s dencal wh x = b, (4).e. he noral equaon assocaed o he proble (7). Then Corollary apples and he proof s coplee. In he res of hs secon we shall prove ha he se of all l pons of he algorh (3)-(4) wh he weghs choce (3) (whch we shall denoe by LPC(;b)) concdes wh LSS(;b). For hs we shall brefly replay soe consrucons and resuls for papers [7] and []. Le B and N be n n real arces (wh N nverble) and d R n such ha he syse Bx = d (5) s conssen. For approxang s soluons, we consder he erave process: x 0 R n, wh x Tx N d = +, 0, (6) T I N B =. (7) The followng resul s proved n [7]. Theore. Le us suppose ha he arx B s syerc and nonnegave defne and consder he followng splng of B = D+ M (8) wh D syerc and nverble. Le E be anoher nverble n n arx and P E defned by Le also N be gven by P E D E D ( ) ( ) E = + B. (9) N NE E D = =. (0) Then, for any x 0 R n he ehod (6)-(7) s convergen o an eleen fro S(B;d) f and only f he arx P E s posve defne. Le now L(N;d) be he se of all l pons of he eraon (6)-(7) (wh respec o x 0 R n ). In he paper [], he followng resul s proved. Proposon. If he ehod (6)-(7) s convergen, hen he followng equaly holds LNd ( ; ) = SBd ( ; ). () We are now able o prove he prevously announced resul. Proposon 3. In he hypohess of Proposon he followng equaly holds
4 Consann POP 4 LPC( ; b) = LSS( ; b). () Proof. Le he n n arx B and d R n be defned by We hen have (see e.g. []) B =, d= b. (3) LSS( ; b) = S( B; d) (4) Moreover, Cno's algorh (3)-(4), wh ω gven by (3) becoes an algorh fro he class (6)-(7) f we defne he arces D and E, n he above Theore by wh ω gven by Then (see (0)) D = I, E = I, (5) ω ω= a (6) N and he above Proposon, (4) and (7) ell us ha = ω I (7) LPC( ; b) = L( N; d) = LSS( ; b) (8) f and only f he syerc arx P=P E, assocaed o he above Cno's algorh and gven by s posve defne or equvalenly Bu, because B s syerc and nonnegave defne and for ω fro (6) we always have P = ωi B (9) E ρ( B) ω>. (30) race( B) = a, (3) ρ( B) ω. (3) If we would have equaly n (3) hen, fro he properes of he arx B and (3) would resul ha B would have only one nonzero egenvalue, wh (algebrac) ulplcy equal o. Then, ran()= whch would conradc (5). Then (30) holds and he proof s coplee. 3. THE SECOND EXTENSION We shall sar he presenaon of hs secon by observng ha he classcal Cno s algorh (3) concdes, n he parcular case wh Jacob's sulaneous proecons ehod (see [4], [0]) ω=γ> 0, =,, (3)
5 5 On Cno s reflecon algorh Indeed, fro (3)-(4) and (7) resuls ha whch concdes wh (8) for x, a b =. (33) x x a a x, a b =, (34) x x a a The convergence condon for (8) (see agan [4], [0]) s wh E = 0 =. (35) 0 <<, (36) ( ) = aa. (37) a Proposon 4. If he assupons (5) hold for, hen he nuber 0 fro (35) sasfes (36). Proof. Because of he syery of E, we successvely ge = E aa = a a = a a, whch ogeher wh (35) gves us 0. (38) If he equaly would hold n (38), we would have (see (35)) =. (39) Bu, because he arx E fro (37) s nonnegave defne and syerc, we now ha σ(e) [ 0, ), whch eans ρ(e) σ(e). Le x R n \{0} be a correspondng egenvecor. We hen successvely oban Fro (40) and he Cauchy-Schwarz nequaly xa, x a =, = = a a. (40) x Exx x x, a x a,,,, (4) we oban he equales n (4), whch eans ha x s collnear wh a,,,. Bu, hs would ean ha ran( ), (4) whch would conradc (5). Thus, src nequaly holds n (38) and he proof s coplee. Le now ϕ, Φ(α; ) be defned by (see [0])
6 Consann POP 6 y, α b ϕ ( y) = α,,, = n, (43) α n Φα ( ; y) = y α ϕ( y), y R, (44) = where α 0 s he -h colun of and α R, α 0. Le D be he n n he arx defned by D = α α We hen consder he followng Cno Exended (CE, for shor) algorh. n. (45) = α LGORITHM CE. Le y 0 = b, x 0 R n and x an already copued approxaon. The nex one, x s gven by y + =Φ( α ; y ), (46) b = b y, (47) x, a b =, (48) x x a a where by b we denoed he -h coponen of he vecor b fro (47). Then, he followng resul holds (as n Theore 6.7 fro [0]). Theore 3. For any arx sasfyng (5) and α 0, =,, n, any vecor b R, any nal approxaon x 0 R n and any α such ha α 0, ρ( D) he sequence (x ) 0 generaed wh he algorh (46)-(48) converges o an eleen x LSS(;b). Conversely, any eleen x LSS(;b) can