SIMULTANEOUS METHODS FOR FINDING ALL ZEROS OF A POLYNOMIAL

Size: px
Start display at page:

Download "SIMULTANEOUS METHODS FOR FINDING ALL ZEROS OF A POLYNOMIAL"

Transcription

1 Joual of athmatcal Sccs: Advacs ad Applcatos Volum, 05, ags 5-8 SIULTANEUS ETHDS FR FINDING ALL ZERS F A LYNIAL JUN-SE SNG ollg of dc Yos Uvsty Soul Rpublc of Koa -mal: usopsog@yos.ac. Abstact Th pupos of ths pap s to pst th w mthods fo fdg all smpl os of polyomals smultaously. Fst, w gv a w mthod fo fdg smultaously all smpl os of polyomals costuctd by applyg th stass mthod to th o th tapodal Nwto s mthod, ad pov th covgc of th mthod. also pst two modfd Nwto s mthods combd wth th dvatv-f mthod, whch a costuctd by applyg th dvatv-f mthod to th o th tapodal Nwto s mthod ad th mdpot Nwto s mthod, spctvly. Fally, w gv a umcal compaso btw vaous smultaous mthods fo fdg os of a polyomal.. Itoducto th a typcal tato mthod such as Nwto s mthod, a tal appoxmato of a o covgs to a spcfc o, but th stass mthod (o Duad-K mthod appoxmats all smpl (al o complx os of polyomal smultaously (s [, 4]. 00 athmatcs Subct lassfcato: 65H04, 65H05. Kywods ad phass: polyomal os, smultaous mthods, stass mthod, covgc, Nwto s mthod. Rcvd Apl 8, Sctfc Advacs ublshs

2 6 JUN-SE SNG Lt a a a b a polyomal of dg havg smpl os wth costats a, a,, a. Lt,, b th dstct os of ad lt dstct complx umbs,, b th appoxmatos. Th stass mthod (Duad-K mthod s dfd as m m m ( m m (, ( fo m 0, ad ths mthod s o of th most fqutly usd tatv mthods whch gv smultaous computato of all os of. If a fucto s dfd by (, th has th sam os as th polyomal, ad so th poblm of fdg th os of ducs to that of os of th fucto. If w dot ( fo,,, th cas of, ( ca b wtt as, ( wh s a cut appoxmato ad ẑ s a w appoxmato to a o of polyomal. Th mthod costuctd by ( s calld th stass-l mthod (bfly, L. Th am of ths pap s to pst th w mthods fo fdg all smpl os of polyomals smultaously. Ths w mthods a basd o th Fot-Soma s mdpot Nwto s mthod ([7] ad th aoo s tapodal Nwto s mthod ([8], whch w modfcatos of th Nwto s mthod though tatv appoxmatos.

3 SIULTANEUS ETHDS FR FINDING ALL 7 f ( x It s wll-ow that Nwto s mthod s dfd by x x f ( x wth a appoxmato x ad a w appoxmato x of a o, ad s ffct to fd a o of a quato f ( x 0 fo a dfftabl fucto f wth pop codtos ad a suffctly clos tal valu (s [8]. I [8], aoo ad Fado poposd th tapodal Nwto s mthod dfd by Thy appld Nwto s mthod to th f ( x x x f ( x f ( x. ( x of th domato. Alog wth (, th mdpot Nwto s mthod that Fot-Soma poposd [7] s costuctd as x x f ( x. f ( x ( x x Thy also appld Nwto s mthod to th f ( x st x x f ( x. (4 x of th domato, ad so Both th tapodal Nwto s mthod ad th mdpot Nwto s mthod a of cubc od, whl th ogal Nwto s mthod was of quadatc od. A vaty of mthods ca b appld to th x addto to Nwto s mthod. tovć t al. [5] dvd th followg smultaous mthod fo fdg all smpl os of polyomals by applyg th stass mthod to th x th mdpot Nwto s mthod: (, (5 (

4 8 JUN-SE SNG whch s calld Nwto-stass mthod (o N. Also, tovć ad tovć [6] foud th followg dvatv-f mthod (o DF dfd as: ( (, (6 whch has a smla fom wth th o abov ad ths mthod s of cubc od. I ths pap, w pst th w mthods fo th smultaous appoxmato of all smpl os of polyomals by applyg th stass-l mthod ad th dvatv-f mthod to x th tapodal Nwto s mthod ad th mdpot Nwto s mthod. Thoughout ths pap, th covgc of os wll b dscussd ad th od wll b calculatd fo w costuctd mthods. wll us th otato a ( b fo two complx umbs a ad b, whos modul a of th sam od, that s, ( b. max { } wth fo,,.,, a I addto, th o s dfd as I all dscussos, th od latd to, whch s a o of th pvously appoxmatd os, s psumd to b th sam. Aft that, w wll show that th od latd to, whch s a o of th appoxmatd os cocg ach mthod, s dtcal. Fo th sam bg, th od latd to th alady appoxmatd os ê s hypothsd to b dtcal as follows: fo all. I Scto, w gv a w mthod fo fdg smultaously all smpl os of polyomals costuctd by applyg th stass mthod to th x th tapodal Nwto s mthod, ad pov th covgc of th mthod. I Scto, w pst two modfd

5 SIULTANEUS ETHDS FR FINDING ALL 9 Nwto s mthods combd wth th dvatv-f mthod. Thy a costuctd by applyg th dvatv-f mthod to th x th tapodal Nwto s mthod ad th mdpot Nwto s mthod, spctvly. I Scto 4, w gv a umcal compaso btw vaous smultaous mthods fo fdg os of a polyomal. Fally, w coclud that th covgc of all w costuctd mthods ths pap a smla o supo tha oth tatv mthods of cubc od.. stass-l Tapodal Nwto s thod I ths scto, w costuct a w mthod fo fdg smultaously all smpl os of polyomals of cubc od. By applyg th stass mthod (5 to th x th tapodal Nwto s mthod (, w dv a w mthod costuctd as follows: ( ( (. (7 call (7 th stass-l tapodal Nwto s mthod, ad fom ths, smply, call t thod. Th calculato ad dscusso of th od of thod a smla to thos of th Nwto-stass mthod, whch s a altato of tovć s mdpot Nwto s mthod (s [5]. Fom (7, w hav th followg: Lmma. Fo a polyomal, w hav ( ( ( ( ( ( (. oof. By th Taylo s xpaso aoud, w hav that ( ( (, ( ( ( (, ( (8 (

