A New Iterative Method for Solving Initial Value Problems

Size: px
Start display at page:

Download "A New Iterative Method for Solving Initial Value Problems"

Transcription

1 A New Ierave Meod or Solvg Ial Value Problems Mgse Wu ad Weu Hog Dearme o Ma., Sascs, ad CS, Uvers o Wscos-Sou, Meomoe, WI 5475, USA Dearme o Maemacs, Clao Sae Uvers, Morrow, GA 36, USA Absrac - I s aer, we roduce a ew arameer erao meod (P-Ierao or sor,wc ca be aled o Adams-Moulo meods o solve al value roblems. Comared w Jacob erao meod, as ree advaages: ( ma sgcal mleme e sable rego o e erave rocess suc a we ca ecel use e large sable rego o mlc ormula; ( allows large se sze o reduce e umber o eraos; ad (3 ca elarge e sabl rego. Kewords: P-Ierao, Adams-Moulo Meod, Sable Rego. Iroduco Cosder a al value roblem: ' ( (, We s dcul o d a aalc soluo, we aurall al a umercal meod o d a aroxmae soluo o ( over a erval [, T]: ( ( or,, L. Toug ere are ma umercal meods or comug (, e ca be classed o wo es o meods : exlc ormulas ad mlc ormulas. Te Adams- Basor (A-B ormulas ad Adams-Moulo (A-M ormulas are oular mul-se exlc ad mlc meods, resecvel. For examles, e exlc wo-se Adams-Basor meod (A-B s ( ( ( (3 (, (, ( ad e mlc wo-se Adams-Moulo meod (A-M s ( ( (5 (, 8 (, (, Te geeral -se A-M ormulas ca be wre as (3 ( ( β (,., Usg a mlc meod, sce aears o bo sdes o e ormula, we ave o al erave rocess o ge (oce a drecl solvg ad comug could be dcul or ( cumbersome. Le be a al aroxmao o, e ( (5 (, 8 (, (, (4 were, m rereses e umber o eraos. I geeral seag, more eraos ca resul a beer aroxmao o e covergece codo olds (see laer dscusso. Ts erave rocess mg coue ul a ges a aroxmao w e requred error boud. O course, e se sze or e calculao mus be roerl seleced because o (a Error corol ere s rucao error a s deedg o, ad (b Sabl eac umercal ormula as a sable rego (also called absolue sable rego suc a λ mus be w e sable rego or covergece, were λ s a egevalue o e ODE. Te sable rego dscusso ca be oud rom oer resources [], we om e deals. For examle, e sable rego o A-B ad e sable rego o A- M are as sow Fgure ad Fgure, resecvel. Fgure Fgure B Comarso, e exlc meod s easer o be erormed a e mlc meod, bu s accurac, geeral, s lower

2 a a o e mlc meod. Te sable rego o e exlc meod s relavel smaller a a o e mlc meod (reer o Fgure ad aga eve oug usg e same ses. Aoer cal examle: e sable rego o e exlc Euler s ormula (, s sde o a ds ceered a (-, w radus wle e sable rego o e mlc Euler s ormula (, s e ere comlex lae exce e u dsc ceered a e o (,. Te sable rego lms e se sze wle usg a umercal meod. Te mlc meod ma allow larger se sze comaravel, bu requre a erave rocess. Almos all umercal aalss exs roduce e oular mulse A-B ad A-M meods. However, mos o em om e sabl dscusso due o comlex. A-B ad A-M ormulas are wdel used scec calculaos suc as a euroal ssem []. Somemes, eole combe wo es o ormulas ogeer o develo so called redcor-correcor meod a uses a exlc ormula as a redcor ad a mlc ormula as e correcor [3]. Te erave rocess or a correcor ca coue ul e requred accurac s aceved. Te erave ormula (4 s called e Jacob erao (also called smle erao. I addo o a a umercal meod lms e se sze, e erao rocess sel also lms e se sze as well. For sace, e sable rego o (4 s e sde o a crcle ceered a e org w radus.4. Eve A-M (3 as a relave large sable rego (see Fgure, bu ol e erseco o e sable regos o (3 ad (4 s useul or acual calculao. I s aer, we roduce a ew erave meod (amed P-Ierao or sor, wc uses a small arameer regular erao meod suc a ma sgcal mleme e sable rego o e erave rocess, ecel use e large sable rego o e mlc ormula ad large se sze a ca resul reducg e umber o eraos, ad sgcal elarge e sabl rego. Ts aer s orgazed as ollows. Te P-Ierao s roduced seco. Te sable rego s dscussed seco 3. I seco 4, we comared P-Ierao w Jacob erao b reseg umercal resuls solvg bo lear ad olear al value roblems. I seco 5, we gve some commes o urer alcaos o e P-Ierao o Mulse ormulas suc as A-M ormulas or solvg ODE ssems. Te P-Ierao meod I s seco, we wll derve our P-Ierao meod. For smlc, we wll use e oaos: (, (,, ad (,. Te small arameer erao ecque dscussed s aer ma be aled o all mul-se A-M ormulas. Le s roduce s rocess b worg w e A-M. Rewre (3 as 5 8 (, ( (, (, c Addg o bo sdes o e equao, were c s a osve arameer, we ave or 5 8 c c 8 c 5 c Tus, e A-M becomes ( c 5 c (5 8 8 Alg smle erao rocess o ge we ave 8 ( c 5 c (5 8 ( m (5 (6 I (6, e eraos ma be erormed a ew mes or reeaed ul acevg e requred accurac, sa < olerace.

