This content has been downloaded from IOPscience. Please scroll down to see the full text.

Size: px
Start display at page:

Download "This content has been downloaded from IOPscience. Please scroll down to see the full text."

Transcription

1 Ths cotet has bee dowloaded from IOPscece. Please scroll dow to see the full text. Dowload detals: IP Address: Ths cotet was dowloaded o 4/09/08 at 04:0 Please ote that terms ad codtos aly. You may also be terested : Hgher-Order Lagraga Equatos of Hgher-Order Motve Mechacal System Zhao Hog-Xa, Ma Sha-Ju ad Sh Yog Why ot eergy coservato? Shaw Carlso Dervg relatvstc mometum ad eergy: II Sebastao Soego ad Massmo P Phase-sace study of a sg quatum artcle a costat magetc feld L M Neto The Adabatc Ivarace of the Acto Varable Classcal Dyamcs Clve G Wells ad Stehe T C Slos Aalytcal mechacs ad feld theory: dervato of equatos from eergy coservato N A Vourov A exaso term Hamlto's equatos M. D. Roberts Feld theory wth hgher dervatves-hamltoa structure L M C Coelho de Souza ad P R Rodrgues Hamltoa formulato of the evoluto of movemets over clusters of oteractg artcles M Adrews

2 Part I Fudametals

3

4 IOP Publshg A Itroducto to Quatum Theory Jeff Greeste Chater The classcal state I the frst quarter of the 0th cetury t was dscovered that the laws of moto formulated by Galleo, Newto, Lagrage, Hamlto, Maxwell, ad may others were adequate to exla a wde rage of heomea volvg electros, atoms, ad lght. After a great deal of effort, a ew theory (together wth a ew law of moto) emerged 94. That theory s ow as quatum mechacs, ad t s ow the basc framewor for uderstadg atomc, uclear, ad subuclear hyscs, as well as codesed-matter hyscs. The laws of moto (due to Galleo, Newto, ) whch receded quatum theory are referred to as classcal mechacs. Although classcal mechacs s ow regarded as oly a aroxmato to quatum mechacs, t s stll true that much of the structure of the quatum theory s herted from the classcal theory that t relaced. So we beg wth a lghtg revew of classcal mechacs, whose formulato begs (but does ot ed!) wth Newto s law F ma.. Baseball, F ma, ad the rcle of least acto Tae a baseball ad throw t straght u the ar. After a fracto of a secod, or erhas a few secods, the baseball wll retur to your had. Deote the heght of the baseball, as a fucto of tme, as x(t). If we mae a lot of x as a fucto of t, the the curve (we wll call t a trajectory ) the x t lae wll, a uform gravtatoal feld assumg ar resstace s eglgble, have the form of a arabola. There are a fte umber of ossble trajectores. Whch oe of these the baseball actually follows s determed by the mometum of the baseball at the momet t leaves your had. However, f we requre that the baseball returs to your had exactly Δt secods after leavg t, the there s oly oe trajectory that the ball ca follow. For a baseball movg a uform gravtatoal feld t s a smle exercse to determe ths trajectory exactly, but we would le to develo a method whch ca be aled do:0.088/ ch - ª IOP Publshg Ltd 07

5 A Itroducto to Quatum Theory to a artcle movg ay otetal feld V(x). So let us beg wth Newto s law F ma, whch s actually a secod-order dfferetal equato d m x dv (.) It s useful to reexress ths secod-order equato as a ar of frst-order equatos m d dv (.) where m s the mass ad s the mometum of the baseball. We wat to fd the soluto of these equatos such that xt ( 0) X ad xt ( 0 +Δ t) Xf, where X ad X f are, resectvely, the (tal) heght of your had whe the baseball leaves t, ad the (fal) heght of your had whe you catch the ball. Wth the advet of the comuter, t s ofte easer to solve equatos of moto umercally, rather tha struggle to fd a aalytc soluto whch may or may ot exst (artcularly whe the equatos are o-lear). Although the object of ths secto s ot really to develo umercal methods for solvg roblems baseball, we wll, for the momet, roceed as though t were. To mae the roblem sutable for a comuter, dvde the tme terval Δt to N smaller tme tervals of durato ϵ ΔtN /, ad deote, for 0,,, N, t t0 + ϵ, x x( t), ( t), x X, x X 0 N f A aroxmato to a cotuous trajectory x(t) s gve by the set of ots { x } coected by straght les, as show fgure.. We ca lewse aroxmate dervatves by fte dffereces,.e. xt ( + ) xt ( ) x+ x t t ϵ ϵ d t ( + ) t ( ) + t t ϵ ϵ (.3) d x ϵ t t t d t t t t ( x+ x) ( x x) ϵ ϵ ϵ We wll allow these ostos to be dfferet, geeral, sce you mght move your had to aother osto whle the ball s flght. -

