A MULTISET-VALUED FIBONACCI-TYPE SEQUENCE

Size: px
Start display at page:

Download "A MULTISET-VALUED FIBONACCI-TYPE SEQUENCE"

Transcription

1 Avces Applctos Dscrete Mthemtcs Volume Number 008 Pes Publshe Ole: Mrch Ths pper s vlble ole t Pushp Publsh House A MULTISET-VALUED IBONACCI-TYPE SEQUENCE TAMÁS KALMÁR-NAGY Deprtmet of Aerospce Eeer Tes A & M Uversty Collee Stto TX 7785 U S A e-ml: scretemth@klmrycom Abstrct We vestte the structure of multset-vlue bocc-type sequece crete by set uo/sum/fferece opertos A close-form eert fucto s erve to eplctly chrcterze the elemets of these sets ther ect veres vrces The eometrc me of the sets s umerclly emostrte to epoetlly row we prove tht the epoet s the squre root of the ole rto Itroucto Oe of the smplest emples of rom mtr proucts s the soclle rom bocc seres (Vswth [0]) Ths sequece s eerte by the rom seco-orer fferece equto ± 0 () where the th term s ether the sum or fferece of the prevous two terms ( or re pcke epeetly wth probblty / t ech step) The structure rowth propertes of the rom bocc sequece hve bee stue by severl uthors (Embree Trefethe [5]; Sre Krpvsky [9]; Ch [3 ]; Jvresse et l [6]; Klmár-Ny [7]; Mkover McGow [8]; B []) Vswth [0] utlze powerful 000 Mthemtcs Subect Clssfcto: B39 05A5 Keywors phrses: bocc sequece eert fucto multset Mkowsk sum Receve November 008

2 86 TAMÁS KALMÁR-NAGY combto of rom mtr theory tervl rthmetc to show tht the sequece lmost surely veres wth epoet of 3 e ν lm 3 Embree Trefethe [5] umerclly vestte ± β eerlzto of the rom bocc sequece The mesure of verece for the rom bocc seres c be epresse s pth vere by coser ll the possble vlues c tt Let h eote the multset of possble - ot ecessrly fferet - vlues of A multset s collecto of obects whch elemets my occur more th oce (Blzr []) The epoet (Vswth s umber) ν c the be formlly epresse s the lmt ( m) m ν lm lm () 0 h 0 h where the vere (eometrc me) s tke over ll the m possble vlues of t step (here m ) Eve thouh there s stro correlto betwee subsequet elemets y prtculr relzto of ths rom sequece the sme epoet ν woul chrcterze the rom sequece whose th elemet s romly chose from h Ths observto proves the motvto for ths stuy Whle recurso relto for the eert fucto of h c be costructe the ol of ths pper s to prove eve smpler emple of multset-vlue recurrece to erve some of ts bsc propertes A Multset-vlue Recurrece Motvte by stues o the rowth of rom bocc sequeces here we trouce multset-vlue bocc-type recurrece Strt wth the two sets f f { } the set f 3 s costructe s the uo of the Mkowsk sum fferece of the prevous two sets ( f f ) ( f f ) { 0 } (3) f3

3 A MULTISET-VALUED IBONACCI-TYPE SEQUENCE 87 where eote the Mkowsk sum fferece of two sets X Y efe s X Y { y X y Y} () X Y { y X y Y} X ( Y ) (5) I eerl we efe f ( f f ) ( f f ) (6) Notce tht f s multset e elemets my occur more th oce The umber of tmes elemet occurs multset s clle ts multplcty (Blzr []) To vo cofuso ste of the str otto for multsets here ( ) eotes the umber of occurreces of elemet The frst few f s re ve by f f {} f 3 { 0 } (7) { 3} { ( ) 3} f { 3 ( ) ( 6) 3( ) 5} f 5 f { 6 ( 7) ( ) 0( 35) ( 35) ( ) 6( 7) 8} (8) 6 ew turl questos rse: Wht ectly re the elemets of f? How my elemets re there f? Wht re vrous sttstcl propertes (me vrce eometrc me) of f? It s the ol of the et secto to swer these questos 3 Geert ucto Sttstcl Propertes of f rst we chrcterze the etreml elemets ( thus the re) of f I the follow eotes the -th bocc umber

4 88 TAMÁS KALMÁR-NAGY Theorem m m f f Proof The frst sttemet follows esly from m f m f m f m f m f The m f m f m f m f The propose soluto m f stsfes ths equto (becuse of the etty ) s well s the tl cotos m f m f f The et theorem proves smple epresso for the crlty of Theorem f Proof The crlty of f s epresse s f ( f f ) ( f f ) f f f f f f Clerly f stsfes ths recurrece the tl cotos f f To compute the frequeces of the vrous teers pper f we use the eert fucto pproch (see the ecellet tretse by Wlf []) I our problem forml power seres ( ) (9) wll ecoe formto bout f I prtculr the e wll correspo to the teer the set f the coeffcet specfes the frequecy (umber of occurreces) of ths teer f Ths proves oe-to-oe mpp betwee the set f the eert fucto ( ) or emple: 3 f { ( ) 3} ( ) f 5 { 3 ( ) ( 6) 3( ) 5} ( 5 ) 6 (0) If the eert fucto of f s ( ) tht of f s obvously

5 A MULTISET-VALUED IBONACCI-TYPE SEQUENCE 89 ( ) The uo Mkowsk sum/fferece of two sets c be esly trslte to opertos betwee ther eert fuctos s f f ( ) ( ) () f f ( ) ( ) () f ( ) ( ) f f f (3) We re ow the posto to epress the eert fucto of f terms of those for f f Recll tht f ( f f ) ( f f ) thus ( ) ( ) ( ) ( ) ( ) ( ) () The tl cotos f f { } specfy those o the eert fucto ( ) ( ) (5) A ce close-form epresso c be fou for ( ): Theorem 3 ( ) ( ) (6) Proof Smple substtuto cofrms tht ths fucto stsfes the tl cotos (5) We wll ow show tht Eq (6) s soluto of the recurrece equto () ( ) ( ) The left h se s smplfe s ( ) (7) ( ) ( ) ( ) ( ) ( ) (8)

6 TAMÁS KALMÁR-NAGY 90 To evlute the rht h se of Eq (7) we frst clculte ( ) (9) The ( ) ( ) ( ) ( ) ( ) ( ) (0) The proof s complete by utlz the etty Corollry The eert fucto ( ) stsfes the fuctol equto ( ) () We c use the eert fucto formlsm to cofrm the erler result o the crlty of f The sze of f s smply the totl umber of the vrous teers t cots ths umber s smply the sum of coeffcets of the eert fucto ( ) f () The eert fucto ( ) c be wrtte s the boml epso ( ) 0 (3) Ths proves eplct escrpto of the set : f the elemets re wth frequecy [ ] 0 Note tht ths rely proves other proof of Theorem

