Quantization in Dynamic Smarandache Multi-Space

Size: px
Start display at page:

Download "Quantization in Dynamic Smarandache Multi-Space"

Transcription

1 Quatzato Dyamc Smaradache Mult-Space Fu Yuhua Cha Offshore Ol Research Ceter, Beg, 7, Cha (E-mal: ) Abstract: Dscussg the applcatos of Dyamc Smaradache Mult-Space (DSMS) Theory. Supposg for the dfferet dyamc spaces ( s a dyamc postve teger ad the fucto of tme) the dfferet equatos have bee establshed, as these dfferet dyamc spaces sythesze the DSMS, ad they are mutually affected, some ew coupled equatos eed to establsh the DSMS to replace some equatos the orgal dyamc spaces, as well as supply other equatos to process the cotact, boudary codtos ad so o. For the ufed processg of all equatos the DSMS, ths paper proposes to ru the quatzato processg to all the varables ad all the equatos ad establsh the ufed varatoal prcple of quatzato wth the collocato method based o the method of weghted resduals, ad smultaeously solve all the equatos the DSMS wth the optmzato method. Thus by usg the ufed varatoal prcple of quatzato the DSMS ad the fractal quatzato method, wll pave the way for the ufed processg of the theory of relatvty ad the quatum mechacs, ad the ufed processg of the four foudatoal teractos. Fally the coupled soluto for the problem of relatvty ad quatum mechacs s dscussed. Key words: Dyamc Smaradache Mult-Space (DSMS), coupled equato, collocato method, ufed varatoal prcple of quatzato, fractal quatzato, four foudatoal teractos, ufed processg Itroducto I ths paper we wll propose the structure of Dyamc Smaradache Mult-Space (DSMS) frstly, the dscuss the problems of the ew coupled equatos the DSMS, ufed processg all the equatos the DSMS, rug the quatzato processg to all the varables ad equatos wth the collocato method based o the method of weghted resduals, varable quatzato method, fractal quatzato method, the ufed varatoal prcple of quatzato, the coupled soluto for the problem of relatvty ad quatum mechacs, ad so o. Smaradache Mult-Space ad Dyamc Smaradache Mult-Space The oto of Smaradache Mult-Space was proposed by Smaradache 969 [-3]. A Smaradache mult-space s a uo of sets or spaces M, M, M. M = M U M U U M where s a teger, ad. For the reaso that s a costat, so the Smaradache Mult-Space s a statc Mult-Space. But may practcal problems, for the cotuous chage of the umber ad structure of the sets or spaces, the dyamc mult-space should be cosdered. We defe that a Dyamc Smaradache Mult-Space (DSMS) s a uo of ( sets or spaces M (, M (, M (.

2 M( = M ( U M ( U U M ( where( s tegers, ad the fucto of tme t(. Ufed varatoal prcple sgle space I the referece [4], a ufed varatoal prcple of flud mechacs was preseted for the sgle flud space.. Optmzato method of weghted resduals Optmzato method of weghted resduals (OMWR) has bee used for solvg some problems mechacs, physcs ad astroomy [5], t ca be stated brefly as follows. Supposg that we have the operator equato F = doma Boudary codto B = o boudary S The the fuctoal Π defed by OMWR reads F) d W A ( B) ds = A m S ( 3 where A (F) s a o-egatve fuctoal about F ts value beg = f F = A ( F) 4 > f F m deotes mmum ad ts value should be equal to zero. It was troduced the referece [6]. W s postve weghted umber, ad may cases t ca be tae as W =. The way for choosg W geeral cases s show the referece [7]. I Eq.(4), f the cases of A ( F) = F, the t gves the Least Square Method. I the referece [7], A ( F) = F F 4 F F e F max ad the le have also bee dscussed. Obvously the codto of zero mmum value of fuctoal Π Eq.(3) s equvalet to the soluto of Eqs.() ad (), the proof s show the referece [4]. It should be oted that, f the doma cossts of soltary pots P ~ P, the boudary S cossts of m soltary pots Q ~ Qm (such as the quatzato problems we wll dscuss ths paper), the the tegral Eq.(3) should be replaced by the sum W A F( P )) W W A ( B( Q )) = m ( m 3 I addto, the establshmet of fuctoal Π OMWR s very easy, ad the mmum value of Π s ow advace (equal to zero). As for fdg the mmum value of Π, two methods ca be used. Oe s solvg the

3 equatos Π '= gve by the statoary codto. Aother s usg the optmzato methods used the refereces [4-8] such as steepest descet method, searchg method.. A ufed varatoal prcple of flud mechacs (UPFM) Basc equatos of flud mechacs ad boudary codto read Cotuty equato F = doma 5 Equato of moto G = doma 6 Eergy equato H = doma 7 Costtutve equato I = doma 8 Equato of state J = doma 9 Boudary codto B = o boudary S Accordg to OMWR, the UPFM the followg form ca be obtaed A ( G) d W A ( H d W A F) d W ) ( 3 A ( J ) d W6 A ( B) ds = W4 A ( I) d W5 m S where W s sutable postve weghted umbers, A (F) A (G) ad the le are o-egatve fuctoals defed by Eq.(4). By usg ths method, the referece [8] ufed processg varous water gravty wave theores, the referece [4] accordg to the OMWR for soltary doma or soltary pot, the soluto of hydrodyamcs equato for the soltary doma or for the soltary pot (pot soluto) s developed. I the solvg process, the compatblty wth other doma or other pot does ot eed to be cosdered. 3 Ufed varatoal prcple the DSMS All the equatos should be cosdered the DSMS are as follows The equatos that are establshed the orgal ( sets or spaces, ad stll correct the DSMS F = doma = ~ ( The boudary codtos that are establshed the orgal ( sets or spaces, ad stll correct the DSMS B = o boudary S = ~ ( 3 As the ( dfferet dyamc sets or spaces sythesze the DSMS, ad they are mutually affected, some ew coupled equatos eed to establsh the DSMS to replace some equatos the orgal dyamc sets or spaces, as well as supply other equatos to process the cotact, boudary codtos ad so o. Here the cotact codto deotes the codto should be satsfed the case that two sets or spaces have partal commo elemets. As establshg the ew coupled equatos the DSMS, the exstg coupled equatos physcs ca be referred, such as the pressure-velocty coupled equato, 3