3 We ca combe e wo exressos o oe: b leg 5, we ave: 5 c ( c 8 ( (6b W a roer se leg ad a seleced value o e arameer c, we ca erorm e erave rocess o A-M b (6 or (6b easl. Prooso : Ierao ormulas (6b ad (4 bo aroac as m. o e same value o Proo: I ac, s eas o cec a, ere s a value * a maes (6b exacl rue,.e., 8 ( c ( * * * Te, b relacg b 5 5 c, we ca reduce o (4: * ( * 5 8. Tus, (6b ad (4 rovde e same value o I s well-ow a egevalues o a sel-sable dereal equao ssem are all locaed o e closed le-al o e comlex lae. Te es equao ' λ s a sel-sable λ re θ w r ad π θ 3π. Te λ ma be cosdered as e egevalue o e es equao. Prooso : Ierave ormula (6b coverges ( cλ <. Proo: Le us dee 8 Φ ( (6b. Clearl, we eed o comue Φ ol oce durg e erave rocess o comug, so we sml wre e erave rocess w arameer c as ( c Φ. Alg e es equao I * ' λ, we ave ( cλ ( cλ Φ Φ (7 s a xed o o e erave rocess, e ( c Φ λ Subracg (8 rom (7 gves Ts mles a (8 m ( λ ( c. * ( * as m, *, ( cλ <. As ou ave see a e sable rego o A-M ormula s gve b e gure. Wle alg A-M o a ssem o equaos, we mus al erave rocess. Oe mg use e Newo s meod, wc, owever, requres comug verse o e marx or eac se. Ts rocess s o ol exesve, bu also volves roud-o errors. Oe mg also use e Jacob erao as sow b ormula (4. However, a smlar argume (as e roo o rooso sows a e Jacob erao o A-M requres λ < / 5. Eve A-M ormula as a relavel large sable rego, e Jacob erao would reduce e se sze < /(5 λ o a ver small umber λ s large. I ex seco, we wll sow a e ormula (6 or (6b ma overcome s roblem. 3 Sable Rego Le us sae a ver mora resul o e sable rego o e erave ormula (6b. Prooso 3: Te sable rego o (6b ca be arbrarl elarged o e closed le-al o e comlex lae as c. Proo: Accordg o rooso, ormula (6 coverges ( cλ <, were 5 5 c <. So, (6b coverges θ cλ <. Alg λ re r cosθ sθ, we ave cλ ( cr θ ( cr θ cos s <, ad (6b coverges c cosθ r <. Tus, c s roerl seleced

4 o sas cosθ c <, e e ormula (6 or (6b coverges r regardless o wa se sze s. I arcular, e ormula (6 or (6b would wor erecl λ s a real umber or close o real umber (θ s close oπ. For coveece, le c. Te (6b coverges ( λ <, or re θ <. We ave or < ( r ( r cosθ s < r < s Te lower boud s less a zero, so we ol eed o ae care o e uer boud. For a xed ad s s π 3π θ,,, ; 5,. Ts meas a e sable rego o (6b ma be elarged arbrarl o e le-al o e comlex lae as, or c. We would le o comare e sable rego o e P- Ierave rocess (6b w a o e A-M because e se sze s lmed b bo A-M ad e erave rocess. Te sable rego w dere values s gve b gure 3. I s obvous a e sable rego o A-M s a roer subse o e sable rego o e P-Ierave rocess (6b. B comarg e sable regos, we sugges o selec a value o rom [.5, ]. Fgure 3: Te sable rego o P-erave (6b w.5,.5, ad, resecvel. Te ermos crcle s e sable rego o e Jacob erao or A-M. 4 Numercal Resuls I s eresg o comare Jacob erao w our P-Ierao. We solved que a ew ODE ssems b Jacob erao ad P-Ierao. We would le o sow e resul o solvg several ssems below. Examle : Solve e x ssem d d d 3 4 d ( Te aalc soluo s ( 5e 4 e, ( 5e 6e. Ts ssem as egevalues - ad -. Sarg w exac al value o ad, we comued (, or o,b A-B ad b A-M. Te erave rocess o A-M was doe b Jacob erao ad P-Ierao searael. We le eac meod comue ses, e.g., comue o. Te 5 erave rocess sos <. We coued e oal umber o eraos (..o.. erormed or e ses. W large se sze, Jacob erao als, bu P- Ierao coverges (see e able. However, due o e rucao error, e soluo as bg error. Sce e rucao 3 error so(, large value o wll resul a bg error. Te eresg o s a P-Ierao as a larger sable rego a a o Jacob erao as we roved earler. Usg small value o, P-Ierao soluo s closer o e exac value.