7 A MULTISET-VALUED IBONACCI-TYPE SEQUENCE 9 ew elemetry sttstcl propertes of f re supple by Theorem 5 The vere µ vrce ν of the elemets f re ve by µ () ν (5) Proof The vere vrce c be compute rectly from the eert fucto ( ) (Wlf []) I prtculr µ ( ) ( ) (6) ν ( ) ( ) (7) Dfferetto of the eert fucto wrt yels ( ) ( ) (( ) ) (8) therefore ( ) Sce ( ) µ The rthmc ervtves re compute s ( ) 3 ( ) ( ) (9) ( ) ( ) 6 3 ( ) ( ) (30) therefore ν lly we coser the eometrc me of f (more precsely tht of the bsolute vlues of ts elemets)

8 9 TAMÁS KALMÁR-NAGY ( f ) G( f ) (3) 0 f where f s the umber of elemets f Tble shows the vlues of the eometrc me for vrous vlues of ther rthms Tble Growth of the eometrc me of f G ( f ) l G( f ) Bse o Tble the rowth of l G( f ) s ler wth slope of ppromtely 0 therefore the eometrc me of f scles wth epoet of κ ep( 0) 7 e κ lm G ( f ) 7 (3) I other wors the (rom) sequece whose th elemet s romly pcke from f ehbts epoetl rowth chrcterze by κ Our ppromto of κ s very close to tht of the squre root of the ole 5 rto φ φ 7 the error s ust 005% Iee Theorem 6 κ lm G ( f ) φ (33) Proof Itrouc the eert fucto (3) c lso be wrtte s

9 A MULTISET-VALUED IBONACCI-TYPE SEQUENCE 93 ( ) (3) The epoets of the eert fuctos re the elemets of f the coeffcets correspo to ther frequeces Thus the eometrc me of f s compute s ( ) f G ep (35) Sce the summ (35) escrbes wehte boml strbuto I the lmt the boml strbuto c be well ppromte by the cotuous orml strbuto (Wesste []) ep ~ π (36) The sum s ppromte s ~ π ep ~ π π 0 ep ep ~ (37) Sce ( ) γ π 0 ep (38)

10 9 TAMÁS KALMÁR-NAGY we obt γ G ( f ) ~ (39) Here γ s the Euler-Mschero costt lly G( f ) ~ ~ φ ~ φ φ (0) thus lm G( f ) e φ κ φ () Coclusos Motvte by stues o the rom bocc sequece we efe vestte recurrece whose elemets re multsets A closeform eert fucto s erve to eplctly chrcterze the elemets of these multsets ther ect veres vrces The eometrc me of the multsets s prove to row epoetlly scl wth the squre root of the ole rto Ackowleemet The uthor woul lke to ckowlee the help of Professor Péter Vlkó cret Tble Refereces [] Z-Q B O the cycle epso for the Lypuov epoet of prouct of rom mtrces J Phys A: Mth Theor 0 (007) [] W D Blzr Multset theory Notre Dme J orml Loc 30() (989) [3] H Ch The symptotc rowth rte of rom bocc type sequeces I bocc Qurterly 3(3) (005) 3-55

11 A MULTISET-VALUED IBONACCI-TYPE SEQUENCE 95 [] H Ch The symptotc rowth rte of rom bocc type sequeces II bocc Qurterly () (006) 73-8 [5] M Embree L N Trefethe Growth ecy of rom bocc sequeces Procees of the Royl Socety Loo Seres A 55(987) (999) 7-85 [6] E Jvresse B Rttu T De L Rue Arv preprt mth PR/06860 (006) [7] T Klmár-Ny The rom bocc recurrece the vsble pots of the ple J Phys A: Mth Ge 39(0) (006) L33-L38 [8] E Mkover J McGow A elemetry proof tht rom bocc sequeces row epoetlly J Number Theory () (006) 0- [9] C Sre P L Krpvsky Rom bocc sequeces J Phys A: Mth Ge 3 (00) [0] D Vswth Rom bocc sequeces the umber 3988 Mth Comp 69(3) (000) 3-55 [] E W Wesste Boml Dstrbuto [] H S Wlf Geertfuctooy Acemc Press Bosto 990

SUM PROPERTIES FOR THE K-LUCAS NUMBERS WITH ARITHMETIC INDEXES

SUM PROPERTIES FOR THE K-LUCAS NUMBERS WITH ARITHMETIC INDEXES Avlble ole t http://sc.org J. Mth. Comput. Sc. 4 (04) No. 05-7 ISSN: 97-507 SUM PROPERTIES OR THE K-UCAS NUMBERS WITH ARITHMETIC INDEXES BIJENDRA SINGH POOJA BHADOURIA AND OMPRAKASH SIKHWA * School of

More information

Sequences and summations

Sequences and summations Lecture 0 Sequeces d summtos Istructor: Kgl Km CSE) E-ml: kkm0@kokuk.c.kr Tel. : 0-0-9 Room : New Mleum Bldg. 0 Lb : New Egeerg Bldg. 0 All sldes re bsed o CS Dscrete Mthemtcs for Computer Scece course

More information

12 Iterative Methods. Linear Systems: Gauss-Seidel Nonlinear Systems Case Study: Chemical Reactions

12 Iterative Methods. Linear Systems: Gauss-Seidel Nonlinear Systems Case Study: Chemical Reactions HK Km Slghtly moded //9 /8/6 Frstly wrtte t Mrch 5 Itertve Methods er Systems: Guss-Sedel Noler Systems Cse Study: Chemcl Rectos Itertve or ppromte methods or systems o equtos cosst o guessg vlue d the

More information

Optimality of Strategies for Collapsing Expanded Random Variables In a Simple Random Sample Ed Stanek

Optimality of Strategies for Collapsing Expanded Random Variables In a Simple Random Sample Ed Stanek Optmlt of Strteges for Collpsg Expe Rom Vrles Smple Rom Smple E Stek troucto We revew the propertes of prectors of ler comtos of rom vrles se o rom vrles su-spce of the orgl rom vrles prtculr, we ttempt

More information

Chapter 2 Intro to Math Techniques for Quantum Mechanics

Chapter 2 Intro to Math Techniques for Quantum Mechanics Wter 3 Chem 356: Itroductory Qutum Mechcs Chpter Itro to Mth Techques for Qutum Mechcs... Itro to dfferetl equtos... Boudry Codtos... 5 Prtl dfferetl equtos d seprto of vrbles... 5 Itroducto to Sttstcs...

