CHAPTER-10 WAVEFUNCTIONS, OBSERVABLES and OPERATORS

Size: px
Start display at page:

Download "CHAPTER-10 WAVEFUNCTIONS, OBSERVABLES and OPERATORS"

Transcription

1 Lecture Notes PH 4/5 ECE 598 A. L Ros INTRODUCTION TO QUANTUM MECHANICS CHAPTER-0 WAVEFUNCTIONS, OBSERVABLES d OPERATORS 0. Represettios i the sptil d mometum spces 0..A Represettio of the wvefuctio i the sptil coordites bsis { positio x } 0..A The Delt Dirc 0..A Comptibility betwee the physicl cocept of mplitude probbility d the ottio used for the ier product. 0..B Represettio of the wvefuctio i the mometum coordites bsis { mometum p } 0..B Represettio of the mometum p stte i spce-coordites bsis { positio x } 0..B Idetifyig the mplitude probbility mometum p s the Fourier trsform of the fuctio ( x) 0..C Tesor Product of Stte Spces 0. The Schrödiger equtio s postulte 0..A The Hmiltoi equtios expressed i the cotiuum sptil coordites. The Schrodiger Equtio. 0..B Iterprettio of the wvefuctio Eistei s view o the grulrity ture of the electromgetic rditio. Mx Bor s probbilistic iterprettio of the wvefuctio. Determiistic evolutio of the wvefuctio Esemble 0..C Normliztio coditio of the wvefuctio Hilbert spce Coservtio of probbility 0..D The Philosophy of Qutum Theory 0.3 Expecttio vlues 0.3.A Expecttio vlue of prticle s positio 0.3.B Expecttio vlue of the prticle s mometum 0.3.C Expecttio (verge) vlues re clculted i esemble of ideticlly prepred systems

2 0.4 Opertors ssocited to observbles 0.4.A Observbles, eigevlues d eigesttes 0.4.B Defiitio of the qutum mechics opertor F ~ to be ssocited with the observble physicl qutity f 0.4.C Defiitio of the Positio Opertor X ~ 0.4.D Defiitio of the Lier Mometum Opertor P ~ 0.4.D. Represettio of the lier mometum opertor P ~ i the mometum bsis { mometum p } 0.4.D. Represettio of the lier mometum opertor P ~ i the sptil coordites bsis { positio x } 0.4.D3 Costructio of the opertors P ~ ~, P ~, P E The Hmiltoi opertor 0.4.E. Evlutio of the me eergy i terms of the Hmiltoi opertor 0.4.E. Represettio of the Hmiltoi opertor i the sptil coordite bsis 0.5 Properties of Opertors 0.5.A Correspodece betwee brs d kets 0.5.B Adoit ) opertors 0.5.C Hermiti or self-doit opertors Properties of Hermiti (or self-doit) opertors: - Opertors ssocited to me vlues re Hermiti (or self-doit) - Eigevlues re rel - Eigevectors with differet eigevlues re orthogol 0.5.D Observble Opertors 0.5.E Opertors o ssocited to me vlues 0.6 The commuttor 0.6.A Expressio for the geerlized ucertity priciple 0.6.B Cougte observbles Stdrd devitio of two cougte observbles 0.6.C Properties of opertors tht do commute 0.7 How to prepre the iitil qutum sttes 0.7.A Kowig wht c we predict bout evetul outcomes from mesuremet? 0.7.B After mesuremet, wht c we sy bout the stte? 0.7.C Simulteous mesuremet of observbles 0.7.C Defiitio of comptible (or simulteously mesurble) opertors 0.7.C Coditio for observbles A ~ d B ~ to be comptible 0.7.C3 Complete set of commutig opertors

3 Refereces: Feym Lectures Vol. III; Chpter 6, 0 Clude Cohe-Toudi, B. Diu, F. Lloe, Qutum Mechics, Wiley. "Itroductio to Qutum Mechics" by Dvid Griffiths; Chpter 3. B. H. Brsde & C. J. Jochi, Qutum Mechics, Pretice Hll, d Ed

4 CHAPTER-0 WAVEFUNCTIONS, OBSERVABLES d OPERATORS Qutum theory is bsed o two mthemticl items: wvefuctios d opertors. The stte of system is represeted by wvefuctio. A exct kowledge of the wvefuctio is the mximum iformtio oe c hve of the system: ll possible iformtio bout the system c be clculted from this wvefuctio. Qutities such s positio, mometum, or eergy, which oe mesures experimetlly, re clled observbles. I clssicl physics, observbles re represeted by ordiry vribles. I qutum mechics observbles re represeted by opertors; i.e. by qutities tht upo opertio o wvefuctio givig ew wvefuctio. This chpter presets three mi sectios: The first icludes descriptio of the sptil-coordites bsis d the mometum-coordites bsis, which re typiclly used to represet qutum stte. The ext describes how to build the qutum mechics opertor correspodig to give observble. The fil sectio ddresses how to build (mthemticl) qutum stte from give set of experimetl results. The key mthemticl cocept used here is the complete set of commutig opertors 0. Represettio of the wvefuctios i the sptil d mometum spces A rbitrry stte c be expded i terms of bse sttes tht coveietly fit the prticulr problem uder study. For geerl descriptios, two bses re frequetly used: the sptil coordite bsis d the lier mometum bsis. These two bsis re ddressed i this sectio. 0..A Represettio of the wvefuctio i the sptil coordites bsis { x, x } Chpter 9 helped to provide some clues o the proper iterprettio of the wvefuctio (the solutios of the Schrodiger equtio.) This cme through the lysis of the prticulr cse of electro movig cross discrete lttice: the wvefuctio is pictured s wve of mplitude probbilities (complex umbers whose mgitude is iterpreted s probbilities). 4

5 Notice, however, tht whe tkig the limitig cse of the lttice spcig tedig to zero, oe eds up with situtio i which the electro is propgtig through cotiuum lie spce. Thus, this limitig cse tkes us to the study of prticle movig i cotiuum spce. I logy to the discrete lttice, where the loctio of the toms guided the selectio of the stte bsis { }, i the cotiuum spce we cosider the followig cotiuum set, { x, x } Cotiuum sptil-coordites bse () I logy to the discrete cse, x stds for stte i which prticle is locted roud the coordite x. x For every vlue x log the lie oe coceives correspodig stte. If oe icludes ll the poits o the lie, complete bsis set results s idicted i (), which will be used to describe geerl qutum stte d, hece, to describe the oe-dimesio motio of prticle. A give stte specifies the prticulr wy i which the mplitude probbility of prticle is distributed log lie. Oe wy of specifyig this stte is by specifyig ll the mplitudeprobbilities tht the prticle will be foud t ech bse stte x; we write ech of these mplitudes s x. We must give ifiite set of mplitudes, oe for ech vlue of x. Thus, = About the x ottio x x Represettio of the wvefuctio () i the sptil coordites bsis We could use ltertive ottios, like, for exmple, = x A Ψ (x ) ; s to mke this expsio to resemble the expsio of vector i terms of bsevectors x with the correspodig coefficiets A Ψ (x ) plyig the role of weightig-fctor coefficiets. Isted of A Ψ (x ) sometimes (x) is preferred; thus, = x (x) ; Multiplyig the expressio bove by prticulr br x, we should obti, x = ( x ) 5

6 (This result will be ustified bit more rigorously i the ext sectio below; see the delt Dirc sectio). Tht is, ( x) x Thus, we will use idistictly the followig ottio = x x.= Amplitude probbility tht the prticle iitilly i the stte be foud (immeditely fter mesuremet) t the stte x. (3) Represettio of the x x. wve-fuctio i the (4) sptil coordites bsis umber Number Cutio: x does ot me [x]* (x). Workig with the sptil coordites bse { x } my costitute the oly occsio i which the ottio x becomes cofusig with the defiitio of the sclr product. Note: I Chpter 8 we used the ottio (t) = A (t), where the mplitudes A (t) = were determied by the Hmiltoi equtios d i = H dt. I this chpter, we re usig isted cotiuum bse { x, x } ; ccordigly the mplitudes will be expressed s x, which is lso writte s x. Soo we hve to ddress the how the Hmiltoi equtios look like whe usig cotiuum bse. The Delt Dirc A subproduct of the mthemticl mipultio expressig stte i give bsis is the closure reltioship tht the compoets of the bsis set must comply: the Delt Dirc reltioship. This is illustrted for the cse of the spce-coordites bsis. = x [ x ] 6

7 x x = x = x [ x ] x x [ x ] x = x x [ x ] ( x ) ( x ) Or, equivletly x = f x x x [ x ] O the other hd, it is ccepted tht for rbitrry fuctio f, the delt fuctio is defied s ( x - x ) f x The lst two expressios re cosistet if, x x = ( x x ). Thus, we hve the followig result: Cse of discrete sttes Delt Kroeecker Cse of cotiuum sttes Delt Dirc (x -x ) (5) m = m x x = ( x - x ) Comptibility betwee the physicl cocept of mplitude probbility d the ottio used for the ier product * x) x We kow tht give stte, the mplitude probbilitiesx tht pper i the expsio = x x re determied by the Hmiltoi equtios. 7