6 A Itroducto to Quatum Theory ad tegrals by sums Fgure.. A dscrete aroxmato to the cotuous ath x(t). t0 t0+δt N d tf( t) ϵft ( ) (.4) where f(t) s ay fucto of tme. As we ow from elemetary calculus, the rghthad sde of (.3) ad (.4) equals the left-had sde the lmt that ϵ 0, whch s also ow as the cotuum lmt. We ca ow aroxmate the laws of moto, by relacg tme dervatves (.) by the corresodg fte dffereces, ad fd x x ϵ m d Vx ( ) ϵ (.5) These are teratve equatos. Gve osto x ad mometum at tme t t,we ca use (.5) to fd the osto ad mometum at tme t t +. The fte dfferece aroxmato of course troduces a slght error; x + ad +, comuted from x ad by (.5) wll dffer from ther exact values by a error of order ϵ. Ths error ca be made eglgble, rcle, by tag ϵ suffcetly small. It s the ossble to use the comuter to fd a aroxmato to the trajectory by the shootg method. The method s to mae a guess for the tal mometum 0 P0, ad the use (.) to solve for x,, x,, ad so o, utl xn, N.If xn Xf, the sto; the set { x } s the (aroxmate) trajectory. If ot, mae a dfferet guess 0 P 0, ad solve aga for { x, }. By tral ad error, oe ca evetually coverge o a tal choce for 0 such that xn Xf. For that choce of tal mometum, the corresodg set of ots { x }, coected by straght-le segmets, gves the aroxmate trajectory of the baseball. Ths rocess s llustrated fgure.. Now let us retur to the secod-order form of Newto s laws, wrtte equato (.). Remarably, F ma ca be exressed as the codto that a certa fucto s statoary. Aga usg (.3) to relace dervatves by fte dffereces, the equato F ma at each tme t becomes -3

7 A Itroducto to Quatum Theory ma { } m x+ x x x d V( x) ϵ ϵ ϵ (.6) The equatos have to be solved for,,, N, wth x 0 X ad x N X f et fxed. Now otce that equato (.6) ca be wrtte as a total dervatve + m x x d ( ) ( + m x x ) ϵvx ( ) 0 (.7) ϵ ϵ so that F ma at some tme t ca be terreted as a codto that the fucto bracets { } should be statoary wth resect to varatos the osto x. But what about all other tmes? We ow troduce a very mortat exresso, crucal both classcal ad quatum hyscs, whch s ow as the acto of the trajectory. The acto s a fucto whch deeds o all the ots { x}, 0,,, N of the trajectory, ad ths case t s N + S x m x x ( ) [{ }] ϵvx ( ) (.8) ϵ 0 The Newto s law F ma ca be restated as the codto that the acto fuctoal S[{ x}] s statoary wth resect to varato of ay of the x (excet for the edots x 0 ad x N, whch are held fxed). I other words d x S x d [{ }] d Fgure.. The shootg method for fdg a trajectory wth fxed ed ots. d N 0 + m x x ( ) ϵvx ( ) ϵ + m x x ( ) ( + m x x ) ϵvx ( ) ϵ ϵ ϵ{ ma( t ) + F ( t )} 0 for,,, N (.9) Ths set of codtos s ow as the rcle of least acto. It s the rcle that the acto S s statoary at ay trajectory { x } satsfyg the equatos of moto F ma, equato (.6), at every tme { t }. -4

8 A Itroducto to Quatum Theory. Euler Lagrage ad Hamlto s equatos I bref, the Euler Lagrage equatos are the secod-order form of the equatos of moto (.), whle Hamlto s equatos are the frst-order form (.). I ether form, the equatos of moto ca be regarded as a cosequece of the rcle of least acto. We wll ow re-wrte those equatos a very geeral way, whch ca be aled to ay mechacal system, cludg those whch are much more comlcated tha a baseball. We beg by wrtg where ad where N S[{ x}] ϵl[ x, x ] (.0) 0 Lx [, x ] mx V( x) (.) x x + x ϵ (.) L[ x, x ] s ow as the Lagraga fucto. The the rcle of least acto requres that, for each, N, d ϵ x S x d 0 [{ }] d Lx [, x ] ϵ N 0 N [, ] + ϵ x Lx x Lx x [, ] x 0 (.3) ad, sce ϵ (.4) ϵ 0 otherwse ths becomes x Lx x [, ] ϵ x Lx [, x ] Lx [, x ] 0 (.5) x Recallg that x x( t ), ths last equato ca be wrtte Lx [, x ] Lx [, x] Lx [, x] x ϵ x x t t t t t tϵ 0 (.6) -5