5 Table. Ierao meod Se sze. Soluos..o.. Jacob Dverge 436 P-Ierao,.5 Coverge 5 P-Ierao,. W large 3 P-Ierao,. Error 7 Ierao meod Se sze. Soluos..o.. Jacob Correc 436 P-Ierao,.5 Beer a 57 P-Ierao,. Jacob s 55 P-Ierao,. 75 Ierao meod Se sze.5 Soluo..o.. Jacob Correc 96 P-Ierao,.5 74 Beer a P-Ierao,. 57 Jacob s P-Ierao,. 48 Ierao meod Se sze. Soluo..o.. Jacob Correc 53 P-Ierao,.5 7 Beer a P-Ierao,. 48 Jacob s P-Ierao,. 37 Examle : Solve e ssem o d 9 3 d d. 9 3 ( d d d Ts s a sable ssem w egevalues -, 4 ± 4. We solved s ssem a smlar maer. Te resul s sow as e able. Te rae o covergece o e P-Ierao s muc aser or large value o a a or small value o. However, we s small, e advaage o usg P-Ierao exss, bu o ver sgca. Te suggesed value wors e. Examle 3: Le s solve a o-lear al value roblem Table. Ierao meod Se sze.4 slouo..o.. Jacob 459 P-Ierao.5 Correc ad P-Ierao,.5 comable 97 P-Ierao,. eac oer. 34 P-Ierao,. 3 Ierao meod Se sze. slouo..o.. Jacob 98 P-Ierao,.5 Correc ad 97 P-Ierao,.5 comable 65 P-Ierao,. eac oer. 54 P-Ierao,. 63 Ierao meod Se sze. slouo..o.. Jacob 58 P-Ierao,.5 Correc ad P-Ierao,.5 comable 7 P-Ierao,. eac oer. 53 P-Ierao,. 43 Table 3. Ierao meod Se sze. slouo..o.. Jacob 49 P-Ierao,.5 Correc ad 7 P-Ierao,.5 comable 43 P-Ierao,. eac oer. 45 P-Ierao,. 64 Ierao meod Se sze.5 slouo..o.. Jacob 3 P-Ierao,.5 Correc ad 79 P-Ierao,.5 comable 48 P-Ierao,. eac oer. 35 P-Ierao,. 54 Ierao meod Se sze. slouo..o.. Jacob 8 P-Ierao,.5 Correc ad 8 P-Ierao,.5 comable 55 P-Ierao,. eac oer. 4 P-Ierao,. 3 d d 3, (. 3 Aga, we solved s ssem a smlar wa. Te resul s sow as e able 3. We.5, Jacob erao s dverge, owever, P-Ierao sll coverges. Te advaage o P-Ierao s sgca or larger.

6 5 Coclusos Te P-Ierao o Mul-se ormulas suc as A-M ormulas s useul sceme or solvg ODE ssems. Lmed b e sace, we coclude s aer w e ollowg commes: (a A-M ormulas w Jacob erao are ece or o-s ODEs. Te A-M ormulas w P-Ierao ca be aled o s ssems [4]. (b I e egevalues o a ssem are real umbers or close o real axs, e advaage o usg P-Ierao s ver sgca ad oe ca use muc larger se sze o do e calculao. (c I e egevalues o a ssem are close o e magar axs, e P-Ierao does o mae oo muc derece comarg w Jacob erao, bu wors e ad rovdes a more accurae soluo. (d Te erave rocess ma be aled o oer A-M ormulas. For examle, or e ree ses A-M ormula ( , ( A M 3 e erave rocess ma be doe b e ollowg sceme ( c ( Or, erave orma, ( c ( (9 Fgure 4: Te sable rego o P-Ierao or A-M3 w.5,.5, ad, resecvel. Te ermos crcle s e sable rego o Jacob erao or A-M3. 6 Reereces [] Oe o e resources: Bra Brade, A Fredl Iroduco o Numercal aalss, 6, [] Meods Neuroal Modelg: From Ios o Newors, Crso Koc & Ida Segev, 998, ISBN:63. [3] Numercal aalss (8 edo, R. Burde ad J. D. Fares, 5, ISPN: , 9 3. [4] Solvg Imlc Equaos Arsg Fromm Adams-Moulo Meods, Tam Ha & Yuua Ha, BIT, Volume 4, No., were. Wle leg c as we dscussed 9 4c earler or A-M, e value ca be ae a smlar maer. Te sable rego o (9 s gve o cure 4.

A Comparison of AdomiansDecomposition Method and Picard Iterations Method in Solving Nonlinear Differential Equations

A Comparison of AdomiansDecomposition Method and Picard Iterations Method in Solving Nonlinear Differential Equations Global Joural of Scece Froer Research Mahemacs a Decso Sceces Volume Issue 7 Verso. Jue Te : Double Bl Peer Revewe Ieraoal Research Joural Publsher: Global Jourals Ic. (USA Ole ISSN: 49-466 & Pr ISSN:

More information

4. Runge-Kutta Formula For Differential Equations

4. Runge-Kutta Formula For Differential Equations NCTU Deprme o Elecrcl d Compuer Egeerg 5 Sprg Course by Pro. Yo-Pg Ce. Ruge-Ku Formul For Derel Equos To solve e derel equos umerclly e mos useul ormul s clled Ruge-Ku ormul

More information

4. Runge-Kutta Formula For Differential Equations. A. Euler Formula B. Runge-Kutta Formula C. An Example for Fourth-Order Runge-Kutta Formula

4. Runge-Kutta Formula For Differential Equations. A. Euler Formula B. Runge-Kutta Formula C. An Example for Fourth-Order Runge-Kutta Formula NCTU Deprme o Elecrcl d Compuer Egeerg Seor Course By Pro. Yo-Pg Ce. Ruge-Ku Formul For Derel Equos A. Euler Formul B. Ruge-Ku Formul C. A Emple or Four-Order Ruge-Ku Formul

More information

Density estimation III.

