Pyramid Algorithms for Barycentric Rational Interpolation

Size: px
Start display at page:

Download "Pyramid Algorithms for Barycentric Rational Interpolation"

Transcription

1 Pyrmd Algorthms for Brycentrc Rtonl Interpolton K Hormnn Scott Schefer Astrct We present new perspectve on the Floter Hormnn nterpolnt. Ths nterpolnt s rtonl of degree (n, d), reproduces polynomls of degree d, nd hs no rel poles. By cstng the evluton of ths nterpolnt s pyrmd lgorthm, we frst demonstrte close relton to Nevlle s lgorthm. We then derve n O(nd) lgorthm for computng the rycentrc weghts of the Floter Hormnn nterpolnt, whch mproves upon the orgnl O(nd 2 ) constructon. Introducton Gven the n + nterpolton nodes x 0 < x < < x n nd the ssocted dt f 0, f..., f n, there re two wys to wrte the rtonl Floter Hormnn nterpolnt [3] of degree d n. On the one hnd, t cn e expressed s the lend n d r(x) = λ (x)p (x) n d λ () (x) of the polynomls p of degree d, whch loclly nterpolte the dt f,..., f +d, wth weghtng functons λ (x) = ( ) (x x ) (x x +d ). On the other hnd, t cn e wrtten n the rycentrc form / n ( ) n ( ) r(x) = w f w (2) x x x x wth postve weghts w = mn(,n d) +d =mx(0, d) k=,k x x k. (3) The rycentrc form s prtculrly suted for evlutng the nterpolnt r n O(n) tme, once the weghts w, whch depend only on the nodes x nd not on the dt f, hve een precomputed. Usng (3), these weghts cn e determned n O(nd 2 ) steps, whch s exctly how common lrres lke Numercl Recpes [5] nd ALGLIB [2] perform the computton. We present two novel procedures for evlutng r(x), whch re nspred y Ron Goldmn s pyrmd lgorthms [4] for the evluton of polynoml nd splne curves. Whle the frst s tlored for Floter Hormnn nterpolnts nd explots the representton of r n (), the second s sed on (2) nd works for generl rycentrc rtonl nterpolnts of degree (n, n) wth rtrry weghts w. Both lgorthms requre O(n 2 ) opertons nd re closely relted to Nevlle s lgorthm for constructng nterpoltng polynomls of degree n. They further led to novel O(nd) lgorthm for computng the weghts n (3). Correspondng uthor; phone: ; fx: ; eml: k.hormnn@us.ch The clm n [5, pge 28] tht the worklod to construct the weghts s of order O(nd) opertons s wrong, ecuse the gven code ust mplements the formul n (3) n strghtforwrd wy nd s clerly of order O(nd 2 ).

2 Fgure : Pyrmd notton for lner comntons. Thck rrows ndcte ffne comntons, where we omt the normlzton fctors of the weghts to keep the dgrm less cluttered. Dshed rrows ndcte tht the weghts need to e multpled wth /2. Q 5 0 (x) Q 4 0 (x) Q4 (x) x x Q 3 0 (x) Q3 (x) Q3 2 (x) x x Q 2 0 (x) Q2 (x) Q2 2 (x) Q2 3 (x) x x Q 0 (x) Q (x) Q 2 (x) Q 3 (x) Q 4 (x) x x x x x x 4 Q 0 0 (x) Q0 (x) Q0 2 (x) Q0 3 (x) Q0 4 (x) Q0 5 (x) Fgure 2: Exmple of the pyrmd lgorthm for Floter Hormnn nterpolton wth n = 5 nd d = 2. 2 Evlutng the rtonl nterpolnt Usng the notton n Fgure, the Floter Hormnn nterpolnt n () cn e evluted y the pyrmd lgorthm n Fgure 2, whch s slght vrton of Nevlle s lgorthm [4, Chpter 2.2], where the weghts n the top n d rows of the pyrmd re not normlzed nd the weghts t the nteror edges n these rows re multpled wth n ddtonl fctor of /2. Tht s, for some gven evluton prmeter x, we strt wth the ntl dt Q 0 (x) = (f, ), = 0,..., n nd compute the ottom d rows of the pyrmd wth Nevlle s lgorthm s Q l (x) = x +l x Q l (x) + x x Q l + (x), = 0,..., n l, x +l x x +l x for l =,..., d, resultng n the vlues Q d (x) = (p (x), ), = 0,..., n d. We then contnue to compute the top n d rows of the pyrmd s Q l (x) = η n l+ for l = d +,..., n, where (x +l x)q l (x) + η n l+ η l = + (x x )Q l + (x), = 0,..., n l, {, f = 0 or = l, /2, otherwse, 2

3 P 0 0 η 0 η P 0 P η 2 0 η 2 η 2 η 2 2 P 2 0 P 2 P 2 2 η 3 0 η 3 η 3 η 3 2 η 3 2 η 3 3 P 3 0 P 3 P 3 2 P 3 3 Fgure 3: Recurrence of the vlues P l (left), whch re ll equl to (rght). resultng n the vlue Q n 0 (x) = ( f(x), g(x) ). We now oserve tht the dgrm n Fgure 2 stsfes the prllel property, tht s, prllel rrows ll hve the sme lels. Hence the product of lels long ny pth from Q d (x) to the pex of the pyrmd s lwys where µ (x) = (x x ) =0 n k=+d+ π(x) = (x k x) = ( ) n d π(x)λ (x), n (x x ). =0 Due to the ddtonl fctors η l, ech of these pths s further multpled y some power of /2. Denotng the sum of these powers y P n d, we hve n d Q n 0 (x) = P n d µ (x)q d (x). As ny pth from the node Q l s cler tht the vlues P l stsfy the recurrence 2 P 0 0 =, (x) to the pex must trverse ether of the nodes Ql P l = η l P l + ηl P l, = 0,..., l, l =,..., n d, s shown n Fgure 3, nd t follows y nducton tht P n d = for = 0,..., n. Therefore, (x) or Ql (x), t n d f(x) = ( ) n d π(x) λ (x)p (x), n d g(x) = ( ) n d π(x) λ (x), nd fnl dvson of these two components of Q n 0 (x) gves r(x) = f(x) g(x). 2 For the ske of smplcty, we tctly follow the conventon tht P l = 0 for < 0 nd > l. 3