More information

DATA FITTING. Intensive Computation 2013/2014. Annalisa Massini

DATA FITTING. Intensive Computation 2013/2014. Annalisa Massini DATA FITTING Itesve Computto 3/4 Als Mss Dt fttg Dt fttg cocers the problem of fttg dscrete dt to obt termedte estmtes. There re two geerl pproches two curve fttg: Iterpolto Dt s ver precse. The strteg

More information

Mathematics HL and further mathematics HL formula booklet

Mathematics HL and further mathematics HL formula booklet Dplom Progrmme Mthemtcs HL d further mthemtcs HL formul boolet For use durg the course d the emtos Frst emtos 04 Publshed Jue 0 Itertol Bcclurete Orgzto 0 5048 Mthemtcs HL d further mthemtcs formul boolet

More information

this is the indefinite integral Since integration is the reverse of differentiation we can check the previous by [ ]

this is the indefinite integral Since integration is the reverse of differentiation we can check the previous by [ ] Atervtves The Itegrl Atervtves Ojectve: Use efte tegrl otto for tervtves. Use sc tegrto rules to f tervtves. Aother mportt questo clculus s gve ervtve f the fucto tht t cme from. Ths s the process kow

More information

Mathematics HL and further mathematics HL formula booklet

Mathematics HL and further mathematics HL formula booklet Dplom Progrmme Mthemtcs HL d further mthemtcs HL formul boolet For use durg the course d the emtos Frst emtos 04 Publshed Jue 0 Itertol Bcclurete Orgzto 0 5048 Cotets Pror lerg Core Topc : Algebr Topc

More information

Chapter 2: Probability and Statistics

Chapter 2: Probability and Statistics Wter 4 Che 35: Sttstcl Mechcs Checl Ketcs Itroucto to sttstcs... 7 Cotuous Dstrbutos... 9 Guss Dstrbuto (D)... Coutg evets to etere probbltes... Bol Coeffcets (Dstrbuto)... 3 Strlg s Appoto... 4 Guss Approto

More information

The z-transform. LTI System description. Prof. Siripong Potisuk

The z-transform. LTI System description. Prof. Siripong Potisuk The -Trsform Prof. Srpog Potsuk LTI System descrpto Prevous bss fucto: ut smple or DT mpulse The put sequece s represeted s ler combto of shfted DT mpulses. The respose s gve by covoluto sum of the put

More information

Mathematics HL and further mathematics HL formula booklet

Mathematics HL and further mathematics HL formula booklet Dplom Progrmme Mthemtcs HL d further mthemtcs HL formul boolet For use durg the course d the emtos Frst emtos 04 Edted 05 (verso ) Itertol Bcclurete Orgzto 0 5048 Cotets Pror lerg Core 3 Topc : Algebr

More information

ICS141: Discrete Mathematics for Computer Science I

ICS141: Discrete Mathematics for Computer Science I Uversty o Hw ICS: Dscrete Mthemtcs or Computer Scece I Dept. Iormto & Computer Sc., Uversty o Hw J Stelovsy bsed o sldes by Dr. Be d Dr. Stll Orgls by Dr. M. P. Fr d Dr. J.L. Gross Provded by McGrw-Hll

More information

POWERS OF COMPLEX PERSYMMETRIC ANTI-TRIDIAGONAL MATRICES WITH CONSTANT ANTI-DIAGONALS

POWERS OF COMPLEX PERSYMMETRIC ANTI-TRIDIAGONAL MATRICES WITH CONSTANT ANTI-DIAGONALS IRRS 9 y 04 wwwrppresscom/volumes/vol9issue/irrs_9 05pdf OWERS OF COLE ERSERIC I-RIIGOL RICES WIH COS I-IGOLS Wg usu * Q e Wg Hbo & ue College of Scece versty of Shgh for Scece d echology Shgh Ch 00093

More information

MTH 146 Class 7 Notes

MTH 146 Class 7 Notes 7.7- Approxmte Itegrto Motvto: MTH 46 Clss 7 Notes I secto 7.5 we lered tht some defte tegrls, lke x e dx, cot e wrtte terms of elemetry fuctos. So, good questo to sk would e: How c oe clculte somethg

More information

CURVE FITTING LEAST SQUARES METHOD

CURVE FITTING LEAST SQUARES METHOD Nuercl Alss for Egeers Ger Jord Uverst CURVE FITTING Although, the for of fucto represetg phscl sste s kow, the fucto tself ot be kow. Therefore, t s frequetl desred to ft curve to set of dt pots the ssued

More information

UNIT 7 RANK CORRELATION

UNIT 7 RANK CORRELATION UNIT 7 RANK CORRELATION Rak Correlato Structure 7. Itroucto Objectves 7. Cocept of Rak Correlato 7.3 Dervato of Rak Correlato Coeffcet Formula 7.4 Te or Repeate Raks 7.5 Cocurret Devato 7.6 Summar 7.7

More information

PubH 7405: REGRESSION ANALYSIS REGRESSION IN MATRIX TERMS

PubH 7405: REGRESSION ANALYSIS REGRESSION IN MATRIX TERMS PubH 745: REGRESSION ANALSIS REGRESSION IN MATRIX TERMS A mtr s dspl of umbers or umercl quttes ld out rectgulr rr of rows d colums. The rr, or two-w tble of umbers, could be rectgulr or squre could be

More information

Preliminary Examinations: Upper V Mathematics Paper 1

Preliminary Examinations: Upper V Mathematics Paper 1 relmr Emtos: Upper V Mthemtcs per Jul 03 Emer: G Evs Tme: 3 hrs Modertor: D Grgortos Mrks: 50 INSTRUCTIONS ND INFORMTION Ths questo pper sts of 0 pges, cludg swer Sheet pge 8 d Iformto Sheet pges 9 d 0

More information

A Technique for Constructing Odd-order Magic Squares Using Basic Latin Squares

A Technique for Constructing Odd-order Magic Squares Using Basic Latin Squares Itertol Jourl of Scetfc d Reserch Publctos, Volume, Issue, My 0 ISSN 0- A Techque for Costructg Odd-order Mgc Squres Usg Bsc Lt Squres Tomb I. Deprtmet of Mthemtcs, Mpur Uversty, Imphl, Mpur (INDIA) tombrom@gml.com

More information

St John s College. UPPER V Mathematics: Paper 1 Learning Outcome 1 and 2. Examiner: GE Marks: 150 Moderator: BT / SLS INSTRUCTIONS AND INFORMATION