6 0 JUN-SE SNG ( ( (. ( ( ( Fom (8, w obta ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( (. Fom Lmma, w hav th followg thom: Thom. If th appoxmat o x goudd fom thod s clos ough to ad th od of s th sam, th th od of ê s dtcal, ad ( s fomd. oof. asly s that th followg quato s satsfd: ( ( ( ( (. That s, ( ( (. (9 If Q (, th Q s a polyomal of od, ad Q ( fo all. Thfo, Q s th Lagag tpolato of pots,,,, ad so w hav

7 SIULTANEUS ETHDS FR FINDING ALL Q. Thfo, w obta. (0 Fom (0, t follows that. ( Substtutg (, w obta. Thfo, w hav (. (

8 JUN-SE SNG Now w wll fd th od of thod. If th Taylo s xpaso s appld to, th w hav (. 4 By (9, (, ad Lmma, w hav that (. Th hbyshv s mthod s dfd by, x f x f x f x f x f x x ad of cubc od (s [7, Subscto 5.]. Accodg to th hbyshv s mthod, w s that.

9 SIULTANEUS ETHDS FR FINDING ALL Thfo, th od of ê s calculatd as follows: ( ( (. ( (. odfd Nwto s thods ombd wth Dvatv-F thod I ths scto, w pst two modfd Nwto s mthods combd wth th dvatv-f mthod (6 fo fdg all smpl os of a polyomals smultaously. Th o s a fom that th dvatv-f mthod s appld to th x th tapodal Nwto s mthod ( as follows: (, ( ( ( whch s calld th dvatv-f tapodal Nwto s mthod, o smply, thod. Fom (, w hav th followg thom: Thom. If th appoxmat o x goudd fom thod s clos ough to ad th od of s th sam, th th od of ê s dtcal, ad ( s fomd. oof. Sc tovć s dvatv-f mthod (6 s of cubc od (, ( ( (4 (s [6]. Usg (4 ad th Taylo s xpaso, ẑ s calculatd as follows. (I ths cas, ( (.! (

10 JUN-SE SNG 4 ( ( ( ( 6 ( ( 4 (. Thfo, th od of ê s calculatd as follows:. Now w apply th dvatv-f mthod to th x th mdpot Nwto s mthod (4 ad costuct th tato as follows:, (5 whch s calld th dvatv-f mdpot Nwto s mthod. Fom ths, w call t thod smply. Fom (5, w hav th followg thom: Thom. If th appoxmat o x goudd fom thod s clos ough to ad th od of s th sam, th th od of ê s dtcal, ad s fomd.

11 SIULTANEUS ETHDS FR FINDING ALL 5 oof. By usg (4 ad Taylo s xpaso, ẑ s calculatd as follows. (I ths cas,!. ( ( 6 ( ( 4 4 ( 4. Thfo, th od of ê ca b calculatd as follows:.

12 6 JUN-SE SNG 4. Numcal ompaso I ths scto, w gv umcal xpmts ad compasos btw vaous smultaous mthods fo fdg os of a polyomal. Ths mthods a all of cubc od. Thy clud thod, thod, thod, th dvatv-f mthod (DF, th tovć s Nwto- stass mthod (N, ad th stass-l mthod (L. Fo a polyomal a a a, w choos tal appoxmatos as Abth s appoach (s []: ( 0 a xp π R. I ths cas, R s a adus of a ccl, wh th tal os by Abth s appoach a locatd complx umb pla. us th followg Hc s fomula to slct R (s []: R max a. Accodg to Hc s fomula, a ds { : < R} ctd at th og cotas all os of polyomal. Th polyomals that w usd o umcal compaso a as follows: ( x ( x ( x ( x 4, ( x ( x ( x ( x 4 ( x 5, (6 ( x ( x ( x ( x 4 ( x 5 ( x 6, x 5x x 7x 6x 8x x x 7. H ( x, ad ( x a lso s polyomals wh 4, 5, 6,, x spctvly.

13 SIULTANEUS ETHDS FR FINDING ALL 7 appoxmatd th os utl t satsfy th followg codto: ( 0 max < 0. m (7 I Tabl, w gv a umcal compaso btw sval mthods to fd all os of thos polyomals (6. It cotas th tato m umb m ad th valu max ( of tatv mthods, aft w appoxmatd (7 to a satsfyg labl. Th small th m, th fast appoxmatd o th os. h m s th sam, t ca b tptd m that a small max ( lads to a hgh accuacy of appoxmato. All computatos hav b do usg ATLAB. Tabl. Th umb of tatos (th o of tatv mthods oly. thod thod thod DF N L (6 (7 ( (5 (6 (5 ( 9(8-4 8(9-4 7(-4 9(9-4 8(-0 (- (7- (4-9(4- (- (- 7(- 4(- (5- (8- (8- (7- (- 4 4(- (- 0(- 4(- (- (- 5. ocluso I ths pap, th w mthods fo th smultaous appoxmato of all smpl os of polyomals by utlg th tapodal Nwto s mthod ad th mdpot Nwto s mthod w poposd. It was pov that ach mthod was of thd od. By smultaously appoxmatg all smpl os of polyomals ad by compag umcal xpmts wth vaous mthods that a of thd od, w obtad that th sults of thod ad thod a smla wth that of pvous mthods. But, w foud out that th sult of thod a supo tha that of ay oth mthods. All mthods w costuctd ths pap a w ad catv. It sms that ths mthods ca b appld to vaous flds, ad th study o th applcatos of thod s ow pogss.