8 Suppose we hve prticle i the stte d we wt to kow the mplitude probbility (fter give mesuremet process) to fid the prticle t the stte. Tht is, we wt to evlute. There re my pth wy for the stte to trsit to stte. It could do it by pssig first through y of the bse-sttes x. Sice ech stte x geertes pth, d ll these pths hve the sme iitil d fil stte, the, ccordig to the rules estblished i Chpter 7, the totl mplitude probbility will be give by, x = ll x x x First, the sum over smll regio of width would be xx multiplied by. The, sice x vries from to the expressio bove c be writte s, = x x (6) We c here itroduce the ottio used bove, mely x = x Also, sice x = x * oe obtis, = * x) x (7) The mplitude probbility is equl to the ier product betwee the fuctios d 0..B Represettio of the wvefuctio i the mometum coordites bsis { p } I the previous sectio, rbitrry stte ws expressed i terms of the compoets of the spce-coordites bsis { x, x }. Here we preset ltertive bsis wy to express the stte, the cotiuum bse of mometum coordites. We will relize tht we re lredy fmilir with the sttes comprisig this ew bsis. Recll tht i Chpter 5 we itroduced the Fourier trsform F= F(k) of fuctio (x). It ivolved the itroductio of the complex hrmoic fuctios e k, where e k (x) = e ikx for k 8

9 ( x )= F(k) e ikx dk Fourier trsform Bse-fuctio e k evluted t x where the weight coefficiets F(k), referred to s the Fourier trsform of the fuctio, re give by, F(k) = e - i k x' ( x' ) ' However, it is coveiet to express the lst two expressios i terms of the vrible p = k, ( x )= (p) e i ( p / ) x d p (8) where (p) = e Fourier trsform of -i(p/ )x ( x' ) ' (9) Expressios (8) d (9) hve clerer physics iterprettio, p ( x ) = π e i ( p / ) x Accordig to de Broglie, p represets ple wve of defiite lier mometum p. (0) We c re-write expressio (8) i terms of fuctios, ( x )= ( p) Fourier trsform of ( x) e i ( p / ) x d p p = ( p) p dp Fuctio Sum of fuctios () Expressios (8) d () re equivlet. 9

10 Plcig p i brcket ottio First, guided by expressio (4), = otice tht expressio (0), p (x) = stte p s follows, x x, e i ( p / ) x, suggests to defie mometum π p = x p ( x ) x e i( p / ) x π Represettio of the mometum stte p i the spce-coordite bsis { x } () x p p ( x ) = π e i ( p / )x Amplitude probbility tht prticle, i stte of mometum p, be foud t the coordite x. (3) This is the de Broglie hypothesis i the lguge of mplitude probbilities. Plcig expressio () i brcket ottio I expressio () we c mke the the followig ssocitio, Hece, = (p) p dp p = (p) p dp (4) Fourier trsform ( x ) Expressio (4) gives s lier combitio of the mometum sttes p defied i () bove. Expressios (8), () d (4) re equivlet. 0

11 The sttes described i () costitute bse (the ustifictio comes from the Fourier trsform theory), { p, - < p < } cotiuum bse of mometum-coordites (5) Exercise: Prove tht p p = ( p p ) Now we formlly c ustify tht i expressio (4): p = (p) I effect, multiplyig (4) with br p, oe obtis, p = p (p ) p d p = (p ) p p dp usig p p = ( p p ) p = (p) (6)

12 Summry bout the mometum coordites: A rbitrry wvefuctio c be expressed s lier combitio of mometum-coordites p, = where (p) = p (p) d p = p p dp -i(p/ )x e ( x' ) ' p =(p) is the Fourier trsform of = (x) (7) p (p) Amplitude probbility tht prticle i the stte c be foud, upo mkig mesuremet, i the stte p. p dp ( p) dp Probbility tht prticle i the stte be foud with mometum withi the itervl ( p, p+ d p). < p > = p (p) dp = p p dp Averge lier mometum of esemble of systems i the stte Expressios (4) d (7) summrizes the obective of this sectio, showig expsio of the stte i two differet bsis, the cotiuum sptil-coordites bse { x, x }; d the lier-mometum bse { p, - < p < }.

13 SUMMARY Coordites bsis = = x x x (x) ; Mometum bsis = = p p dp p (p) d p (p) is the Fourier trsform of = (x) p is such tht, x p p ( x ) = π e i ( p / )x 0..C Tesor Product of Stte Spces Cosider the stte spce comprised of wvefuctios describig the sttes of give system ( electro, for exmple). We use the idex- to differetite it from the stte spce comprised of wvefuctios describig the stte of other system (other electro, for exmple) which is iitilly locted fr wy from the system. The systems (the electros) my evetully get closer, iterct, d the go fr wy gi. How to describe the spce stte of the globl system? The cocept of tesor product is itroduced to llows such descriptio Let { u i (), i,, 3,...} be bsis i the spce ε, d { v i (), i,, 3,...} be bsis i the spce The tesor product of of ε d ε, deoted by ε ε ε, is defied s spce whose bsis is formed by elemets of the type, { u i () v () } ε 3

14 Tht is, elemet of ε ε is lier combitio of the form, The tesor product i, c i u i () v () is defied with the followig properties, [ () ] () [ () () ] () [ () ] [ () () ] [ () () ] () [ () () () [ () () ] [ () ] + [ () () ] + [ () () ] () ] Two importt cses my rise. Cse : The wvefuctio is the tesor product of the type, () () Tht is c be expressed s the tesor product of stte from ε d stte from ε. i, i i b i u i () b v () ui () v () Cse : The wvefuctio cot be expressed s the tesor product betwee stte purely from the spce ε d stte purely from the spce ε. I this cse, the wvefutio tkes the form, i, c i ui () v () Let s cosider, for exmple the cse i which ech spce ε d ε hs dimesio. i, c i u i() v () i c i u v() ci u v() i() i () i c u () v() c u () v() 4

15 c u() v() c u() v() Notice, cot be expressed i the form () () To mke the cse eve simpler, let s ssume c = c = 0, c u () v () + c u () v () etglemet cot be expressed i the form of sigle term of the form () () 0. The Schrödiger Equtio s postulte 0..A The Hmiltoi equtios expressed i the cotiuum sptil coordites. The Schrödiger Equtio. 3 I chpter 8 we obtied the geerl Hmiltoi equtios tht describe the time evolutio of the wvefuctio (t) = A ( t ), da H ( t) A (8) dt i Chpter 9 described the prticulr cse of electro movig i lttice (the ltter costituted by toms seprted distce b. Whe we took the limit b 0 the Hmiltoi equtios i (5) took the form ( x,t) ( x,t) i V ( x,t) ( x,t) (9) t m x Let s cosider ow rbitrry geerl cse. eff How does the Hmiltoi equtios (8) look like whe expressed i the i the cotiuum spce coordites { x, x }? Let s fid out such geerl forml expressio (oe tht is more geerl th expressio (9) ). First otice tht the mplitudes system, = A A i (8) ccout for the stte describig the qutum Sice A c lso be writte s A =, Eq. (5) c lso be expressed s, d i = H dt 5

16 Let s lso recll, from Chpter 7, tht the coefficiets H opertor H ~ (specific to the problem beig solved.) Tht is, deped o t. d i = H ~ dt I the cotiuum spce coordites we should expect, d i x = dt x H ~ x x d x (x) ( x ) re obtied from the Hmiltoi H H ~. I geerl, H ~ d d i ( x ) = dt H( x, x )( x ) d x (0) where we hve defied H( x, x ) x H ~ x Quotig Feym, 4 Accordig to (0), the rte of chge of t x would deped o the vlue of t ll other poits x. x H ~ x is the mplitude per uit time tht the electro will ump from x to x. It turs out i ture, however, tht this mplitude is zero except for poits x very close to x. This mes (s we sw i the exmple of the chi of toms) tht the right-hd side of Eq. (0) c be expressed completely i terms of d the sptil derivtives of, ll evluted t x. The correct lw of physics is d H( x, x ) ( x ) d x = ( x ) + V ( x) ( x ) Postulte () m Where did we get tht from? Nowhere. It cme out of the mid of Schrodiger, iveted i his struggle to fid uderstdig of the experimetl observtio of the experimetl world. Usig () i (0) oe obtis, 6

17 i t V ( x, t) x m Schrodiger Equtio This equtio mrked historic momet costitutig the birth of the qutum mechicl descriptio of mtter. The gret historicl momet mrkig the birth of the qutum mechicl descriptio of mtter occurred whe Schrodiger first wrote dow his equtio i 96. For my yers the iterl tomic structure of the mtter hd bee gret mystery. No oe hd bee ble to uderstd wht held mtter together, why there ws chemicl bidig, d especilly how it could be tht toms could be stble. (Although Bohr hd bee ble to give descriptio of the iterl motio of electro i hydroge tom which seemed to expli the observed spectrum of light emitted by this tom, the reso tht electros moved this wy remied mystery.) Schrodiger s discovery of the proper equtios of motio for electros o tomic scle provided theory from which tomic pheome could be clculted qutittively, ccurtely d i detil. Feym s Lectures, Vol III, pge 6-3. Although the result () is kid of postulte, we do hve some clues bout how to iterpret it, bsed o the prticulr cse of the dymics of electro i crystl lttice, studied i Chpter 9. () 0..B Iterprettio of the Wvefuctio Eistei s view o the grulrity ture of the electromgetic rditio I Chpter 5, hrmoic fuctio ws used to describe the motio of free prticle i logy to the existet formlism to describe electromgetic wves, π ε ( x, t) ε o Cos [ x νt] electromgetic wve λ where, the electromgetic itesity I (eergy per uit time crossig uit cross-sectio re perpediculr to the directio of rditio propgtio) is proportiol to ε ( x, t). Eistei (i the cotext of tryig to expli the results from the photoelectric effect) itroduced the grulrity iterprettio of the electromgetic wves (lter clled photos), bdoig the more clssicl cotiuum iterprettio. I Eistei s view, the itesity is iterpreted s sttisticl vrible I c o ε N h. Here N costitutes the verge umber of photos per secod crossig uit re perpediculr to the directio of rditio propgtio; ε ~ N Averge vlues re used i this iterprettio becuse the emissio process of photos by give source is sttisticl i ture. The exct umber of photos crossig uit re per uit time fluctutes roud verge vlue N. 7