St John s College. UPPER V Mathematics: Paper 1 Learning Outcome 1 and 2. Examiner: GE Marks: 150 Moderator: BT / SLS INSTRUCTIONS AND INFORMATION St Joh s College UPPER V Mthemtcs: Pper Lerg Outcome d ugust 00 Tme: 3 hours Emer: GE Mrks: 50 Modertor: BT / SLS INSTRUCTIONS ND INFORMTION Red the followg structos crefull. Ths questo pper cossts of

More information

Chapter Unary Matrix Operations

Chapter Unary Matrix Operations Chpter 04.04 Ury trx Opertos After redg ths chpter, you should be ble to:. kow wht ury opertos mes,. fd the trspose of squre mtrx d t s reltoshp to symmetrc mtrces,. fd the trce of mtrx, d 4. fd the ermt

More information

CS473-Algorithms I. Lecture 3. Solving Recurrences. Cevdet Aykanat - Bilkent University Computer Engineering Department

CS473-Algorithms I. Lecture 3. Solving Recurrences. Cevdet Aykanat - Bilkent University Computer Engineering Department CS473-Algorthms I Lecture 3 Solvg Recurreces Cevdet Aykt - Blket Uversty Computer Egeerg Deprtmet Solvg Recurreces The lyss of merge sort Lecture requred us to solve recurrece. Recurreces re lke solvg

More information

Differential Entropy 吳家麟教授

Differential Entropy 吳家麟教授 Deretl Etropy 吳家麟教授 Deto Let be rdom vrble wt cumultve dstrbuto ucto I F s cotuous te r.v. s sd to be cotuous. Let = F we te dervtve s deed. I te s clled te pd or. Te set were > 0 s clled te support set

More information

Strategies for the AP Calculus Exam

Strategies for the AP Calculus Exam Strteges for the AP Clculus Em Strteges for the AP Clculus Em Strtegy : Kow Your Stuff Ths my seem ovous ut t ees to e metoe. No mout of cochg wll help you o the em f you o t kow the mterl. Here s lst

More information

Regression. By Jugal Kalita Based on Chapter 17 of Chapra and Canale, Numerical Methods for Engineers

Regression. By Jugal Kalita Based on Chapter 17 of Chapra and Canale, Numerical Methods for Engineers Regresso By Jugl Klt Bsed o Chpter 7 of Chpr d Cle, Numercl Methods for Egeers Regresso Descrbes techques to ft curves (curve fttg) to dscrete dt to obt termedte estmtes. There re two geerl pproches two

More information

Available online through

Available online through Avlble ole through wwwmfo FIXED POINTS FOR NON-SELF MAPPINGS ON CONEX ECTOR METRIC SPACES Susht Kumr Moht* Deprtmet of Mthemtcs West Begl Stte Uverst Brst 4 PrgsNorth) Kolt 76 West Begl Id E-ml: smwbes@yhoo

More information

Methods for solving the radiative transfer equation. Part 3: Discreteordinate. 1. Discrete-ordinate method for the case of isotropic scattering.

Methods for solving the radiative transfer equation. Part 3: Discreteordinate. 1. Discrete-ordinate method for the case of isotropic scattering. ecture Metos for sov te rtve trsfer equto. rt 3: Dscreteorte eto. Obectves:. Dscrete-orte eto for te cse of sotropc sctter..geerzto of te screte-orte eto for ooeeous tospere. 3. uerc peetto of te screte-orte

More information

Union, Intersection, Product and Direct Product of Prime Ideals

Union, Intersection, Product and Direct Product of Prime Ideals Globl Jourl of Pure d Appled Mthemtcs. ISSN 0973-1768 Volume 11, Number 3 (2015), pp. 1663-1667 Reserch Id Publctos http://www.rpublcto.com Uo, Itersecto, Product d Drect Product of Prme Idels Bdu.P (1),

More information

24 Concept of wave function. x 2. Ae is finite everywhere in space.

24 Concept of wave function. x 2. Ae is finite everywhere in space. 4 Cocept of wve fucto Chpter Cocept of Wve Fucto. Itroucto : There s lwys qutty sscocte wth y type of wves, whch vres peroclly wth spce te. I wter wves, the qutty tht vres peroclly s the heght of the wter

More information

C.11 Bang-bang Control

C.11 Bang-bang Control Itroucto to Cotrol heory Iclug Optmal Cotrol Nguye a e -.5 C. Bag-bag Cotrol. Itroucto hs chapter eals wth the cotrol wth restrctos: s boue a mght well be possble to have scotutes. o llustrate some of

More information

Chapter 2 Intro to Math Techniques for Quantum Mechanics

Chapter 2 Intro to Math Techniques for Quantum Mechanics Fll 4 Chem 356: Itroductory Qutum Mechcs Chpter Itro to Mth Techques for Qutum Mechcs... Itro to dfferetl equtos... Boudry Codtos... 5 Prtl dfferetl equtos d seprto of vrbles... 5 Itroducto to Sttstcs...

More information

On Solution of Min-Max Composition Fuzzy Relational Equation

On Solution of Min-Max Composition Fuzzy Relational Equation U-Sl Scece Jourl Vol.4()7 O Soluto of M-Mx Coposto Fuzzy eltol Equto N.M. N* Dte of cceptce /5/7 Abstrct I ths pper, M-Mx coposto fuzzy relto equto re studed. hs study s geerlzto of the works of Ohsto

More information

Solutions to Odd-Numbered End-of-Chapter Exercises: Chapter 17

Solutions to Odd-Numbered End-of-Chapter Exercises: Chapter 17 Itroucto to Ecoometrcs (3 r Upate Eto) by James H. Stock a Mark W. Watso Solutos to O-Numbere E-of-Chapter Exercses: Chapter 7 (Ths erso August 7, 04) 05 Pearso Eucato, Ic. Stock/Watso - Itroucto to Ecoometrcs

More information

LINEARLY CONSTRAINED MINIMIZATION BY USING NEWTON S METHOD

LINEARLY CONSTRAINED MINIMIZATION BY USING NEWTON S METHOD Jural Karya Asl Loreka Ahl Matematk Vol 8 o 205 Page 084-088 Jural Karya Asl Loreka Ahl Matematk LIEARLY COSTRAIED MIIMIZATIO BY USIG EWTO S METHOD Yosza B Dasrl, a Ismal B Moh 2 Faculty Electrocs a Computer

More information

Answer: First, I ll show how to find the terms analytically then I ll show how to use the TI to find them.