Density estimation III. Lecure 4 esy esmao III. Mlos Hauskrec mlos@cs..edu 539 Seo Square Oule Oule: esy esmao: Mamum lkelood ML Bayesa arameer esmaes MP Beroull dsrbuo. Bomal dsrbuo Mulomal dsrbuo Normal dsrbuo Eoeal famly Eoeal

More information

Density estimation. Density estimations. CS 2750 Machine Learning. Lecture 5. Milos Hauskrecht 5329 Sennott Square

Density estimation. Density estimations. CS 2750 Machine Learning. Lecture 5. Milos Hauskrecht 5329 Sennott Square Lecure 5 esy esmao Mlos Hauskrec mlos@cs..edu 539 Seo Square esy esmaos ocs: esy esmao: Mamum lkelood ML Bayesa arameer esmaes M Beroull dsrbuo. Bomal dsrbuo Mulomal dsrbuo Normal dsrbuo Eoeal famly Noaramerc

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

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

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

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

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

Observations on the transcendental Equation

Observations on the transcendental Equation IOSR Jourl o Mecs IOSR-JM e-issn: 78-78-ISSN: 9-7 Volue 7 Issue Jul. - u. -7 www.osrjourls.or Oservos o e rscedel Euo M..Gol S.Vds T.R.Us R Dere o Mecs Sr Idr Gd Collee Trucrll- src: Te rscedel euo w ve

More information

NUMERICAL SOLUTIONS TO ORDINARY DIFFERENTIAL EQUATIONS

NUMERICAL SOLUTIONS TO ORDINARY DIFFERENTIAL EQUATIONS NUMERICAL SOLUTIONS TO ORDINARY DIFFERENTIAL EQUATIONS If e eqao coas dervaves of a - order s sad o be a - order dffereal eqao. For eample a secod-order eqao descrbg e oscllao of a weg aced po b a sprg

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

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

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

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

Initial-Value Problems for ODEs. numerical errors (round-off and truncation errors) Consider a perturbed system: dz dt

Initial-Value Problems for ODEs. numerical errors (round-off and truncation errors) Consider a perturbed system: dz dt Ital-Value Problems or ODEs d GIVEN: t t,, a FIND: t or atb umercal errors (roud-o ad trucato errors) Cosder a perturbed sstem: dz t, z t, at b z a a Does z(t) (t)? () (uqueess) a uque soluto (t) exsts

More information

Density estimation III.

Density estimation III. Lecure 6 esy esmao III. Mlos Hausrec mlos@cs..eu 539 Seo Square Oule Oule: esy esmao: Bomal srbuo Mulomal srbuo ormal srbuo Eoeal famly aa: esy esmao {.. } a vecor of arbue values Objecve: ry o esmae e

More information

THE PUBLISHING HOUSE PROCEEDINGS OF THE ROMANIAN ACADEMY, Series A, OF THE ROMANIAN ACADEMY Volume 10, Number 2/2009, pp

THE PUBLISHING HOUSE PROCEEDINGS OF THE ROMANIAN ACADEMY, Series A, OF THE ROMANIAN ACADEMY Volume 10, Number 2/2009, pp THE PUBLIHING HOUE PROCEEDING OF THE ROMANIAN ACADEMY, eres A, OF THE ROMANIAN ACADEMY Volume 0, Number /009,. 000-000 ON ZALMAI EMIPARAMETRIC DUALITY MODEL FOR MULTIOBJECTIVE FRACTIONAL PROGRAMMING WITH

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

Comparison of the Bayesian and Maximum Likelihood Estimation for Weibull Distribution

Comparison of the Bayesian and Maximum Likelihood Estimation for Weibull Distribution Joural of Mahemacs ad Sascs 6 (2): 1-14, 21 ISSN 1549-3644 21 Scece Publcaos Comarso of he Bayesa ad Maxmum Lkelhood Esmao for Webull Dsrbuo Al Omar Mohammed Ahmed, Hadeel Salm Al-Kuub ad Noor Akma Ibrahm

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

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

Available online Journal of Scientific and Engineering Research, 2014, 1(1): Research Article

Available online  Journal of Scientific and Engineering Research, 2014, 1(1): Research Article Avalable ole wwwjsaercom Joural o Scec ad Egeerg Research, 0, ():0-9 Research Arcle ISSN: 39-630 CODEN(USA): JSERBR NEW INFORMATION INEUALITIES ON DIFFERENCE OF GENERALIZED DIVERGENCES AND ITS APPLICATION

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

Calculation of Effective Resonance Integrals

Calculation of Effective Resonance Integrals Clculo of ffecve Resoce egrls S.B. Borzkov FLNP JNR Du Russ Clculo of e effecve oce egrl wc cludes e rel eerg deedece of euro flux des d correco o e euro cure e smle s eeded for ccure flux deermo d euro

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

Binary Time-Frame Expansion

Binary Time-Frame Expansion Bary Tme-Frame Expaso Farza Fallah Fujsu Labs. o Amerca Suyvale, CA 9485 Absrac- Ths paper roduces a ew mehod or perormg me-rame expaso based o wrg he umber o me rames erms o powers o wo. I he proposed

More information

Density estimation III. Linear regression.

Density estimation III. Linear regression. Lecure 6 Mlos Hauskrec mlos@cs.p.eu 539 Seo Square Des esmao III. Lear regresso. Daa: Des esmao D { D D.. D} D a vecor of arbue values Obecve: r o esmae e uerlg rue probabl srbuo over varables X px usg

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

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

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

Dr. Shalabh Department of Mathematics and Statistics Indian Institute of Technology Kanpur

Dr. Shalabh Department of Mathematics and Statistics Indian Institute of Technology Kanpur Aalyss of Varace ad Desg of Exermets-I MODULE II LECTURE - GENERAL LINEAR HYPOTHESIS AND ANALYSIS OF VARIANCE Dr Shalabh Deartmet of Mathematcs ad Statstcs Ida Isttute of Techology Kaur Tukey s rocedure

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

Chapter Trapezoidal Rule of Integration

Chapter Trapezoidal Rule of Integration Cper 7 Trpezodl Rule o Iegro Aer redg s per, you sould e le o: derve e rpezodl rule o egro, use e rpezodl rule o egro o solve prolems, derve e mulple-segme rpezodl rule o egro, 4 use e mulple-segme rpezodl