4 R 5 0 (x) R 4 0 (x) R4 (x) x x R 3 0 (x) R3 (x) R3 2 (x) x x R 2 0 (x) R2 (x) R2 2 (x) R2 3 (x) x x R 0 (x) R (x) R 2 (x) R 3 (x) R 4 (x) x x x x x x 4 R 0 0 (x) R0 (x) R0 2 (x) R0 3 (x) R0 4 (x) R0 5 (x) Fgure 4: Exmple of the pyrmd lgorthm for generl rycentrc rtonl nterpolton wth n = 5. Another opton s to evlute the nterpolnt r y modfyng ll the rows of Nevlle s lgorthm, s shown n Fgure 4, nd to use the ntl dt R 0 (x) = w (f, ) = (w f, w ), = 0,..., n. We then compute the rows of the pyrmd from ottom to top s R l (x) = η n l+ for l =,..., n wth η l (x +l x)r l (x) + η n l+ defned s ove, resultng n the vlue + (x x )R l (x), = 0,..., n l, R n 0 (x) = ( f(x), g(x) ). Wth rguments smlr to the ones ove, t cn e shown tht n f(x) = ( ) n ( ) n π(x) w f, g(x) = ( ) n ( ) π(x) w, x x x x nd fnl dvson gves r(x) = f(x) g(x). Note tht ths second lgorthm works for ny set of weghts w, hence for ny rtonl nterpolnt of degree (n, n). Clerly, oth lgorthms requre O(n 2 ) steps nd re thus slower nd possly not s roust s the O(n) evluton of r(x) wth the rycentrc form (2), ut we eleve tht ths formulton sheds some nterestng new lght on the de of rycentrc rtonl nterpolton. We would further lke to pont out tht the lgorthm n Fgure 2 ers strong resemlnce to the lgorthm of Brry nd Goldmn [] for evlutng Ctmull Rom splnes. Both lgorthms frst crry out few rounds of Nevlle s lgorthm to compute the locl polynoml nterpolnts p (x), ut they dffer n the wy these vlues re comned whle gong through the top rows of the pyrmd: for Ctmull Rom splnes we lend the locl polynomls wth B-splne ss functons, nd for Floter Hormnn nterpolnts we use λ / n d =0 λ s lendng functons. 4 +

5 x x 0 x x 0 x 2 x 0 x 2 x 0 V0 2 V 2 V2 2 V3 2 V0 V V2 V3 V4 x 2 x x 2 x x 3 x x 3 x x 3 x 2 x 3 x 2 x 4 x 2 x 4 x 2 x 4 x 3 x 4 x 3 x 5 x 3 x 5 x 3 x 5 x 4 x 5 x 4 V0 0 V 0 V2 0 V3 0 V4 0 V5 0 Fgure 5: Exmple of the pyrmd lgorthm for computng the Floter Hormnn weghts w n (3) wth n = 5 nd d = 2. 3 Computng the weghts Comprng the two lgorthms n Fgures 2 nd 4, we notce tht they re dentcl n the top n d rows nd tht the nput dt Q 0 (x) nd R0 (x) dffer y the fctors w. Ths oservton suggests tht there mght exst n effcent pyrmd lgorthm for computng the Floter Hormnn weghts. Indeed, we cn determne the weghts w f we strt wth the vlues V d =, = 0,..., n d nd then work our wy down the ottom d rows of the pyrmd, usng the normlzton fctors from Nevlle s lgorthm, to compute 3 V l = V l+ x +l x + for l = d, d 2,..., 0, s shown n Fgure 5. To see tht V 0 vlues U l = mn(,n l) =mx(0, l) V l +l k=,k V l+ x +l+ x, = 0,..., n l, (4) = w for = 0,..., n, consder the, = 0,..., n, l = 0,..., d. (5) x x k For these vlues we clerly hve U 0 = V 0 nd U d = w for = 0,..., n, nd we further show tht U l = U l+ for ny l = 0,..., d. The mn de s frst to expnd ech ddend A l of U l usng (4) nd then to ugment the products y one fctor, A l = V l +l k=,k ( x x k = = V l+ V l+ x +l x + V l+ ) +l x +l+ x k=,k +l x x x +l x x x k k=,k }{{} =B l Next oserve tht the terms C l from Al nd Bl + from Al + C l + B+ l = V l+ x +l+ x x +l+ x = V l+ +l+ k=,k +l+ k=,k x x k = Al+. 3 For the ske of smplcty, we tctly follow the conventon tht V l x x k + V l+ x +l+ x x +l+ x x x k + V l+ x x x +l+ x +l+ k=,k x x k } {{ } =C l sum up to the ddend Al+ of U l+, +l+ k=,k = 0 for < 0 nd > n l. x x k. 5

6 Fnlly, notce tht B0 l = 0, ecuse V l+ = 0, nd B l l = Al+ l for > l, nd smlrly Cl n l = 0, ecuse V l+ n l = 0, nd Cl = Al+ for < n l. If we now denote the lower nd upper ounds of the summton ndex n (5) y l = mx(0, l), l = mn(, n l) nd dstngush the dfferent cses of the ndex, where ether l = 0 or l = l nd ether l = or l = n l, we fnd tht n ll cses U l = l = l A l = B l l + l = l + B l + l = l C l + C l l l = B l + l = l A l+ + C l l = l+ = l+ A l+ = U l+. Therefore, V 0 = U 0 = U = = U d = w, whch sserts tht the Floter Hormnn weghts re determned y the lgorthm ove n O(nd) steps. References [] P. J. Brry nd R. N. Goldmn. A recursve evluton lgorthm for clss of Ctmull Rom splnes. ACM SIGGRAPH Computer Grphcs, 22(4):99 204, Aug [2] S. Bochknov. ALGLIB User Gude Interpolton nd fttng Rtonl nterpolton. Dec [Onlne; ccessed 0-June- 205]. [3] M. S. Floter nd K. Hormnn. Brycentrc rtonl nterpolton wth no poles nd hgh rtes of pproxmton. Numersche Mthemtk, 07(2):35 33, Aug [4] R. Goldmn. Pyrmd Algorthms: A Dynmc Progrmmng Approch to Curves nd Surfces for Geometrc Modelng. The Morgn Kufmnn Seres n Computer Grphcs nd Geometrc Modelng. Morgn Kufmnn, Sn Frncsco, [5] S. Teukolsky, B. P. Flnnery, W. T. Vetterlng, nd W. H. Press. Numercl Recpes n C: The Art of Scentfc Computng, chpter 3.4., pges Cmrdge Unversty Press, New York, thrd edton,