9 A Itroducto to Quatum Theory Ths s the Euler Lagrage equato for the baseball. It becomes smler whe we tae the ϵ 0 lmt (the cotuum lmt). I that lmt, we have x x N x ϵ xt () S ϵl[ x, x ] S d t L[ x( t), x ( t)] + t0+δt where the Lagraga fucto for the baseball s t0 (.7) Lxt [ ( ), xt ( )] mx () t Vxt [ ()] (.8) ad the Euler Lagrage equato, the cotuum lmt, becomes L xt () d L 0 (.9) d t xt ( ) I tag artal dervatves of L wth resect to x ad ẋ, t s mortat to uderstad that we have to reted that x ad ẋ are deedet varables whch have othg to do wth oe aother, deste the fact that ẋ s really the tme dervatve of x. That s just the meag of the artal dervatve otato. For the Lagraga of the baseball, equato (.8), the relevat artal dervatves are L d Vxt [ ( )] xt () d xt ( ) (.0) L mx () t xt () whch, whe substtuted to equato (.9) gve m x dv + 0 (.) t Ths s smly Newto s law F ma, the secod-order form of equato (.). We ow wat to rewrte the Euler Lagrage equato frst-order form. Of course, we already ow the aswer, whch s equato (.), but let us forget ths aswer for a momet, order to troduce a very geeral method. The reaso the Euler Lagrage equato s secod-order the tme dervatves s that L/ x s frstorder the tme dervatve. So let us defe the mometum corresodg to the coordate x to be L x (.) Ths gves as a fucto of x ad ẋ, but, alteratvely, we ca solve for ẋ as a fucto of x ad,.e. x x ( x, ) (.3) -6

10 A Itroducto to Quatum Theory Next, we troduce the Hamltoa fucto H[, x] x ( x, ) L[ x, x ( x, )] (.4) Sce ẋ s a fucto of x ad, H s also a fucto of x ad. The reaso for troducg the Hamltoa s that ts frst dervatves wth resect to x ad have a remarable roerty; amely, o a trajectory satsfyg the Euler Lagrage equatos, the x ad dervatves of H are roortoal to the tme dervatves of ad x. To see ths, frst dfferetate the Hamltoa wth resect to, H + x x xx (, ) L x (, x) x where we have aled (.). Next, dfferetatg H wth resect to x, H xx (, ) L L x (, x) x x x x x L x (.5) (.6) Usg the Euler Lagrage equato (.9) (ad ths s where the equatos of moto eter), we fd H d L x x d (.7) Thus, wth the hel of the Hamltoa fucto, we have rewrtte the sgle secod-order Euler Lagrage equato (.9) as a ar of frst-order dfferetal equatos H (.8) d H x whch are ow as Hamlto s equatos. For a baseball, the Lagraga s gve by equato (.8), ad therefore the mometum s L mx x (.9) Ths s verted to gve x x (, x) (.30) m -7

11 A Itroducto to Quatum Theory ad the Hamltoa s H x ( x, ) L[ x, x ( x, )] m Vx ( ) m m + m Vx ( ) (.3) Note that the Hamltoa for the baseball s smly the etc eergy lus the otetal eergy;.e. the Hamltoa s a exresso for the total eergy of the baseball. Substtutg H to Hamlto s equatos, oe fds + t m Vx ( ) d m d + t x m Vx dv ( ) d whch s smly the frst-order form of Newto s law (.)..3 Classcal mechacs a utshell (.3) All the machery of the least acto rcle, the Lagraga fucto, ad Hamlto s equatos, s overll the case of a baseball. I that case, we ew the equato of moto from the begg. But for more volved dyamcal systems, volvg, say, wheels, srgs, levers, ad edulums, all couled together some comlcated way, the equatos of moto are ofte far from obvous, ad what s eeded s some systematc way to derve them. For ay mechacal system, the geeralzed coordates { q } are a set of varables eeded to descrbe the cofgurato of the system at a gve tme. These could be a set of Cartesa coordates of a umber of dfferet artcles, or the agular dslacemet of a edulum, or the dslacemet of a srg from equlbrum, or all of the above. The dyamcs of the system, terms of these coordates, s gve by a Lagraga fucto L, whch deeds o the geeralzed coordates { q } ad ther frst tme dervatves { q }. Normally, o-relatvstc mechacs, we frst secfy. The Lagraga L { q, q} Ketc Eergy Potetal Eergy (.33) Oe the defes. The acto S d t L { q, q} (.34) From the least acto rcle, followg a method smlar to the oe we used for the baseball, we derve -8

12 A Itroducto to Quatum Theory 3. The Euler Lagrage equatos L q d L q 0 (.35) These are the secod-order equatos of moto. To go to the frst-order form, frst defe 4. The geeralzed mometa L q (.36) whch ca be verted to gve the tme dervatves q of the geeralzed coordates terms of the geeralzed coordates ad mometa q q { q, } (.37) Vewg q as a fucto of ad q, oe the defes 5. The Hamltoa { } { } H q, q L q, q (.38) Usually the Hamltoa has the form H [, q] Ketc Eergy + Potetal Eergy (.39) Fally, the equatos of moto frst-order form are gve by 6. Hamlto s equatos H q H q (.40) If the otetal eergy does ot deed exlctly o tme, the Hamlto s equatos mly that H s tme-deedet, dh H q + H q t t + q q t t t t 0 (.4) as we would exect f H s the total eergy of the system. -9