Answer: First, I ll show how to find the terms analytically then I ll show how to use the TI to find them. . CHAPTER 0 SEQUENCE, SERIES, d INDUCTION Secto 0. Seqece A lst of mers specfc order. E / Fd the frst terms : of the gve seqece: Aswer: Frst, I ll show how to fd the terms ltcll the I ll show how to se

More information

MATH2999 Directed Studies in Mathematics Matrix Theory and Its Applications

MATH2999 Directed Studies in Mathematics Matrix Theory and Its Applications MATH999 Drected Studes Mthemtcs Mtr Theory d Its Applctos Reserch Topc Sttory Probblty Vector of Hgher-order Mrkov Ch By Zhg Sho Supervsors: Prof. L Ch-Kwog d Dr. Ch Jor-Tg Cotets Abstrct. Itroducto: Bckgroud.

More information

Analytical Approach for the Solution of Thermodynamic Identities with Relativistic General Equation of State in a Mixture of Gases

Analytical Approach for the Solution of Thermodynamic Identities with Relativistic General Equation of State in a Mixture of Gases Itertol Jourl of Advced Reserch Physcl Scece (IJARPS) Volume, Issue 5, September 204, PP 6-0 ISSN 2349-7874 (Prt) & ISSN 2349-7882 (Ole) www.rcourls.org Alytcl Approch for the Soluto of Thermodymc Idettes

More information

Chapter 3 Supplemental Text Material

Chapter 3 Supplemental Text Material S3-. The Defto of Fctor Effects Chpter 3 Supplemetl Text Mterl As oted Sectos 3- d 3-3, there re two wys to wrte the model for sglefctor expermet, the mes model d the effects model. We wll geerlly use

More information

Almost Unbiased Estimation of the Poisson Regression Model

Almost Unbiased Estimation of the Poisson Regression Model Ecoometrcs Worg Pper EWP0909 ISSN 485-644 Deprtmet of Ecoomcs Almost Ubsed Estmto of the Posso Regresso Model Dvd E. Gles Deprtmet of Ecoomcs, Uversty of Vctor Vctor, BC, Cd V8W Y & Hu Feg Deprtmet of

More information

Soo King Lim Figure 1: Figure 2: Figure 3: Figure 4: Figure 5: Figure 6: Figure 7: Figure 8: Figure 9: Figure 10: Figure 11:

Soo King Lim Figure 1: Figure 2: Figure 3: Figure 4: Figure 5: Figure 6: Figure 7: Figure 8: Figure 9: Figure 10: Figure 11: Soo Kg Lm 1.0 Nested Fctorl Desg... 1.1 Two-Fctor Nested Desg... 1.1.1 Alss of Vrce... Exmple 1... 5 1.1. Stggered Nested Desg for Equlzg Degree of Freedom... 7 1.1. Three-Fctor Nested Desg... 8 1.1..1

More information

On Several Inequalities Deduced Using a Power Series Approach

On Several Inequalities Deduced Using a Power Series Approach It J Cotemp Mth Sceces, Vol 8, 203, o 8, 855-864 HIKARI Ltd, wwwm-hrcom http://dxdoorg/02988/jcms2033896 O Severl Iequltes Deduced Usg Power Seres Approch Lored Curdru Deprtmet of Mthemtcs Poltehc Uversty

More information

Chapter 4: Distributions

Chapter 4: Distributions Chpter 4: Dstrbutos Prerequste: Chpter 4. The Algebr of Expecttos d Vrces I ths secto we wll mke use of the followg symbols: s rdom vrble b s rdom vrble c s costt vector md s costt mtrx, d F m s costt

More information

Probabilistic approach to the distribution of primes and to the proof of Legendre and Elliott-Halberstam conjectures VICTOR VOLFSON

Probabilistic approach to the distribution of primes and to the proof of Legendre and Elliott-Halberstam conjectures VICTOR VOLFSON Probblstc pproch to the dstrbuto of prmes d to the proof of Legedre d Ellott-Hlberstm cojectures VICTOR VOLFSON ABSTRACT. Probblstc models for the dstrbuto of prmes the turl umbers re costructed the rtcle.

More information

THE PUBLISHING HOUSE PROCEEDINGS OF THE ROMANIAN ACADEMY, Series A, OF THE ROMANIAN ACADEMY Volume 9, Number 3/2008, pp

THE PUBLISHING HOUSE PROCEEDINGS OF THE ROMANIAN ACADEMY, Series A, OF THE ROMANIAN ACADEMY Volume 9, Number 3/2008, pp THE PUBLISHIN HOUSE PROCEEDINS OF THE ROMANIAN ACADEMY, Seres A, OF THE ROMANIAN ACADEMY Volume 9, Number 3/8, THE UNITS IN Stela Corelu ANDRONESCU Uversty of Pteşt, Deartmet of Mathematcs, Târgu Vale

More information

In Calculus I you learned an approximation method using a Riemann sum. Recall that the Riemann sum is

In Calculus I you learned an approximation method using a Riemann sum. Recall that the Riemann sum is Mth Sprg 08 L Approxmtg Dete Itegrls I Itroducto We hve studed severl methods tht llow us to d the exct vlues o dete tegrls However, there re some cses whch t s ot possle to evlute dete tegrl exctly I

More information

4 Linear Homogeneous Recurrence Relation 4-1 Fibonacci Rabbits. 组合数学 Combinatorics

4 Linear Homogeneous Recurrence Relation 4-1 Fibonacci Rabbits. 组合数学 Combinatorics 4 Ler Homogeeous Recurrece Relto 4- bocc Rbbts 组合数学 ombtorcs The delt of the th moth d - th moth s gve brth by the rbbts - moth. o = - + - Moth Moth Moth Moth 4 I the frst moth there s pr of ewly-bor rbbts;

More information

ME 501A Seminar in Engineering Analysis Page 1

ME 501A Seminar in Engineering Analysis Page 1 Mtr Trsformtos usg Egevectors September 8, Mtr Trsformtos Usg Egevectors Lrry Cretto Mechcl Egeerg A Semr Egeerg Alyss September 8, Outle Revew lst lecture Trsformtos wth mtr of egevectors: = - A ermt

More information

Chapter Gauss-Seidel Method

Chapter Gauss-Seidel Method Chpter 04.08 Guss-Sedel Method After redg ths hpter, you should be ble to:. solve set of equtos usg the Guss-Sedel method,. reogze the dvtges d ptflls of the Guss-Sedel method, d. determe uder wht odtos