be obaned as he l pon of such a sequence, for an approprae choce of x 0. Moreover, for x 0 n he range of, he sequence (x ) 0 converges o he nal nor soluon of he proble (7). REFERENCES. BJÖRK,., Nuercal ehods for leas squares probles, SIM Phladelpha, CENSOR, Y., ELFVING, T., Bloc-erave algorhs wh dagonally scaled oblque proecons for he lnear feasbly proble, SIM Marx nal. and ppl., 4(00), CIMMINO, G., Calcolo approssoao per le soluzon de sse d equazon lnear, Rc. Sc. progr. ecn. econo. naz.,, pp , ELFVING, T., Bloc-Ierave Mehods for Conssen and Inconssen Lnear Equaons, Nuer. Mah., 35, pp. -, GROETSCH, C.W., Inverse probles n he aheacal scences, Veweg, Wesbaden, Gerany, KCZMRZ, S., ngenahere uflosung von Syseen lneare Glechungen, Bull. cad. Polonase Sc. e Leres.,, pp , KELLER, H., On he soluon of sngular and sedefne lnear syses by eraon, SIM J. Nuer. nal., Ser. B, (), pp. 8-90, MOHR, M., POP, C., RUEDE, U., n experenal analyss of a dfferenal nverse proble, Techncal Repor 99-, IMMD0, FU Erlangen-Nurnberg, Gerany, MOHR, M., Coparson of solvers for a boelecrc feld proble, Techncal Repor 00-, IMMD0, FU Erlangen-Nurnberg, Gerany, 00.
7 7 On Cno s reflecon algorh 0. POP, C., Exensons of bloc-proecons ehods wh relaxaon paraeers o nconssen and ran-defcen leas-squares probles, BIT, 38(), pp. 5-76, POP, C., Characerzaon of he soluons se of nconssen leas-squares probles by an exended Kaczarz algorh, Korean Journal on Cop. and ppl. Mah., 6(), pp. 5-64, YOUNG, D.M., On he conssency of lnear saonary erave ehods, SIM J. Nuer. nal., 9(), pp , 97. Receved Deceber 8, 007
. 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. 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 informationNormal 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 informationV.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 informationResponse 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( ) () 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 informationComparison 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 informationNotes 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 informationLecture 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 informationCS434a/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 informationTHEORETICAL 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 informationOn 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 informationGENERATING 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 informationJohn 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 informationCH.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 informationHEAT 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 informationVolatility Interpolation
Volaly Inerpolaon Prelmnary Verson March 00 Jesper Andreasen and Bran Huge Danse Mares, Copenhagen wan.daddy@danseban.com brno@danseban.com Elecronc copy avalable a: hp://ssrn.com/absrac=69497 Inro Local
More informationA 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 informationExistence 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 informationStatic Output-Feedback Simultaneous Stabilization of Interval Time-Delay Systems
Sac Oupu-Feedback Sulaneous Sablzaon of Inerval e-delay Syses YUAN-CHANG CHANG SONG-SHYONG CHEN Deparen of Elecrcal Engneerng Lee-Mng Insue of echnology No. - Lee-Juan Road a-shan ape Couny 4305 AIWAN
More informationRobust 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 informationA TWO-LEVEL LOAN PORTFOLIO OPTIMIZATION PROBLEM
Proceedngs of he 2010 Wner Sulaon Conference B. Johansson, S. Jan, J. Monoya-Torres, J. Hugan, and E. Yücesan, eds. A TWO-LEVEL LOAN PORTFOLIO OPTIMIZATION PROBLEM JanQang Hu Jun Tong School of Manageen
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
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 informationSolution 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 informationOnline 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 informationLinear 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 informatione-journal Reliability: Theory& Applications No 2 (Vol.2) Vyacheslav Abramov