Rank One Update And the Google Matrix by Al Bernstein Signal Science, LLC

Rank One Update And the Google Matrix by Al Bernstein Signal Science, LLC Introducton Rnk One Updte And the Google Mtrx y Al Bernsten Sgnl Scence, LLC www.sgnlscence.net here re two dfferent wys to perform mtrx multplctons. he frst uses dot product formulton nd the second uses

More information

Lecture 4: Piecewise Cubic Interpolation

Lecture 4: Piecewise Cubic Interpolation Lecture notes on Vrtonl nd Approxmte Methods n Appled Mthemtcs - A Perce UBC Lecture 4: Pecewse Cubc Interpolton Compled 6 August 7 In ths lecture we consder pecewse cubc nterpolton n whch cubc polynoml

More information

UNIVERSITY OF IOANNINA DEPARTMENT OF ECONOMICS. M.Sc. in Economics MICROECONOMIC THEORY I. Problem Set II

UNIVERSITY OF IOANNINA DEPARTMENT OF ECONOMICS. M.Sc. in Economics MICROECONOMIC THEORY I. Problem Set II Mcroeconomc Theory I UNIVERSITY OF IOANNINA DEPARTMENT OF ECONOMICS MSc n Economcs MICROECONOMIC THEORY I Techng: A Lptns (Note: The number of ndctes exercse s dffculty level) ()True or flse? If V( y )

More information

Katholieke Universiteit Leuven Department of Computer Science

Katholieke Universiteit Leuven Department of Computer Science Updte Rules for Weghted Non-negtve FH*G Fctorzton Peter Peers Phlp Dutré Report CW 440, Aprl 006 Ktholeke Unverstet Leuven Deprtment of Computer Scence Celestjnenln 00A B-3001 Heverlee (Belgum) Updte Rules

More information

THE COMBINED SHEPARD ABEL GONCHAROV UNIVARIATE OPERATOR

THE COMBINED SHEPARD ABEL GONCHAROV UNIVARIATE OPERATOR REVUE D ANALYSE NUMÉRIQUE ET DE THÉORIE DE L APPROXIMATION Tome 32, N o 1, 2003, pp 11 20 THE COMBINED SHEPARD ABEL GONCHAROV UNIVARIATE OPERATOR TEODORA CĂTINAŞ Abstrct We extend the Sheprd opertor by

More information

Introduction to Numerical Integration Part II

Introduction to Numerical Integration Part II Introducton to umercl Integrton Prt II CS 75/Mth 75 Brn T. Smth, UM, CS Dept. Sprng, 998 4/9/998 qud_ Intro to Gussn Qudrture s eore, the generl tretment chnges the ntegrton prolem to ndng the ntegrl w

More information

Jens Siebel (University of Applied Sciences Kaiserslautern) An Interactive Introduction to Complex Numbers

Jens Siebel (University of Applied Sciences Kaiserslautern) An Interactive Introduction to Complex Numbers Jens Sebel (Unversty of Appled Scences Kserslutern) An Interctve Introducton to Complex Numbers 1. Introducton We know tht some polynoml equtons do not hve ny solutons on R/. Exmple 1.1: Solve x + 1= for

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

International Journal of Pure and Applied Sciences and Technology

International Journal of Pure and Applied Sciences and Technology Int. J. Pure Appl. Sc. Technol., () (), pp. 44-49 Interntonl Journl of Pure nd Appled Scences nd Technolog ISSN 9-67 Avlle onlne t www.jopst.n Reserch Pper Numercl Soluton for Non-Lner Fredholm Integrl

More information

INTRODUCTION TO COMPLEX NUMBERS

INTRODUCTION TO COMPLEX NUMBERS INTRODUCTION TO COMPLEX NUMBERS The numers -4, -3, -, -1, 0, 1,, 3, 4 represent the negtve nd postve rel numers termed ntegers. As one frst lerns n mddle school they cn e thought of s unt dstnce spced

More information

CISE 301: Numerical Methods Lecture 5, Topic 4 Least Squares, Curve Fitting

CISE 301: Numerical Methods Lecture 5, Topic 4 Least Squares, Curve Fitting CISE 3: umercl Methods Lecture 5 Topc 4 Lest Squres Curve Fttng Dr. Amr Khouh Term Red Chpter 7 of the tetoo c Khouh CISE3_Topc4_Lest Squre Motvton Gven set of epermentl dt 3 5. 5.9 6.3 The reltonshp etween

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

Partially Observable Systems. 1 Partially Observable Markov Decision Process (POMDP) Formalism

Partially Observable Systems. 1 Partially Observable Markov Decision Process (POMDP) Formalism CS294-40 Lernng for Rootcs nd Control Lecture 10-9/30/2008 Lecturer: Peter Aeel Prtlly Oservle Systems Scre: Dvd Nchum Lecture outlne POMDP formlsm Pont-sed vlue terton Glol methods: polytree, enumerton,

More information

Chapter Newton-Raphson Method of Solving a Nonlinear Equation

Chapter Newton-Raphson Method of Solving a Nonlinear Equation Chpter 0.04 Newton-Rphson Method o Solvng Nonlner Equton Ater redng ths chpter, you should be ble to:. derve the Newton-Rphson method ormul,. develop the lgorthm o the Newton-Rphson method,. use the Newton-Rphson

More information

Lecture 36. Finite Element Methods

Lecture 36. Finite Element Methods CE 60: Numercl Methods Lecture 36 Fnte Element Methods Course Coordntor: Dr. Suresh A. Krth, Assocte Professor, Deprtment of Cvl Engneerng, IIT Guwht. In the lst clss, we dscussed on the ppromte methods

More information

Abhilasha Classes Class- XII Date: SOLUTION (Chap - 9,10,12) MM 50 Mob no

Abhilasha Classes Class- XII Date: SOLUTION (Chap - 9,10,12) MM 50 Mob no hlsh Clsses Clss- XII Dte: 0- - SOLUTION Chp - 9,0, MM 50 Mo no-996 If nd re poston vets of nd B respetvel, fnd the poston vet of pont C n B produed suh tht C B vet r C B = where = hs length nd dreton

More information

Applied Statistics Qualifier Examination

Applied Statistics Qualifier Examination Appled Sttstcs Qulfer Exmnton Qul_june_8 Fll 8 Instructons: () The exmnton contns 4 Questons. You re to nswer 3 out of 4 of them. () You my use ny books nd clss notes tht you mght fnd helpful n solvng

More information

INTERPOLATION(1) ELM1222 Numerical Analysis. ELM1222 Numerical Analysis Dr Muharrem Mercimek

INTERPOLATION(1) ELM1222 Numerical Analysis. ELM1222 Numerical Analysis Dr Muharrem Mercimek ELM Numercl Anlss Dr Muhrrem Mercmek INTEPOLATION ELM Numercl Anlss Some of the contents re dopted from Lurene V. Fusett, Appled Numercl Anlss usng MATLAB. Prentce Hll Inc., 999 ELM Numercl Anlss Dr Muhrrem

More information

Remember: Project Proposals are due April 11.