More information

ITERATIVE METHODS FOR SOLVING SYSTEMS OF LINEAR ALGEBRAIC EQUATIONS

ITERATIVE METHODS FOR SOLVING SYSTEMS OF LINEAR ALGEBRAIC EQUATIONS Numercl Alyss for Egeers Germ Jord Uversty ITERATIVE METHODS FOR SOLVING SYSTEMS OF LINEAR ALGEBRAIC EQUATIONS Numercl soluto of lrge systems of ler lgerc equtos usg drect methods such s Mtr Iverse, Guss

More information

Bond Additive Modeling 5. Mathematical Properties of the Variable Sum Exdeg Index

Bond Additive Modeling 5. Mathematical Properties of the Variable Sum Exdeg Index CROATICA CHEMICA ACTA CCACAA ISSN 00-6 e-issn -7X Crot. Chem. Act 8 () (0) 9 0. CCA-5 Orgl Scetfc Artcle Bod Addtve Modelg 5. Mthemtcl Propertes of the Vrble Sum Edeg Ide Dmr Vukčevć Fculty of Nturl Sceces

More information

= 2. Statistic - function that doesn't depend on any of the known parameters; examples:

= 2. Statistic - function that doesn't depend on any of the known parameters; examples: of Samplg Theory amples - uemploymet househol cosumpto survey Raom sample - set of rv's... ; 's have ot strbuto [ ] f f s vector of parameters e.g. Statstc - fucto that oes't epe o ay of the ow parameters;

More information

On a class of analytic functions defined by Ruscheweyh derivative

On a class of analytic functions defined by Ruscheweyh derivative Lfe Scece Jourl ;9( http://wwwlfescecestecom O clss of lytc fuctos defed by Ruscheweyh dervtve S N Ml M Arf K I Noor 3 d M Rz Deprtmet of Mthemtcs GC Uversty Fslbd Pujb Pst Deprtmet of Mthemtcs Abdul Wl

More information

COMPLEX NUMBERS AND DE MOIVRE S THEOREM

COMPLEX NUMBERS AND DE MOIVRE S THEOREM COMPLEX NUMBERS AND DE MOIVRE S THEOREM OBJECTIVE PROBLEMS. s equl to b d. 9 9 b 9 9 d. The mgr prt of s 5 5 b 5. If m, the the lest tegrl vlue of m s b 8 5. The vlue of 5... s f s eve, f s odd b f s eve,

More information

Chapter 7. Bounds for weighted sums of Random Variables

Chapter 7. Bounds for weighted sums of Random Variables Chpter 7. Bouds for weghted sums of Rdom Vrbles 7. Itroducto Let d 2 be two depedet rdom vrbles hvg commo dstrbuto fucto. Htczeko (998 d Hu d L (2000 vestgted the Rylegh dstrbuto d obted some results bout

More information

Chapter 3. Differentiation 3.3 Differentiation Rules

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

More information

1 4 6 is symmetric 3 SPECIAL MATRICES 3.1 SYMMETRIC MATRICES. Defn: A matrix A is symmetric if and only if A = A, i.e., a ij =a ji i, j. Example 3.1.

1 4 6 is symmetric 3 SPECIAL MATRICES 3.1 SYMMETRIC MATRICES. Defn: A matrix A is symmetric if and only if A = A, i.e., a ij =a ji i, j. Example 3.1. SPECIAL MATRICES SYMMETRIC MATRICES Def: A mtr A s symmetr f d oly f A A, e,, Emple A s symmetr Def: A mtr A s skew symmetr f d oly f A A, e,, Emple A s skew symmetr Remrks: If A s symmetr or skew symmetr,

More information

Density estimation II

Density estimation II CS 750 Mche Lerg Lecture 6 esty estmto II Mlos Husrecht mlos@tt.edu 539 Seott Squre t: esty estmto {.. } vector of ttrute vlues Ojectve: estmte the model of the uderlyg rolty dstruto over vrles X X usg

More information

Two Coefficients of the Dyson Product

Two Coefficients of the Dyson Product Two Coeffcents of the Dyson Product rxv:07.460v mth.co 7 Nov 007 Lun Lv, Guoce Xn, nd Yue Zhou 3,,3 Center for Combntorcs, LPMC TJKLC Nnk Unversty, Tnjn 30007, P.R. Chn lvlun@cfc.nnk.edu.cn gn@nnk.edu.cn

More information

Chapter 3. Differentiation 3.2 Differentiation Rules for Polynomials, Exponentials, Products and Quotients

Chapter 3. Differentiation 3.2 Differentiation Rules for Polynomials, Exponentials, Products and Quotients 3.2 Dfferetato Rules 1 Capter 3. Dfferetato 3.2 Dfferetato Rules for Polyomals, Expoetals, Proucts a Quotets Rule 1. Dervatve of a Costat Fucto. If f as te costat value f(x) = c, te f x = [c] = 0. x Proof.

More information

ON NILPOTENCY IN NONASSOCIATIVE ALGEBRAS

ON NILPOTENCY IN NONASSOCIATIVE ALGEBRAS Jourl of Algebr Nuber Theory: Advces d Applctos Volue 6 Nuber 6 ges 85- Avlble t http://scetfcdvces.co. DOI: http://dx.do.org/.864/t_779 ON NILOTENCY IN NONASSOCIATIVE ALGERAS C. J. A. ÉRÉ M. F. OUEDRAOGO

More information

ZETA REGULARIZATION METHOD APPLIED TO THE CALCULATION OF DIVERGENT DIVERGENT INTEGRALS

ZETA REGULARIZATION METHOD APPLIED TO THE CALCULATION OF DIVERGENT DIVERGENT INTEGRALS ZETA REGULARIZATION METOD APPLIED TO TE CALCULATION OF DIVERGENT DIVERGENT INTEGRALS Jose Jver Grc Moret Grdute studet of Physcs t the UPV/EU (Uversty of Bsque coutry) I Sold Stte Physcs Addres: Prctctes

More information

CS321. Introduction to Numerical Methods

CS321. Introduction to Numerical Methods CS Itroducto to Numercl Metods Lecture Revew Proessor Ju Zg Deprtmet o Computer Scece Uversty o Ketucky Legto, KY 6 6 Mrc 7, Number Coverso A geerl umber sould be coverted teger prt d rctol prt seprtely

More information

Area and the Definite Integral. Area under Curve. The Partition. y f (x) We want to find the area under f (x) on [ a, b ]

Area and the Definite Integral. Area under Curve. The Partition. y f (x) We want to find the area under f (x) on [ a, b ] Are d the Defte Itegrl 1 Are uder Curve We wt to fd the re uder f (x) o [, ] y f (x) x The Prtto We eg y prttog the tervl [, ] to smller su-tervls x 0 x 1 x x - x -1 x 1 The Bsc Ide We the crete rectgles