June 7 e-ournal Relably: Theory& Applcaons No (Vol. CONFIDENCE INTERVALS ASSOCIATED WITH PERFORMANCE ANALYSIS OF SYMMETRIC LARGE CLOSED CLIENT/SERVER COMPUTER NETWORKS Absrac Vyacheslav Abramov School
More informationApproximate 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 informationPeriodic motions of a class of forced infinite lattices with nearest neighbor interaction
J. Mah. Anal. Appl. 34 28 44 52 www.elsever.co/locae/jaa Peroc oons of a class of force nfne laces wh neares neghbor neracon Chao Wang a,b,, Dngban Qan a a School of Maheacal Scence, Suzhou Unversy, Suzhou
More informationHow 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 informationLearning Objectives. Self Organization Map. Hamming Distance(1/5) Introduction. Hamming Distance(3/5) Hamming Distance(2/5) 15/04/2015
/4/ Learnng Objecves Self Organzaon Map Learnng whou Exaples. Inroducon. MAXNET 3. Cluserng 4. Feaure Map. Self-organzng Feaure Map 6. Concluson 38 Inroducon. Learnng whou exaples. Daa are npu o he syse
More informationSurvival 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 informationRelative 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 information3. OVERVIEW OF NUMERICAL METHODS
3 OVERVIEW OF NUMERICAL METHODS 3 Inroducory remarks Ths chaper summarzes hose numercal echnques whose knowledge s ndspensable for he undersandng of he dfferen dscree elemen mehods: he Newon-Raphson-mehod,
More informationChapter 6 DETECTION AND ESTIMATION: Model of digital communication system. Fundamental issues in digital communications are
Chaper 6 DCIO AD IMAIO: Fndaenal sses n dgal concaons are. Deecon and. saon Deecon heory: I deals wh he desgn and evalaon of decson ang processor ha observes he receved sgnal and gesses whch parclar sybol
More informationJ 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 informationTesting 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 informationDEEP UNFOLDING FOR MULTICHANNEL SOURCE SEPARATION SUPPLEMENTARY MATERIAL
DEEP UNFOLDING FOR MULTICHANNEL SOURCE SEPARATION SUPPLEMENTARY MATERIAL Sco Wsdom, John Hershey 2, Jonahan Le Roux 2, and Shnj Waanabe 2 Deparmen o Elecrcal Engneerng, Unversy o Washngon, Seale, WA, USA
More informationOn elements with index of the form 2 a 3 b in a parametric family of biquadratic elds
On elemens wh ndex of he form a 3 b n a paramerc famly of bquadrac elds Bora JadrevĆ Absrac In hs paper we gve some resuls abou prmve negral elemens p(c p n he famly of bcyclc bquadrac elds L c = Q ) c;
More informationIn 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 informationFirst-order piecewise-linear dynamic circuits
Frs-order pecewse-lnear dynamc crcus. Fndng he soluon We wll sudy rs-order dynamc crcus composed o a nonlnear resse one-por, ermnaed eher by a lnear capacor or a lnear nducor (see Fg.. Nonlnear resse one-por
More informationCubic 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 informationON 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 informationSOME 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 informationMath 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 informationPart II CONTINUOUS TIME STOCHASTIC PROCESSES
Par II CONTINUOUS TIME STOCHASTIC PROCESSES 4 Chaper 4 For an advanced analyss of he properes of he Wener process, see: Revus D and Yor M: Connuous marngales and Brownan Moon Karazas I and Shreve S E:
More informationSupporting information How to concatenate the local attractors of subnetworks in the HPFP
n Effcen lgorh for Idenfyng Prry Phenoype rcors of Lrge-Scle Boolen Newor Sng-Mo Choo nd Kwng-Hyun Cho Depren of Mhecs Unversy of Ulsn Ulsn 446 Republc of Kore Depren of Bo nd Brn Engneerng Kore dvnced
More informationShould 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 informationVariants 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 informationChapter 6: AC Circuits
Chaper 6: AC Crcus Chaper 6: Oulne Phasors and he AC Seady Sae AC Crcus A sable, lnear crcu operang n he seady sae wh snusodal excaon (.e., snusodal seady sae. Complee response forced response naural response.