18 Mx Bor s Probbilistic Iterprettio of the wvefuctio I logy to Eistei s view of rditio, Mx Bor proposed similr view to iterpret the prticle s wve-fuctios. I Mx Bor s view, ( x, t) plys role similr to ε ( x, t), ( x, t) is mesure of the probbility of fidig the prticle roud give plce x d t give time t. This iterprettio ws itroduced yers fter Schrodiger (96) hd developed forml qutum mechics descriptio. More specificlly, ( x, y,z, t) plys the role of probbility desity. Pictorilly, the prticle is more likely to be t loctios where the wvefutio hs pprecible vlue. Determiistic evolutio of the wvefuctio The predictios of qutum mechics re sttisticl. I order to kow the stte of motio of prticle, we must mke mesuremet But mesuremet ecessrily disturbs the system i wy tht cot be completely determied. However, otice tht, beig the solutio of differetil equtio (the Schrodiger equtio), vries with time i wy tht is completely determiistic. Tht is, if were kow t t=0, the Schrodiger equtio determies precisely its form t y future time. Tht is, QM mkes determiistic predictio of mplitude probbility wve. However, the ltter does ot covey to determiistic outcomes. There is oe further poit to cosider. How to determie the wvefuctio t t=0? How do the experimetl mesuremets led to the recostructio of the wvefuctio? Or, how to prepre system i defiitely uique stte? If we could ot recostruct wvefuctio, wht would be the beefit of hvig theoreticl formultio tht describes determiistic evolutio of somethig we do ot kow? As it turs out, despite the fct tht mesuremet i QM i geerl ffect the stte of system, such recostructio is possible i some cses (thik of system tht re i sttiory stes.) But i lrger cotext, to beefit of the QM determiistic formultio wht we eed is to prepre system (or my systems) i defiite stte; for the theory could the be used to mke predictios bout the evolutio of tht prticulr stte. We will ddress this issue i the ext chpters, fter the itroductio of observbles d eigesttes. We will see tht fidig eigesttes commo to differet observbles rrows the selectio pool of sttes i which the system c be foud. This procedure leds to the cocept of esemble of system costituted by (i this wy) eqully prepred systems, which costitutes the lbortory i which the QM cocept re develop. (A the ed, 8

19 systems cot be determied with bsolute certity simply becuse set of mesuremets t t=0 t most my led to the determitio of but ot to uiquely defie ). Let s expli the sttisticl iterprettio bit further i the cotext of esemble of ideticlly prepred systems. Esemble 5 Imgie very lrge umber of ideticlly prepred idepedet system (ssumed to be ll of them i the sme stte), ech of them cosistig of sigle prticle movig uder the ifluece of give exterl force. All these systems re ideticlly prepred. The whole esemble is ssumed to be described by complex-vrible sigle wvefuctio ( x, y, z, t), which cotis ll the iformtio tht c be obtied bout them. describes the whole esemble... Esemble is used to mke probbilistic predictio o wht my hppe i prticulr member of the esemble. N It is postulted tht: If mesuremet of the prticle s positio re mde o ech of the N member of the esemble, the frctio of times the prticle will be foud withi the volume elemet d 3 r = dy dz roud the positio r ( x, y, z, t) t the time t is give by (3) * ( x, y, z, t) ( x, y, z, t) d 3 r where * stds for the complex cougte umber. Notice tht this is othig but the lguge of probbility; i this cse, positio probbility desity P. P * ( x, y, z,t) ( x, y, z,t) ( x, y, z,t) ( x, y, z, t ) 9

20 Cutio: For coveiece, we shll ofte spek of the wvefuctio of prticulr system, BUT it must lwys be uderstood tht this is shorthd for the wvefuctio ssocited with esemble of ideticl d ideticlly prepred systems, s required by the sttisticl ture of the theory C Normliztio coditio for the wvefuctio The probbilistic iterprettio of the wvefuctio implies, therefore, the followig requiremet: * 3 ( x, y, z,t) ( x, y, z,t) d r (4) All spce becuse give prticle, the likelihood to fid it ywhere should be oe. Iheret to this requiremet is tht, ( r, t) 0 (5) r Notice tht if is solutio of the Schrodiger equtio, the fuctio c (c beig costt) is lso solutio. The multiplictive fctor c therefore hs to be chose such tht the fuctio c stisfies the coditio (4). This process is clled ormlizig the wvefuctio. I geerl, there will be solutios to the Schrodiger equtio () whose solutio ted to ifiite vlue. This mes they re o-ormlizble d therefore c ot represet prticle probbility desity. Such fuctios must be reected o the grouds of Bor s probbility iterprettio. Qutum mechics sttes re represeted by squre- itegrble fuctios tht stisfy the Schrodiger equtio. The prticulr subset of squre itegrble fuctios form vector spce clled the Hilbert spce. QUESTION: Suppose tht is ormlized t t 0. As the time evolves, will chge. How do we kow if it will remi ormlized? Here we show tht the Schrodiger equtio hs the remrkble property tht it utomticlly preserves the ormliztio of the wvefuctio: 0

21 If stisfies the Schrodiger equtio The if the potetil is rel dt Proof: Let s strt with d dt ( x, t) = 0 (6) d ( x,t) ( x,t) (7) t We provide below grphic ustifictio of (7). fx, t ) fx, t +) x x t O the other hd, fx, t +) - fx, t ) = t [fx, t + ) - fx, t) ] t ) t t t (8) We use the Schrodiger equtio (9) to clculte the time derivtives, i V ( x,t) t m x i i V ( x,t) m x i i V ( x,t) t m x Tkig the complex cougte, d ssumig tht the potetil is rel, i i V( x,t) t m x Addig the lst two expressios, t t i m x x

22 i m Replcig (9) i (8) we obti, Accordigly, d dt t i m x x x x x x ( x,t) ( x,t) t i m x x x i m x x The expressio o the right is zero becuse x 0 x (9) 0..D The Philosophy of Qutum Theory There hs bee cotroversy over the Qutum Theory s philosophic foudtios. Neils Bohr hs bee the pricipl rchitect of wht is kow s the Copehge iterprettio (sttisticl iterprettio) Eistei ws the pricipl critic of Bohr s iterprettio. His sttemet God does ot ply dice with the uiverse, refers to the bdomet of strict cuslity d idividul evets by qutum theory. Heiseberg coutercts rguig: We hve ot ssumed tht the qutum theory (s opposed to the clssicl theory) is sttisticl theory, i the sese tht oly sttisticl coclusios c be drw from exct dt. I the formultio of the cusl lw, mely, if we kow the preset exctly, we c predict the future it is ot the coclusio, but rther the premise which is flse. We cot kow, s mtter of priciple, the preset i ll its detils. Louis de Broglie, o the other hd, rgues tht tht limited kowledge of the preset my be rther limittio of the curret mesuremet methods beig used. He recogizes tht

23 ) it is certi tht the methods of mesuremet do ot llow us to determie simulteously ll the mgitude which would be ecessry to obti picture of the clssicl type, d tht b) perturbtios itroduced by the mesuremet, which re impossible to elimite, prevet us i geerl from predictig precisely the results which it will produce d llow oly sttisticl predictios. The costructio of purely probbilistic formule ws thus completely ustified. But, the ssertio tht i) The ucerti d icomplete chrcter of the kowledge tht experimet t its preset stge gives us bout wht relly hppes i microphysics, is the result of ii) rel idetermicy of the physicl sttes d of their evolutio, costitutes extrpoltio tht does ot pper i y wy to be ustified. De Broglie cosiders possible tht lookig ito the future we will be ble to iterpret the lws of probbility d qutum physics s beig the sttisticl results of the developmet of completely determied vlues of vribles which re t preset hidde from us. Louis de Broglie s view give bove highlights the obectio to qutum mechics philosophic idetermiism. Accordig to Eistei: The belief of exterl world idepedet of the perceivig subect is the bsis of ll turl sciece. Qutum mechics, however, regrds the iterctio betwee obect d observer s the ultimte relity; reects s meigless d useless the otio tht behid the uiverse of our perceptio there lies hidde obective world ruled by cuslity; cofies itself to the descriptio of the reltios mog perceptios 7 Physics hs give up o the problem of tryig predictig exctly wht will hppe i defiite circumstce. 0.3 Expecttio vlues 0.3.A Expecttio (or me) vlue of prticle s positio Let s ssume we hve system cosistig of box cotiig sigle prticle, which is (we ssume) i stte. The expecttio vlue of the prticle s positio is defied by, x - x ( x) (30) But wht does this itegrl exctly me? It is worth to emphsize first wht type of iterprettio should be voided. 8 3