4 temperature-stress coupled equato, ad so o. For the sae of coveece, all the ew coupled equatos, the cotact ad boudary codtos ad so o wll be wrtte the ufed form as follows ( whch doma or boudary) C = for = ~ m( 4 Accordg to OMWR, the ufed varatoal prcple the DSMS s as follows ( W A ( F ) d W A ( B) ds m( ( W3 A ( C ) d = m 5 S may be It also should be oted that, f the doma cossts of '( soltary pots P ~ P ', the boudary S cossts of m '( soltary pots Q ~ Q m ', the doma cossts of ( soltary pots P ~ P, the the tegral Eq.(5) should be replaced by the sum, ad the ufed varatoal prcple of quatzato may be obtaed ( W '( W A ( F ( P )) ' ' ( W m'( W A ( B ( Q " " )) m( 3 ( W W A ( C ( P )) = m 5 ' ' 4 arable quatzato ad equato quatzato The varable quatzato cossts two methods: average value method ad represetatve value method. The average value method meas that the value of a whole terval s represeted by the average value of ths terval. For example, a spaceshp avgates alog a straght le. Its speed orgally s cotual. But we tae the cotual fve le segmets, ad tae the average speed of each le segmet, moreover the speed of etre le segmet s represeted by the average speed. The the speed has ot bee cotual, we may th that the speed has bee carred o the quatzato processg (smlarly, other parameters such as eergy, temperature ad so o, may be carred o the quatzato processg). The represetatve value method meas that the value of a whole terval s represeted by the value of a represetatve pot that s chose sutably ths terval. For the equato quatzato, the represetatve value method ca be used oly, because the soluto of equato s ot ow advace. The fte dfferece method ad the fte elemet method are all the typcal 4

5 represetatve value method for the equato quatzato. Obvously, quatzato methods for varable ad equato are sutable for all the cotuty problems. 5 Fractal quatzato The quatzato realzed by fractal method, could be reached through tag certa varables fractal formula for the tegers. The fractal formula reads C N = 6 D r Now, the fractal formula, we carry o the quatzato processg to value of N, amely tae the value of N for the arrage sequece umber, ad N =,, 3. For example, the solar system for the orbtal moto average veloctes of the e plaets (ut: m/s), tag the characterstc dmeso r for some plaet orbtal moto average velocty, tag the value of N for the seral umber accordg to the sze of the orbtal moto average velocty, frstly cosderg the case of Mercury r=47.89, the we have N= (Mercury's orbtal moto average velocty s the greates, thereupo we have the coordates pot (47.89, ), accordg to aalogzes other 8 plaets coordates pots are as follows: (35.3, ), (9.79, 3), (4.3, 4), (3.6, 5), (9.64, 6), (6.8, 7), (5.43, 8), (4.74, 9). The above 9 coordates pots may be plotted o the double logarthmc coordates, the we may obta 8 straght-le segmets. I order to adapt the applcato of Smaradache geometrc ad eutrosophc methods, here we do ot fd the fttg curve of these 8 straght-le segmets wth least squares method, but for the 8 straght les, usg the coordates pots data to determe accurately ther fractal parameters (costat C ad fractal dmeso D ). For example, accordg to Mercury's coordates (47.89, ) ad eus's coordates (35.3, ), may obta the fractal parameters for the frst straght-le segmet: C =53.684, D = The fractal formula for the frst straght-le segmet reads: N =. Ths formula r may be used as the extrapolato formula to forecast the orbtal moto average velocty of ext plaet (Earth) by substtutg N =3 to ths formula ad solvg the value of r. Smlarly, all the forecastg results for other plaets may be reached. By usg the st straght-le segmet, the forecastg result of the orbtal moto average velocty of ext plaet (Earth) reads: =9.7, the error s.7%. By usg the d straght-le segmet, the forecastg result of ext plaet (Mars) reads: =6.55, the error s.%. By usg the 3rd straght-le segmet, the forecastg result of ext plaet (Jupter) reads: =.49, the error s 59.9%. By usg the 4th straght-le segmet, the forecastg result of ext plaet (Satur) reads: =7.9, the error s 8.%. By usg the 5th straght-le segmet, the forecastg result of ext plaet (Uraus) reads: =7.46, the error s 9.5%. By usg the 6th straght-le segmet, the forecastg result of ext plaet (Neptue) reads: =5.4, the error s 7.9%. 5

6 By usg the 7th straght-le segmet, the forecastg result of ext plaet (Pluto) reads: =4.45, the error s 6.8%. By usg the 8th straght-le segmet, the forecastg result of ext plaet (teth plae reads: =4., the error s determacy, because the teth plaet s ot dscovered. I addto, the referece [9], the cocept of varable dmeso fractal was troduced. I whch the fractal dmeso D s a varable stead of a costat, for example D = a ar ar... a r 7 I some cases, for the sae of coveece, the fractal formula ca be wrtte as the followg form l N = l C D l r 8 Substtutg Eq.7 to Eq.8, the form easy to hadle the fractal quatzato ca be obtaed l N = l C ( a a r a r... a r ) l r 9 Namely F = l C ( a ar ar... a r ) l r l N = 6 Applcato of ufed varatoal prcple of quatzato ad fractal quatzato Now we dscuss the ufed processg for the problems of specal relatvty ad quatum mechacs. Cosderg the ufed processg for the problems of the wavelegth of Balmer seres atomc spectrum of hydroge ad the ultmate speed expermet. Wth the method of quatum mechacs, the wavelegth of Balmer seres atomc spectrum of hydroge may be obtaed theoretcally 4 λ ( ) = 9. = 3,4, 5 4 From table we ca see that comparg wth the expermetal data λ 3), λ (4), (5), the results of Eq.() have some small errors. ( λ I order to obta the better results, we choose the pots wth = 3,4, 5 as the represetatve pots, usg the fractal quatzato method ad supposg C λ ( ) = = 3,4, 5 D The cocrete form wll be decded wth varatoal prcple of quatzato. Table. Wavelegth of Balmer seres atomc spectrum of hydroge 6