More information

Review of Linear Algebra

Review of Linear Algebra PGE 30: Forulto d Soluto Geosstes Egeerg Dr. Blhoff Sprg 0 Revew of Ler Alger Chpter 7 of Nuercl Methods wth MATLAB, Gerld Recktewld Vector s ordered set of rel (or cople) uers rrged s row or colu sclr

More information

Computer Programming

Computer Programming Computer Progrmmg I progrmmg, t s ot eough to be vetve d geous. Oe lso eeds to be dscpled d cotrolled order ot be become etgled oe's ow completes. Hrl D. Mlls, Forwrd to Progrmmg Proverbs b Her F. Ledgrd

More information

Fibonacci and Lucas Numbers as Tridiagonal Matrix Determinants

Fibonacci and Lucas Numbers as Tridiagonal Matrix Determinants Rochester Isttute of echology RI Scholr Wors Artcles 8-00 bocc d ucs Nubers s rdgol trx Deterts Nth D. Chll Est Kod Copy Drre Nry Rochester Isttute of echology ollow ths d ddtol wors t: http://scholrwors.rt.edu/rtcle

More information

Random variables and sampling theory

Random variables and sampling theory Revew Rdom vrbles d smplg theory [Note: Beg your study of ths chpter by redg the Overvew secto below. The red the correspodg chpter the textbook, vew the correspodg sldeshows o the webste, d do the strred

More information

Section 6.3: Geometric Sequences

Section 6.3: Geometric Sequences 40 Chpter 6 Sectio 6.: Geometric Sequeces My jobs offer ul cost-of-livig icrese to keep slries cosistet with ifltio. Suppose, for exmple, recet college grdute fids positio s sles mger erig ul slry of $6,000.

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

under the curve in the first quadrant.

under the curve in the first quadrant. NOTES 5: INTEGRALS Nme: Dte: Perod: LESSON 5. AREAS AND DISTANCES Are uder the curve Are uder f( ), ove the -s, o the dom., Prctce Prolems:. f ( ). Fd the re uder the fucto, ove the - s, etwee,.. f ( )

More information

DCDM BUSINESS SCHOOL NUMERICAL METHODS (COS 233-8) Solutions to Assignment 3. x f(x)

DCDM BUSINESS SCHOOL NUMERICAL METHODS (COS 233-8) Solutions to Assignment 3. x f(x) DCDM BUSINESS SCHOOL NUMEICAL METHODS (COS -8) Solutons to Assgnment Queston Consder the followng dt: 5 f() 8 7 5 () Set up dfference tble through fourth dfferences. (b) Wht s the mnmum degree tht n nterpoltng

More information

14.2 Line Integrals. determines a partition P of the curve by points Pi ( xi, y

14.2 Line Integrals. determines a partition P of the curve by points Pi ( xi, y 4. Le Itegrls I ths secto we defe tegrl tht s smlr to sgle tegrl except tht sted of tegrtg over tervl [ ] we tegrte over curve. Such tegrls re clled le tegrls lthough curve tegrls would e etter termology.

More information

DISCRETE TIME MODELS OF FORWARD CONTRACTS INSURANCE

DISCRETE TIME MODELS OF FORWARD CONTRACTS INSURANCE G Tstsshvl DSCRETE TME MODELS OF FORWARD CONTRACTS NSURANCE (Vol) 008 September DSCRETE TME MODELS OF FORWARD CONTRACTS NSURANCE GSh Tstsshvl e-ml: gurm@mdvoru 69004 Vldvosto Rdo str 7 sttute for Appled

More information

The definite Riemann integral

The definite Riemann integral Roberto s Notes o Itegrl Clculus Chpter 4: Defte tegrls d the FTC Secto 4 The defte Rem tegrl Wht you eed to kow lredy: How to ppromte the re uder curve by usg Rem sums. Wht you c ler here: How to use

More information

Discrete Mathematics and Probability Theory Fall 2016 Seshia and Walrand DIS 10b

Discrete Mathematics and Probability Theory Fall 2016 Seshia and Walrand DIS 10b CS 70 Dscrete Mathematcs ad Probablty Theory Fall 206 Sesha ad Walrad DIS 0b. Wll I Get My Package? Seaky delvery guy of some compay s out delverg packages to customers. Not oly does he had a radom package

More information

Application of Modified Gravitational Search Algorithm to Solve the Problem of Teaching Hidden Markov Model

Application of Modified Gravitational Search Algorithm to Solve the Problem of Teaching Hidden Markov Model IJCSI Itertol Jourl of Computer Scece Issues, Vol. 10, Issue 3, No 2, y 2013 ISSN (Pr: 1694-0814 ISSN (Ole): 1694-0784 www.ijcsi.org 1 Applcto of ofe Grvttol Serch Algorthm to Solve the Problem of Techg

More information

Linear Algebra Concepts

Linear Algebra Concepts Ler Algebr Cocepts Ke Kreutz-Delgdo (Nuo Vscocelos) ECE 75A Wter 22 UCSD Vector spces Defto: vector spce s set H where ddto d sclr multplcto re defed d stsf: ) +( + ) (+ )+ 5) l H 2) + + H 6) 3) H, + 7)

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

Roberto s Notes on Integral Calculus Chapter 4: Definite integrals and the FTC Section 2. Riemann sums

Roberto s Notes on Integral Calculus Chapter 4: Definite integrals and the FTC Section 2. Riemann sums Roerto s Notes o Itegrl Clculus Chpter 4: Defte tegrls d the FTC Secto 2 Rem sums Wht you eed to kow lredy: The defto of re for rectgle. Rememer tht our curret prolem s how to compute the re of ple rego

More information

Cooper and McGillem Chapter 4: Moments Linear Regression

Cooper and McGillem Chapter 4: Moments Linear Regression Cooper d McGllem Chpter 4: Momets Ler Regresso Chpter 4: lemets of Sttstcs 4-6 Curve Fttg d Ler Regresso 4-7 Correlto Betwee Two Sets of Dt Cocepts How close re the smple vlues to the uderlg pdf vlues?