Remember: Project Proposals are due April 11. Bonformtcs ecture Notes Announcements Remember: Project Proposls re due Aprl. Clss 22 Aprl 4, 2002 A. Hdden Mrov Models. Defntons Emple - Consder the emple we tled bout n clss lst tme wth the cons. However,

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

Least squares. Václav Hlaváč. Czech Technical University in Prague

Least squares. Václav Hlaváč. Czech Technical University in Prague Lest squres Václv Hlváč Czech echncl Unversty n Prgue hlvc@fel.cvut.cz http://cmp.felk.cvut.cz/~hlvc Courtesy: Fred Pghn nd J.P. Lews, SIGGRAPH 2007 Course; Outlne 2 Lner regresson Geometry of lest-squres

More information

Dynamic Programming. Preview. Dynamic Programming. Dynamic Programming. Dynamic Programming (Example: Fibonacci Sequence)

Dynamic Programming. Preview. Dynamic Programming. Dynamic Programming. Dynamic Programming (Example: Fibonacci Sequence) /24/27 Prevew Fbonacc Sequence Longest Common Subsequence Dynamc programmng s a method for solvng complex problems by breakng them down nto smpler sub-problems. It s applcable to problems exhbtng the propertes

More information

The Schur-Cohn Algorithm

The Schur-Cohn Algorithm Modelng, Estmton nd Otml Flterng n Sgnl Processng Mohmed Njm Coyrght 8, ISTE Ltd. Aendx F The Schur-Cohn Algorthm In ths endx, our m s to resent the Schur-Cohn lgorthm [] whch s often used s crteron for

More information

NUMERICAL DIFFERENTIATION

NUMERICAL DIFFERENTIATION NUMERICAL DIFFERENTIATION 1 Introducton Dfferentaton s a method to compute the rate at whch a dependent output y changes wth respect to the change n the ndependent nput x. Ths rate of change s called the

More information

8. INVERSE Z-TRANSFORM

8. INVERSE Z-TRANSFORM 8. INVERSE Z-TRANSFORM The proce by whch Z-trnform of tme ere, nmely X(), returned to the tme domn clled the nvere Z-trnform. The nvere Z-trnform defned by: Computer tudy Z X M-fle trn.m ued to fnd nvere

More information

6 Roots of Equations: Open Methods

6 Roots of Equations: Open Methods HK Km Slghtly modfed 3//9, /8/6 Frstly wrtten t Mrch 5 6 Roots of Equtons: Open Methods Smple Fed-Pont Iterton Newton-Rphson Secnt Methods MATLAB Functon: fzero Polynomls Cse Study: Ppe Frcton Brcketng

More information

GAUSS ELIMINATION. Consider the following system of algebraic linear equations

GAUSS ELIMINATION. Consider the following system of algebraic linear equations Numercl Anlyss for Engneers Germn Jordnn Unversty GAUSS ELIMINATION Consder the followng system of lgebrc lner equtons To solve the bove system usng clsscl methods, equton () s subtrcted from equton ()

More information

4. Eccentric axial loading, cross-section core

4. Eccentric axial loading, cross-section core . Eccentrc xl lodng, cross-secton core Introducton We re strtng to consder more generl cse when the xl force nd bxl bendng ct smultneousl n the cross-secton of the br. B vrtue of Snt-Vennt s prncple we

More information

Multiple view geometry

Multiple view geometry EECS 442 Computer vson Multple vew geometry Perspectve Structure from Moton - Perspectve structure from moton prolem - mgutes - lgerc methods - Fctorzton methods - Bundle djustment - Self-clrton Redng:

More information

Foundations of Arithmetic

Foundations of Arithmetic Foundatons of Arthmetc Notaton We shall denote the sum and product of numbers n the usual notaton as a 2 + a 2 + a 3 + + a = a, a 1 a 2 a 3 a = a The notaton a b means a dvdes b,.e. ac = b where c s an

More information

7.2 Volume. A cross section is the shape we get when cutting straight through an object.

7.2 Volume. A cross section is the shape we get when cutting straight through an object. 7. Volume Let s revew the volume of smple sold, cylnder frst. Cylnder s volume=se re heght. As llustrted n Fgure (). Fgure ( nd (c) re specl cylnders. Fgure () s rght crculr cylnder. Fgure (c) s ox. A

More information

The Number of Rows which Equal Certain Row

The Number of Rows which Equal Certain Row Interntonl Journl of Algebr, Vol 5, 011, no 30, 1481-1488 he Number of Rows whch Equl Certn Row Ahmd Hbl Deprtment of mthemtcs Fcult of Scences Dmscus unverst Dmscus, Sr hblhmd1@gmlcom Abstrct Let be X

More information

Study of Trapezoidal Fuzzy Linear System of Equations S. M. Bargir 1, *, M. S. Bapat 2, J. D. Yadav 3 1

Study of Trapezoidal Fuzzy Linear System of Equations S. M. Bargir 1, *, M. S. Bapat 2, J. D. Yadav 3 1 mercn Interntonl Journl of Reserch n cence Technology Engneerng & Mthemtcs vlble onlne t http://wwwsrnet IN (Prnt: 38-349 IN (Onlne: 38-3580 IN (CD-ROM: 38-369 IJRTEM s refereed ndexed peer-revewed multdscplnry

More information

Principle Component Analysis

Principle Component Analysis Prncple Component Anlyss Jng Go SUNY Bufflo Why Dmensonlty Reducton? We hve too mny dmensons o reson bout or obtn nsghts from o vsulze oo much nose n the dt Need to reduce them to smller set of fctors