7 alue of Expermetal value of λ Quatum mechacs method aratoal prcple of quatzatoo-coupled soluto aratoal prcple of quatzatocoupled soluto I specal relatvty, as dscussg the ultmate speed, t gves E v ( E ) = c [ ( ) ] 3 m c From table we ca see that comparg wth the Bertozz expermetal value of v, the results of Eq.(3) also have some small errors. I order to obta the better results, usg the fractal quatzato method ad supposg C v ( E ) = E =.,.8, E D Table. Bertozz ultmate speed expermet alue of eergy E Expermetal value of v Specal relatvty aratoal prcple of quatzatoo-coupled soluto aratoal prcple of quatzatocoupled soluto I varatoal prcple 5, adoptg Least Square Method, ad the weghted umber beg, the we have Π 5 Π = m where [l λ ( ) l λ( )] Π = 3,4,5 = [l v ( E ) l v ( E )] E =.,.8,4.7 Because the coupled equato of quatum mechacs ad specal relatvty has ot bee establshed, there s ot such term Eq.(5). Now we dscuss the soluto of Eq.(5). Frst s the o-coupled soluto, amely the quatum mechacs soluto ad the specal relatvty soluto wll have ot ay relatos. I Eqs.() ad (4), supposg 7

8 D = a a D = b b E The the soluto of Eq.(5) s decded wth the followg equatos Π C = Π a = Π b = 6 Solvg these equatos, the o-coupled expressos of fractal quatzato for the wavelegth ad ultmate speed are as follows λ ( ) = = 3,4, v ( E ) = E =.,.8,4. E E From Eqs.(7) ad (8), we ca see that C = C a = b. The results of fractal formulas Eqs.(7) ad (8) are show Table ad table, these results ad the expermetal results are completely same. Secodly dscussg the coupled soluto, we tae all the costat terms the quatum mechacs soluto to be equal to all these the specal relatvty soluto. Namely Eqs.() ad (4) we should have C = C a = b D = a a a D = b b E b E Solvg Eqs.(6), the coupled expressos of fractal quatzato for the wavelegth ad ultmate speed are as follows λ ( ) = = 3,4, v ( E ) = E =.,.8,4. E E E The results of fractal formulas Eqs.(9) ad (3) are show Table ad table, these results ad the expermetal results are completely same. From the above results we may see that, for the questos of two completely dfferet domas of quatum mechacs ad specal relatvty, the extremely smlar solutos ca be obtaed wth the method preseted ths paper. Moreover, although the coupled equato of quatum mechacs ad specal relatvty has ot bee establshed, the 8

9 coupled soluto for the quatum mechacs ad the theory of relatvty really ca be obtaed. 7 Further dscusso The ufed varatoal prcple of quatzato DSMS ad the fractal quatzato method, smlarly ca be used for the ufed processg of the questos dfferet domas. Thus wll pave the way for the ufed processg of the theory of relatvty ad the quatum mechacs, ad the ufed processg of the four foudatoal teractos. For example, we may dscuss the smplest stuato, amely the equatos for descrbg the four foudatoal teractos read: F = = ~ 4, structurg ther acto domas as a DSMS, supposg that all the coupled equatos ad supplemetary cotact ad boudary codtos read: C =, the the varatoal prcple for the ufed processg of the four foudatoal teractos may be wrtte as follows 4 = W F d W C d = m The further topcs are how to derve the coupled equatos DSMS, as well as supplemet sutable cotact ad boudary codtos ad so o. Whe these equatos have ot bee establshed, the coupled soluto for dfferet domas may be obtaed frstly, order to dscover the commo groud of them, ad create the codtos for fally dervg the coupled equatos ad so o. Refereces F. Smaradache. Mxed oeucldea geometres. eprt arxv: math/9. /. F. Smaradache. A Ufyg Feld Logcs. Neutrosopy: Neutrosophc Probablty, Set, ad Logc. Amerca research Press. Rehoboth L. F. Mao. Smaradache mult-space theory. Hexs. Phoex. AZ, 6. 4 Fu Yuhua. A Ufed aratoal Prcple of Flud Mechacs ad Applcato o Soltary Subdoma or Pot. Cha Ocea Egeerg, 994, ol.8, No., Fu Yuhua. The applcato of optmum MWR mechacs, physcs ad astroomy, : The advace ad egeerg applcato of the method of weghted resdual, Wuha Uversty Press, 99. ( Chese) 6 Fu Yuhua. New soluto for problem advace of Mercury s perhelo. Acta Astroomca Sca, 3(4). ( Chese) 7 Fu Yuhua. A ew way for solvg boudary layer equatos, Cha Offshore Ol Gas (Egeerg), (6). ( Chese) 8 Fu Yuhua. Ufed Water Gravty Wave Theory ad Improved Lear Wave. Cha Ocea Egeerg, 6(). 9 Fu Yuhua. arable dmeso fractal flud mechacs. Proceedgs of secod computatoal hydrodyamcs coferece. Wuha, 993~7. ( Chese) 9

Functions of Random Variables

Functions of Random Variables Fuctos of Radom Varables Chapter Fve Fuctos of Radom Varables 5. Itroducto A geeral egeerg aalyss model s show Fg. 5.. The model output (respose) cotas the performaces of a system or product, such as weght,

More information

DIFFERENTIAL GEOMETRIC APPROACH TO HAMILTONIAN MECHANICS

DIFFERENTIAL GEOMETRIC APPROACH TO HAMILTONIAN MECHANICS DIFFERENTIAL GEOMETRIC APPROACH TO HAMILTONIAN MECHANICS Course Project: Classcal Mechacs (PHY 40) Suja Dabholkar (Y430) Sul Yeshwath (Y444). Itroducto Hamltoa mechacs s geometry phase space. It deals

More information

Mu Sequences/Series Solutions National Convention 2014

Mu Sequences/Series Solutions National Convention 2014 Mu Sequeces/Seres Solutos Natoal Coveto 04 C 6 E A 6C A 6 B B 7 A D 7 D C 7 A B 8 A B 8 A C 8 E 4 B 9 B 4 E 9 B 4 C 9 E C 0 A A 0 D B 0 C C Usg basc propertes of arthmetc sequeces, we fd a ad bm m We eed

More information

L5 Polynomial / Spline Curves

L5 Polynomial / Spline Curves L5 Polyomal / Sple Curves Cotets Coc sectos Polyomal Curves Hermte Curves Bezer Curves B-Sples No-Uform Ratoal B-Sples (NURBS) Mapulato ad Represetato of Curves Types of Curve Equatos Implct: Descrbe a