24 Expressio (30) does ot imply tht if you mesure the positio of the prticle over d over gi the - x ( x) would be the verge of the results. I fct, if repeted mesuremets were to be mde o the sme prticle, the first mesuremet (whose outcome is upredictble) will mke the wvefuctio to collpse to stte of correspodig prticle s positio x (let s sy x 0 ); subsequet mesuremets (if they re performed quickly) will simply repet tht sme result x 0. O the cotrry, x - x ( x) mes the verge obtied from mesuremets performed o my systems, ll i the sme stte. Tht is, A esemble of systems is prepred, ech i the sme stte, d mesuremet of the positio is performed i ll of them. x is the verge from such mesuremet. describes the whole esemble... Esemble is used to mke probbilistic predictio o wht my hppe i prticulr member of the esemble. N Fig. 0. Esemble of ideticlly prepred systems. Whe we sy tht system is i the stte, we re ctully referrig to esemble of systems ll of them i the sme stte. Thus, represets the whole esemble. d x 0.3.B Clcultio of m dt As time goes o, the expecttio vlue x my chge with time, sice the wvefuctio evolves with time. Let s clculte its rte of chge. d x dt d dt x ( x, t) x t t x ( x, t) t x t For the cse where the potetil is rel, we obtied i expressio (9) tht, 4

25 t t i m x x d x dt i x m x x i x m x x x After itegrtig by prts, it gives d x dt i m x x Itegrtig oe more time by prts (ust the secod term o the right side,) d x i dt m x (3) We would be tempted to postulte tht the expecttio (or me) vlue of the lier d x mometum is equl to p m. I tht cse, expressio (3) would give, dt d x p m dt = (3) i x i x However, look t the fuctios iside the itegrl. How to uderstd tht term like x would led to the verge vlue of the lier mometum? This ppers bit strge, to sy the lest. (We will more sese of this lst result i the sectios below, whe the cocept of qutum mechics opertors is itroduced). 0.3.C Expecttio (verge) vlues re clculted i esemble of ideticlly prepred systems I geerl, the me-vlue of give physicl property f (mely, eergy, lier mometum, positio, etc.), more geericlly clled observble, is obtied by mkig mesuremet i ech of the N eqully prepred systems of esemble (d ot by vergig repeted mesuremets o sigle system.) The whole esemble is ssumed to be described by complex-vrible sigle wvefuctio ( x, y, z, t). Whe mkig mesuremets o ech of the N ideticlly prepred systems of the esemble (see left-side of the figure below) let s ssume we get series of results (see rightside of the figure below) like this: 5

26 N systems re foud to hve vlue of f equl to f, from which we deduce tht the prticulr system collpsed to the stte f right fter the mesuremet N systems re foud to hve vlue of f equl to f, from which we deduce tht the prticulr system collpsed to the stte f right fter the mesuremet etc. Accordigly, the verge vlue of f is clculted s follows, f v N f N f N N Usul procedure to clculte verge vlues (33) N Notice tht whe the totl umber of mesuremet N is very lrge umber, the rtio N is othig but the probbility of fidig the system i the prticulr stte f. QM postultes tht the vlue of the vlue f, f ψ should be iterpreted s the probbility to obti f ψ = N Qutum Mechics postulte (34) N Represetig the esemble ψ Hece, expressio (33) c be writte s, f v f f ψ (35) 6

27 ... Esemble... N Before the mesuremets The vlue of f i ech system is ukow. N After the mesuremets The vlue of f hs bee mesured i ech system; fterwrds we proceed to clculte the verge vlue < f >. 0.4 Opertors ssocited to Observbles Qutities such s positio, mometum, or eergy (which re mesured experimetlly) re clled observbles. I clssicl physics, observbles re represeted by ordiry vribles (E, p, for exmple). I qutum mechics observbles re represeted by opertors (qutities tht operte o fuctio to give ew fuctio.) Whe system i stte eters some pprtus, like, for exmple, mgetic field i the Ster Gerlch experimet, or mser resot cvity, it my leve i differet stte. Tht is, s result of its iterctio with the pprtus, the stte of the system is modified. Symboliclly, the pprtus c be represeted by correspodig opertor F ~ such tht, = F ~ (36) Note: We will distiguish the opertors (from other qutities) by puttig smll ht ~ o top of its correspodig symbol. We show below how to ssocite qutum mechics opertor F ~ to give physicl qutity f. 7

28 0.4.A Observbles, eigevlues d eige-sttes Let s cosider physicl qutity or observble f ( f could be the prticle s mometum for exmple) tht chrcterizes the stte of qutum system. gulr I qutum mechics, the differet vlues tht give physicl qutity f (observble) c tke, re clled its eigevlues; f, f, f 3, (37) The set of these qutum eigevlues is referred to s the spectrum of eigevlues of the correspodig qutity f. For simplicity, let ssume for the momet tht the spectrum of eigevlues is discrete. will deote the stte where the qutity f hs the vlue f ; These sttes will be clled eigesttes (38) We will ssume tht these eigestes stisfy, m = ( x) m ( x) = m (39) Let s ssume lso tht the eigesttes ssocited to the observble f costitute bsis, { ; =,, 3, } Bsis of eigesttes A rbitrry stte c the be represeted by the expsio, = A =. (40) where A = = ( x) ( x) Sice the stte must be ormlized stte, the A = = Accordig to expressio (35), the me vlue of f, whe the system is i the stte, is give by, f = v f A = f (4) Expressio (4) is still very geerl, sice we do ot kow yet the eigesttes. (We will describe below how to figure out these sttes). 8

29 0.4.B Defiitio of the QM opertor F ~ to be ssocited with the observble physicl qutity f. 9,0 The opertor F ~ to be ssocited with the observble f is such tht, whe ctig o rbitrry stte, stisfies the followig: F ~ f v Defiitio of the Opertor F ~ ssocited to the observble f (4) (the verge vlue o the right side is clculted over the esemble represeted by the stte ) But, how to obti explicit expressio for such opertor F ~? If the observble qutity were, for exmple, the lier mometum, how to build its correspodig qutum mechics opertor? Before buildig the opertors explicitly, we first we derive i this sectio geerl selfcosistet expressio tht shows how opertor, ssocited to give observble, should look like (see expressio (47) below). Subsequetly, sectios 0.4.C d 0.4.D will provide specific procedure o how to costruct the positio d mometum opertors. Derivig self-cosistet expressio of QM opertor F ~ If the system is i the stte, = A =, the verge vlue of the qutity f is give by (4), f = v f A = f The requiremet to build the opertor F ~ is, F ~ = f = = v f A = f f * f (43) 9

30 Notice, we c obti more compct expressio for the opertor F ~ if we use the ottio, P ~ pro, Proectio opertor (44) Expressio (44) describes opertor tht whe ctig o stte gives the proectio of tht stte log the stte ; tht is, P ~ pro, = = (45) Accordigly, (43) c be expressed s, F ~ = [ f ] (46) stte umber opertor where deote the stte where the qutity f hs the vlue f ; This ottio trick llows us to express the opertor i more compct form, F ~ = f (47) eigevlues Proectio opertorp ~ pro, built out of eigesttes ssocited to the physicl qutity f. This is self-cosistet expressio for the qutum opertor F ~ tht will be ssocited with the clssicl qutity f. It is expressio tht is comptible with the ~ requiremet tht Ψ F Ψ f. Note: Although the opertor defied i (47) would pper to deped o the prticulr bsis composed by eigesttes, the opertor should remi the sme if we chged the bsis. This clim is supported by the fct tht the vlue of f, which itervees i the defiitio of F ~, should be idepedet of the bsis chose. v v 30

31 Notice (47) is self-cosistet with its defiitio. By pplyig the opertor eigesttes, it gives, F ~ to oe of the Tht is, oce the opertor F ~ F ~ = f (48) (ssocited to the physicl qutity f ) is kow, the eigefuctios of tht give physicl qutity re the solutios of the equtio F ~ = where is costt. Still, otice tht (47) d (48) give ust self-cosistet expressios for the opertor F ~. It is expressio tht is comptible with the requiremet tht F ~ f v, but it is defied i terms of eigesttes tht, for give physics qutity f, we do ot kow yet but would like to fid out. Accordigly, F ~ is ot completely kow yet. But, prphrsig Ldu, Although the opertor F ~ is defied by (48), which itself cotis the eigefuctios, o further coclusios c be drw from the results we hve obtied. However, s we shll see below, the form of the opertors for vrious physicl qutities c be determied from direct physicl cosidertios, which subsequetly, usig the bove properties of the opertors, will eble us to fid the eigefuctios d eigevlues by solvig the equtio F ~ = Expressios (4) d (47) will guide us to build the qutum opertors. 0.4.C Defiitio of the Positio Opertor X ~ We re lookig for opertor X ~ such tht X ~ gives us the me vlue of the positio whe the system is i the stte, which requires, X ~ *( x) [ ( X ~ )( x) ] x - x x ( x), X ~ =? (49) [ *( x) ] x [ ( x) ] ( X ~ ) ( x ) = x ( x ) (50) Notice, we cot sy X ~ = x; tht would be icorrect. (For istce, wht vlue of x would you choose to mke the expressio X ~ = x vlid?). More pproprite is first to defie the idetity fuctio I, which stisfies, 3