More information

Department of Mechanical Engineering, University of Bath. Mathematics ME Problem sheet 11 Least Squares Fitting of data

Department of Mechanical Engineering, University of Bath. Mathematics ME Problem sheet 11 Least Squares Fitting of data Deprtment of Mechncl Engneerng, Unversty of Bth Mthemtcs ME10305 Prolem sheet 11 Lest Squres Fttng of dt NOTE: If you re gettng just lttle t concerned y the length of these questons, then do hve look t

More information

Exercises. 18 Algorithms

Exercises. 18 Algorithms 18 Algorthms Exercses 0.1. In each of the followng stuatons, ndcate whether f = O(g), or f = Ω(g), or both (n whch case f = Θ(g)). f(n) g(n) (a) n 100 n 200 (b) n 1/2 n 2/3 (c) 100n + log n n + (log n)

More information

Effects of polarization on the reflected wave

Effects of polarization on the reflected wave Lecture Notes. L Ros PPLIED OPTICS Effects of polrzton on the reflected wve Ref: The Feynmn Lectures on Physcs, Vol-I, Secton 33-6 Plne of ncdence Z Plne of nterfce Fg. 1 Y Y r 1 Glss r 1 Glss Fg. Reflecton

More information

10. AREAS BETWEEN CURVES

10. AREAS BETWEEN CURVES . AREAS BETWEEN CURVES.. Ares etween curves So res ove the x-xis re positive nd res elow re negtive, right? Wrong! We lied! Well, when you first lern out integrtion it s convenient fiction tht s true in

More information

Triangle-based Consistencies for Cost Function Networks

Triangle-based Consistencies for Cost Function Networks Nonme mnuscrpt No. (wll e nserted y the edtor) Trngle-sed Consstences for Cost Functon Networks Hep Nguyen Chrstn Bessere Smon de Gvry Thoms Schex Receved: dte / Accepted: dte Astrct Cost Functon Networks

More information

Chapter 5 Supplemental Text Material R S T. ij i j ij ijk

Chapter 5 Supplemental Text Material R S T. ij i j ij ijk Chpter 5 Supplementl Text Mterl 5-. Expected Men Squres n the Two-fctor Fctorl Consder the two-fctor fxed effects model y = µ + τ + β + ( τβ) + ε k R S T =,,, =,,, k =,,, n gven s Equton (5-) n the textook.

More information

COMPLEX NUMBER & QUADRATIC EQUATION

COMPLEX NUMBER & QUADRATIC EQUATION MCQ COMPLEX NUMBER & QUADRATIC EQUATION Syllus : Comple numers s ordered prs of rels, Representton of comple numers n the form + nd ther representton n plne, Argnd dgrm, lger of comple numers, modulus

More information

Bases for Vector Spaces

Bases for Vector Spaces Bses for Vector Spces 2-26-25 A set is independent if, roughly speking, there is no redundncy in the set: You cn t uild ny vector in the set s liner comintion of the others A set spns if you cn uild everything

More information

Online Appendix to. Mandating Behavioral Conformity in Social Groups with Conformist Members

Online Appendix to. Mandating Behavioral Conformity in Social Groups with Conformist Members Onlne Appendx to Mndtng Behvorl Conformty n Socl Groups wth Conformst Members Peter Grzl Andrze Bnk (Correspondng uthor) Deprtment of Economcs, The Wllms School, Wshngton nd Lee Unversty, Lexngton, 4450

More information

Math1110 (Spring 2009) Prelim 3 - Solutions

Math1110 (Spring 2009) Prelim 3 - Solutions Math 1110 (Sprng 2009) Solutons to Prelm 3 (04/21/2009) 1 Queston 1. (16 ponts) Short answer. Math1110 (Sprng 2009) Prelm 3 - Solutons x a 1 (a) (4 ponts) Please evaluate lm, where a and b are postve numbers.

More information

CIS587 - Artificial Intelligence. Uncertainty CIS587 - AI. KB for medical diagnosis. Example.

CIS587 - Artificial Intelligence. Uncertainty CIS587 - AI. KB for medical diagnosis. Example. CIS587 - rtfcl Intellgence Uncertnty K for medcl dgnoss. Exmple. We wnt to uld K system for the dgnoss of pneumon. rolem descrpton: Dsese: pneumon tent symptoms fndngs, l tests: Fever, Cough, leness, WC

More information

Dennis Bricker, 2001 Dept of Industrial Engineering The University of Iowa. MDP: Taxi page 1

Dennis Bricker, 2001 Dept of Industrial Engineering The University of Iowa. MDP: Taxi page 1 Denns Brcker, 2001 Dept of Industrl Engneerng The Unversty of Iow MDP: Tx pge 1 A tx serves three djcent towns: A, B, nd C. Ech tme the tx dschrges pssenger, the drver must choose from three possble ctons:

More information

Calculation of time complexity (3%)

Calculation of time complexity (3%) Problem 1. (30%) Calculaton of tme complexty (3%) Gven n ctes, usng exhaust search to see every result takes O(n!). Calculaton of tme needed to solve the problem (2%) 40 ctes:40! dfferent tours 40 add

More information

COS 511: Theoretical Machine Learning. Lecturer: Rob Schapire Lecture #16 Scribe: Yannan Wang April 3, 2014

COS 511: Theoretical Machine Learning. Lecturer: Rob Schapire Lecture #16 Scribe: Yannan Wang April 3, 2014 COS 511: Theoretcal Machne Learnng Lecturer: Rob Schapre Lecture #16 Scrbe: Yannan Wang Aprl 3, 014 1 Introducton The goal of our onlne learnng scenaro from last class s C comparng wth best expert and

More information

An Introduction to Support Vector Machines

An Introduction to Support Vector Machines An Introducton to Support Vector Mchnes Wht s good Decson Boundry? Consder two-clss, lnerly seprble clssfcton problem Clss How to fnd the lne (or hyperplne n n-dmensons, n>)? Any de? Clss Per Lug Mrtell

More information

3/6/00. Reading Assignments. Outline. Hidden Markov Models: Explanation and Model Learning

3/6/00. Reading Assignments. Outline. Hidden Markov Models: Explanation and Model Learning 3/6/ Hdden Mrkov Models: Explnton nd Model Lernng Brn C. Wllms 6.4/6.43 Sesson 2 9/3/ courtesy of JPL copyrght Brn Wllms, 2 Brn C. Wllms, copyrght 2 Redng Assgnments AIMA (Russell nd Norvg) Ch 5.-.3, 2.3

More information

Definite Integrals. The area under a curve can be approximated by adding up the areas of rectangles = 1 1 +

Definite Integrals. The area under a curve can be approximated by adding up the areas of rectangles = 1 1 + Definite Integrls --5 The re under curve cn e pproximted y dding up the res of rectngles. Exmple. Approximte the re under y = from x = to x = using equl suintervls nd + x evluting the function t the left-hnd

More information

For now, let us focus on a specific model of neurons. These are simplified from reality but can achieve remarkable results.