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

Introduction to local (nonparametric) density estimation. methods

Introduction to local (nonparametric) density estimation. methods Itroducto to local (oparametrc) desty estmato methods A slecture by Yu Lu for ECE 66 Sprg 014 1. Itroducto Ths slecture troduces two local desty estmato methods whch are Parze desty estmato ad k-earest

More information

MOLECULAR VIBRATIONS

MOLECULAR VIBRATIONS MOLECULAR VIBRATIONS Here we wsh to vestgate molecular vbratos ad draw a smlarty betwee the theory of molecular vbratos ad Hückel theory. 1. Smple Harmoc Oscllator Recall that the eergy of a oe-dmesoal

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

BERNSTEIN COLLOCATION METHOD FOR SOLVING NONLINEAR DIFFERENTIAL EQUATIONS. Aysegul Akyuz Dascioglu and Nese Isler

BERNSTEIN COLLOCATION METHOD FOR SOLVING NONLINEAR DIFFERENTIAL EQUATIONS. Aysegul Akyuz Dascioglu and Nese Isler Mathematcal ad Computatoal Applcatos, Vol. 8, No. 3, pp. 293-300, 203 BERNSTEIN COLLOCATION METHOD FOR SOLVING NONLINEAR DIFFERENTIAL EQUATIONS Aysegul Ayuz Dascoglu ad Nese Isler Departmet of Mathematcs,

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

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

{ }{ ( )} (, ) = ( ) ( ) ( ) 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 Mathematical Appendix

The Mathematical Appendix The Mathematcal Appedx Defto A: If ( Λ, Ω, where ( λ λ λ whch the probablty dstrbutos,,..., Defto A. uppose that ( Λ,,..., s a expermet type, the σ-algebra o λ λ λ are defed s deoted by ( (,,...,, σ Ω.

More information

CHAPTER VI Statistical Analysis of Experimental Data

CHAPTER VI Statistical Analysis of Experimental Data Chapter VI Statstcal Aalyss of Expermetal Data CHAPTER VI Statstcal Aalyss of Expermetal Data Measuremets do ot lead to a uque value. Ths s a result of the multtude of errors (maly radom errors) that ca

More information

1 Onto functions and bijections Applications to Counting

1 Onto functions and bijections Applications to Counting 1 Oto fuctos ad bectos Applcatos to Coutg Now we move o to a ew topc. Defto 1.1 (Surecto. A fucto f : A B s sad to be surectve or oto f for each b B there s some a A so that f(a B. What are examples of

More information

ESS Line Fitting

ESS Line Fitting ESS 5 014 17. Le Fttg A very commo problem data aalyss s lookg for relatoshpetwee dfferet parameters ad fttg les or surfaces to data. The smplest example s fttg a straght le ad we wll dscuss that here

More information

Block-Based Compact Thermal Modeling of Semiconductor Integrated Circuits

Block-Based Compact Thermal Modeling of Semiconductor Integrated Circuits Block-Based Compact hermal Modelg of Semcoductor Itegrated Crcuts Master s hess Defese Caddate: Jg Ba Commttee Members: Dr. Mg-Cheg Cheg Dr. Daqg Hou Dr. Robert Schllg July 27, 2009 Outle Itroducto Backgroud

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

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

Chapter 11 Systematic Sampling

Chapter 11 Systematic Sampling Chapter stematc amplg The sstematc samplg techue s operatoall more coveet tha the smple radom samplg. It also esures at the same tme that each ut has eual probablt of cluso the sample. I ths method of

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

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

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

EVALUATION OF FUNCTIONAL INTEGRALS BY MEANS OF A SERIES AND THE METHOD OF BOREL TRANSFORM

EVALUATION OF FUNCTIONAL INTEGRALS BY MEANS OF A SERIES AND THE METHOD OF BOREL TRANSFORM EVALUATION OF FUNCTIONAL INTEGRALS BY MEANS OF A SERIES AND THE METHOD OF BOREL TRANSFORM Jose Javer Garca Moreta Ph. D research studet at the UPV/EHU (Uversty of Basque coutry) Departmet of Theoretcal

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

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

Analysis of Variance with Weibull Data

Analysis of Variance with Weibull Data Aalyss of Varace wth Webull Data Lahaa Watthaacheewaul Abstract I statstcal data aalyss by aalyss of varace, the usual basc assumptos are that the model s addtve ad the errors are radomly, depedetly, ad

More information

PGE 310: Formulation and Solution in Geosystems Engineering. Dr. Balhoff. Interpolation

PGE 310: Formulation and Solution in Geosystems Engineering. Dr. Balhoff. Interpolation PGE 30: Formulato ad Soluto Geosystems Egeerg Dr. Balhoff Iterpolato Numercal Methods wth MATLAB, Recktewald, Chapter 0 ad Numercal Methods for Egeers, Chapra ad Caale, 5 th Ed., Part Fve, Chapter 8 ad

More information

ON THE MOTION OF PLANAR BARS SYSTEMS WITH CLEARANCES IN JOINTS

ON THE MOTION OF PLANAR BARS SYSTEMS WITH CLEARANCES IN JOINTS ON THE MOTION OF PLANAR BARS SYSTEMS WITH CLEARANCES IN JOINTS Şl uv dr g Ja-Crsta GRIGORE, Uverstatea d Pteşt, strtîrgu dvale Nr Prof uv dr g Ncolae PANDREA, Uverstatea d Pteşt, strtîrgu dvale Nr Cof

More information

Lecture 12 APPROXIMATION OF FIRST ORDER DERIVATIVES

Lecture 12 APPROXIMATION OF FIRST ORDER DERIVATIVES FDM: Appromato of Frst Order Dervatves Lecture APPROXIMATION OF FIRST ORDER DERIVATIVES. INTRODUCTION Covectve term coservato equatos volve frst order dervatves. The smplest possble approach for dscretzato

More information

Some Notes on the Probability Space of Statistical Surveys

Some Notes on the Probability Space of Statistical Surveys Metodološk zvezk, Vol. 7, No., 200, 7-2 ome Notes o the Probablty pace of tatstcal urveys George Petrakos Abstract Ths paper troduces a formal presetato of samplg process usg prcples ad cocepts from Probablty

More information

MATH 247/Winter Notes on the adjoint and on normal operators.