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

Outline. Energy-Efficient Target Coverage in Wireless Sensor Networks. Sensor Node. Introduction. Characteristics of WSN

Outline. Energy-Efficient Target Coverage in Wireless Sensor Networks. Sensor Node. Introduction. Characteristics of WSN Ener-Effcen Tare Coverae n Wreless Sensor Newors Presened b M Trà Tá -4-4 Inroducon Bacround Relaed Wor Our Proosal Oulne Maxmum Se Covers (MSC) Problem MSC Problem s NP-Comlee MSC Heursc Concluson Sensor

More information

Geometric Modeling

Geometric Modeling Geomerc Modelg 9.58. Crves coed Cc Bezer ad B-Sle Crves Far Chaers 4-5 8 Moreso Chaers 4 5 4 Tycal Tyes of Paramerc Crves Corol os flece crve shae. Ierolag Crve asses hrogh all corol os. Herme Defed y

More information

NUMERICAL SIMULATION OF INTERNAL WAVES USING A SET OF FULLY NONLINEAR INTERNAL-WAVE EQUATIONS

NUMERICAL SIMULATION OF INTERNAL WAVES USING A SET OF FULLY NONLINEAR INTERNAL-WAVE EQUATIONS Aual Joural o Hydraulc Eeer JSCE Vol.5 7 February NUMERICAL SIMULATION OF INTERNAL WAVES USING A SET OF FULLY NONLINEAR INTERNAL-WAVE EQUATIONS Taro KAKINUMA ad Kesuke NAKAYAMA Member o JSCE Dr. o E. Mare

More information

Derivation of 3-Point Block Method Formula for Solving First Order Stiff Ordinary Differential Equations

Derivation of 3-Point Block Method Formula for Solving First Order Stiff Ordinary Differential Equations Dervato of -Pot Block Method Formula for Solvg Frst Order Stff Ordary Dfferetal Equatos Kharul Hamd Kharul Auar, Kharl Iskadar Othma, Zara Bb Ibrahm Abstract Dervato of pot block method formula wth costat

More information

Fourth Order Four-Stage Diagonally Implicit Runge-Kutta Method for Linear Ordinary Differential Equations ABSTRACT INTRODUCTION

Fourth Order Four-Stage Diagonally Implicit Runge-Kutta Method for Linear Ordinary Differential Equations ABSTRACT INTRODUCTION Malasa Joural of Mathematcal Sceces (): 95-05 (00) Fourth Order Four-Stage Dagoall Implct Ruge-Kutta Method for Lear Ordar Dfferetal Equatos Nur Izzat Che Jawas, Fudzah Ismal, Mohamed Sulema, 3 Azm Jaafar

More information

Midterm Exam. Thursday, April hour, 15 minutes

Midterm Exam. Thursday, April hour, 15 minutes Economcs of Grow, ECO560 San Francsco Sae Unvers Mcael Bar Sprng 04 Mderm Exam Tursda, prl 0 our, 5 mnues ame: Insrucons. Ts s closed boo, closed noes exam.. o calculaors of an nd are allowed. 3. Sow all

More information

BSDEs with polynomial growth generators Philippe Briand IRMAR, Universite Rennes 1, Rennes Cedex, FRANCE Rene Carmona Statistics & Operations R

BSDEs with polynomial growth generators Philippe Briand IRMAR, Universite Rennes 1, Rennes Cedex, FRANCE Rene Carmona Statistics & Operations R BSDEs wh olyomal growh geeraors Phle Brad IRMAR, Uverse Rees 1, 35 42 Rees Cedex, FRANCE Ree Carmoa Sascs & Oeraos Research, Prceo Uversy, Prceo NJ 8544, USA July 22, 1998 revsed December 15, 1998 Absrac

More information

Integral Φ0-Stability of Impulsive Differential Equations

Integral Φ0-Stability of Impulsive Differential Equations Ope Joural of Appled Sceces, 5, 5, 65-66 Publsed Ole Ocober 5 ScRes p://wwwscrporg/joural/ojapps p://ddoorg/46/ojapps5564 Iegral Φ-Sably of Impulsve Dffereal Equaos Aju Sood, Sajay K Srvasava Appled Sceces

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

Inner-Outer Synchronization Analysis of Two Complex Networks with Delayed and Non-Delayed Coupling

Inner-Outer Synchronization Analysis of Two Complex Networks with Delayed and Non-Delayed Coupling ISS 746-7659, Eglad, UK Joural of Iformao ad Compug Scece Vol. 7, o., 0, pp. 0-08 Ier-Ouer Sycrozao Aalyss of wo Complex eworks w Delayed ad o-delayed Couplg Sog Zeg + Isue of Appled Maemacs, Zeag Uversy

More information

Continuous Random Variables: Conditioning, Expectation and Independence

Continuous Random Variables: Conditioning, Expectation and Independence Cotuous Radom Varables: Codtog, xectato ad Ideedece Berl Che Deartmet o Comuter cece & Iormato geerg atoal Tawa ormal Uverst Reerece: - D.. Bertsekas, J.. Tstskls, Itroducto to robablt, ectos 3.4-3.5 Codtog

More information

Advection! Discontinuous! solutions shocks! Shock Speed! ! f. !t + U!f. ! t! x. dx dt = U; t = 0

Advection! Discontinuous! solutions shocks! Shock Speed! ! f. !t + U!f. ! t! x. dx dt = U; t = 0 p://www.d.edu/~gryggva/cfd-course/ Advecio Discoiuous soluios socks Gréar Tryggvaso Sprig Discoiuous Soluios Cosider e liear Advecio Equaio + U = Te aalyic soluio is obaied by caracerisics d d = U; d d