For now, let us focus on a specific model of neurons. These are simplified from reality but can achieve remarkable results. Neural Networks : Dervaton compled by Alvn Wan from Professor Jtendra Malk s lecture Ths type of computaton s called deep learnng and s the most popular method for many problems, such as computer vson

More information

Linear Feature Engineering 11

Linear Feature Engineering 11 Lnear Feature Engneerng 11 2 Least-Squares 2.1 Smple least-squares Consder the followng dataset. We have a bunch of nputs x and correspondng outputs y. The partcular values n ths dataset are x y 0.23 0.19

More information

Complex Numbers Alpha, Round 1 Test #123

Complex Numbers Alpha, Round 1 Test #123 Complex Numbers Alpha, Round Test #3. Wrte your 6-dgt ID# n the I.D. NUMBER grd, left-justfed, and bubble. Check that each column has only one number darkened.. In the EXAM NO. grd, wrte the 3-dgt Test

More information

2.4 Linear Inequalities and Interval Notation

2.4 Linear Inequalities and Interval Notation .4 Liner Inequlities nd Intervl Nottion We wnt to solve equtions tht hve n inequlity symol insted of n equl sign. There re four inequlity symols tht we will look t: Less thn , Less thn or

More information

Review of Gaussian Quadrature method

Review of Gaussian Quadrature method Review of Gussin Qudrture method Nsser M. Asi Spring 006 compiled on Sundy Decemer 1, 017 t 09:1 PM 1 The prolem To find numericl vlue for the integrl of rel vlued function of rel vrile over specific rnge

More information

Lecture 2: Numerical Methods for Differentiations and Integrations

Lecture 2: Numerical Methods for Differentiations and Integrations Numercal Smulaton of Space Plasmas (I [AP-4036] Lecture 2 by Lng-Hsao Lyu March, 2018 Lecture 2: Numercal Methods for Dfferentatons and Integratons As we have dscussed n Lecture 1 that numercal smulaton

More information

EEE 241: Linear Systems

EEE 241: Linear Systems EEE : Lnear Systems Summary #: Backpropagaton BACKPROPAGATION The perceptron rule as well as the Wdrow Hoff learnng were desgned to tran sngle layer networks. They suffer from the same dsadvantage: they

More information

Quiz: Experimental Physics Lab-I

Quiz: Experimental Physics Lab-I Mxmum Mrks: 18 Totl tme llowed: 35 mn Quz: Expermentl Physcs Lb-I Nme: Roll no: Attempt ll questons. 1. In n experment, bll of mss 100 g s dropped from heght of 65 cm nto the snd contner, the mpct s clled

More information

Quadratic Forms. Quadratic Forms

Quadratic Forms. Quadratic Forms Qudrtic Forms Recll the Simon & Blume excerpt from n erlier lecture which sid tht the min tsk of clculus is to pproximte nonliner functions with liner functions. It s ctully more ccurte to sy tht we pproximte

More information

ragsdale (zdr82) HW6 ditmire (58335) 1 the direction of the current in the figure. Using the lower circuit in the figure, we get

ragsdale (zdr82) HW6 ditmire (58335) 1 the direction of the current in the figure. Using the lower circuit in the figure, we get rgsdle (zdr8) HW6 dtmre (58335) Ths prnt-out should hve 5 questons Multple-choce questons my contnue on the next column or pge fnd ll choces efore nswerng 00 (prt of ) 00 ponts The currents re flowng n

More information

Point-based methods for estimating the length of a parametric curve

Point-based methods for estimating the length of a parametric curve Journl of Computtonl nd Appled Mthemtcs 196 (006) 51 5 www.elsever.com/locte/cm Pont-bsed methods for estmtng the length of prmetrc curve Mchel S. Floter, Atgerr F. Rsmussen CMA, Unversty of Oslo, P.O.

More information

4.4 Areas, Integrals and Antiderivatives

4.4 Areas, Integrals and Antiderivatives . res, integrls nd ntiderivtives 333. Ares, Integrls nd Antiderivtives This section explores properties of functions defined s res nd exmines some connections mong res, integrls nd ntiderivtives. In order

More information

The area under the graph of f and above the x-axis between a and b is denoted by. f(x) dx. π O

The area under the graph of f and above the x-axis between a and b is denoted by. f(x) dx. π O 1 Section 5. The Definite Integrl Suppose tht function f is continuous nd positive over n intervl [, ]. y = f(x) x The re under the grph of f nd ove the x-xis etween nd is denoted y f(x) dx nd clled the

More information

I1 = I2 I1 = I2 + I3 I1 + I2 = I3 + I4 I 3

I1 = I2 I1 = I2 + I3 I1 + I2 = I3 + I4 I 3 2 The Prllel Circuit Electric Circuits: Figure 2- elow show ttery nd multiple resistors rrnged in prllel. Ech resistor receives portion of the current from the ttery sed on its resistnce. The split is

More information

Variable time amplitude amplification and quantum algorithms for linear algebra. Andris Ambainis University of Latvia

Variable time amplitude amplification and quantum algorithms for linear algebra. Andris Ambainis University of Latvia Vrble tme mpltude mplfcton nd quntum lgorthms for lner lgebr Andrs Ambns Unversty of Ltv Tlk outlne. ew verson of mpltude mplfcton;. Quntum lgorthm for testng f A s sngulr; 3. Quntum lgorthm for solvng

More information

Outline and Reading. Dynamic Programming. Dynamic Programming revealed. Computing Fibonacci. The General Dynamic Programming Technique

Outline and Reading. Dynamic Programming. Dynamic Programming revealed. Computing Fibonacci. The General Dynamic Programming Technique Outlne and Readng Dynamc Programmng The General Technque ( 5.3.2) -1 Knapsac Problem ( 5.3.3) Matrx Chan-Product ( 5.3.1) Dynamc Programmng verson 1.4 1 Dynamc Programmng verson 1.4 2 Dynamc Programmng

More information

378 Relations Solutions for Chapter 16. Section 16.1 Exercises. 3. Let A = {0,1,2,3,4,5}. Write out the relation R that expresses on A.