32 I(x) = x, The (50) could the be writte s, ( X ~ ) ( x) = (I ) ( x) becuse tht would give [ X ~ ]( x) = [I ] ( x) = I( x) ( x) = x ( x). Tht is, X ~ = I Aswer (5) I summry, The Positio Opertor X ~ X ~ is the opertor ssocited to x X ~ = I where I is the idetity fuctio; I (x) = x ( X ~ ) x= x x (5) X ~ = [ * x ] [ X ~ x ] = [ * x ] x [x] x Notice, it is strightforwrd to relize tht X is the opertor ssocited to ~ x, ~ 3 X is the opertor ssocited to x 3, etc. More geerl, X ~ is the opertor ssocited to Averge vlue of physicl qutity x (53) Qutum mechics opertor x v X ~ ~ x X v x v ~ X 3

33 if h(x) is polyomil or coverget series, (54) the h( X ~ ) will be the opertor ssocited to h ( x) For exmple: If physicl qutity chges s, h(x) = 3 x - 7 x 3 the the correspodig opertor will be 3 X ~ 3-7 X ~, tht is, the lst expressio is obtied by evlutig h( X ~ ). Averge vlue Qutum mechics of physicl qutity opertor h (x) h( X ~ ) v For exmple, if h(x) were clssicl potetil tht depeds oly o positio x, the h( X ~ ) would be the the correspodig qutum mechics potetil opertor. Tke the cse of the hrmoic oscilltor, where V(x) = (/)k x the correspodig QM opertor is ~ V ~ = (/)k X. 0.4.D Defiitio of the Lier Mometum Opertor Here we costruct the qutum Lier Mometum Opertor, d give its represettio i both the mometum-coordites d sptil-coordites bsis. This exmple will help to illustrte tht opertors re geerl mthemticl cocepts whose represettio depeds o the bse sttes beig used. The tsk of defiig the mometum opertor is fcilitted by the fct tht the eigefuctios d eigevlues re lredy kow. Thus Sectios 0.4.D d 0.4.D my pper to be trivil; still it helps to illustrte the coectio betwee opertor d the me vlue of the correspodig observble. 0.4.D The Lier Mometum Opertor P ~ expressed i the mometum sttes bsis { p } I this cse, the observble f refers to the lier mometum p. Before buildig the lier mometum opertor let s summrize wht we kow bout the mometum sttes. Accordig to de Broglie, for give p we ssocite mometum stte p give by expressio () bove, 33

34 p x p ( x ) = x π e i ( p / ) x (55) Represettio of the mometum stte p i the spcecoordite bsis { x } x p p ( x ) = e i ( p / ) x π Sometimes, for coveiece, we will use the ottio mometum p or p isted of p. A rbitrry stte c be expressed s lier combitio of the mometum sttes, where = (p) = p = p p dp = p (p) d p (56) -i(p/ )x e ( x' ) ' Now we wt to build the Lier Mometum Opertor tht will be ssocited to the physicl qutity p. Accordig to expressio (47), F ~ = p, but with the summtio exteded to itegrl over cotiuum vrible, the mometum opertor should hve the form, P ~ = p p p dp Lier Mometum Opertor (57) Applyig the mometum opertor P ~ to rbitrry stte gives, P ~ = p p p dp, Accordig to (56) p =(p), which gives P ~ = p (p) p dp (58) ~ P Ψ is lier combitio of sttes p. 34

35 Let verify if the opertor P ~ defied i (57) stisfies the requiremet tht P ~ is equl to the verge mometum by ). Sice = p (p) d p, we hve, = p (the verge tke over esemble chrcterized p (p ) d p (59) Similr to the delt Dirc obtied i expressio (6) bove, it c be demostrted tht, p p = ( p p ) (60) From (58) d (59), P ~ = [ p (p ) d p ] [ p (p) p dp ] = d p p (p) (p ) p p dp = d p p (p) (p ) ( p p ) dp = d p p (p ) (p ) Usig p isted of p = p (p) d p Sice =(p) is the Fourier trsform of = ( x ), the term o the right side of the lst expressio is the the verge lier mometum of system i the stte. (See the summry fter expressio (7) bove). P ~ = p (p) d p = p Averge mometum (6) Hece, the opertor P ~ defied i (57), therefore, fulfills the geerl requiremet (4) impossed to qutum mechics opertors. 35

36 Mtrix represettio of the Lier Mometum Opertor P ~ i the mometum coordites bsis { p, - < p < } Usig (60), otice i (57) tht pplyig P ~ to prticulr stte p gives, P ~ p = p p (6) Usig gi (60) we obti ~ P = p P ~ p = p ( p p ) (63) pp' Elemets of the mtrix represettio of the opertor i the mometum bsis Summry The Mometum Opertor P ~ P ~ = p p p dp (64) The remiig clcultios re reltively strightforwrd becuse of the fct tht we ssume rbitrry stte c expressed s lier combitio of moemetum sttes = p p dp = p (p) d p where = (p) is the Fourier trsform of (x) Accordigly, d P ~ = P ~ = = p p p dp = p (p) p dp [ * x ] [P ~ x ] p (p) d p p 36

37 0.4.D The lier mometum opertor P ~ expressed i the sptil coordites bsis { x, x } Wht bout if the expsio of i the mometum bsis { p, - < p < } is ot kow, d, isted, its expsio i the sptil coordite is vilble? (i.e. (x) is kow for every vlue of x, but its Fourier trsform of (p), is ot redy vilble). Wht to do to fid P ~ (without hvig to go through the trouble of expressig i terms of the mometum bsis s expressio (64) requires)? It is show below tht ltertive wy (ltertive to (64) ) to express the lier mometum does exist. Applyig (57), P ~ = we hve = P ~ = p p p dp, to the stte fuctio, where p p dp = p p p dp = p (p ) dp p (p) p dp x P ~ = p (p) x p dp, [P ~ x= p (p) p x dp Usig (53) x p p ( x ) = π e i ( p / ) x [P ~ x= p (p) π e i ( p / ) x dp, Otherwise, if the Fourier tyrsform (p) of (x) were kow, the P ~ = [ p p p dp ] = p p p dp = p (p) p dp 37

38 [P ~ x = (p) p π e i ( p / ) x dp, = (p) i d π e i ( p / ) x dp = i d (p) π e i (p / ) x dp = i d (p) p xdp, Hece, P ~ = = i i d d x (65) The Mometum Opertor P ~ (i the sptil-coordites spce) P ~ = i d (66) The remiig clcultio is fcilitted if rbitrry stte is expressed s lier combitio of sptil coordites, so the derivtive c be strightforrdly evluted. P ~ = which leds to i d p v = P ~ d = i 38

39 I pssig, otice tht the lst expressio is ideticl to (3) where we clculted the rte of d x chge of the verge positio of prticle m. At tht time, the presece of sptilderivtive ppered to mke o sese i beig ivolved i wht could be iterpreted s dt d i the velocity of prticle. Now we see tht such sptil derivtive ppers becuse we re usig the represettio of the mometum opertor i the sptil coordites (tht sptil derivtive does ot pper whe workig i the mometum bsis). 0.4.D3 Systemtic wy to express the opertors P ~, bsis ~ P, ~ 3 P, etc., i the sptil-coordites We lredy kow how to express the opertors P ~, P, P, i the mometum coordite bsis; we ust eed to pply 57) repetitive wys. Tht expressio will give us the opertors redy to pply o sttes whose expsio i the p sttes is redy vilble. But wht we wt ow is to hve those opertors i the sptil-coordites; i.e. expressed i wy redy to be pplied whe the sttes re expressed i the sptil-coordites. Here we show systemtic wy to obti such expressios. ~ ~ 3 Costructio of the Lier MometumP ~ i the sptil- coordites bse We lredy kow how to express P ~ i the mometum bsis, P ~ = p p p dp. Which, whe opertig i esemble of systems chrcterized by the wvefuctio = p p dp = p (p ) dp, stisfies, p v = p p p dp (67) First, let s work out the fctor p p iside the itegrl i expressio (67). Sice p = (p) is the Fourier trsform of x, we hve p p = p (p) = p -i(p/ )x e ' ( x' ) 39

40 = -i(p/ )x p e ( x' ) ' = i [ d e -i(p/ )x ] ( x' ) ' Itegrtig by prts = p p = p i Replcig (68) i (67), p v = p p i [e -i(p/ )x ] i = p p i d ( x') ' d (68) d dp (69) d dp. p v = i Wht is this? Notice, if we wted to expd the stte i the mometum bse we would write, i d = p p i d d i This is exctly wht we hve i the expressio bove. Accordigly, d (70) 40

41 = * x,t i d x,t ] If we defie the opertor P ~ = The lst expressio becomes, Tht is, P ~ = i d i p v = d (7) * x,t P ~ x,t ] (7) stisfies the requiremet for beig the lier mometum opertor. Costructio of the opertor ssocited to By defiitio: p v p = v p p p dp (73) The systemtic procedure cosists i evlutig first the fctor p p iside the itegrl. p p = p -i(p/ )x e ' ( x' ) = p -i(p/ )x e ( x' ) ' Cotiuig with similr procedure s bove (itegrtig by prts), we obti, p p = p Replcig (74) i (73) leds to, i d (74) p = v p p i d dp 4

42 Fctorig out = p p i d dp The itegrl term is the expsio of the ket i d i the mometum bse p = v i d = * x,t i d (75) x,t ] If we defie the opertor ~ P = The lst expressio becomes, i d (76) Tht is, ~ P = i d ssocited with p. p = v * x,t ~ P x,t ] (77) stisfies the requiremet for beig the qutum opertor to be More geerl, for positive iteger, Tht is, ~ P = i p = v d * x, t i d x,t ] (78) is the qutum mechics opertor ssocited to p Averge vlue of physicl qutity Qutum mechics opertor 4