14 8 JUN-SE SNG Rfcs []. Abth, Itato mthods fo fdg all os of a polyomal smultaously, ath. omput. 7 (97, [] E. Duad, Soluto Numéqus ds Équatos Algébaqus, Tom. I: Équatos du Typ F ( x 0, Racs d u olyôm, asso, as, 960. []. Hc, Appld ad omputatoal omplx Aalyss, Vol., Joh ly ad Sos Ic., Nw Yo, 974. [4] I.. K, E Gsamtschttvfah u Bchug d Nullstll vo olyom, Num. ath. 8 (966, [5]. S. tovć, D. Hcg ad I. tovć, a smultaous mthod of Nwto- stass' typ fo fdg all os fo a polyomal, Appl. ath. omput. 5 (009, [6]. S. tovć ad L. D. tovć, a cubcally covgt dvatv f oot fdg mthod, It. J. omput. ath. 84 (007, [7] J. F. Taub, Itatv thods fo th Soluto of Equatos, tc-hall, Eglwood lffs, Nw Jsy, 964. [8] S. aoo ad T. G. I. Fado, A vaat of Nwto s mthod wth acclatd thd-od covgc, Appl. ath. Ltt. (000, g

New bounds on Poisson approximation to the distribution of a sum of negative binomial random variables

New bounds on Poisson approximation to the distribution of a sum of negative binomial random variables Sogklaaka J. Sc. Tchol. 4 () 4-48 Ma. -. 8 Ogal tcl Nw bouds o Posso aomato to th dstbuto of a sum of gatv bomal adom vaabls * Kat Taabola Datmt of Mathmatcs Faculty of Scc Buaha Uvsty Muag Chobu 3 Thalad

More information

Today s topics. How did we solve the H atom problem? CMF Office Hours

Today s topics. How did we solve the H atom problem? CMF Office Hours CMF Offc ous Wd. Nov. 4 oo-p Mo. Nov. 9 oo-p Mo. Nov. 6-3p Wd. Nov. 8 :30-3:30 p Wd. Dc. 5 oo-p F. Dc. 7 4:30-5:30 Mo. Dc. 0 oo-p Wd. Dc. 4:30-5:30 p ouly xa o Th. Dc. 3 Today s topcs Bf vw of slctd sults

More information

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

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

More information

International Journal of Advanced Scientific Research and Management, Volume 3 Issue 11, Nov

International Journal of Advanced Scientific Research and Management, Volume 3 Issue 11, Nov 199 Algothm ad Matlab Pogam fo Softwa Rlablty Gowth Modl Basd o Wbull Od Statstcs Dstbuto Akladswa Svasa Vswaatha 1 ad Saavth Rama 2 1 Mathmatcs, Saaatha Collg of Egg, Tchy, Taml Nadu, Ida Abstact I ths

More information

Control Systems. Lecture 8 Root Locus. Root Locus. Plant. Controller. Sensor

Control Systems. Lecture 8 Root Locus. Root Locus. Plant. Controller. Sensor Cotol Syt ctu 8 Root ocu Clacal Cotol Pof. Eugo Schut hgh Uvty Root ocu Cotoll Plat R E C U Y - H C D So Y C C R C H Wtg th loo ga a w a ttd tackg th clod-loo ol a ga va Clacal Cotol Pof. Eugo Schut hgh

More information

Statics. Consider the free body diagram of link i, which is connected to link i-1 and link i+1 by joint i and joint i-1, respectively. = r r r.

Statics. Consider the free body diagram of link i, which is connected to link i-1 and link i+1 by joint i and joint i-1, respectively. = r r r. Statcs Th cotact btw a mapulato ad ts vomt sults tactv ocs ad momts at th mapulato/vomt tac. Statcs ams at aalyzg th latoshp btw th actuato dv tous ad th sultat oc ad momt appld at th mapulato dpot wh

More information

Homework 1: Solutions

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

More information

3.4 Properties of the Stress Tensor

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

More information

Numerical Method: Finite difference scheme

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

More information

The Odd Generalized Exponential Modified. Weibull Distribution

The Odd Generalized Exponential Modified. Weibull Distribution Itatoal Mathmatcal oum Vol. 6 o. 9 943-959 HIKARI td www.m-ha.com http://d.do.og/.988/m.6.6793 Th Odd Galzd Epotal Modd Wbull Dstbuto Yassm Y. Abdlall Dpatmt o Mathmatcal Statstcs Isttut o Statstcal Studs

More information

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

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

More information

CBSE , ˆj. cos CBSE_2015_SET-1. SECTION A 1. Given that a 2iˆ ˆj. We need to find. 3. Consider the vector equation of the plane.

CBSE , ˆj. cos CBSE_2015_SET-1. SECTION A 1. Given that a 2iˆ ˆj. We need to find. 3. Consider the vector equation of the plane. CBSE CBSE SET- SECTION. Gv tht d W d to fd 7 7 Hc, 7 7 7. Lt,. W ow tht.. Thus,. Cosd th vcto quto of th pl.. z. - + z = - + z = Thus th Cts quto of th pl s - + z = Lt d th dstc tw th pot,, - to th pl.

More information

Chapter 2 Reciprocal Lattice. An important concept for analyzing periodic structures

Chapter 2 Reciprocal Lattice. An important concept for analyzing periodic structures Chpt Rcpocl Lttc A mpott cocpt o lyzg podc stuctus Rsos o toducg cpocl lttc Thoy o cystl dcto o x-ys, utos, d lctos. Wh th dcto mxmum? Wht s th tsty? Abstct study o uctos wth th podcty o Bvs lttc Fou tsomto.

More information

On Jackson's Theorem

On Jackson's Theorem It. J. Cotm. Math. Scics, Vol. 7, 0, o. 4, 49 54 O Jackso's Thom Ema Sami Bhaya Datmt o Mathmatics, Collg o Educatio Babylo Uivsity, Babil, Iaq mabhaya@yahoo.com Abstact W ov that o a uctio W [, ], 0

More information

CBSE SAMPLE PAPER SOLUTIONS CLASS-XII MATHS SET-2 CBSE , ˆj. cos. SECTION A 1. Given that a 2iˆ ˆj. We need to find

CBSE SAMPLE PAPER SOLUTIONS CLASS-XII MATHS SET-2 CBSE , ˆj. cos. SECTION A 1. Given that a 2iˆ ˆj. We need to find BSE SMLE ER SOLUTONS LSS-X MTHS SET- BSE SETON Gv tht d W d to fd 7 7 Hc, 7 7 7 Lt, W ow tht Thus, osd th vcto quto of th pl z - + z = - + z = Thus th ts quto of th pl s - + z = Lt d th dstc tw th pot,,