378 Relations Solutions for Chapter 16. Section 16.1 Exercises. 3. Let A = {0,1,2,3,4,5}. Write out the relation R that expresses on A. 378 Reltions 16.7 Solutions for Chpter 16 Section 16.1 Exercises 1. Let A = {0,1,2,3,4,5}. Write out the reltion R tht expresses > on A. Then illustrte it with digrm. 2 1 R = { (5,4),(5,3),(5,2),(5,1),(5,0),(4,3),(4,2),(4,1),

More information

Name: SID: Discussion Session:

Name: SID: Discussion Session: Nme: SID: Dscusson Sesson: hemcl Engneerng hermodynmcs -- Fll 008 uesdy, Octoer, 008 Merm I - 70 mnutes 00 onts otl losed Book nd Notes (5 ponts). onsder n del gs wth constnt het cpctes. Indcte whether

More information

1 Convex Optimization

1 Convex Optimization Convex Optmzaton We wll consder convex optmzaton problems. Namely, mnmzaton problems where the objectve s convex (we assume no constrants for now). Such problems often arse n machne learnng. For example,

More information

A Family of Multivariate Abel Series Distributions. of Order k

A Family of Multivariate Abel Series Distributions. of Order k Appled Mthemtcl Scences, Vol. 2, 2008, no. 45, 2239-2246 A Fmly of Multvrte Abel Seres Dstrbutons of Order k Rupk Gupt & Kshore K. Ds 2 Fculty of Scence & Technology, The Icf Unversty, Agrtl, Trpur, Ind

More information

Math 497C Sep 17, Curves and Surfaces Fall 2004, PSU

Math 497C Sep 17, Curves and Surfaces Fall 2004, PSU Mth 497C Sep 17, 004 1 Curves nd Surfces Fll 004, PSU Lecture Notes 3 1.8 The generl defnton of curvture; Fox-Mlnor s Theorem Let α: [, b] R n be curve nd P = {t 0,...,t n } be prtton of [, b], then the

More information

Multilayer Perceptron (MLP)

Multilayer Perceptron (MLP) Multlayer Perceptron (MLP) Seungjn Cho Department of Computer Scence and Engneerng Pohang Unversty of Scence and Technology 77 Cheongam-ro, Nam-gu, Pohang 37673, Korea seungjn@postech.ac.kr 1 / 20 Outlne

More information

Computation of Fifth Degree of Spline Function Model by Using C++ Programming

Computation of Fifth Degree of Spline Function Model by Using C++ Programming www.ijci.org 89 Computton o Ft Degree o plne Functon Model b Usng C Progrmmng Frdun K. Hml, Aln A. Abdull nd Knd M. Qdr Mtemtcs Dept, Unverst o ulmn, ulmn, IRAQ Mtemtcs Dept, Unverst o ulmn, ulmn, IRAQ

More information

Formulated Algorithm for Computing Dominant Eigenvalue. and the Corresponding Eigenvector

Formulated Algorithm for Computing Dominant Eigenvalue. and the Corresponding Eigenvector Int. J. Contemp. Mth. Scences Vol. 8 23 no. 9 899-9 HIKARI Ltd www.m-hkr.com http://dx.do.org/.2988/jcms.23.3674 Formulted Algorthm for Computng Domnnt Egenlue nd the Correspondng Egenector Igob Dod Knu

More information

CALCULUS CLASSROOM CAPSULES

CALCULUS CLASSROOM CAPSULES CALCULUS CLASSROOM CAPSULES SESSION S86 Dr. Sham Alfred Rartan Valley Communty College salfred@rartanval.edu 38th AMATYC Annual Conference Jacksonvlle, Florda November 8-, 202 2 Calculus Classroom Capsules

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

Improper Integrals. The First Fundamental Theorem of Calculus, as we ve discussed in class, goes as follows:

Improper Integrals. The First Fundamental Theorem of Calculus, as we ve discussed in class, goes as follows: Improper Integrls The First Fundmentl Theorem of Clculus, s we ve discussed in clss, goes s follows: If f is continuous on the intervl [, ] nd F is function for which F t = ft, then ftdt = F F. An integrl

More information

#64. ΔS for Isothermal Mixing of Ideal Gases

#64. ΔS for Isothermal Mixing of Ideal Gases #64 Carnot Heat Engne ΔS for Isothermal Mxng of Ideal Gases ds = S dt + S T V V S = P V T T V PV = nrt, P T ds = v T = nr V dv V nr V V = nrln V V = - nrln V V ΔS ΔS ΔS for Isothermal Mxng for Ideal Gases

More information

Section 6: Area, Volume, and Average Value

Section 6: Area, Volume, and Average Value Chpter The Integrl Applied Clculus Section 6: Are, Volume, nd Averge Vlue Are We hve lredy used integrls to find the re etween the grph of function nd the horizontl xis. Integrls cn lso e used to find

More information

Electrochemical Thermodynamics. Interfaces and Energy Conversion

Electrochemical Thermodynamics. Interfaces and Energy Conversion CHE465/865, 2006-3, Lecture 6, 18 th Sep., 2006 Electrochemcl Thermodynmcs Interfces nd Energy Converson Where does the energy contrbuton F zϕ dn come from? Frst lw of thermodynmcs (conservton of energy):

More information

ESCI 342 Atmospheric Dynamics I Lesson 1 Vectors and Vector Calculus

ESCI 342 Atmospheric Dynamics I Lesson 1 Vectors and Vector Calculus ESI 34 tmospherc Dnmcs I Lesson 1 Vectors nd Vector lculus Reference: Schum s Outlne Seres: Mthemtcl Hndbook of Formuls nd Tbles Suggested Redng: Mrtn Secton 1 OORDINTE SYSTEMS n orthonorml coordnte sstem

More information

Review of linear algebra. Nuno Vasconcelos UCSD

Review of linear algebra. Nuno Vasconcelos UCSD Revew of lner lgebr Nuno Vsconcelos UCSD Vector spces Defnton: vector spce s set H where ddton nd sclr multplcton re defned nd stsf: ) +( + ) (+ )+ 5) λ H 2) + + H 6) 3) H, + 7) λ(λ ) (λλ ) 4) H, - + 8)

More information

VECTORS VECTORS VECTORS VECTORS. 2. Vector Representation. 1. Definition. 3. Types of Vectors. 5. Vector Operations I. 4. Equal and Opposite Vectors

VECTORS VECTORS VECTORS VECTORS. 2. Vector Representation. 1. Definition. 3. Types of Vectors. 5. Vector Operations I. 4. Equal and Opposite Vectors 1. Defnton A vetor s n entt tht m represent phsl quntt tht hs mgntude nd dreton s opposed to slr tht ls dreton.. Vetor Representton A vetor n e represented grphll n rrow. The length of the rrow s the mgntude

More information

How do we solve these things, especially when they get complicated? How do we know when a system has a solution, and when is it unique?