43 p v p v p v i i i d d d If g(p, t) is polyomil, or bsolutely coverget series, i the clssicl mometum vrible p, oe obtis, g ( p, t) v = * x, t d g(, t ) i x,t ] (79) Averge vlue of physicl qutity v Qutum mechics opertor d g i g ( p, t) (, t) A exmple of (79) costitutes the QM opertor ssocited to the kietic eergy. m p v ~ P m = = m i d d (80) m 0.4.E The Hmiltoi opertor 0.4.E. Me eergy i terms of the Hmiltoi opertor Oe of the virtues of usig QM opertors is tht me vlues c be expressed idepedet of prticulr bsis. For exmple, recll tht < x > = X ~ d <p> = P ~. We re goig to do somethig similr here for the verge eergy <E>. I Chpter 8, the Hmiltoi Opertor H ~ ws recogized through its mtrix represettio [H], the ltter beig iterpreted s eergy mtrix due to the fct tht, whe workig with 43

44 sttiory sttes, the compoets of the mtrix were the eergy of the correspodig sttiory sttes. I the lguge of eigevlues used i this chpter we c write, H ~ E = E E (8) where { E, =,, 3, } is the bsis costituted by eergy sttiory sttes For system i rbitrry stte let s clculte the expecttio (or me) vlue of eergy. = E E (8) Its me eergy is give by, E E v E ψ = E * E E = E E E = H ~ E E This fctor c be expressed i terms of the Hmiltoi opertor From (74): H ~ E = E E = H ~ E E = E = H ~ (83) v Thus, we hve foud gi (s we did for the lier mometum d the positio opertors) elegt wy to express me vlue (i this cse for the eergy) tht does ot mke referece to the prticulr bse sttes. Whe explicit ottio of the bsis sttes is required to clculte sttiory sttes bsis { E, =,, 3, } E v, we my use the E E v E But, i some cses it my be coveiet to use differet bse. For geerl bsis { for =,,3, } we will hve, ψ 44

45 E = H ~ v Expressig d i the { } bsis = [ i i* i ] H ~ [ ] = i i H ~ (84) i, 0.4.E. Represettio of the Hmiltoi Opertor i the sptil coordite bsis Similr to the cse of the lier mometum opertor ddressed i the previous sectios, the Hmiltoi opertor c dopt differet shpes depedig o whether we re workig i the sptil-coordites bsis or i the mometum coordite bses. Here we ddress the represettio of the Hmiltoi opertor i the sptil-coordites I (83), let s tke the cse of sptil coordite bsis Defiig E E v v = H ~ = [ - = [ - = - x * x ] H ~ x * x ] [ - x * [ - x x ] H ~ x x ] x H ~ x x ] - H( x, x ) x H ~ x (85) (see lso expressio (0) bove). E = v - = - x * ( x) * - - H( x, x ) x H( x, x ) ( x ) But Schrodiger estblished tht (see expressio () bove) - H( x, x ) ( x ) d x = ( x ) + V ( x)( x ) d m 45

46 Accordigly E v = - ( x) * [ d m If we defie the potetil opertor V ~, ~ V Ψ V Ψ ( x ) + V ( x)( x )] (86) E = v - ( x) * d [ m + V ~ ] (x) H ~ E = H ~ v ~ Represettio of the Hmiltoi d H Opertor i the sptil coordites + V ~ (87) m bsis ~ Notice, ccordig to expressio (80) P m = d m, therefore we c write, ~ H ~ P m ~ V Summry Observble Me Opertor Opertor i the sptil vlue coordites represettio positio X ~ X ~ x x = X ~ X ~ = x mometum P ~ d i p p = P ~ P ~ = i d 46

47 Eergy ~ d H + V ~ m E E = H ~ 0.5 Properties of Opertors 0.5.A Correspodece betwee brs d kets Br Ket χ χ m m m m b b m * m m m m m b m b m bφ bφ Φ b * Φ b * Φ * Φ Φ Φ * Util ow we hve defied the ctio of lier opertor A ~ o kets the ctio of opertor o brs. χ. We wt to defie 0.5.B The Adoit opertor Cosider opertor A ~. We picture opertor A ~ tht whe ctig o rbitrry d wvefuctio the result is 3, or, or I, etc. For give A ~, we will defie its 47

48 correspodet doit opertor o wvefuctio ; tht is is, A ~. Through the defiitio we will fid out wht does A ~ do if if ~ A ~ = 3 the we eed to fid A =?; or A ~ d = etc. the we eed to fid ~ A =? I dditio, we would like to kow lso how to express the ctio of opertor oto wvefuctios i the lguge of brs d kets. For opertor ctig o ket, wht does A ~ χ me? For opertor ctig o br, wht does A ~ me? First, by defiitio, A ~ χ A ~ χ A ~ χ mes the opertor A ~ ctig o the ket χ. Let be give br, d A ~ give opertor. For ech rbitrry ket χ oe c ssocited complex umber ( A ~ χ ) ( A ~ ~ χ ) Give d A Notice tht through this ssocitio, the br is ssocited with ew br u, which stisfies, u ( A ~ χ ) (i) Let s defie the opertor A ~ such tht, A ~ u (ii) From (i) d (ii) ~ A ~ A (88) 48

49 A ~ is clled the doit opertor of the lier opertor A ~. Exercise: Show tht the doit opertor A ~ is lier. Exmple: Show tht Let s pply (88), ( (B ~ ) = B ~ ~ ~ A A, to the opertor A ~ = ~ B ) ~ B B ~ ~ [ B *] * ~ [ B ] * ~ [ B ] * B ~ Hece, (B ~ ) = B ~ (iii) Notice: Accordig to (iii) the defiitio (88) c lso be stted s, 49

50 ~ A ~ A (iv) Exercise. Give the opertor ~ D ( D ~, ) D ~ = = d, wht is the opertor D ~? d x * x) [ d ψ * x) x d x Itegrtig by prts = - d φ x) x = d x d ]x d x [ - D ~ x) ]* x This implies, = ( - D ~, ) D ~ = - D ~ (v) Expressio (iv) bove, ~ ~ A A, is very ppelig to be re-expressed s A ~ ( A ~ ) A ~ A ~ (vi) ~ which prompts the followig iterprettio, A ~ A ~ = A ~ A ~ A ~ A ~ A ~ ssigs to the br the br A ~ 50

51 Similrly, ~ A ~ A ~ = A ~ ~ ( A ) ~ A A ~ = A ~ From the expressio A ~ = A ~, we obti, ( A ~ ) = A ~ Usig the defiitio (88) = A ~ = ( A ~ ) Tht is, A ~ = ( A ~ ) = ( A ~ ) = A ~ (vii) We hve rrived to the result tht it is uecessry to put the prethesis. We emphsize lso the results show bove i the shded blocks: A ~ = A ~ A ~ = A ~ (viii) A ~ = A ~ Mtrix Represettio of the doit opertor I the expressio (88) bove, if we tke s the bse-stte, d s the bse stte m, we obti, (, ~ m ) = ( m, ~ ) * Hece their mtrix represettio re relted through, [ ~ m = [ ~ m * (89) 5

52 (Notice the order of the idexes is reversed) ~ is lso clled the Hermiti doit opertor of ~. Properties: ~ ~ ( A B) = B ~ A ~ (90) Exmple: Wht is the Hermiti cougte opertor of the positio opertor X ~? ( X ~, ) (, X ~ ) = * x) [ X ~ ]x = * x) x x = x * x) x = [x x) ] * x = [ X ~ x) ] * x ( X ~, ) = ( X ~ ) Sice this expressio is vlid for y rbitrry sttes d, the X ~ = X ~ Tht is, the Hermiti cougte of the positio opertor is itself. (9) Exmple: Give the opertor ~ D D ~ =? ( D ~, ) D ~ = = d, wht is the Hermiti cougte opertor? d x * x) [ d ψ * x) x d x Itegrtig by prts = - = ( - D ~, ) d φ x) x = d x d ]x d x [ - D ~ x) ]* x 5

53 Tht is, D ~ = - D ~ (9) 0.5.C Hermiti or self-doit opertors My importt opertors of qutum mechics hve the specil property tht whe you tke the Hermiti doit you get bck the sme opertor. ~ = ~. (93) Such opertors re clled the self-doit or Hermiti opertors. Exmple: The positio opertor X ~ is self doit opertor becuse the exmple i the previous sectio (see expressio 9). Exmple: Let s see if the lier mometum opertor P ~ is self doit ( P ~, ) (,P ~ ) = * x) [ i = i * x) d ]x d x d ψ x d x Itegrtig by prts = - i d φ x) x d x X ~ = X ~, s show i Tht is, = [ i d φ d x = ( P ~, ) x) ]* x P ~ = P ~ (94) Properties of Hermiti (or self-doit) opertors Opertors ssocited to me vlues re Hermiti (or self-doit) I sectio 0.4 bove we defied qutum mechics opertors ssocited to clssicl observble qutity. The defiitio ivolved the clcultio of me vlues of observbles. From the fct tht me vlues re rel, we c drw some coclusios cocerig the properties of those opertors. f v = F ~ 53

Chapter 04.05 System of Equations

Chapter 04.05 System of Equations hpter 04.05 System of Equtios After redig th chpter, you should be ble to:. setup simulteous lier equtios i mtrix form d vice-vers,. uderstd the cocept of the iverse of mtrix, 3. kow the differece betwee

More information

Repeated multiplication is represented using exponential notation, for example:

Repeated multiplication is represented using exponential notation, for example: Appedix A: The Lws of Expoets Expoets re short-hd ottio used to represet my fctors multiplied together All of the rules for mipultig expoets my be deduced from the lws of multiplictio d divisio tht you

More information

MATHEMATICS FOR ENGINEERING BASIC ALGEBRA

MATHEMATICS FOR ENGINEERING BASIC ALGEBRA MATHEMATICS FOR ENGINEERING BASIC ALGEBRA TUTORIAL - INDICES, LOGARITHMS AND FUNCTION This is the oe of series of bsic tutorils i mthemtics imed t begiers or yoe wtig to refresh themselves o fudmetls.