More informationAT&T Labs Research, Shannon Laboratory, 180 Park Avenue, Room A279, Florham Park, NJ , USA
Machne Learnng, 43, 65 91, 001 c 001 Kluwer Acadec Publshers. Manufacured n The Neherlands. Drfng Gaes ROBERT E. SCHAPIRE schapre@research.a.co AT&T Labs Research, Shannon Laboraory, 180 Park Avenue, Roo
More informationMechanics 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 information2/20/2013. 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 informationAdvanced Macroeconomics II: Exchange economy
Advanced Macroeconomcs II: Exchange economy Krzyszof Makarsk 1 Smple deermnsc dynamc model. 1.1 Inroducon Inroducon Smple deermnsc dynamc model. Defnons of equlbrum: Arrow-Debreu Sequenal Recursve Equvalence
More informationLet s treat the problem of the response of a system to an applied external force. Again,
Page 33 QUANTUM LNEAR RESPONSE FUNCTON Le s rea he problem of he response of a sysem o an appled exernal force. Agan, H() H f () A H + V () Exernal agen acng on nernal varable Hamlonan for equlbrum sysem
More informationCS286.2 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 informationDensity 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 informationCONSISTENT ESTIMATION OF THE NUMBER OF DYNAMIC FACTORS IN A LARGE N AND T PANEL. Detailed Appendix
COSISE ESIMAIO OF HE UMBER OF DYAMIC FACORS I A LARGE AD PAEL Dealed Aendx July 005 hs verson: May 9, 006 Dane Amengual Dearmen of Economcs, Prnceon Unversy and Mar W Wason* Woodrow Wlson School and Dearmen
More information( ) [ ] MAP Decision Rule
Announcemens Bayes Decson Theory wh Normal Dsrbuons HW0 due oday HW o be assgned soon Proec descrpon posed Bomercs CSE 90 Lecure 4 CSE90, Sprng 04 CSE90, Sprng 04 Key Probables 4 ω class label X feaure
More informationSymmetry and Asymmetry of MIMO Fading Channels
Symmery and Asymmery of MIMO Fadng Channels mmanuel Abbe, mre Telaar, Member, I, and Lzhong Zheng, Member, I Absrac We consder ergodc coheren MIMO channels, and characerze he opmal npu dsrbuon under general
More informationScattering 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 informationA 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 informationOrdinary 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 informationGMM parameter estimation. Xiaoye Lu CMPS290c Final Project
GMM paraeer esaon Xaoye Lu M290c Fnal rojec GMM nroducon Gaussan ure Model obnaon of several gaussan coponens Noaon: For each Gaussan dsrbuon:, s he ean and covarance ar. A GMM h ures(coponens): p ( 2π
More informationEpistemic 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 informationTHE 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 informationFI 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 informationPHYS 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 informationDensity 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 informationLecture Slides for INTRODUCTION TO. Machine Learning. ETHEM ALPAYDIN The MIT Press,
Lecure ldes for INRODUCION O Machne Learnng EHEM ALPAYDIN he MI Press, 004 alpaydn@boun.edu.r hp://.cpe.boun.edu.r/~ehe/l CHAPER 6: Densonaly Reducon Why Reduce Densonaly?. Reduces e copley: Less copuaon.
More informationMethod 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@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 informationLecture 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 informationOperations Research Letters
Operaons Research Leers 39 (2011) 323 328 Conens lss avalable a ScVerse ScenceDrec Operaons Research Leers journal homepage: www.elsever.com/locae/orl Rank of Handelman herarchy for Max-Cu Myoung-Ju Park,
More informationAdvanced 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 informationTSS = 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 informationRobustness 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 informationNATIONAL 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 informationTight 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 informationComb 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 informationLecture 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 informationTime-interval analysis of β decay. V. Horvat and J. C. Hardy
Tme-nerval analyss of β decay V. Horva and J. C. Hardy Work on he even analyss of β decay [1] connued and resuled n he developmen of a novel mehod of bea-decay me-nerval analyss ha produces hghly accurae
More informationEXPONENTIAL PROBABILITY DISTRIBUTION