13 A Itroducto to Quatum Theory Examle: the lae edulum Our edulum s a mass m at the ed of a weghtless rgd rod of legth l, whch vots a lae aroud the ot P. The geeralzed coordate, whch secfes the osto of the edulum at ay gve tme, s the agle θ betwee the rod ad the vertcal axs.. The Lagraga L θ ml ( V0 mgl cos( θ)) (.4) where V 0 s the gravtatoal otetal at the heght of ot P, whch the edulum reaches at θ π/. Sce V 0 s arbtrary, we wll just set t to V0 0.. The acto 3. The Euler Lagrage equatos We have t S θ + θ ml mgl cos( ) (.43) t0 L θ θ mgl s L θ θ ml (.44) ad therefore ml θ + mgl s θ 0 (.45) s the Euler Lagrage form of the equatos of moto. 4. The geeralzed mometum L θ θ ml (.46) 5. The Hamltoa Isert to θ ml (.47) θ θ H + θ ml mgl cos( ) (.48) -0

14 A Itroducto to Quatum Theory to obta H mgl cos( θ) (.49) ml 6. Hamlto s equatos θ H ml H mgl s θ θ whch are easly see to be equvalet to the Euler Lagrage equatos..4 The classcal state (.50) Predcto s rather mortat hyscs, sce the oly relable test of a scetfc theory s the ablty, gve the state of affars at reset, to redct the future. Stated rather abstractly, the rocess of redcto wors as follows. By a slght dsturbace ow as a measuremet, a object s assged a mathematcal reresetato whch we wll call ts hyscal state. The laws of moto are mathematcal rules by whch, gve a hyscal state at a artcular tme, oe ca deduce the hyscal state of the object at some later tme. The later hyscal state s the redcto, whch ca be checed by a subsequet measuremet of the object. From the dscusso so far, t s easy to see that what s meat classcal hyscs by the hyscal state of a system s smly ts set of geeralzed coordates ad the geeralzed mometa a { q, a }. These are suosed to be obtaed, at some tme t 0, by the measuremet rocess. Gve the hyscal state at some tme t, the state at t + ϵ s obtaed by the rule a a H q ( t + ϵ) q ( t) + ϵ a t H a( t + ϵ) a( t) ϵ a q t (.5) I ths way, the hyscal state at ay later tme ca be obtaed ( rcle) to a arbtrary degree of accuracy, by mag the tme-ste ϵ suffcetly small (or else, f ossble, by solvg the equatos of moto exactly). Note that the coordates { q a } aloe are ot eough to secfy the hyscal state, because they are ot suffcet to redct the future. Iformato about the mometa { a } s also requred. The sace of all ossble a { q, a } s ow as hase sace. For a sgle artcle movg three dmesos, there are three comoets of osto ad three comoets of mometum, so the hyscal state s secfed by sx umbers ( x, y, z, x, y, z ), whch ca be vewed as a ot sx-dmesoal hase sace. Lewse, the hyscal state of a system of N artcles cossts of three coordates -

15 A Itroducto to Quatum Theory for each artcle ( 3N coordates all), ad three comoets of mometum for each artcle ( 3N mometum comoets all), so the state s gve by a set of 6N umbers, whch ca be vewed as a sgle ot 6N-dmesoal sace. As we wll see the ext chaters, classcal mechacs fals to redct correctly the behavor of both lght ad matter at the atomc level, ad s relaced by quatum mechacs. But classcal ad quatum mechacs have a lot commo: they both assg hyscal states to objects, ad these hyscal states evolve accordg to dfferetal equatos whch are frst order the tme dervatves. The dfferece les maly the cotrast betwee a hyscal state as uderstood by classcal mechacs, the classcal state, ad ts quatum couterart, the quatum state. Ths dfferece wll be exlored the ext few chaters. Problems. Two ot-le artcles movg three dmesos have masses m ad m resectvely, ad teract va a otetal V ( x x ). Fd Hamlto s equatos of moto for the artcles.. Suose, stead of a rgd rod, the mass of the lae edulum s coected to ot P by a weghtless srg. The otetal eergy of the srg s L ( L) 0, where L s the legth of the srg, ad L 0 s ts legth whe ot dslaced by a exteral force. Choosg L ad θ as the geeralzed coordates, fd Hamlto s equatos. -

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

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

Unit 9. The Tangent Bundle

Unit 9. The Tangent Bundle Ut 9. The Taget Budle ========================================================================================== ---------- The taget sace of a submafold of R, detfcato of taget vectors wth dervatos at

More information

å 1 13 Practice Final Examination Solutions - = CS109 Dec 5, 2018

å 1 13 Practice Final Examination Solutions - = CS109 Dec 5, 2018 Chrs Pech Fal Practce CS09 Dec 5, 08 Practce Fal Examato Solutos. Aswer: 4/5 8/7. There are multle ways to obta ths aswer; here are two: The frst commo method s to sum over all ossbltes for the rak of