More information

GENERALIZED METHOD OF LIE-ALGEBRAIC DISCRETE APPROXIMATIONS FOR SOLVING CAUCHY PROBLEMS WITH EVOLUTION EQUATION

GENERALIZED METHOD OF LIE-ALGEBRAIC DISCRETE APPROXIMATIONS FOR SOLVING CAUCHY PROBLEMS WITH EVOLUTION EQUATION Joural of Appled Maemacs ad ompuaoal Mecacs 24 3(2 5-62 GENERALIZED METHOD OF LIE-ALGEBRAI DISRETE APPROXIMATIONS FOR SOLVING AUHY PROBLEMS WITH EVOLUTION EQUATION Arkad Kdybaluk Iva Frako Naoal Uversy

More information

Differential Equation of Eigenvalues for Sturm Liouville Boundary Value Problem with Neumann Boundary Conditions

Differential Equation of Eigenvalues for Sturm Liouville Boundary Value Problem with Neumann Boundary Conditions Ierol Reserc Jorl o Aled d Bsc Sceces 3 Avlle ole www.rjs.co ISSN 5-838X / Vol 4 : 997-33 Scece Exlorer Plcos Derel Eqo o Eevles or Sr Lovlle Bodry Vle Prole w Ne Bodry Codos Al Kll Gold Dere o Mecs Azr

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

CS537. Numerical Analysis

CS537. Numerical Analysis CS57 Numerical Analsis Lecure Numerical Soluion o Ordinar Dierenial Equaions Proessor Jun Zang Deparmen o Compuer Science Universi o enuck Leingon, Y 4006 0046 April 5, 00 Wa is ODE An Ordinar Dierenial

More information

Additional Exercises for Chapter What is the slope-intercept form of the equation of the line given by 3x + 5y + 2 = 0?

Additional Exercises for Chapter What is the slope-intercept form of the equation of the line given by 3x + 5y + 2 = 0? ddiional Eercises for Caper 5 bou Lines, Slopes, and Tangen Lines 39. Find an equaion for e line roug e wo poins (, 7) and (5, ). 4. Wa is e slope-inercep form of e equaion of e line given by 3 + 5y +

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

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

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

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

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

NUMERICAL EVALUATION of DYNAMIC RESPONSE

NUMERICAL EVALUATION of DYNAMIC RESPONSE NUMERICAL EVALUATION of YNAMIC RESPONSE Aalycal solo of he eqao of oo for a sgle degree of freedo syse s sally o ossble f he excao aled force or grod accelerao ü g -vares arbrarly h e or f he syse s olear.

More information

Numerical Analysis Formulae Booklet

Numerical Analysis Formulae Booklet Numercal Aalyss Formulae Booklet. Iteratve Scemes for Systems of Lear Algebrac Equatos:.... Taylor Seres... 3. Fte Dfferece Approxmatos... 3 4. Egevalues ad Egevectors of Matrces.... 3 5. Vector ad Matrx

More information

Can we take the Mysticism Out of the Pearson Coefficient of Linear Correlation?

Can we take the Mysticism Out of the Pearson Coefficient of Linear Correlation? Ca we tae the Mstcsm Out of the Pearso Coeffcet of Lear Correlato? Itroducto As the ttle of ths tutoral dcates, our purpose s to egeder a clear uderstadg of the Pearso coeffcet of lear correlato studets

More information

A Remark on Generalized Free Subgroups. of Generalized HNN Groups

A Remark on Generalized Free Subgroups. of Generalized HNN Groups Ieraoal Mahemacal Forum 5 200 o 503-509 A Remar o Geeralzed Free Subroup o Geeralzed HNN Group R M S Mahmood Al Ho Uvery Abu Dhab POBo 526 UAE raheedmm@yahoocom Abrac A roup ermed eeralzed ree roup a ree

More information

Introduction to Numerical Differentiation and Interpolation March 10, !=1 1!=1 2!=2 3!=6 4!=24 5!= 120

Introduction to Numerical Differentiation and Interpolation March 10, !=1 1!=1 2!=2 3!=6 4!=24 5!= 120 Itroducto to Numercal Deretato ad Iterpolato Marc, Itroducto to Numercal Deretato ad Iterpolato Larr Caretto Mecacal Egeerg 9 Numercal Aalss o Egeerg stems Marc, Itroducto Iterpolato s te use o a dscrete

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

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

Introducing Sieve of Eratosthenes as a Theorem

Introducing Sieve of Eratosthenes as a Theorem ISSN(Ole 9-8 ISSN (Prt - Iteratoal Joural of Iovatve Research Scece Egeerg ad echolog (A Hgh Imact Factor & UGC Aroved Joural Webste wwwrsetcom Vol Issue 9 Setember Itroducg Seve of Eratosthees as a heorem

More information

Numerical approximatons for solving partial differentıal equations with variable coefficients

Numerical approximatons for solving partial differentıal equations with variable coefficients Appled ad Copuaoal Maheacs ; () : 9- Publshed ole Februar (hp://www.scecepublshggroup.co/j/ac) do:.648/j.ac.. Nuercal approaos for solvg paral dffereıal equaos wh varable coeffces Ves TURUT Depare of Maheacs

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

Linear Regression Linear Regression with Shrinkage

Linear Regression Linear Regression with Shrinkage Lear Regresso Lear Regresso h Shrkage Iroduco Regresso meas predcg a couous (usuall scalar oupu from a vecor of couous pus (feaures x. Example: Predcg vehcle fuel effcec (mpg from 8 arbues: Lear Regresso

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

Third handout: On the Gini Index

Third handout: On the Gini Index Thrd hadout: O the dex Corrado, a tala statstca, proposed (, 9, 96) to measure absolute equalt va the mea dfferece whch s defed as ( / ) where refers to the total umber of dvduals socet. Assume that. The

More information

û s L u t 0 s a ; i.e., û s 0

û s L u t 0 s a ; i.e., û s 0 Te Hille-Yosida Teorem We ave seen a wen e absrac IVP is uniquely solvable en e soluion operaor defines a semigroup of bounded operaors. We ave no ye discussed e condiions under wic e IVP is uniquely solvable.