More information

Convolution of Generated Random Variable from. Exponential Distribution with Stabilizer Constant

Convolution of Generated Random Variable from. Exponential Distribution with Stabilizer Constant Appld Mamacal Scc Vol 9 5 o 9 78-789 HIKARI Ld wwwm-acom p://dxdoog/988/am5559 Covoluo of Gad Radom Vaabl fom Expoal Dbuo w Sablz Coa Dod Dvao Maa Lufaa Oaa ad Maa Aa Dpam of Mamac Facul of Mamac ad Naual

More information

( m is the length of columns of A ) spanned by the columns of A : . Select those columns of B that contain a pivot; say those are Bi

( m is the length of columns of A ) spanned by the columns of A : . Select those columns of B that contain a pivot; say those are Bi Assgmet /MATH 47/Wte Due: Thusday Jauay The poblems to solve ae umbeed [] to [] below Fst some explaatoy otes Fdg a bass of the colum-space of a max ad povg that the colum ak (dmeso of the colum space)

More information

such that for 1 From the definition of the k-fibonacci numbers, the firsts of them are presented in Table 1. Table 1: First k-fibonacci numbers F 1

such that for 1 From the definition of the k-fibonacci numbers, the firsts of them are presented in Table 1. Table 1: First k-fibonacci numbers F 1 Scholas Joual of Egeeg ad Techology (SJET) Sch. J. Eg. Tech. 0; (C):669-67 Scholas Academc ad Scetfc Publshe (A Iteatoal Publshe fo Academc ad Scetfc Resouces) www.saspublshe.com ISSN -X (Ole) ISSN 7-9

More information

A study on Ricci soliton in S -manifolds.

A study on Ricci soliton in S -manifolds. IO Joual of Mathmatc IO-JM -IN: 78-578 p-in: 9-765 olum Iu I Ja - Fb 07 PP - wwwojoualo K dyavath ad Bawad Dpatmt of Mathmatc Kuvmpu vtyhaaahatta - 577 5 hmoa Kaataa Ida Abtact: I th pap w tudy m ymmtc

More information

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

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

More information

IFYFM002 Further Maths Appendix C Formula Booklet

IFYFM002 Further Maths Appendix C Formula Booklet Ittol Foudto Y (IFY) IFYFM00 Futh Mths Appd C Fomul Booklt Rltd Documts: IFY Futh Mthmtcs Syllbus 07/8 Cotts Mthmtcs Fomul L Equtos d Mtcs... Qudtc Equtos d Rmd Thom... Boml Epsos, Squcs d Ss... Idcs,

More information

Development of indirect EFBEM for radiating noise analysis including underwater problems

Development of indirect EFBEM for radiating noise analysis including underwater problems csnk 03 It. J. Naval cht. Oca E. 03 5:39~403 http://dx.do.o/0.478/ijnoe-03-04 Dvlopmt of dct EFBEM fo adat os aalyss clud udwat poblms Hyu-Wu Kwo Su-Yoo Ho ad J-Hu So 3 Rsach Isttut of Ma Systms E RIMSE

More information

Reliability of time dependent stress-strength system for various distributions

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

More information

ENGG 1203 Tutorial. Difference Equations. Find the Pole(s) Finding Equations and Poles

ENGG 1203 Tutorial. Difference Equations. Find the Pole(s) Finding Equations and Poles ENGG 03 Tutoial Systms ad Cotol 9 Apil Laig Obctivs Z tasfom Complx pols Fdbac cotol systms Ac: MIT OCW 60, 6003 Diffc Equatios Cosid th systm pstd by th followig diffc quatio y[ ] x[ ] (5y[ ] 3y[ ]) wh

More information

In 1991 Fermat s Last Theorem Has Been Proved

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

More information

GREEN S FUNCTION FOR HEAT CONDUCTION PROBLEMS IN A MULTI-LAYERED HOLLOW CYLINDER

GREEN S FUNCTION FOR HEAT CONDUCTION PROBLEMS IN A MULTI-LAYERED HOLLOW CYLINDER Joual of ppled Mathematcs ad Computatoal Mechacs 4, 3(3), 5- GREE S FUCTIO FOR HET CODUCTIO PROBLEMS I MULTI-LYERED HOLLOW CYLIDER Stasław Kukla, Uszula Sedlecka Isttute of Mathematcs, Czestochowa Uvesty

More information

Beam Warming Second-Order Upwind Method

Beam Warming Second-Order Upwind Method Beam Warmg Secod-Order Upwd Method Petr Valeta Jauary 6, 015 Ths documet s a part of the assessmet work for the subject 1DRP Dfferetal Equatos o Computer lectured o FNSPE CTU Prague. Abstract Ths documet

More information

Fairing of Parametric Quintic Splines

Fairing of Parametric Quintic Splines ISSN 46-69 Eglad UK Joual of Ifomato ad omputg Scece Vol No 6 pp -8 Fag of Paametc Qutc Sples Yuau Wag Shagha Isttute of Spots Shagha 48 ha School of Mathematcal Scece Fuda Uvesty Shagha 4 ha { P t )}

More information

Independent Domination in Line Graphs

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

More information

Order Statistics from Exponentiated Gamma. Distribution and Associated Inference

Order Statistics from Exponentiated Gamma. Distribution and Associated Inference It J otm Mth Scc Vo 4 9 o 7-9 Od Stttc fom Eottd Gmm Dtto d Aoctd Ifc A I Shw * d R A Bo G og of Edcto PO Bo 369 Jddh 438 Sd A G og of Edcto Dtmt of mthmtc PO Bo 469 Jddh 49 Sd A Atct Od tttc fom ottd

More information

k of the incident wave) will be greater t is too small to satisfy the required kinematics boundary condition, (19)

k of the incident wave) will be greater t is too small to satisfy the required kinematics boundary condition, (19) TOTAL INTRNAL RFLTION Kmacs pops Sc h vcos a coplaa, l s cosd h cd pla cocds wh h X pla; hc 0. y y y osd h cas whch h lgh s cd fom h mdum of hgh dx of faco >. Fo cd agls ga ha h ccal agl s 1 ( /, h hooal