More information

CHAPTER 4 SCRODINGER S EQUATION

CHAPTER 4 SCRODINGER S EQUATION CHAPTER 4 SCRODIGER S EQUATIO A. Itroducto The old quatum theory have elaed successfully about le sectral hydroge atom. Ths theary also have show hyscal heomea atomc ad subatomc order fulflled rcle ad

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

Channel Models with Memory. Channel Models with Memory. Channel Models with Memory. Channel Models with Memory

Channel Models with Memory. Channel Models with Memory. Channel Models with Memory. Channel Models with Memory Chael Models wth Memory Chael Models wth Memory Hayder radha Electrcal ad Comuter Egeerg Mchga State Uversty I may ractcal etworkg scearos (cludg the Iteret ad wreless etworks), the uderlyg chaels are

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

Laboratory I.10 It All Adds Up

Laboratory I.10 It All Adds Up Laboratory I. It All Adds Up Goals The studet wll work wth Rema sums ad evaluate them usg Derve. The studet wll see applcatos of tegrals as accumulatos of chages. The studet wll revew curve fttg sklls.

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

On the characteristics of partial differential equations

On the characteristics of partial differential equations Sur les caractérstques des équatos au dérvées artelles Bull Soc Math Frace 5 (897) 8- O the characterstcs of artal dfferetal equatos By JULES BEUDON Traslated by D H Delhech I a ote that was reseted to

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

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

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

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

Random Variables. ECE 313 Probability with Engineering Applications Lecture 8 Professor Ravi K. Iyer University of Illinois

Random Variables. ECE 313 Probability with Engineering Applications Lecture 8 Professor Ravi K. Iyer University of Illinois Radom Varables ECE 313 Probablty wth Egeerg Alcatos Lecture 8 Professor Rav K. Iyer Uversty of Illos Iyer - Lecture 8 ECE 313 Fall 013 Today s Tocs Revew o Radom Varables Cumulatve Dstrbuto Fucto (CDF

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

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

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

Overview of the weighting constants and the points where we evaluate the function for The Gaussian quadrature Project two

Overview of the weighting constants and the points where we evaluate the function for The Gaussian quadrature Project two Overvew of the weghtg costats ad the pots where we evaluate the fucto for The Gaussa quadrature Project two By Ashraf Marzouk ChE 505 Fall 005 Departmet of Mechacal Egeerg Uversty of Teessee Koxvlle, TN

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

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

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

Quantization in Dynamic Smarandache Multi-Space

Quantization in Dynamic Smarandache Multi-Space Quatzato Dyamc Smaradache Mult-Space Fu Yuhua Cha Offshore Ol Research Ceter, Beg, 7, Cha (E-mal: fuyh@cooc.com.c ) Abstract: Dscussg the applcatos of Dyamc Smaradache Mult-Space (DSMS) Theory. Supposg

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 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

Bounds on the expected entropy and KL-divergence of sampled multinomial distributions. Brandon C. Roy

Bounds on the expected entropy and KL-divergence of sampled multinomial distributions. Brandon C. Roy Bouds o the expected etropy ad KL-dvergece of sampled multomal dstrbutos Brado C. Roy bcroy@meda.mt.edu Orgal: May 18, 2011 Revsed: Jue 6, 2011 Abstract Iformato theoretc quattes calculated from a sampled

More information

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

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

More information

2SLS Estimates ECON In this case, begin with the assumption that E[ i

2SLS Estimates ECON In this case, begin with the assumption that E[ i SLS Estmates ECON 3033 Bll Evas Fall 05 Two-Stage Least Squares (SLS Cosder a stadard lear bvarate regresso model y 0 x. I ths case, beg wth the assumto that E[ x] 0 whch meas that OLS estmates of wll

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

Lecture 5: Interpolation. Polynomial interpolation Rational approximation

Lecture 5: Interpolation. Polynomial interpolation Rational approximation Lecture 5: Iterpolato olyomal terpolato Ratoal appromato Coeffcets of the polyomal Iterpolato: Sometme we kow the values of a fucto f for a fte set of pots. Yet we wat to evaluate f for other values perhaps

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

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

IS 709/809: Computational Methods in IS Research. Simple Markovian Queueing Model

IS 709/809: Computational Methods in IS Research. Simple Markovian Queueing Model IS 79/89: Comutatoal Methods IS Research Smle Marova Queueg Model Nrmalya Roy Deartmet of Iformato Systems Uversty of Marylad Baltmore Couty www.umbc.edu Queueg Theory Software QtsPlus software The software

More information

On A Two Dimensional Finsler Space Whose Geodesics Are Semi- Elipses and Pair of Straight Lines

On A Two Dimensional Finsler Space Whose Geodesics Are Semi- Elipses and Pair of Straight Lines IOSR Joural of Mathematcs (IOSR-JM) e-issn: 78-578 -ISSN:39-765X Volume 0 Issue Ver VII (Mar-Ar 04) PP 43-5 wwwosrjouralsorg O A Two Dmesoal Fsler Sace Whose Geodescs Are Sem- Elses ad Par of Straght es

More information

Probability and Statistics. What is probability? What is statistics?