MTH/STA 56 EXPONENTIAL PROBABILITY DISTRIBUTION As discussed in Exaple (of Secion of Unifor Probabili Disribuion), in a Poisson process, evens are occurring independenl a rando and a a unifor rae per uni
More informationChapter 6 DETECTION AND ESTIMATION: Model of digital communication system. Fundamental issues in digital communications are
Chaper 6 DEECIO AD EIMAIO: Fundamenal ssues n dgal communcaons are. Deecon and. Esmaon Deecon heory: I deals wh he desgn and evaluaon of decson makng processor ha observes he receved sgnal and guesses
More informationReview: Transformations. Transformations - Viewing. Transformations - Modeling. world CAMERA OBJECT WORLD CSE 681 CSE 681 CSE 681 CSE 681
Revew: Trnsforons Trnsforons Modelng rnsforons buld cople odels b posonng (rnsforng sple coponens relve o ech oher ewng rnsforons plcng vrul cer n he world rnsforon fro world coordnes o cer coordnes Perspecve
More informationHomework 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( t) Outline of program: BGC1: Survival and event history analysis Oslo, March-May Recapitulation. The additive regression model
BGC1: Survval and even hsory analyss Oslo, March-May 212 Monday May 7h and Tuesday May 8h The addve regresson model Ørnulf Borgan Deparmen of Mahemacs Unversy of Oslo Oulne of program: Recapulaon Counng
More informationSELFSIMILAR PROCESSES WITH STATIONARY INCREMENTS IN THE SECOND WIENER CHAOS
POBABILITY AD MATEMATICAL STATISTICS Vol., Fasc., pp. SELFSIMILA POCESSES WIT STATIOAY ICEMETS I TE SECOD WIEE CAOS BY M. M A E J I M A YOKOAMA AD C. A. T U D O LILLE Absrac. We sudy selfsmlar processes
More informationApplied Mathematics Letters
Appled Matheatcs Letters 2 (2) 46 5 Contents lsts avalable at ScenceDrect Appled Matheatcs Letters journal hoepage: wwwelseverco/locate/al Calculaton of coeffcents of a cardnal B-splne Gradr V Mlovanovć
More informationON THE CORRECTNESS OF A NONLOCAL PROBLEM FOR THE SECOND ORDER MIXED TYPE EQUATION OF THE SECOND KIND IN A RECTANGLE
IIUM Engneerng Journal, Vol. 17, No., 16 Daolov ON THE CORRECTNESS OF A NONLOCAL PROBLEM FOR THE SECOND ORDER MIXED TYPE EUATION OF THE SECOND KIND IN A RECTANGLE SIROZHIDDIN ZUHRIDDINOVICH DJAMOLOV *
More informationOnline 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 informationCONSISTENT EARTHQUAKE ACCELERATION AND DISPLACEMENT RECORDS
APPENDX J CONSSTENT EARTHQUAKE ACCEERATON AND DSPACEMENT RECORDS Earhqake Acceleraons can be Measred. However, Srcres are Sbjeced o Earhqake Dsplacemens J. NTRODUCTON { XE "Acceleraon Records" }A he presen
More informationA Modified Genetic Algorithm Comparable to Quantum GA
A Modfed Genec Algorh Coparable o Quanu GA Tahereh Kahookar Toos Ferdows Unversy of Mashhad _k_oos@wal.u.ac.r Habb Rajab Mashhad Ferdows Unversy of Mashhad h_rajab@ferdows.u.ac.r Absrac: Recenly, researchers
More informationSystem in Weibull Distribution
Internatonal Matheatcal Foru 4 9 no. 9 94-95 Relablty Equvalence Factors of a Seres-Parallel Syste n Webull Dstrbuton M. A. El-Dacese Matheatcs Departent Faculty of Scence Tanta Unversty Tanta Egypt eldacese@yahoo.co
More informationThe Finite Element Method for the Analysis of Non-Linear and Dynamic Systems
Swss Federal Insue of Page 1 The Fne Elemen Mehod for he Analyss of Non-Lnear and Dynamc Sysems Prof. Dr. Mchael Havbro Faber Dr. Nebojsa Mojslovc Swss Federal Insue of ETH Zurch, Swzerland Mehod of Fne
More informationOn 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 informationChapter 2 Linear dynamic analysis of a structural system
Chaper Lnear dynamc analyss of a srucural sysem. Dynamc equlbrum he dynamc equlbrum analyss of a srucure s he mos general case ha can be suded as akes no accoun all he forces acng on. When he exernal loads
More informationSupplementary Online Material
Suppleenary Onlne Maeral In he followng secons, we presen our approach o calculang yapunov exponens. We derve our cenral resul Λ= τ n n pτλ ( A pbt λ( = τ, = A ( drecly fro he growh equaon x ( = AE x (
More informationThis document is downloaded from DR-NTU, Nanyang Technological University Library, Singapore.
Ths documen s downloaded from DR-NTU, Nanyang Technologcal Unversy Lbrary, Sngapore. Tle A smplfed verb machng algorhm for word paron n vsual speech processng( Acceped verson ) Auhor(s) Foo, Say We; Yong,
More informationA distributed optimization-based approach for hierarchical MPC of large-scale systems with coupled dynamics and constraints
Delf Unversy of Technology Delf Cener for Sysems and Conrol Techncal repor 11-039 A dsrbued opmzaon-based approach for herarchcal MPC of large-scale sysems wh coupled dynamcs and consrans M.D. Doan, T.
More information