MATH 247/Winter Notes on the adjoint and on normal operators. MATH 47/Wter 00 Notes o the adjot ad o ormal operators I these otes, V s a fte dmesoal er product space over, wth gve er * product uv, T, S, T, are lear operators o V U, W are subspaces of V Whe we say

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

best estimate (mean) for X uncertainty or error in the measurement (systematic, random or statistical) best

best estimate (mean) for X uncertainty or error in the measurement (systematic, random or statistical) best Error Aalyss Preamble Wheever a measuremet s made, the result followg from that measuremet s always subject to ucertaty The ucertaty ca be reduced by makg several measuremets of the same quatty or by mprovg

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

A Remark on the Uniform Convergence of Some Sequences of Functions

A Remark on the Uniform Convergence of Some Sequences of Functions Advaces Pure Mathematcs 05 5 57-533 Publshed Ole July 05 ScRes. http://www.scrp.org/joural/apm http://dx.do.org/0.436/apm.05.59048 A Remark o the Uform Covergece of Some Sequeces of Fuctos Guy Degla Isttut

More information

Entropy ISSN by MDPI

Entropy ISSN by MDPI Etropy 2003, 5, 233-238 Etropy ISSN 1099-4300 2003 by MDPI www.mdp.org/etropy O the Measure Etropy of Addtve Cellular Automata Hasa Aı Arts ad Sceces Faculty, Departmet of Mathematcs, Harra Uversty; 63100,

More information

Chapter 3 Sampling For Proportions and Percentages

Chapter 3 Sampling For Proportions and Percentages Chapter 3 Samplg For Proportos ad Percetages I may stuatos, the characterstc uder study o whch the observatos are collected are qualtatve ature For example, the resposes of customers may marketg surveys

More information

Module 1 : The equation of continuity. Lecture 5: Conservation of Mass for each species. & Fick s Law

Module 1 : The equation of continuity. Lecture 5: Conservation of Mass for each species. & Fick s Law Module : The equato of cotuty Lecture 5: Coservato of Mass for each speces & Fck s Law NPTEL, IIT Kharagpur, Prof. Sakat Chakraborty, Departmet of Chemcal Egeerg 2 Basc Deftos I Mass Trasfer, we usually

More information

Simple Linear Regression

Simple Linear Regression Statstcal Methods I (EST 75) Page 139 Smple Lear Regresso Smple regresso applcatos are used to ft a model descrbg a lear relatoshp betwee two varables. The aspects of least squares regresso ad correlato

More information

Point Estimation: definition of estimators

Point Estimation: definition of estimators Pot Estmato: defto of estmators Pot estmator: ay fucto W (X,..., X ) of a data sample. The exercse of pot estmato s to use partcular fuctos of the data order to estmate certa ukow populato parameters.

More information

1 Lyapunov Stability Theory

1 Lyapunov Stability Theory Lyapuov Stablty heory I ths secto we cosder proofs of stablty of equlbra of autoomous systems. hs s stadard theory for olear systems, ad oe of the most mportat tools the aalyss of olear systems. It may

More information

8.1 Hashing Algorithms

8.1 Hashing Algorithms CS787: Advaced Algorthms Scrbe: Mayak Maheshwar, Chrs Hrchs Lecturer: Shuch Chawla Topc: Hashg ad NP-Completeess Date: September 21 2007 Prevously we looked at applcatos of radomzed algorthms, ad bega

More information

Investigation of Partially Conditional RP Model with Response Error. Ed Stanek

Investigation of Partially Conditional RP Model with Response Error. Ed Stanek Partally Codtoal Radom Permutato Model 7- vestgato of Partally Codtoal RP Model wth Respose Error TRODUCTO Ed Staek We explore the predctor that wll result a smple radom sample wth respose error whe a

More information

. The set of these sums. be a partition of [ ab, ]. Consider the sum f( x) f( x 1)

. The set of these sums. be a partition of [ ab, ]. Consider the sum f( x) f( x 1) Chapter 7 Fuctos o Bouded Varato. Subject: Real Aalyss Level: M.Sc. Source: Syed Gul Shah (Charma, Departmet o Mathematcs, US Sargodha Collected & Composed by: Atq ur Rehma (atq@mathcty.org, http://www.mathcty.org

More information

Lecture 7. Confidence Intervals and Hypothesis Tests in the Simple CLR Model

Lecture 7. Confidence Intervals and Hypothesis Tests in the Simple CLR Model Lecture 7. Cofdece Itervals ad Hypothess Tests the Smple CLR Model I lecture 6 we troduced the Classcal Lear Regresso (CLR) model that s the radom expermet of whch the data Y,,, K, are the outcomes. The

More information

Maps on Triangular Matrix Algebras

Maps on Triangular Matrix Algebras Maps o ragular Matrx lgebras HMED RMZI SOUROUR Departmet of Mathematcs ad Statstcs Uversty of Vctora Vctora, BC V8W 3P4 CND sourour@mathuvcca bstract We surveys results about somorphsms, Jorda somorphsms,

More information

Some results and conjectures about recurrence relations for certain sequences of binomial sums.

Some results and conjectures about recurrence relations for certain sequences of binomial sums. Soe results ad coectures about recurrece relatos for certa sequeces of boal sus Joha Cgler Faultät für Matheat Uverstät We A-9 We Nordbergstraße 5 Joha Cgler@uveacat Abstract I a prevous paper [] I have

More information

d dt d d dt dt Also recall that by Taylor series, / 2 (enables use of sin instead of cos-see p.27 of A&F) dsin

d dt d d dt dt Also recall that by Taylor series, / 2 (enables use of sin instead of cos-see p.27 of A&F) dsin Learzato of the Swg Equato We wll cover sectos.5.-.6 ad begg of Secto 3.3 these otes. 1. Sgle mache-fte bus case Cosder a sgle mache coected to a fte bus, as show Fg. 1 below. E y1 V=1./_ Fg. 1 The admttace

More information

Assignment 5/MATH 247/Winter Due: Friday, February 19 in class (!) (answers will be posted right after class)

Assignment 5/MATH 247/Winter Due: Friday, February 19 in class (!) (answers will be posted right after class) Assgmet 5/MATH 7/Wter 00 Due: Frday, February 9 class (!) (aswers wll be posted rght after class) As usual, there are peces of text, before the questos [], [], themselves. Recall: For the quadratc form