Probability and Statistics. What is probability? What is statistics? robablt ad Statstcs What s robablt? What s statstcs? robablt ad Statstcs robablt Formall defed usg a set of aoms Seeks to determe the lkelhood that a gve evet or observato or measuremet wll or has haeed

More information

MATH 371 Homework assignment 1 August 29, 2013

MATH 371 Homework assignment 1 August 29, 2013 MATH 371 Homework assgmet 1 August 29, 2013 1. Prove that f a subset S Z has a smallest elemet the t s uque ( other words, f x s a smallest elemet of S ad y s also a smallest elemet of S the x y). We kow

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

Manipulator Dynamics. Amirkabir University of Technology Computer Engineering & Information Technology Department

Manipulator Dynamics. Amirkabir University of Technology Computer Engineering & Information Technology Department Mapulator Dyamcs mrkabr Uversty of echology omputer Egeerg formato echology Departmet troducto obot arm dyamcs deals wth the mathematcal formulatos of the equatos of robot arm moto. hey are useful as:

More information

Introduction to Econometrics (3 rd Updated Edition, Global Edition) Solutions to Odd-Numbered End-of-Chapter Exercises: Chapter 9

Introduction to Econometrics (3 rd Updated Edition, Global Edition) Solutions to Odd-Numbered End-of-Chapter Exercises: Chapter 9 Itroducto to Ecoometrcs (3 rd Udated Edto, Global Edto) by James H. Stock ad Mark W. Watso Solutos to Odd-Numbered Ed-of-Chater Exercses: Chater 9 (Ths verso August 7, 04) 05 Pearso Educato, Ltd. Stock/Watso

More information

called the state descriptors of Newtonian physics. The evolution of the state represented by the trajectory is given by m r t V r t

called the state descriptors of Newtonian physics. The evolution of the state represented by the trajectory is given by m r t V r t Revew of Phys 3 The Classcal Pot of Vew A system s a collecto of artcles that teracts themselves va teral forces ad that may teract wth the world outsde va exteral felds. Itrsc roertes of a classcal system

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

Nonparametric Density Estimation Intro

Nonparametric Density Estimation Intro Noarametrc Desty Estmato Itro Parze Wdows No-Parametrc Methods Nether robablty dstrbuto or dscrmat fucto s kow Haes qute ofte All we have s labeled data a lot s kow easer salmo bass salmo salmo Estmate

More information

Ideal multigrades with trigonometric coefficients

Ideal multigrades with trigonometric coefficients Ideal multgrades wth trgoometrc coeffcets Zarathustra Brady December 13, 010 1 The problem A (, k) multgrade s defed as a par of dstct sets of tegers such that (a 1,..., a ; b 1,..., b ) a j = =1 for all

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

Summary of the lecture in Biostatistics

Summary of the lecture in Biostatistics Summary of the lecture Bostatstcs Probablty Desty Fucto For a cotuos radom varable, a probablty desty fucto s a fucto such that: 0 dx a b) b a dx A probablty desty fucto provdes a smple descrpto of the

More information

( ) 2 2. Multi-Layer Refraction Problem Rafael Espericueta, Bakersfield College, November, 2006

( ) 2 2. Multi-Layer Refraction Problem Rafael Espericueta, Bakersfield College, November, 2006 Mult-Layer Refracto Problem Rafael Espercueta, Bakersfeld College, November, 006 Lght travels at dfferet speeds through dfferet meda, but refracts at layer boudares order to traverse the least-tme path.

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

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

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

PHYS Look over. examples 2, 3, 4, 6, 7, 8,9, 10 and 11. How To Make Physics Pay PHYS Look over. Examples: 1, 4, 5, 6, 7, 8, 9, 10,

PHYS Look over. examples 2, 3, 4, 6, 7, 8,9, 10 and 11. How To Make Physics Pay PHYS Look over. Examples: 1, 4, 5, 6, 7, 8, 9, 10, PHYS Look over Chapter 9 Sectos - Eamples:, 4, 5, 6, 7, 8, 9, 0, PHYS Look over Chapter 7 Sectos -8 8, 0 eamples, 3, 4, 6, 7, 8,9, 0 ad How To ake Phscs Pa We wll ow look at a wa of calculatg where the

More information

Computations with large numbers

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

More information

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

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

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

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

Periodic Table of Elements. EE105 - Spring 2007 Microelectronic Devices and Circuits. The Diamond Structure. Electronic Properties of Silicon

Periodic Table of Elements. EE105 - Spring 2007 Microelectronic Devices and Circuits. The Diamond Structure. Electronic Properties of Silicon EE105 - Srg 007 Mcroelectroc Devces ad Crcuts Perodc Table of Elemets Lecture Semcoductor Bascs Electroc Proertes of Slco Slco s Grou IV (atomc umber 14) Atom electroc structure: 1s s 6 3s 3 Crystal electroc