How do we solve these things, especially when they get complicated? How do we know when a system has a solution, and when is it unique? XII. LINEAR ALGEBRA: SOLVING SYSTEMS OF EQUATIONS Tody we re going to tlk out solving systems of liner equtions. These re prolems tht give couple of equtions with couple of unknowns, like: 6= x + x 7=

More information

Difference Equations

Difference Equations Dfference Equatons c Jan Vrbk 1 Bascs Suppose a sequence of numbers, say a 0,a 1,a,a 3,... s defned by a certan general relatonshp between, say, three consecutve values of the sequence, e.g. a + +3a +1

More information

A Tri-Valued Belief Network Model for Information Retrieval

A Tri-Valued Belief Network Model for Information Retrieval December 200 A Tr-Vlued Belef Networ Model for Informton Retrevl Fernndo Ds-Neves Computer Scence Dept. Vrgn Polytechnc Insttute nd Stte Unversty Blcsburg, VA 24060. IR models t Combnng Evdence Grphcl

More information

Things to Memorize: A Partial List. January 27, 2017

Things to Memorize: A Partial List. January 27, 2017 Things to Memorize: A Prtil List Jnury 27, 2017 Chpter 2 Vectors - Bsic Fcts A vector hs mgnitude (lso clled size/length/norm) nd direction. It does not hve fixed position, so the sme vector cn e moved

More information

Expected Value and Variance

Expected Value and Variance MATH 38 Expected Value and Varance Dr. Neal, WKU We now shall dscuss how to fnd the average and standard devaton of a random varable X. Expected Value Defnton. The expected value (or average value, or

More information

Finding Dense Subgraphs in G(n, 1/2)

Finding Dense Subgraphs in G(n, 1/2) Fndng Dense Subgraphs n Gn, 1/ Atsh Das Sarma 1, Amt Deshpande, and Rav Kannan 1 Georga Insttute of Technology,atsh@cc.gatech.edu Mcrosoft Research-Bangalore,amtdesh,annan@mcrosoft.com Abstract. Fndng

More information

Min Cut, Fast Cut, Polynomial Identities

Min Cut, Fast Cut, Polynomial Identities Randomzed Algorthms, Summer 016 Mn Cut, Fast Cut, Polynomal Identtes Instructor: Thomas Kesselhem and Kurt Mehlhorn 1 Mn Cuts n Graphs Lecture (5 pages) Throughout ths secton, G = (V, E) s a mult-graph.

More information

Announcements. Image Formation: Outline. The course. Image Formation and Cameras (cont.)

Announcements. Image Formation: Outline. The course. Image Formation and Cameras (cont.) nnouncements Imge Formton nd Cmers (cont.) ssgnment : Cmer & Lenses, gd Trnsformtons, nd Homogrph wll be posted lter tod. CSE 5 Lecture 5 CS5, Fll CS5, Fll CS5, Fll The course rt : The phscs of mgng rt

More information

Report on Image warping

Report on Image warping Report on Image warpng Xuan Ne, Dec. 20, 2004 Ths document summarzed the algorthms of our mage warpng soluton for further study, and there s a detaled descrpton about the mplementaton of these algorthms.

More information

VECTORS AND TENSORS IV.1.1. INTRODUCTION

VECTORS AND TENSORS IV.1.1. INTRODUCTION Chpter IV Vector nd Tensor Anlyss IV. Vectors nd Tensors Septemer 5, 08 05 IV. VECTORS AND TENSORS IV... INTRODUCTION In mthemtcs nd mechncs, we he to operte wth qunttes whch requre dfferent mthemtcl ojects

More information

Bridging the gap: GCSE AS Level

Bridging the gap: GCSE AS Level Bridging the gp: GCSE AS Level CONTENTS Chpter Removing rckets pge Chpter Liner equtions Chpter Simultneous equtions 8 Chpter Fctors 0 Chpter Chnge the suject of the formul Chpter 6 Solving qudrtic equtions

More information

Chemical Reaction Engineering

Chemical Reaction Engineering Lecture 20 hemcl Recton Engneerng (RE) s the feld tht studes the rtes nd mechnsms of chemcl rectons nd the desgn of the rectors n whch they tke plce. Lst Lecture Energy Blnce Fundmentls F 0 E 0 F E Q W

More information

Bernoulli Numbers and Polynomials

Bernoulli Numbers and Polynomials Bernoull Numbers and Polynomals T. Muthukumar tmk@tk.ac.n 17 Jun 2014 The sum of frst n natural numbers 1, 2, 3,..., n s n n(n + 1 S 1 (n := m = = n2 2 2 + n 2. Ths formula can be derved by notng that

More information

Week 5: Neural Networks

Week 5: Neural Networks Week 5: Neural Networks Instructor: Sergey Levne Neural Networks Summary In the prevous lecture, we saw how we can construct neural networks by extendng logstc regresson. Neural networks consst of multple

More information

Problem Set 6: Trees Spring 2018

Problem Set 6: Trees Spring 2018 Problem Set 6: Trees 1-29 Sprng 2018 A Average dstance Gven a tree, calculate the average dstance between two vertces n the tree. For example, the average dstance between two vertces n the followng tree

More information

Errors for Linear Systems

Errors for Linear Systems Errors for Lnear Systems When we solve a lnear system Ax b we often do not know A and b exactly, but have only approxmatons  and ˆb avalable. Then the best thng we can do s to solve ˆx ˆb exactly whch

More information