More information

Cubic Nonpolynomial Spline Approach to the Solution of a Second Order Two-Point Boundary Value Problem

Cubic Nonpolynomial Spline Approach to the Solution of a Second Order Two-Point Boundary Value Problem Joural of Amerca Scece ;6( Cubc Nopolyomal Sple Approach to the Soluto of a Secod Order Two-Pot Boudary Value Problem W.K. Zahra, F.A. Abd El-Salam, A.A. El-Sabbagh ad Z.A. ZAk * Departmet of Egeerg athematcs

More information

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

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

More information

Unbalanced Panel Data Models

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

More information

Lecture 7 Diffusion. Our fluid equations that we developed before are: v t v mn t

Lecture 7 Diffusion. Our fluid equations that we developed before are: v t v mn t Cla ot fo EE6318/Phy 6383 Spg 001 Th doumt fo tutoal u oly ad may ot b opd o dtbutd outd of EE6318/Phy 6383 tu 7 Dffuo Ou flud quato that w dvlopd bfo a: f ( )+ v v m + v v M m v f P+ q E+ v B 13 1 4 34

More information

ANALYSIS ON THE NATURE OF THE BASIC EQUATIONS IN SYNERGETIC INTER-REPRESENTATION NETWORK

ANALYSIS ON THE NATURE OF THE BASIC EQUATIONS IN SYNERGETIC INTER-REPRESENTATION NETWORK Far East Joural of Appled Mathematcs Volume, Number, 2008, Pages Ths paper s avalable ole at http://www.pphm.com 2008 Pushpa Publshg House ANALYSIS ON THE NATURE OF THE ASI EQUATIONS IN SYNERGETI INTER-REPRESENTATION

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

Anouncements. Conjugate Gradients. Steepest Descent. Outline. Steepest Descent. Steepest Descent

Anouncements. Conjugate Gradients. Steepest Descent. Outline. Steepest Descent. Steepest Descent oucms Couga Gas Mchal Kazha (6.657) Ifomao abou h Sma (6.757) hav b pos ol: hp://www.cs.hu.u/~msha Tch Spcs: o M o Tusay afoo. o Two paps scuss ach w. o Vos fo w s caa paps u by Thusay vg. Oul Rvw of Sps

More information

On Estimation of Unknown Parameters of Exponential- Logarithmic Distribution by Censored Data

On Estimation of Unknown Parameters of Exponential- Logarithmic Distribution by Censored Data saqartvlos mcrbata rovul akadms moamb, t 9, #2, 2015 BULLETIN OF THE GEORGIAN NATIONAL ACADEMY OF SCIENCES, vol 9, o 2, 2015 Mathmatcs O Estmato of Ukow Paramtrs of Epotal- Logarthmc Dstrbuto by Csord

More information

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

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

More information

Given a table of data poins of an unknown or complicated function f : we want to find a (simpler) function p s.t. px (

Given a table of data poins of an unknown or complicated function f : we want to find a (simpler) function p s.t. px ( Iterpolato 1 Iterpolato Gve a table of data pos of a ukow or complcated fucto f : y 0 1 2 y y y y 0 1 2 we wat to fd a (smpler) fucto p s.t. p ( ) = y for = 0... p s sad to terpolate the table or terpolate

More information

Aotomorphic Functions And Fermat s Last Theorem(4)

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

More information

CH E 374 Computational Methods in Engineering Fall 2007

CH E 374 Computational Methods in Engineering Fall 2007 CH E 7 Computatoal Methods Egeerg Fall 007 Sample Soluto 5. The data o the varato of the rato of stagato pressure to statc pressure (r ) wth Mach umber ( M ) for the flow through a duct are as follows:

More information

SOME IMPUTATION METHODS IN DOUBLE SAMPLING SCHEME FOR ESTIMATION OF POPULATION MEAN

SOME IMPUTATION METHODS IN DOUBLE SAMPLING SCHEME FOR ESTIMATION OF POPULATION MEAN aoal Joual of Mod Egg Rsach (JMER) www.jm.com ol. ssu. Ja-F 0 pp-00-07 N: 9- OME MPUTATON METHOD N DOUBLE AMPLNG HEME FOR ETMATON OF POPULATON MEAN ABTRAT Nada gh Thaku Kalpaa adav fo Mahmacal ccs (M)

More information

Trace of Positive Integer Power of Adjacency Matrix

Trace of Positive Integer Power of Adjacency Matrix Global Joual of Pue ad Appled Mathematcs. IN 097-78 Volume, Numbe 07), pp. 079-087 Reseach Ida Publcatos http://www.publcato.com Tace of Postve Itege Powe of Adacecy Matx Jagdsh Kuma Pahade * ad Mao Jha

More information

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

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

More information

Different types of Domination in Intuitionistic Fuzzy Graph

Different types of Domination in Intuitionistic Fuzzy Graph Aals of Pur ad Appld Mathmatcs Vol, No, 07, 87-0 ISSN: 79-087X P, 79-0888ol Publshd o July 07 wwwrsarchmathscorg DOI: http://dxdoorg/057/apama Aals of Dffrt typs of Domato Itutostc Fuzzy Graph MGaruambga,

More information

COMPLEX NUMBERS AND ELEMENTARY FUNCTIONS OF COMPLEX VARIABLES

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

More information

In the name of Allah Proton Electromagnetic Form Factors

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

More information

Lecture 9 Multiple Class Models

Lecture 9 Multiple Class Models Lectue 9 Multple Class Models Multclass MVA Appoxmate MVA 8.4.2002 Copyght Teemu Keola 2002 1 Aval Theoem fo Multple Classes Wth jobs the system, a job class avg to ay seve sees the seve as equlbum wth

More information

XII. Addition of many identical spins

XII. Addition of many identical spins XII. Addto of may detcal sps XII.. ymmetc goup ymmetc goup s the goup of all possble pemutatos of obects. I total! elemets cludg detty opeato. Each pemutato s a poduct of a ceta fte umbe of pawse taspostos.