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

18.413: Error Correcting Codes Lab March 2, Lecture 8

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

More information

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

Centroids & Moments of Inertia of Beam Sections

Centroids & Moments of Inertia of Beam Sections RCH 614 Note Set 8 S017ab Cetrods & Momets of erta of Beam Sectos Notato: b C d d d Fz h c Jo L O Q Q = ame for area = ame for a (base) wdth = desgato for chael secto = ame for cetrod = calculus smbol

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

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

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

Lecture 9. Some Useful Discrete Distributions. Some Useful Discrete Distributions. The observations generated by different experiments have

Lecture 9. Some Useful Discrete Distributions. Some Useful Discrete Distributions. The observations generated by different experiments have NM 7 Lecture 9 Some Useful Dscrete Dstrbutos Some Useful Dscrete Dstrbutos The observatos geerated by dfferet eermets have the same geeral tye of behavor. Cosequetly, radom varables assocated wth these

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

D. VQ WITH 1ST-ORDER LOSSLESS CODING

D. VQ WITH 1ST-ORDER LOSSLESS CODING VARIABLE-RATE VQ (AKA VQ WITH ENTROPY CODING) Varable-Rate VQ = Quatzato + Lossless Varable-Legth Bary Codg A rage of optos -- from smple to complex A. Uform scalar quatzato wth varable-legth codg, oe

More information

Semiconductor Device Physics

Semiconductor Device Physics 1 Semcoductor evce Physcs Lecture 7 htt://ztomul.wordress.com 0 1 3 Semcoductor evce Physcs Chater 6 Jucto odes: I-V Characterstcs 3 Chater 6 Jucto odes: I-V Characterstcs Qualtatve ervato Majorty carrers

More information

AN EULER-MC LAURIN FORMULA FOR INFINITE DIMENSIONAL SPACES

AN EULER-MC LAURIN FORMULA FOR INFINITE DIMENSIONAL SPACES AN EULER-MC LAURIN FORMULA FOR INFINITE DIMENSIONAL SPACES Jose Javer Garca Moreta Graduate Studet of Physcs ( Sold State ) at UPV/EHU Address: P.O 6 890 Portugalete, Vzcaya (Spa) Phoe: (00) 3 685 77 16

More information

ON BIVARIATE GEOMETRIC DISTRIBUTION. K. Jayakumar, D.A. Mundassery 1. INTRODUCTION

ON BIVARIATE GEOMETRIC DISTRIBUTION. K. Jayakumar, D.A. Mundassery 1. INTRODUCTION STATISTICA, ao LXVII, 4, 007 O BIVARIATE GEOMETRIC DISTRIBUTIO ITRODUCTIO Probablty dstrbutos of radom sums of deedetly ad detcally dstrbuted radom varables are maly aled modelg ractcal roblems that deal

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

TESTS BASED ON MAXIMUM LIKELIHOOD

TESTS BASED ON MAXIMUM LIKELIHOOD ESE 5 Toy E. Smth. The Basc Example. TESTS BASED ON MAXIMUM LIKELIHOOD To llustrate the propertes of maxmum lkelhood estmates ad tests, we cosder the smplest possble case of estmatg the mea of the ormal

More information

CHAPTER 6. d. With success = observation greater than 10, x = # of successes = 4, and

CHAPTER 6. d. With success = observation greater than 10, x = # of successes = 4, and CHAPTR 6 Secto 6.. a. We use the samle mea, to estmate the oulato mea µ. Σ 9.80 µ 8.407 7 ~ 7. b. We use the samle meda, 7 (the mddle observato whe arraged ascedg order. c. We use the samle stadard devato,

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

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 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

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

Statistics MINITAB - Lab 5

Statistics MINITAB - Lab 5 Statstcs 10010 MINITAB - Lab 5 PART I: The Correlato Coeffcet Qute ofte statstcs we are preseted wth data that suggests that a lear relatoshp exsts betwee two varables. For example the plot below s of

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

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

Two Fuzzy Probability Measures

Two Fuzzy Probability Measures Two Fuzzy robablty Measures Zdeěk Karíšek Isttute of Mathematcs Faculty of Mechacal Egeerg Bro Uversty of Techology Techcká 2 66 69 Bro Czech Reublc e-mal: karsek@umfmevutbrcz Karel Slavíček System dmstrato

More information

Lecture 3. Sampling, sampling distributions, and parameter estimation

Lecture 3. Sampling, sampling distributions, and parameter estimation Lecture 3 Samplg, samplg dstrbutos, ad parameter estmato Samplg Defto Populato s defed as the collecto of all the possble observatos of terest. The collecto of observatos we take from the populato s called

More information

16 Homework lecture 16

16 Homework lecture 16 Quees College, CUNY, Departmet of Computer Scece Numercal Methods CSCI 361 / 761 Fall 2018 Istructor: Dr. Sateesh Mae c Sateesh R. Mae 2018 16 Homework lecture 16 Please emal your soluto, as a fle attachmet,