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

Chapter 2 - Free Vibration of Multi-Degree-of-Freedom Systems - II

Chapter 2 - Free Vibration of Multi-Degree-of-Freedom Systems - II CEE49b Chapter - Free Vbrato of Mult-Degree-of-Freedom Systems - II We ca obta a approxmate soluto to the fudametal atural frequecy through a approxmate formula developed usg eergy prcples by Lord Raylegh

More information

Lecture 16: Backpropogation Algorithm Neural Networks with smooth activation functions

Lecture 16: Backpropogation Algorithm Neural Networks with smooth activation functions CO-511: Learg Theory prg 2017 Lecturer: Ro Lv Lecture 16: Bacpropogato Algorthm Dsclamer: These otes have ot bee subected to the usual scruty reserved for formal publcatos. They may be dstrbuted outsde

More information

Generalized Convex Functions on Fractal Sets and Two Related Inequalities

Generalized Convex Functions on Fractal Sets and Two Related Inequalities Geeralzed Covex Fuctos o Fractal Sets ad Two Related Iequaltes Huxa Mo, X Su ad Dogya Yu 3,,3School of Scece, Bejg Uversty of Posts ad Telecommucatos, Bejg,00876, Cha, Correspodece should be addressed

More information

Research Article Some Strong Limit Theorems for Weighted Product Sums of ρ-mixing Sequences of Random Variables

Research Article Some Strong Limit Theorems for Weighted Product Sums of ρ-mixing Sequences of Random Variables Hdaw Publshg Corporato Joural of Iequaltes ad Applcatos Volume 2009, Artcle ID 174768, 10 pages do:10.1155/2009/174768 Research Artcle Some Strog Lmt Theorems for Weghted Product Sums of ρ-mxg Sequeces

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

A Method for Damping Estimation Based On Least Square Fit

A Method for Damping Estimation Based On Least Square Fit Amerca Joural of Egeerg Research (AJER) 5 Amerca Joural of Egeerg Research (AJER) e-issn: 3-847 p-issn : 3-936 Volume-4, Issue-7, pp-5-9 www.ajer.org Research Paper Ope Access A Method for Dampg Estmato

More information

A New Measure of Probabilistic Entropy. and its Properties

A New Measure of Probabilistic Entropy. and its Properties Appled Mathematcal Sceces, Vol. 4, 200, o. 28, 387-394 A New Measure of Probablstc Etropy ad ts Propertes Rajeesh Kumar Departmet of Mathematcs Kurukshetra Uversty Kurukshetra, Ida rajeesh_kuk@redffmal.com

More information

PROJECTION PROBLEM FOR REGULAR POLYGONS

PROJECTION PROBLEM FOR REGULAR POLYGONS Joural of Mathematcal Sceces: Advaces ad Applcatos Volume, Number, 008, Pages 95-50 PROJECTION PROBLEM FOR REGULAR POLYGONS College of Scece Bejg Forestry Uversty Bejg 0008 P. R. Cha e-mal: sl@bjfu.edu.c

More information

STRONG CONSISTENCY FOR SIMPLE LINEAR EV MODEL WITH v/ -MIXING

STRONG CONSISTENCY FOR SIMPLE LINEAR EV MODEL WITH v/ -MIXING Joural of tatstcs: Advaces Theory ad Alcatos Volume 5, Number, 6, Pages 3- Avalable at htt://scetfcadvaces.co. DOI: htt://d.do.org/.864/jsata_7678 TRONG CONITENCY FOR IMPLE LINEAR EV MODEL WITH v/ -MIXING

More information

Multiple Choice Test. Chapter Adequacy of Models for Regression

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

More information

Unimodality Tests for Global Optimization of Single Variable Functions Using Statistical Methods

Unimodality Tests for Global Optimization of Single Variable Functions Using Statistical Methods Malaysa Umodalty Joural Tests of Mathematcal for Global Optmzato Sceces (): of 05 Sgle - 5 Varable (007) Fuctos Usg Statstcal Methods Umodalty Tests for Global Optmzato of Sgle Varable Fuctos Usg Statstcal

More information

Ordinary Least Squares Regression. Simple Regression. Algebra and Assumptions.

Ordinary Least Squares Regression. Simple Regression. Algebra and Assumptions. Ordary Least Squares egresso. Smple egresso. Algebra ad Assumptos. I ths part of the course we are gog to study a techque for aalysg the lear relatoshp betwee two varables Y ad X. We have pars of observatos

More information

Lecture 3 Probability review (cont d)

Lecture 3 Probability review (cont d) STATS 00: Itroducto to Statstcal Iferece Autum 06 Lecture 3 Probablty revew (cot d) 3. Jot dstrbutos If radom varables X,..., X k are depedet, the ther dstrbuto may be specfed by specfyg the dvdual dstrbuto

More information

A tighter lower bound on the circuit size of the hardest Boolean functions

A tighter lower bound on the circuit size of the hardest Boolean functions Electroc Colloquum o Computatoal Complexty, Report No. 86 2011) A tghter lower boud o the crcut sze of the hardest Boolea fuctos Masak Yamamoto Abstract I [IPL2005], Fradse ad Mlterse mproved bouds o the

More information

2.28 The Wall Street Journal is probably referring to the average number of cubes used per glass measured for some population that they have chosen.

2.28 The Wall Street Journal is probably referring to the average number of cubes used per glass measured for some population that they have chosen. .5 x 54.5 a. x 7. 786 7 b. The raked observatos are: 7.4, 7.5, 7.7, 7.8, 7.9, 8.0, 8.. Sce the sample sze 7 s odd, the meda s the (+)/ 4 th raked observato, or meda 7.8 c. The cosumer would more lkely

More information

Integral Equation Methods. Jacob White. Thanks to Deepak Ramaswamy, Michal Rewienski, Xin Wang and Karen Veroy

Integral Equation Methods. Jacob White. Thanks to Deepak Ramaswamy, Michal Rewienski, Xin Wang and Karen Veroy Itroducto to Smulato - Lecture 22 Itegral Equato ethods Jacob Whte Thaks to Deepak Ramaswamy, chal Rewesk, X Wag ad Kare Veroy Outle Itegral Equato ethods Exteror versus teror problems Start wth usg pot

More information

ABOUT ONE APPROACH TO APPROXIMATION OF CONTINUOUS FUNCTION BY THREE-LAYERED NEURAL NETWORK

ABOUT ONE APPROACH TO APPROXIMATION OF CONTINUOUS FUNCTION BY THREE-LAYERED NEURAL NETWORK ABOUT ONE APPROACH TO APPROXIMATION OF CONTINUOUS FUNCTION BY THREE-LAYERED NEURAL NETWORK Ram Rzayev Cyberetc Isttute of the Natoal Scece Academy of Azerbaa Republc ramrza@yahoo.com Aygu Alasgarova Khazar

More information

Lecture Notes 2. The ability to manipulate matrices is critical in economics.