More information

Summation Notation The sum of the first n terms of a sequence is represented by the summation notation i the index of summation

Summation Notation The sum of the first n terms of a sequence is represented by the summation notation i the index of summation Lesso 0.: Sequeces d Summtio Nottio Def. of Sequece A ifiite sequece is fuctio whose domi is the set of positive rel itegers (turl umers). The fuctio vlues or terms of the sequece re represeted y, 2, 3,...,....

More information

Application: Volume. 6.1 Overture. Cylinders

Application: Volume. 6.1 Overture. Cylinders Applictio: Volume 61 Overture I this chpter we preset other pplictio of the defiite itegrl, this time to fid volumes of certi solids As importt s this prticulr pplictio is, more importt is to recogize

More information

MATHEMATICS SYLLABUS SECONDARY 7th YEAR

MATHEMATICS SYLLABUS SECONDARY 7th YEAR Europe Schools Office of the Secretry-Geerl Pedgogicl developmet Uit Ref.: 2011-01-D-41-e-2 Orig.: DE MATHEMATICS SYLLABUS SECONDARY 7th YEAR Stdrd level 5 period/week course Approved y the Joit Techig

More information

A. Description: A simple queueing system is shown in Fig. 16-1. Customers arrive randomly at an average rate of

A. Description: A simple queueing system is shown in Fig. 16-1. Customers arrive randomly at an average rate of Queueig Theory INTRODUCTION Queueig theory dels with the study of queues (witig lies). Queues boud i rcticl situtios. The erliest use of queueig theory ws i the desig of telehoe system. Alictios of queueig

More information

n Using the formula we get a confidence interval of 80±1.64

n Using the formula we get a confidence interval of 80±1.64 9.52 The professor of sttistics oticed tht the rks i his course re orlly distributed. He hs lso oticed tht his orig clss verge is 73% with stdrd devitio of 12% o their fil exs. His fteroo clsses verge

More information

Gray level image enhancement using the Bernstein polynomials

Gray level image enhancement using the Bernstein polynomials Buletiul Ştiiţiic l Uiersităţii "Politehic" di Timişor Seri ELECTRONICĂ şi TELECOMUNICAŢII TRANSACTIONS o ELECTRONICS d COMMUNICATIONS Tom 47(6), Fscicol -, 00 Gry leel imge ehcemet usig the Berstei polyomils

More information

PREMIUMS CALCULATION FOR LIFE INSURANCE

PREMIUMS CALCULATION FOR LIFE INSURANCE ls of the Uiversity of etroşi, Ecoomics, 2(3), 202, 97-204 97 REIUS CLCULTIO FOR LIFE ISURCE RE, RI GÎRBCI * BSTRCT: The pper presets the techiques d the formuls used o itertiol prctice for estblishig

More information

INVESTIGATION OF PARAMETERS OF ACCUMULATOR TRANSMISSION OF SELF- MOVING MACHINE

INVESTIGATION OF PARAMETERS OF ACCUMULATOR TRANSMISSION OF SELF- MOVING MACHINE ENGINEEING FO UL DEVELOENT Jelgv, 28.-29.05.2009. INVESTIGTION OF ETES OF CCUULTO TNSISSION OF SELF- OVING CHINE leksdrs Kirk Lithui Uiversity of griculture, Kus leksdrs.kirk@lzuu.lt.lt bstrct. Uder the

More information

Present and future value formulae for uneven cash flow Based on performance of a Business

Present and future value formulae for uneven cash flow Based on performance of a Business Advces i Mgemet & Applied Ecoomics, vol., o., 20, 93-09 ISSN: 792-7544 (prit versio), 792-7552 (olie) Itertiol Scietific Press, 20 Preset d future vlue formule for ueve csh flow Bsed o performce of Busiess

More information

Infinite Sequences and Series

Infinite Sequences and Series CHAPTER 4 Ifiite Sequeces ad Series 4.1. Sequeces A sequece is a ifiite ordered list of umbers, for example the sequece of odd positive itegers: 1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29...

More information

Discontinuous Simulation Techniques for Worm Drive Mechanical Systems Dynamics

Discontinuous Simulation Techniques for Worm Drive Mechanical Systems Dynamics Discotiuous Simultio Techiques for Worm Drive Mechicl Systems Dymics Rostyslv Stolyrchuk Stte Scietific d Reserch Istitute of Iformtio Ifrstructure Ntiol Acdemy of Scieces of Ukrie PO Box 5446, Lviv-3,

More information

LINEAR TRANSFORMATIONS AND THEIR REPRESENTING MATRICES

LINEAR TRANSFORMATIONS AND THEIR REPRESENTING MATRICES LINEAR TRANSFORMATIONS AND THEIR REPRESENTING MATRICES DAVID WEBB CONTENTS Liner trnsformtions 2 The representing mtrix of liner trnsformtion 3 3 An ppliction: reflections in the plne 6 4 The lgebr of

More information

Example 2 Find the square root of 0. The only square root of 0 is 0 (since 0 is not positive or negative, so those choices don t exist here).

Example 2 Find the square root of 0. The only square root of 0 is 0 (since 0 is not positive or negative, so those choices don t exist here). BEGINNING ALGEBRA Roots ad Radicals (revised summer, 00 Olso) Packet to Supplemet the Curret Textbook - Part Review of Square Roots & Irratioals (This portio ca be ay time before Part ad should mostly

More information

5.2. LINE INTEGRALS 265. Let us quickly review the kind of integrals we have studied so far before we introduce a new one.

5.2. LINE INTEGRALS 265. Let us quickly review the kind of integrals we have studied so far before we introduce a new one. 5.2. LINE INTEGRALS 265 5.2 Line Integrls 5.2.1 Introduction Let us quickly review the kind of integrls we hve studied so fr before we introduce new one. 1. Definite integrl. Given continuous rel-vlued

More information

THE REGRESSION MODEL IN MATRIX FORM. For simple linear regression, meaning one predictor, the model is. for i = 1, 2, 3,, n

THE REGRESSION MODEL IN MATRIX FORM. For simple linear regression, meaning one predictor, the model is. for i = 1, 2, 3,, n We will cosider the liear regressio model i matrix form. For simple liear regressio, meaig oe predictor, the model is i = + x i + ε i for i =,,,, This model icludes the assumptio that the ε i s are a sample

More information

Graphs on Logarithmic and Semilogarithmic Paper

Graphs on Logarithmic and Semilogarithmic Paper 0CH_PHClter_TMSETE_ 3//00 :3 PM Pge Grphs on Logrithmic nd Semilogrithmic Pper OBJECTIVES When ou hve completed this chpter, ou should be ble to: Mke grphs on logrithmic nd semilogrithmic pper. Grph empiricl

More information

Helicopter Theme and Variations

Helicopter Theme and Variations Helicopter Theme nd Vritions Or, Some Experimentl Designs Employing Pper Helicopters Some possible explntory vribles re: Who drops the helicopter The length of the rotor bldes The height from which the

More information

Groundwater Management Tools: Analytical Procedure and Case Studies. MAF Technical Paper No: 2003/06. Prepared for MAF Policy by Vince Bidwell

Groundwater Management Tools: Analytical Procedure and Case Studies. MAF Technical Paper No: 2003/06. Prepared for MAF Policy by Vince Bidwell Groudwter Mgemet Tools: Alyticl Procedure d Cse Studies MAF Techicl Pper No: 00/06 Prepred for MAF Policy by Vice Bidwell ISBN No: 0-78-0777-8 ISSN No: 7-66 October 00 Disclimer While every effort hs bee

More information

Here are a couple of warnings to my students who may be here to get a copy of what happened on a day that you missed.

Here are a couple of warnings to my students who may be here to get a copy of what happened on a day that you missed. This documet was writte ad copyrighted by Paul Dawkis. Use of this documet ad its olie versio is govered by the Terms ad Coditios of Use located at http://tutorial.math.lamar.edu/terms.asp. The olie versio

More information

Treatment Spring Late Summer Fall 0.10 5.56 3.85 0.61 6.97 3.01 1.91 3.01 2.13 2.99 5.33 2.50 1.06 3.53 6.10 Mean = 1.33 Mean = 4.88 Mean = 3.

Treatment Spring Late Summer Fall 0.10 5.56 3.85 0.61 6.97 3.01 1.91 3.01 2.13 2.99 5.33 2.50 1.06 3.53 6.10 Mean = 1.33 Mean = 4.88 Mean = 3. The nlysis of vrince (ANOVA) Although the t-test is one of the most commonly used sttisticl hypothesis tests, it hs limittions. The mjor limittion is tht the t-test cn be used to compre the mens of only

More information

Authorized licensed use limited to: University of Illinois. Downloaded on July 27,2010 at 06:52:39 UTC from IEEE Xplore. Restrictions apply.