More information

Geometric Modeling

Geometric Modeling Geometrc Modelg 9.580.0 Crves Morteso Chater -5 ad Agel Chater 9 Crve Bascs Crve: Locs of a ot movg wth degree of freedom. Some tyes of eqatos to descrbe crves: Itrsc o relace o exteral frame of referece

More information

Ahmed Elgamal. MDOF Systems & Modal Analysis

Ahmed Elgamal. MDOF Systems & Modal Analysis DOF Systems & odal Aalyss odal Aalyss (hese otes cover sectos from Ch. 0, Dyamcs of Structures, Al Chopra, Pretce Hall, 995). Refereces Dyamcs of Structures, Al K. Chopra, Pretce Hall, New Jersey, ISBN

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

UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS

UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS Postpoed exam: ECON430 Statstcs Date of exam: Jauary 0, 0 Tme for exam: 09:00 a.m. :00 oo The problem set covers 5 pages Resources allowed: All wrtte ad prted

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

A Study of the Reproducibility of Measurements with HUR Leg Extension/Curl Research Line

A Study of the Reproducibility of Measurements with HUR Leg Extension/Curl Research Line HUR Techcal Report 000--9 verso.05 / Frak Borg (borgbros@ett.f) A Study of the Reproducblty of Measuremets wth HUR Leg Eteso/Curl Research Le A mportat property of measuremets s that the results should

More information

General Method for Calculating Chemical Equilibrium Composition

General Method for Calculating Chemical Equilibrium Composition AE 6766/Setzma Sprg 004 Geeral Metod for Calculatg Cemcal Equlbrum Composto For gve tal codtos (e.g., for gve reactats, coose te speces to be cluded te products. As a example, for combusto of ydroge wt

More information

COMPUTERISED ALGEBRA USED TO CALCULATE X n COST AND SOME COSTS FROM CONVERSIONS OF P-BASE SYSTEM WITH REFERENCES OF P-ADIC NUMBERS FROM

COMPUTERISED ALGEBRA USED TO CALCULATE X n COST AND SOME COSTS FROM CONVERSIONS OF P-BASE SYSTEM WITH REFERENCES OF P-ADIC NUMBERS FROM U.P.B. Sc. Bull., Seres A, Vol. 68, No. 3, 6 COMPUTERISED ALGEBRA USED TO CALCULATE X COST AND SOME COSTS FROM CONVERSIONS OF P-BASE SYSTEM WITH REFERENCES OF P-ADIC NUMBERS FROM Z AND Q C.A. MURESAN Autorul

More information

MMJ 1113 FINITE ELEMENT METHOD Introduction to PART I

MMJ 1113 FINITE ELEMENT METHOD Introduction to PART I MMJ FINITE EEMENT METHOD Cotut requremets Assume that the fuctos appearg uder the tegral the elemet equatos cota up to (r) th order To esure covergece N must satsf Compatblt requremet the fuctos must have

More information

Lecture Notes Types of economic variables

Lecture Notes Types of economic variables Lecture Notes 3 1. Types of ecoomc varables () Cotuous varable takes o a cotuum the sample space, such as all pots o a le or all real umbers Example: GDP, Polluto cocetrato, etc. () Dscrete varables fte

More information

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

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

More information

Hamilton s principle for non-holonomic systems

Hamilton s principle for non-holonomic systems Das Hamltosche Przp be chtholoome Systeme, Math. A. (935), pp. 94-97. Hamlto s prcple for o-holoomc systems by Georg Hamel Berl Traslate by: D. H. Delphech I the paper Le prcpe e Hamlto et l holoomsme,

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

Complex Numbers Primer

Complex Numbers Primer Complex Numbers Prmer Before I get started o ths let me frst make t clear that ths documet s ot teded to teach you everythg there s to kow about complex umbers. That s a subject that ca (ad does) take

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

1 0, x? x x. 1 Root finding. 1.1 Introduction. Solve[x^2-1 0,x] {{x -1},{x 1}} Plot[x^2-1,{x,-2,2}] 3

1 0, x? x x. 1 Root finding. 1.1 Introduction. Solve[x^2-1 0,x] {{x -1},{x 1}} Plot[x^2-1,{x,-2,2}] 3 Adrew Powuk - http://www.powuk.com- Math 49 (Numercal Aalyss) Root fdg. Itroducto f ( ),?,? Solve[^-,] {{-},{}} Plot[^-,{,-,}] Cubc equato https://e.wkpeda.org/wk/cubc_fucto Quartc equato https://e.wkpeda.org/wk/quartc_fucto

More information

EECE 301 Signals & Systems

EECE 301 Signals & Systems EECE 01 Sgals & Systems Prof. Mark Fowler Note Set #9 Computg D-T Covoluto Readg Assgmet: Secto. of Kame ad Heck 1/ Course Flow Dagram The arrows here show coceptual flow betwee deas. Note the parallel

More information