Lecture Notes 2. The ability to manipulate matrices is critical in economics. Lecture Notes. Revew of Matrces he ablt to mapulate matrces s crtcal ecoomcs.. Matr a rectagular arra of umbers, parameters, or varables placed rows ad colums. Matrces are assocated wth lear equatos. lemets

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

Lebesgue Measure of Generalized Cantor Set

Lebesgue Measure of Generalized Cantor Set Aals of Pure ad Appled Mathematcs Vol., No.,, -8 ISSN: -8X P), -888ole) Publshed o 8 May www.researchmathsc.org Aals of Lebesgue Measure of Geeralzed ator Set Md. Jahurul Islam ad Md. Shahdul Islam Departmet

More information

Evaluating Polynomials

Evaluating Polynomials Uverst of Nebraska - Lcol DgtalCommos@Uverst of Nebraska - Lcol MAT Exam Expostor Papers Math the Mddle Isttute Partershp 7-7 Evaluatg Polomals Thomas J. Harrgto Uverst of Nebraska-Lcol Follow ths ad addtoal

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

Generalization of the Dissimilarity Measure of Fuzzy Sets

Generalization of the Dissimilarity Measure of Fuzzy Sets Iteratoal Mathematcal Forum 2 2007 o. 68 3395-3400 Geeralzato of the Dssmlarty Measure of Fuzzy Sets Faramarz Faghh Boformatcs Laboratory Naobotechology Research Ceter vesa Research Isttute CECR Tehra

More information

We have already referred to a certain reaction, which takes place at high temperature after rich combustion.

We have already referred to a certain reaction, which takes place at high temperature after rich combustion. ME 41 Day 13 Topcs Chemcal Equlbrum - Theory Chemcal Equlbrum Example #1 Equlbrum Costats Chemcal Equlbrum Example #2 Chemcal Equlbrum of Hot Bured Gas 1. Chemcal Equlbrum We have already referred to a

More information

STK4011 and STK9011 Autumn 2016

STK4011 and STK9011 Autumn 2016 STK4 ad STK9 Autum 6 Pot estmato Covers (most of the followg materal from chapter 7: Secto 7.: pages 3-3 Secto 7..: pages 3-33 Secto 7..: pages 35-3 Secto 7..3: pages 34-35 Secto 7.3.: pages 33-33 Secto

More information

MULTIDIMENSIONAL HETEROGENEOUS VARIABLE PREDICTION BASED ON EXPERTS STATEMENTS. Gennadiy Lbov, Maxim Gerasimov

MULTIDIMENSIONAL HETEROGENEOUS VARIABLE PREDICTION BASED ON EXPERTS STATEMENTS. Gennadiy Lbov, Maxim Gerasimov Iteratoal Boo Seres "Iformato Scece ad Computg" 97 MULTIIMNSIONAL HTROGNOUS VARIABL PRICTION BAS ON PRTS STATMNTS Geady Lbov Maxm Gerasmov Abstract: I the wors [ ] we proposed a approach of formg a cosesus

More information

ONE GENERALIZED INEQUALITY FOR CONVEX FUNCTIONS ON THE TRIANGLE

ONE GENERALIZED INEQUALITY FOR CONVEX FUNCTIONS ON THE TRIANGLE Joural of Pure ad Appled Mathematcs: Advaces ad Applcatos Volume 4 Number 205 Pages 77-87 Avalable at http://scetfcadvaces.co. DOI: http://.do.org/0.8642/jpamaa_7002534 ONE GENERALIZED INEQUALITY FOR CONVEX

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

Exercises for Square-Congruence Modulo n ver 11

Exercises for Square-Congruence Modulo n ver 11 Exercses for Square-Cogruece Modulo ver Let ad ab,.. Mark True or False. a. 3S 30 b. 3S 90 c. 3S 3 d. 3S 4 e. 4S f. 5S g. 0S 55 h. 8S 57. 9S 58 j. S 76 k. 6S 304 l. 47S 5347. Fd the equvalece classes duced

More information

means the first term, a2 means the term, etc. Infinite Sequences: follow the same pattern forever.

means the first term, a2 means the term, etc. Infinite Sequences: follow the same pattern forever. 9.4 Sequeces ad Seres Pre Calculus 9.4 SEQUENCES AND SERIES Learg Targets:. Wrte the terms of a explctly defed sequece.. Wrte the terms of a recursvely defed sequece. 3. Determe whether a sequece s arthmetc,

More information

Homework 1: Solutions Sid Banerjee Problem 1: (Practice with Asymptotic Notation) ORIE 4520: Stochastics at Scale Fall 2015

Homework 1: Solutions Sid Banerjee Problem 1: (Practice with Asymptotic Notation) ORIE 4520: Stochastics at Scale Fall 2015 Fall 05 Homework : Solutos Problem : (Practce wth Asymptotc Notato) A essetal requremet for uderstadg scalg behavor s comfort wth asymptotc (or bg-o ) otato. I ths problem, you wll prove some basc facts

More information

2. Independence and Bernoulli Trials

2. Independence and Bernoulli Trials . Ideedece ad Beroull Trals Ideedece: Evets ad B are deedet f B B. - It s easy to show that, B deedet mles, B;, B are all deedet ars. For examle, ad so that B or B B B B B φ,.e., ad B are deedet evets.,

More information

Module 7: Probability and Statistics

Module 7: Probability and Statistics Lecture 4: Goodess of ft tests. Itroducto Module 7: Probablty ad Statstcs I the prevous two lectures, the cocepts, steps ad applcatos of Hypotheses testg were dscussed. Hypotheses testg may be used to

More information

Physics 114 Exam 2 Fall Name:

Physics 114 Exam 2 Fall Name: Physcs 114 Exam Fall 015 Name: For gradg purposes (do ot wrte here): Questo 1. 1... 3. 3. Problem Aswer each of the followg questos. Pots for each questo are dcated red. Uless otherwse dcated, the amout

More information

Lecture 07: Poles and Zeros

Lecture 07: Poles and Zeros Lecture 07: Poles ad Zeros Defto of poles ad zeros The trasfer fucto provdes a bass for determg mportat system respose characterstcs wthout solvg the complete dfferetal equato. As defed, the trasfer fucto

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

Complete Convergence and Some Maximal Inequalities for Weighted Sums of Random Variables

Complete Convergence and Some Maximal Inequalities for Weighted Sums of Random Variables Joural of Sceces, Islamc Republc of Ira 8(4): -6 (007) Uversty of Tehra, ISSN 06-04 http://sceces.ut.ac.r Complete Covergece ad Some Maxmal Iequaltes for Weghted Sums of Radom Varables M. Am,,* H.R. Nl

More information

Econometric Methods. Review of Estimation

Econometric Methods. Review of Estimation Ecoometrc Methods Revew of Estmato Estmatg the populato mea Radom samplg Pot ad terval estmators Lear estmators Ubased estmators Lear Ubased Estmators (LUEs) Effcecy (mmum varace) ad Best Lear Ubased Estmators

More information

A COMPARATIVE STUDY OF THE METHODS OF SOLVING NON-LINEAR PROGRAMMING PROBLEM

A COMPARATIVE STUDY OF THE METHODS OF SOLVING NON-LINEAR PROGRAMMING PROBLEM DAODIL INTERNATIONAL UNIVERSITY JOURNAL O SCIENCE AND TECHNOLOGY, VOLUME, ISSUE, JANUARY 9 A COMPARATIVE STUDY O THE METHODS O SOLVING NON-LINEAR PROGRAMMING PROBLEM Bmal Chadra Das Departmet of Tetle

More information

MEASURES OF DISPERSION

MEASURES OF DISPERSION MEASURES OF DISPERSION Measure of Cetral Tedecy: Measures of Cetral Tedecy ad Dsperso ) Mathematcal Average: a) Arthmetc mea (A.M.) b) Geometrc mea (G.M.) c) Harmoc mea (H.M.) ) Averages of Posto: a) Meda

More information

α1 α2 Simplex and Rectangle Elements Multi-index Notation of polynomials of degree Definition: The set P k will be the set of all functions:

α1 α2 Simplex and Rectangle Elements Multi-index Notation of polynomials of degree Definition: The set P k will be the set of all functions: Smplex ad Rectagle Elemets Mult-dex Notato = (,..., ), o-egatve tegers = = β = ( β,..., β ) the + β = ( + β,..., + β ) + x = x x x x = x x β β + D = D = D D x x x β β Defto: The set P of polyomals of degree

More information

2006 Jamie Trahan, Autar Kaw, Kevin Martin University of South Florida United States of America

2006 Jamie Trahan, Autar Kaw, Kevin Martin University of South Florida United States of America SOLUTION OF SYSTEMS OF SIMULTANEOUS LINEAR EQUATIONS Gauss-Sedel Method 006 Jame Traha, Autar Kaw, Kev Mart Uversty of South Florda Uted States of Amerca kaw@eg.usf.edu Itroducto Ths worksheet demostrates

More information

LINEAR RECURRENT SEQUENCES AND POWERS OF A SQUARE MATRIX

LINEAR RECURRENT SEQUENCES AND POWERS OF A SQUARE MATRIX INTEGERS: ELECTRONIC JOURNAL OF COMBINATORIAL NUMBER THEORY 6 2006, #A12 LINEAR RECURRENT SEQUENCES AND POWERS OF A SQUARE MATRIX Hacèe Belbachr 1 USTHB, Departmet of Mathematcs, POBox 32 El Ala, 16111,

More information

Multiple Regression. More than 2 variables! Grade on Final. Multiple Regression 11/21/2012. Exam 2 Grades. Exam 2 Re-grades

Multiple Regression. More than 2 variables! Grade on Final. Multiple Regression 11/21/2012. Exam 2 Grades. Exam 2 Re-grades STAT 101 Dr. Kar Lock Morga 11/20/12 Exam 2 Grades Multple Regresso SECTIONS 9.2, 10.1, 10.2 Multple explaatory varables (10.1) Parttog varablty R 2, ANOVA (9.2) Codtos resdual plot (10.2) Trasformatos

More information

A Markov Chain Competition Model

A Markov Chain Competition Model Academc Forum 3 5-6 A Marov Cha Competto Model Mchael Lloyd, Ph.D. Mathematcs ad Computer Scece Abstract A brth ad death cha for two or more speces s examed aalytcally ad umercally. Descrpto of the Model

More information

X ε ) = 0, or equivalently, lim

X ε ) = 0, or equivalently, lim Revew for the prevous lecture Cocepts: order statstcs Theorems: Dstrbutos of order statstcs Examples: How to get the dstrbuto of order statstcs Chapter 5 Propertes of a Radom Sample Secto 55 Covergece

More information

Lecture Notes Forecasting the process of estimating or predicting unknown situations

Lecture Notes Forecasting the process of estimating or predicting unknown situations Lecture Notes. Ecoomc Forecastg. Forecastg the process of estmatg or predctg ukow stuatos Eample usuall ecoomsts predct future ecoomc varables Forecastg apples to a varet of data () tme seres data predctg

More information

Research on SVM Prediction Model Based on Chaos Theory

Research on SVM Prediction Model Based on Chaos Theory Advaced Scece ad Techology Letters Vol.3 (SoftTech 06, pp.59-63 http://dx.do.org/0.457/astl.06.3.3 Research o SVM Predcto Model Based o Chaos Theory Sog Lagog, Wu Hux, Zhag Zezhog 3, College of Iformato

More information

7.0 Equality Contraints: Lagrange Multipliers

7.0 Equality Contraints: Lagrange Multipliers Systes Optzato 7.0 Equalty Cotrats: Lagrage Multplers Cosder the zato of a o-lear fucto subject to equalty costrats: g f() R ( ) 0 ( ) (7.) where the g ( ) are possbly also olear fuctos, ad < otherwse

More information