More information

18.413: Error Correcting Codes Lab March 2, Lecture 8

18.413: Error Correcting Codes Lab March 2, Lecture 8 18.413: Error Correctg Codes Lab March 2, 2004 Lecturer: Dael A. Spelma Lecture 8 8.1 Vector Spaces A set C {0, 1} s a vector space f for x all C ad y C, x + y C, where we take addto to be compoet wse

More information

Estimation of Stress- Strength Reliability model using finite mixture of exponential distributions

Estimation of Stress- Strength Reliability model using finite mixture of exponential distributions Iteratoal Joural of Computatoal Egeerg Research Vol, 0 Issue, Estmato of Stress- Stregth Relablty model usg fte mxture of expoetal dstrbutos K.Sadhya, T.S.Umamaheswar Departmet of Mathematcs, Lal Bhadur

More information

Introduction to logistic regression

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

More information

C-1: Aerodynamics of Airfoils 1 C-2: Aerodynamics of Airfoils 2 C-3: Panel Methods C-4: Thin Airfoil Theory

C-1: Aerodynamics of Airfoils 1 C-2: Aerodynamics of Airfoils 2 C-3: Panel Methods C-4: Thin Airfoil Theory ROAD MAP... AE301 Aerodyamcs I UNIT C: 2-D Arfols C-1: Aerodyamcs of Arfols 1 C-2: Aerodyamcs of Arfols 2 C-3: Pael Methods C-4: Th Arfol Theory AE301 Aerodyamcs I Ut C-3: Lst of Subects Problem Solutos?

More information

LECTURE 6 TRANSFORMATION OF RANDOM VARIABLES

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

More information

Professor Wei Zhu. 1. Sampling from the Normal Population

Professor Wei Zhu. 1. Sampling from the Normal Population AMS570 Pofesso We Zhu. Samplg fom the Nomal Populato *Example: We wsh to estmate the dstbuto of heghts of adult US male. It s beleved that the heght of adult US male follows a omal dstbuto N(, ) Def. Smple

More information

Existence of Nonoscillatory Solutions for a Class of N-order Neutral Differential Systems

Existence of Nonoscillatory Solutions for a Class of N-order Neutral Differential Systems Vo 3 No Mod Appd Scc Exsc of Nooscaoy Souos fo a Cass of N-od Nua Dffa Sysms Zhb Ch & Apg Zhag Dpam of Ifomao Egg Hua Uvsy of Tchoogy Hua 4 Cha E-ma: chzhbb@63com Th sach s facd by Hua Povc aua sccs fud

More information

Recent Advances in Computers, Communications, Applied Social Science and Mathematics

Recent Advances in Computers, Communications, Applied Social Science and Mathematics Recet Advaces Computes, Commucatos, Appled ocal cece ad athematcs Coutg Roots of Radom Polyomal Equatos mall Itevals EFRAI HERIG epatmet of Compute cece ad athematcs Ael Uvesty Cete of amaa cece Pa,Ael,4487

More information

Edge Product Cordial Labeling of Some Cycle Related Graphs

Edge Product Cordial Labeling of Some Cycle Related Graphs Op Joua o Dsct Mathmatcs, 6, 6, 68-78 http://.scp.o/joua/ojdm ISSN O: 6-7643 ISSN Pt: 6-7635 Ed Poduct Coda Lab o Som Cyc Ratd Gaphs Udaya M. Pajapat, Ntta B. Pat St. Xav s Co, Ahmdabad, Ida Shaksh Vaha

More information

Decomposition of Hadamard Matrices

Decomposition of Hadamard Matrices Chapter 7 Decomposto of Hadamard Matrces We hae see Chapter that Hadamard s orgal costructo of Hadamard matrces states that the Kroecer product of Hadamard matrces of orders m ad s a Hadamard matrx of

More information

Correlation in tree The (ferromagnetic) Ising model

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

More information

Best Linear Unbiased Estimators of the Three Parameter Gamma Distribution using doubly Type-II censoring

Best Linear Unbiased Estimators of the Three Parameter Gamma Distribution using doubly Type-II censoring Best Lea Ubased Estmatos of the hee Paamete Gamma Dstbuto usg doubly ype-ii cesog Amal S. Hassa Salwa Abd El-Aty Abstact Recetly ode statstcs ad the momets have assumed cosdeable teest may applcatos volvg

More information

Exponential Generating Functions - J. T. Butler

Exponential Generating Functions - J. T. Butler Epoetal Geeatg Fuctos - J. T. Butle Epoetal Geeatg Fuctos Geeatg fuctos fo pemutatos. Defto: a +a +a 2 2 + a + s the oday geeatg fucto fo the sequece of teges (a, a, a 2, a, ). Ep. Ge. Fuc.- J. T. Butle

More information

Course 10 Shading. 1. Basic Concepts: Radiance: the light energy. Light Source:

Course 10 Shading. 1. Basic Concepts: Radiance: the light energy. Light Source: Cour 0 Shadg Cour 0 Shadg. Bac Coct: Lght Sourc: adac: th lght rg radatd from a ut ara of lght ourc or urfac a ut old agl. Sold agl: $ # r f lght ourc a ot ourc th ut ara omttd abov dfto. llumato: lght

More information

School of Aerospace Engineering Origins of Quantum Theory. Measurements of emission of light (EM radiation) from (H) atoms found discrete lines

School of Aerospace Engineering Origins of Quantum Theory. Measurements of emission of light (EM radiation) from (H) atoms found discrete lines Ogs of Quatu Thoy Masuts of sso of lght (EM adato) fo (H) atos foud dsct ls 5 4 Abl to ft to followg ss psso ν R λ c λwavlgth, νfqucy, cspd lght RRydbg Costat (~09,7677.58c - ),,, +, +,..g.,,.6, 0.6, (Lya

More information

Lecture 1: Empirical economic relations

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

More information

Arithmetic Mean and Geometric Mean

Arithmetic Mean and Geometric Mean Acta Mathematca Ntresa Vol, No, p 43 48 ISSN 453-6083 Arthmetc Mea ad Geometrc Mea Mare Varga a * Peter Mchalča b a Departmet of Mathematcs, Faculty of Natural Sceces, Costate the Phlosopher Uversty Ntra,