More information

P-Convexity Property in Musielak-Orlicz Function Space of Bohner Type

P-Convexity Property in Musielak-Orlicz Function Space of Bohner Type J N Sce & Mh Res Vol 3 No (7) -7 Alble ole h://orlwlsogocd/deh/sr P-Coey Proery Msel-Orlcz Fco Sce o Boher ye Yl Rodsr Mhecs Edco Deree Fcly o Ss d echology Uerss sl Neger Wlsogo Cerl Jdoes Absrcs Corresodg

More information

P a g e 5 1 of R e p o r t P B 4 / 0 9

P a g e 5 1 of R e p o r t P B 4 / 0 9 P a g e 5 1 of R e p o r t P B 4 / 0 9 J A R T a l s o c o n c l u d e d t h a t a l t h o u g h t h e i n t e n t o f N e l s o n s r e h a b i l i t a t i o n p l a n i s t o e n h a n c e c o n n e

More information

Brownian Motion and Stochastic Calculus. Brownian Motion and Stochastic Calculus

Brownian Motion and Stochastic Calculus. Brownian Motion and Stochastic Calculus Browa Moo Sochasc Calculus Xogzh Che Uversy of Hawa a Maoa earme of Mahemacs Seember, 8 Absrac Ths oe s abou oob decomoso he bascs of Suare egrable margales Coes oob-meyer ecomoso Suare Iegrable Margales

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

On an algorithm of the dynamic reconstruction of inputs in systems with time-delay

On an algorithm of the dynamic reconstruction of inputs in systems with time-delay Ieraoal Joural of Advaces Appled Maemacs ad Mecacs Volume, Issue 2 : (23) pp. 53-64 Avalable ole a www.jaamm.com IJAAMM ISSN: 2347-2529 O a algorm of e dyamc recosruco of pus sysems w me-delay V. I. Maksmov

More information

CHAPTER 4 RADICAL EXPRESSIONS

CHAPTER 4 RADICAL EXPRESSIONS 6 CHAPTER RADICAL EXPRESSIONS. The th Root of a Real Number A real umber a s called the th root of a real umber b f Thus, for example: s a square root of sce. s also a square root of sce ( ). s a cube

More information

Our main purpose in this section is to undertake an examination of the stock

Our main purpose in this section is to undertake an examination of the stock 3. Caial gains ax and e sock rice volailiy Our main urose in is secion is o underake an examinaion of e sock rice volailiy by considering ow e raional seculaor s olding canges afer e ax rae on caial gains

More information

CONTROLLABILITY OF A CLASS OF SINGULAR SYSTEMS

CONTROLLABILITY OF A CLASS OF SINGULAR SYSTEMS 44 Asa Joural o Corol Vol 8 No 4 pp 44-43 December 6 -re Paper- CONTROLLAILITY OF A CLASS OF SINGULAR SYSTEMS Guagmg Xe ad Log Wag ASTRACT I hs paper several dere coceps o corollably are vesgaed or a class

More information

Upper Bound For Matrix Operators On Some Sequence Spaces

Upper Bound For Matrix Operators On Some Sequence Spaces Suama Uer Bou formar Oeraors Uer Bou For Mar Oeraors O Some Sequece Saces Suama Dearme of Mahemacs Gaah Maa Uersy Yogyaara 558 INDONESIA Emal: suama@ugmac masomo@yahoocom Isar D alam aer aa susa masalah

More information

Unit 10. The Lie Algebra of Vector Fields

Unit 10. The Lie Algebra of Vector Fields U 10. The Le Algebra of Vecor Felds ================================================================================================================================================================ -----------------------------------

More information

Assignment 7/MATH 247/Winter, 2010 Due: Friday, March 19. Powers of a square matrix

Assignment 7/MATH 247/Winter, 2010 Due: Friday, March 19. Powers of a square matrix Assgmet 7/MATH 47/Wter, 00 Due: Frday, March 9 Powers o a square matrx Gve a square matrx A, ts powers A or large, or eve arbtrary, teger expoets ca be calculated by dagoalzg A -- that s possble (!) Namely,

More information

A L A BA M A L A W R E V IE W

A L A BA M A L A W R E V IE W A L A BA M A L A W R E V IE W Volume 52 Fall 2000 Number 1 B E F O R E D I S A B I L I T Y C I V I L R I G HT S : C I V I L W A R P E N S I O N S A N D TH E P O L I T I C S O F D I S A B I L I T Y I N

More information

Modeling and Predicting Sequences: HMM and (may be) CRF. Amr Ahmed Feb 25

Modeling and Predicting Sequences: HMM and (may be) CRF. Amr Ahmed Feb 25 Modelg d redcg Sequeces: HMM d m be CRF Amr Ahmed 070 Feb 25 Bg cure redcg Sgle Lbel Ipu : A se of feures: - Bg of words docume - Oupu : Clss lbel - Topc of he docume - redcg Sequece of Lbels Noo Noe:

More information

Computations with large numbers

Computations with large numbers Comutatos wth large umbers Wehu Hog, Det. of Math, Clayto State Uversty, 2 Clayto State lvd, Morrow, G 326, Mgshe Wu, Det. of Mathematcs, Statstcs, ad Comuter Scece, Uversty of Wscos-Stout, Meomoe, WI

More information

Idea is to sample from a different distribution that picks points in important regions of the sample space. Want ( ) ( ) ( ) E f X = f x g x dx

Idea is to sample from a different distribution that picks points in important regions of the sample space. Want ( ) ( ) ( ) E f X = f x g x dx Importace Samplg Used for a umber of purposes: Varace reducto Allows for dffcult dstrbutos to be sampled from. Sestvty aalyss Reusg samples to reduce computatoal burde. Idea s to sample from a dfferet

More information

Outline. simplest HMM (1) simple HMMs? simplest HMM (2) Parameter estimation for discrete hidden Markov models

Outline. simplest HMM (1) simple HMMs? simplest HMM (2) Parameter estimation for discrete hidden Markov models Oulie Parameer esimaio for discree idde Markov models Juko Murakami () ad Tomas Taylor (2). Vicoria Uiversiy of Welligo 2. Arizoa Sae Uiversiy Descripio of simple idde Markov models Maximum likeliood esimae

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

Chapter 3. Differentiation 3.3 Differentiation Rules

Chapter 3. Differentiation 3.3 Differentiation Rules 3.3 Dfferetato Rules 1 Capter 3. Dfferetato 3.3 Dfferetato Rules Dervatve of a Costat Fucto. If f as te costat value f(x) = c, te f x = [c] = 0. x Proof. From te efto: f (x) f(x + ) f(x) o c c 0 = 0. QED

More information

A Remark on Polynomial Mappings from to

A Remark on Polynomial Mappings from to Aled Mahemacs 6 7 868-88 h://www.scr.org/joural/am ISSN Ole: 5-7393 ISSN Pr: 5-7385 A Remar o Polyomal Mags from o ad a Alcao of he Sofware Male Research Th Bch Thuy Nguye UNESP Uversdade Esadual Paulsa

More information

Part 4b Asymptotic Results for MRR2 using PRESS. Recall that the PRESS statistic is a special type of cross validation procedure (see Allen (1971))

Part 4b Asymptotic Results for MRR2 using PRESS. Recall that the PRESS statistic is a special type of cross validation procedure (see Allen (1971)) art 4b Asymptotc Results for MRR usg RESS Recall that the RESS statstc s a specal type of cross valdato procedure (see Alle (97)) partcular to the regresso problem ad volves fdg Y $,, the estmate at the

More information

EE 6885 Statistical Pattern Recognition

EE 6885 Statistical Pattern Recognition EE 6885 Sascal Paer Recogo Fall 005 Prof. Shh-Fu Chag hp://www.ee.columba.edu/~sfchag Lecure 5 (9//05 4- Readg Model Parameer Esmao ML Esmao, Chap. 3. Mure of Gaussa ad EM Referece Boo, HTF Chap. 8.5 Teboo,

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

Solution to Some Open Problems on E-super Vertex Magic Total Labeling of Graphs

Solution to Some Open Problems on E-super Vertex Magic Total Labeling of Graphs Aalable a hp://paed/aa Appl Appl Mah ISS: 9-9466 Vol 0 Isse (Deceber 0) pp 04- Applcaos ad Appled Maheacs: A Ieraoal Joral (AAM) Solo o Soe Ope Probles o E-sper Verex Magc Toal Labelg o Graphs G Marh MS

More information

Chapter 5. Long Waves

Chapter 5. Long Waves ape 5. Lo Waes Wae e s o compaed ae dep: < < L π Fom ea ae eo o s s ; amos ozoa moo z p s ; dosac pesse Dep-aeaed coseao o mass

More information

Numerical Methods for a Class of Hybrid. Weakly Singular Integro-Differential Equations.

Numerical Methods for a Class of Hybrid. Weakly Singular Integro-Differential Equations. Ale Mahemacs 7 8 956-966 h://www.scr.org/joural/am ISSN Ole: 5-7393 ISSN Pr: 5-7385 Numercal Mehos for a Class of Hybr Wealy Sgular Iegro-Dffereal Equaos Shhchug Chag Dearme of Face Chug Hua Uversy Hschu

More information

Optimal control for multi-input bilinear systems with an application in cancer chemotherapy.

Optimal control for multi-input bilinear systems with an application in cancer chemotherapy. Ieraoal Joural of Scefc ad Iovave Mahemacal Research (IJSIMR) Volume 3, Issue 2, February 2015, PP 22-31 ISSN 2347-307X (Pr) & ISSN 2347-3142 (Ole) www.arcjourals.org Omal corol for mul-u blear sysems

More information

Integral Equations and their Relationship to Differential Equations with Initial Conditions

Integral Equations and their Relationship to Differential Equations with Initial Conditions Scece Refleco SR Vol 6 wwwscecereflecocom Geerl Leers Mhemcs GLM 6 3-3 Geerl Leers Mhemcs GLM Wese: hp://wwwscecereflecocom/geerl-leers--mhemcs/ Geerl Leers Mhemcs Scece Refleco Iegrl Equos d her Reloshp

More information

Mean Cordial Labeling of Certain Graphs

Mean Cordial Labeling of Certain Graphs J Comp & Math Sc Vol4 (4), 74-8 (03) Mea Cordal Labelg o Certa Graphs ALBERT WILLIAM, INDRA RAJASINGH ad S ROY Departmet o Mathematcs, Loyola College, Chea, INDIA School o Advaced Sceces, VIT Uversty,

More information