More information

Introduction to mathematical Statistics

Introduction to mathematical Statistics Itroducto to mthemtcl ttstcs Fl oluto. A grou of bbes ll of whom weghed romtely the sme t brth re rdomly dvded to two grous. The bbes smle were fed formul A; those smle were fed formul B. The weght gs

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

Chapter Simpson s 1/3 Rule of Integration. ( x)

Chapter Simpson s 1/3 Rule of Integration. ( x) Cpter 7. Smpso s / Rule o Itegrto Ater redg ts pter, you sould e le to. derve te ormul or Smpso s / rule o tegrto,. use Smpso s / rule t to solve tegrls,. develop te ormul or multple-segmet Smpso s / rule

More information

MATRIX AND VECTOR NORMS

MATRIX AND VECTOR NORMS Numercl lyss for Egeers Germ Jord Uversty MTRIX ND VECTOR NORMS vector orm s mesure of the mgtude of vector. Smlrly, mtr orm s mesure of the mgtude of mtr. For sgle comoet etty such s ordry umers, the

More information

Objective of curve fitting is to represent a set of discrete data by a function (curve). Consider a set of discrete data as given in table.

Objective of curve fitting is to represent a set of discrete data by a function (curve). Consider a set of discrete data as given in table. CURVE FITTING Obectve curve ttg s t represet set dscrete dt b uct curve. Csder set dscrete dt s gve tble. 3 3 = T use the dt eectvel, curve epress s tted t the gve dt set, s = + = + + = e b ler uct plml

More information

Differential Method of Thin Layer for Retaining Wall Active Earth Pressure and Its Distribution under Seismic Condition Li-Min XU, Yong SUN

Differential Method of Thin Layer for Retaining Wall Active Earth Pressure and Its Distribution under Seismic Condition Li-Min XU, Yong SUN Itertol Coferece o Mechcs d Cvl Egeerg (ICMCE 014) Dfferetl Method of Th Lyer for Retg Wll Actve Erth Pressure d Its Dstrbuto uder Sesmc Codto L-M XU, Yog SUN Key Lbortory of Krst Evromet d Geologcl Hzrd

More information

UNIT 6 CORRELATION COEFFICIENT

UNIT 6 CORRELATION COEFFICIENT UNIT CORRELATION COEFFICIENT Correlato Coeffcet Structure. Itroucto Objectves. Cocept a Defto of Correlato.3 Tpes of Correlato.4 Scatter Dagram.5 Coeffcet of Correlato Assumptos for Correlato Coeffcet.

More information

INTRODUCTION ( ) 1. Errors

INTRODUCTION ( ) 1. Errors INTRODUCTION Numercl lyss volves the study, developmet d lyss of lgorthms for obtg umercl solutos to vrous mthemtcl problems. Frequetly umercl lyss s clled the mthemtcs of scetfc computg. Numercl lyss

More information

Lecture 3: Review of Linear Algebra and MATLAB

Lecture 3: Review of Linear Algebra and MATLAB eture 3: Revew of er Aler AAB Vetor mtr otto Vetors tres Vetor spes er trsformtos Eevlues eevetors AAB prmer Itrouto to Ptter Reoto Rro Guterrez-su Wrht Stte Uverst Vetor mtr otto A -mesol (olum) vetor

More information

Integration by Parts for D K

Integration by Parts for D K Itertol OPEN ACCESS Jourl Of Moder Egeerg Reserc IJMER Itegrto y Prts for D K Itegrl T K Gr, S Ry 2 Deprtmet of Mtemtcs, Rgutpur College, Rgutpur-72333, Purul, West Begl, Id 2 Deprtmet of Mtemtcs, Ss Bv,

More information

More Regression Lecture Notes CE 311K - McKinney Introduction to Computer Methods Department of Civil Engineering The University of Texas at Austin

More Regression Lecture Notes CE 311K - McKinney Introduction to Computer Methods Department of Civil Engineering The University of Texas at Austin More Regresso Lecture Notes CE K - McKe Itroducto to Coputer Methods Deprtet of Cvl Egeerg The Uverst of Tes t Aust Polol Regresso Prevousl, we ft strght le to os dt (, ), (, ), (, ) usg the lest-squres

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

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

Chapter Newton-Raphson Method of Solving a Nonlinear Equation

Chapter Newton-Raphson Method of Solving a Nonlinear Equation Chpter.4 Newton-Rphson Method of Solvng Nonlner Equton After redng ths chpter, you should be ble to:. derve the Newton-Rphson method formul,. develop the lgorthm of the Newton-Rphson method,. use the Newton-Rphson

More information

GCE AS/A Level MATHEMATICS GCE AS/A Level FURTHER MATHEMATICS

GCE AS/A Level MATHEMATICS GCE AS/A Level FURTHER MATHEMATICS GCE AS/A Level MATHEMATICS GCE AS/A Level FURTHER MATHEMATICS FORMULA BOOKLET Fom Septembe 07 Issued 07 Mesuto Pue Mthemtcs Sufce e of sphee = 4 Ae of cuved sufce of coe = slt heght Athmetc Sees S l d

More information

Chapter 6 Notes, Larson/Hostetler 3e

Chapter 6 Notes, Larson/Hostetler 3e Contents 6. Antiderivtives nd the Rules of Integrtion.......................... 6. Are nd the Definite Integrl.................................. 6.. Are............................................ 6. Reimnn

More information

EVALUATING COMPARISON BETWEEN CONSISTENCY IMPROVING METHOD AND RESURVEY IN AHP

EVALUATING COMPARISON BETWEEN CONSISTENCY IMPROVING METHOD AND RESURVEY IN AHP ISAHP 00, Bere, Stzerld, August -4, 00 EVALUATING COMPARISON BETWEEN CONSISTENCY IMPROVING METHOD AND RESURVEY IN AHP J Rhro, S Hlm d Set Wto Petr Chrst Uversty, Surby, Idoes @peter.petr.c.d sh@peter.petr.c.d

More information

02/15/04 INTERESTING FINITE AND INFINITE PRODUCTS FROM SIMPLE ALGEBRAIC IDENTITIES

02/15/04 INTERESTING FINITE AND INFINITE PRODUCTS FROM SIMPLE ALGEBRAIC IDENTITIES 0/5/04 ITERESTIG FIITE AD IFIITE PRODUCTS FROM SIMPLE ALGEBRAIC IDETITIES Thomas J Osler Mathematcs Departmet Rowa Uversty Glassboro J 0808 Osler@rowaedu Itroducto The dfferece of two squares, y = + y

More information

European Journal of Mathematics and Computer Science Vol. 3 No. 1, 2016 ISSN ISSN

European Journal of Mathematics and Computer Science Vol. 3 No. 1, 2016 ISSN ISSN Euroe Jour of Mthemtcs d omuter Scece Vo. No. 6 ISSN 59-995 ISSN 59-995 ON AN INVESTIGATION O THE MATRIX O THE SEOND PARTIA DERIVATIVE IN ONE EONOMI DYNAMIS MODE S. I. Hmdov Bu Stte Uverst ABSTRAT The

More information