More information

Section 5.1/5.2: Areas and Distances the Definite Integral

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

More information

Handout 7. Properties of Bloch States and Electron Statistics in Energy Bands

Handout 7. Properties of Bloch States and Electron Statistics in Energy Bands Hdout 7 Popts of Bloch Stts d Elcto Sttstcs Eg Bds I ths lctu ou wll l: Popts of Bloch fuctos Podc boud codtos fo Bloch fuctos Dst of stts -spc Elcto occupto sttstcs g bds ECE 407 Spg 009 Fh R Coll Uvst

More information

{ }{ ( )} (, ) = ( ) ( ) ( ) Chapter 14 Exercises in Sampling Theory. Exercise 1 (Simple random sampling): Solution:

{ }{ ( )} (, ) = ( ) ( ) ( ) Chapter 14 Exercises in Sampling Theory. Exercise 1 (Simple random sampling): Solution: Chapter 4 Exercses Samplg Theory Exercse (Smple radom samplg: Let there be two correlated radom varables X ad A sample of sze s draw from a populato by smple radom samplg wthout replacemet The observed

More information

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

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

More information

Analysis of Lagrange Interpolation Formula

Analysis of Lagrange Interpolation Formula P IJISET - Iteratoal Joural of Iovatve Scece, Egeerg & Techology, Vol. Issue, December 4. www.jset.com ISS 348 7968 Aalyss of Lagrage Iterpolato Formula Vjay Dahya PDepartmet of MathematcsMaharaja Surajmal

More information

Log1 Contest Round 2 Theta Complex Numbers. 4 points each. 5 points each

Log1 Contest Round 2 Theta Complex Numbers. 4 points each. 5 points each 01 Log1 Cotest Roud Theta Complex Numbers 1 Wrte a b Wrte a b form: 1 5 form: 1 5 4 pots each Wrte a b form: 65 4 4 Evaluate: 65 5 Determe f the followg statemet s always, sometmes, or ever true (you may

More information

CIVL 7/ D Boundary Value Problems - Axisymmetric Elements 1/8

CIVL 7/ D Boundary Value Problems - Axisymmetric Elements 1/8 CIVL 7/8 -D Bounday Valu Poblms - xsymmtc Elmnts /8 xsymmtc poblms a somtms fd to as adally symmtc poblms. hy a gomtcally th-dmnsonal but mathmatcally only two-dmnsonal n th physcs of th poblm. In oth

More information

Almost all Cayley Graphs Are Hamiltonian

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

More information

On the convergence of derivatives of Bernstein approximation

On the convergence of derivatives of Bernstein approximation O the covergece of dervatves of Berste approxmato Mchael S. Floater Abstract: By dfferetatg a remader formula of Stacu, we derve both a error boud ad a asymptotc formula for the dervatves of Berste approxmato.

More information

Inequalities for Dual Orlicz Mixed Quermassintegrals.

Inequalities for Dual Orlicz Mixed Quermassintegrals. Advaces Pue Mathematcs 206 6 894-902 http://wwwscpog/joual/apm IN Ole: 260-0384 IN Pt: 260-0368 Iequaltes fo Dual Olcz Mxed Quemasstegals jua u chool of Mathematcs ad Computatoal cece Hua Uvesty of cece

More information

The Linear Probability Density Function of Continuous Random Variables in the Real Number Field and Its Existence Proof

The Linear Probability Density Function of Continuous Random Variables in the Real Number Field and Its Existence Proof MATEC Web of Cofeeces ICIEA 06 600 (06) DOI: 0.05/mateccof/0668600 The ea Pobablty Desty Fucto of Cotuous Radom Vaables the Real Numbe Feld ad Its Estece Poof Yya Che ad Ye Collee of Softwae, Taj Uvesty,

More information

Non-axial symmetric loading on axial symmetric. Final Report of AFEM

Non-axial symmetric loading on axial symmetric. Final Report of AFEM No-axal symmetc loadg o axal symmetc body Fal Repot of AFEM Ths poject does hamoc aalyss of o-axal symmetc loadg o axal symmetc body. Shuagxg Da, Musket Kamtokat 5//009 No-axal symmetc loadg o axal symmetc

More information

Solving Constrained Flow-Shop Scheduling. Problems with Three Machines

Solving Constrained Flow-Shop Scheduling. Problems with Three Machines It J Cotemp Math Sceces, Vol 5, 2010, o 19, 921-929 Solvg Costraed Flow-Shop Schedulg Problems wth Three Maches P Pada ad P Rajedra Departmet of Mathematcs, School of Advaced Sceces, VIT Uversty, Vellore-632

More information

Dual adaptive control of mechanical arm

Dual adaptive control of mechanical arm Itatoal Joual of Avac Rsach Comput Egg & chology (IJARCE Volum 6 Issu 9 Sptmb 07 ISSN: 78 33 Dual aaptv cotol of mchacal am Bgtao Lu Jx Pu Jg Lu Abstact Amg at th fctoal foc a th molg o caus by th chag

More information

Ruin Probability in a Generalized Risk Process under Rates of Interest with Homogenous Markov Chain Claims and Homogenous Markov Chain Interests

Ruin Probability in a Generalized Risk Process under Rates of Interest with Homogenous Markov Chain Claims and Homogenous Markov Chain Interests Appld Mathmatc 3, 3(5: 85-97 DOI:.593/.am.335.5 u Pbablty a Gald Pc ud at f Itt wth Hmgu Mav Cha Clam ad Hmgu Mav Cha Itt Quag Phug Duy Dpatmt f Mathmatc, Fg Tad Uvty, Ha, Vt Nam Abtact Th am f th pap

More information

UNIT 2 SOLUTION OF ALGEBRAIC AND TRANSCENDENTAL EQUATIONS

UNIT 2 SOLUTION OF ALGEBRAIC AND TRANSCENDENTAL EQUATIONS Numercal Computg -I UNIT SOLUTION OF ALGEBRAIC AND TRANSCENDENTAL EQUATIONS Structure Page Nos..0 Itroducto 6. Objectves 7. Ital Approxmato to a Root 7. Bsecto Method 8.. Error Aalyss 9.4 Regula Fals Method

More information

Chapter 9 Jordan Block Matrices

Chapter 9 Jordan Block Matrices Chapter 9 Jorda Block atrces I ths chapter we wll solve the followg problem. Gve a lear operator T fd a bass R of F such that the matrx R (T) s as smple as possble. f course smple s a matter of taste.

More information

signal amplification; design of digital logic; memory circuits

signal amplification; design of digital logic; memory circuits hatr Th lctroc dvc that s caabl of currt ad voltag amlfcato, or ga, cojucto wth othr crcut lmts, s th trasstor, whch s a thr-trmal dvc. Th dvlomt of th slco trasstor by Bard, Bratta, ad chockly at Bll

More information

ROOT-LOCUS ANALYSIS. Lecture 11: Root Locus Plot. Consider a general feedback control system with a variable gain K. Y ( s ) ( ) K

ROOT-LOCUS ANALYSIS. Lecture 11: Root Locus Plot. Consider a general feedback control system with a variable gain K. Y ( s ) ( ) K ROOT-LOCUS ANALYSIS Coder a geeral feedback cotrol yte wth a varable ga. R( Y( G( + H( Root-Locu a plot of the loc of the pole of the cloed-loop trafer fucto whe oe of the yte paraeter ( vared. Root locu

More information

Application Of Alternating Group Explicit Method For Parabolic Equations

Application Of Alternating Group Explicit Method For Parabolic Equations WSEAS RANSACIONS o INFORMAION SCIENCE ad APPLICAIONS Qghua Feg Applcato Of Alteatg oup Explct Method Fo Paabolc Equatos Qghua Feg School of Scece Shadog uvesty of techology Zhagzhou Road # Zbo Shadog 09

More information

D. Bertsekas and R. Gallager, "Data networks." Q: What are the labels for the x-axis and y-axis of Fig. 4.2?

D. Bertsekas and R. Gallager, Data networks. Q: What are the labels for the x-axis and y-axis of Fig. 4.2? pd by J. Succ ECE 543 Octob 22 2002 Outl Slottd Aloh Dft Stblzd Slottd Aloh Uslottd Aloh Splttg Algoths Rfc D. Btsks d R. llg "Dt twoks." Rvw (Slottd Aloh): : Wht th lbls fo th x-xs d y-xs of Fg. 4.2?

More information

ME 501A Seminar in Engineering Analysis Page 1

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

More information

Chapter 5 Properties of a Random Sample

Chapter 5 Properties of a Random Sample Lecture 6 o BST 63: Statstcal Theory I Ku Zhag, /0/008 Revew for the prevous lecture Cocepts: t-dstrbuto, F-dstrbuto Theorems: Dstrbutos of sample mea ad sample varace, relatoshp betwee sample mea ad sample

More information

2. THERMODYNAMICS and ENSEMBLES (Part B) R. Bhattacharya, Department of Physics, Jadavpur University, Kolkata 32

2. THERMODYNAMICS and ENSEMBLES (Part B) R. Bhattacharya, Department of Physics, Jadavpur University, Kolkata 32 . THRMODYAMICS ad SMBLS (Pat B R. Battacaya, Dpatmt of Pyscs, Jadavpu Uvsty, Kolkata.4 smbls A smbl s a collcto of plcas of mmb systms wc av dtcal macoscopc paamts, but wos mcoscopc dscptos of ts mmbs

More information

Introduction to logistic regression

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

More information

Non-degenerate Perturbation Theory

Non-degenerate Perturbation Theory No-degeerate Perturbato Theory Proble : H E ca't solve exactly. But wth H H H' H" L H E Uperturbed egevalue proble. Ca solve exactly. E Therefore, kow ad. H ' H" called perturbatos Copyrght Mchael D. Fayer,

More information

On the Interval Zoro Symmetric Single Step. Procedure IZSS1-5D for the Simultaneous. Bounding of Real Polynomial Zeros

On the Interval Zoro Symmetric Single Step. Procedure IZSS1-5D for the Simultaneous. Bounding of Real Polynomial Zeros It. Joural of Math. Aalyss, Vol. 7, 2013, o. 59, 2947-2951 HIKARI Ltd, www.m-hkar.com http://dx.do.org/10.12988/ma.2013.310259 O the Iterval Zoro Symmetrc Sgle Step Procedure IZSS1-5D for the Smultaeous

More information

Boyce/DiPrima 9 th ed, Ch 7.6: Complex Eigenvalues

Boyce/DiPrima 9 th ed, Ch 7.6: Complex Eigenvalues BocDPm 9 h d Ch 7.6: Compl Egvlus Elm Dffl Equos d Boud Vlu Poblms 9 h do b Wllm E. Boc d Rchd C. DPm 9 b Joh Wl & Sos Ic. W cosd g homogous ssm of fs od l quos wh cos l coffcs d hus h ssm c b w s ' A

More information

The R Package PK for Basic Pharmacokinetics

The R Package PK for Basic Pharmacokinetics Wolfsggr, h R Pacag PK St 6 h R Pacag PK for Basc Pharmacotcs Mart J. Wolfsggr Dpartmt of Bostatstcs, Baxtr AG, Va, Austra Addrss of th author: Mart J. Wolfsggr Dpartmt of Bostatstcs Baxtr AG Wagramr Straß

More information

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

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

More information

Chapter 4 (Part 1): Non-Parametric Classification (Sections ) Pattern Classification 4.3) Announcements

Chapter 4 (Part 1): Non-Parametric Classification (Sections ) Pattern Classification 4.3) Announcements Aoucemets No-Parametrc Desty Estmato Techques HW assged Most of ths lecture was o the blacboard. These sldes cover the same materal as preseted DHS Bometrcs CSE 90-a Lecture 7 CSE90a Fall 06 CSE90a Fall

More information

F. Inequalities. HKAL Pure Mathematics. 進佳數學團隊 Dr. Herbert Lam 林康榮博士. [Solution] Example Basic properties

F. Inequalities. HKAL Pure Mathematics. 進佳數學團隊 Dr. Herbert Lam 林康榮博士. [Solution] Example Basic properties 進佳數學團隊 Dr. Herbert Lam 林康榮博士 HKAL Pure Mathematcs F. Ieualtes. Basc propertes Theorem Let a, b, c be real umbers. () If a b ad b c, the a c. () If a b ad c 0, the ac bc, but f a b ad c 0, the ac bc. Theorem

More information