Authorized licensed use limited to: University of Illinois. Downloaded on July 27,2010 at 06:52:39 UTC from IEEE Xplore. Restrictions apply. Uiversl Dt Compressio d Lier Predictio Meir Feder d Adrew C. Siger y Jury, 998 The reltioship betwee predictio d dt compressio c be exteded to uiversl predictio schemes d uiversl dt compressio. Recet work

More information

Lecture 3 Gaussian Probability Distribution

Lecture 3 Gaussian Probability Distribution Lecture 3 Gussin Probbility Distribution Introduction l Gussin probbility distribution is perhps the most used distribution in ll of science. u lso clled bell shped curve or norml distribution l Unlike

More information

SPECIAL PRODUCTS AND FACTORIZATION

SPECIAL PRODUCTS AND FACTORIZATION MODULE - Specil Products nd Fctoriztion 4 SPECIAL PRODUCTS AND FACTORIZATION In n erlier lesson you hve lernt multipliction of lgebric epressions, prticulrly polynomils. In the study of lgebr, we come

More information

CS103A Handout 23 Winter 2002 February 22, 2002 Solving Recurrence Relations

CS103A Handout 23 Winter 2002 February 22, 2002 Solving Recurrence Relations CS3A Hadout 3 Witer 00 February, 00 Solvig Recurrece Relatios Itroductio A wide variety of recurrece problems occur i models. Some of these recurrece relatios ca be solved usig iteratio or some other ad

More information

Soving Recurrence Relations

Soving Recurrence Relations Sovig Recurrece Relatios Part 1. Homogeeous liear 2d degree relatios with costat coefficiets. Cosider the recurrece relatio ( ) T () + at ( 1) + bt ( 2) = 0 This is called a homogeeous liear 2d degree

More information

In nite Sequences. Dr. Philippe B. Laval Kennesaw State University. October 9, 2008

In nite Sequences. Dr. Philippe B. Laval Kennesaw State University. October 9, 2008 I ite Sequeces Dr. Philippe B. Laval Keesaw State Uiversity October 9, 2008 Abstract This had out is a itroductio to i ite sequeces. mai de itios ad presets some elemetary results. It gives the I ite Sequeces

More information

Reasoning to Solve Equations and Inequalities

Reasoning to Solve Equations and Inequalities Lesson4 Resoning to Solve Equtions nd Inequlities In erlier work in this unit, you modeled situtions with severl vriles nd equtions. For exmple, suppose you were given usiness plns for concert showing

More information

Chapter 13 Volumetric analysis (acid base titrations)

Chapter 13 Volumetric analysis (acid base titrations) Chpter 1 Volumetric lysis (cid se titrtios) Ope the tp d ru out some of the liquid util the tp coectio is full of cid d o ir remis (ir ules would led to iccurte result s they will proly dislodge durig

More information

Mathematics. Vectors. hsn.uk.net. Higher. Contents. Vectors 128 HSN23100

Mathematics. Vectors. hsn.uk.net. Higher. Contents. Vectors 128 HSN23100 hsn.uk.net Higher Mthemtics UNIT 3 OUTCOME 1 Vectors Contents Vectors 18 1 Vectors nd Sclrs 18 Components 18 3 Mgnitude 130 4 Equl Vectors 131 5 Addition nd Subtrction of Vectors 13 6 Multipliction by

More information

Polynomial Functions. Polynomial functions in one variable can be written in expanded form as ( )

Polynomial Functions. Polynomial functions in one variable can be written in expanded form as ( ) Polynomil Functions Polynomil functions in one vrible cn be written in expnded form s n n 1 n 2 2 f x = x + x + x + + x + x+ n n 1 n 2 2 1 0 Exmples of polynomils in expnded form re nd 3 8 7 4 = 5 4 +

More information

Physics 43 Homework Set 9 Chapter 40 Key

Physics 43 Homework Set 9 Chapter 40 Key Physics 43 Homework Set 9 Chpter 4 Key. The wve function for n electron tht is confined to x nm is. Find the normliztion constnt. b. Wht is the probbility of finding the electron in. nm-wide region t x

More information

Section 11.3: The Integral Test

Section 11.3: The Integral Test Sectio.3: The Itegral Test Most of the series we have looked at have either diverged or have coverged ad we have bee able to fid what they coverge to. I geeral however, the problem is much more difficult

More information

DEPARTMENT OF ACTUARIAL STUDIES RESEARCH PAPER SERIES

DEPARTMENT OF ACTUARIAL STUDIES RESEARCH PAPER SERIES DEPARTMENT OF ACTUARIAL STUDIES RESEARCH PAPER SERIES The ulti-bioil odel d pplictios by Ti Kyg Reserch Pper No. 005/03 July 005 Divisio of Ecooic d Ficil Studies Mcqurie Uiversity Sydey NSW 09 Austrli

More information

Department of Computer Science, University of Otago

Department of Computer Science, University of Otago Departmet of Computer Sciece, Uiversity of Otago Techical Report OUCS-2006-09 Permutatios Cotaiig May Patters Authors: M.H. Albert Departmet of Computer Sciece, Uiversity of Otago Micah Colema, Rya Fly

More information

I. Chi-squared Distributions

I. Chi-squared Distributions 1 M 358K Supplemet to Chapter 23: CHI-SQUARED DISTRIBUTIONS, T-DISTRIBUTIONS, AND DEGREES OF FREEDOM To uderstad t-distributios, we first eed to look at aother family of distributios, the chi-squared distributios.

More information

Factoring Polynomials

Factoring Polynomials Fctoring Polynomils Some definitions (not necessrily ll for secondry school mthemtics): A polynomil is the sum of one or more terms, in which ech term consists of product of constnt nd one or more vribles

More information

Lecture 4: Cauchy sequences, Bolzano-Weierstrass, and the Squeeze theorem

Lecture 4: Cauchy sequences, Bolzano-Weierstrass, and the Squeeze theorem Lecture 4: Cauchy sequeces, Bolzao-Weierstrass, ad the Squeeze theorem The purpose of this lecture is more modest tha the previous oes. It is to state certai coditios uder which we are guarateed that limits

More information

We will begin this chapter with a quick refresher of what an exponent is.

We will begin this chapter with a quick refresher of what an exponent is. .1 Exoets We will egi this chter with quick refresher of wht exoet is. Recll: So, exoet is how we rereset reeted ultilictio. We wt to tke closer look t the exoet. We will egi with wht the roerties re for

More information

Transformer Maintenance Policies Selection Based on an Improved Fuzzy Analytic Hierarchy Process

Transformer Maintenance Policies Selection Based on an Improved Fuzzy Analytic Hierarchy Process JOURNAL OF COMPUTERS, VOL. 8, NO. 5, MAY 203 343 Trsformer Mitece Policies Selectio Bsed o Improved Fuzzy Alytic Hierrchy Process Hogxi Xie School of Computer sciece d Techology Chi Uiversity of Miig &

More information

15.6. The mean value and the root-mean-square value of a function. Introduction. Prerequisites. Learning Outcomes. Learning Style

15.6. The mean value and the root-mean-square value of a function. Introduction. Prerequisites. Learning Outcomes. Learning Style The men vlue nd the root-men-squre vlue of function 5.6 Introduction Currents nd voltges often vry with time nd engineers my wish to know the verge vlue of such current or voltge over some prticulr time

More information

m n Use technology to discover the rules for forms such as a a, various integer values of m and n and a fixed integer value a.

m n Use technology to discover the rules for forms such as a a, various integer values of m and n and a fixed integer value a. TIth.co Alger Expoet Rules ID: 988 Tie required 25 iutes Activity Overview This ctivity llows studets to work idepedetly to discover rules for workig with expoets, such s Multiplictio d Divisio of Like

More information

PROF. BOYAN KOSTADINOV NEW YORK CITY COLLEGE OF TECHNOLOGY, CUNY

PROF. BOYAN KOSTADINOV NEW YORK CITY COLLEGE OF TECHNOLOGY, CUNY MAT 0630 INTERNET RESOURCES, REVIEW OF CONCEPTS AND COMMON MISTAKES PROF. BOYAN KOSTADINOV NEW YORK CITY COLLEGE OF TECHNOLOGY, CUNY Contents 1. ACT Compss Prctice Tests 1 2. Common Mistkes 2 3. Distributive

More information

Your organization has a Class B IP address of 166.144.0.0 Before you implement subnetting, the Network ID and Host ID are divided as follows:

Your organization has a Class B IP address of 166.144.0.0 Before you implement subnetting, the Network ID and Host ID are divided as follows: Subettig Subettig is used to subdivide a sigle class of etwork i to multiple smaller etworks. Example: Your orgaizatio has a Class B IP address of 166.144.0.0 Before you implemet subettig, the Network

More information

Chapter 5: Inner Product Spaces

Chapter 5: Inner Product Spaces Chapter 5: Ier Product Spaces Chapter 5: Ier Product Spaces SECION A Itroductio to Ier Product Spaces By the ed of this sectio you will be able to uderstad what is meat by a ier product space give examples

More information

Lesson 17 Pearson s Correlation Coefficient

Lesson 17 Pearson s Correlation Coefficient Outlie Measures of Relatioships Pearso s Correlatio Coefficiet (r) -types of data -scatter plots -measure of directio -measure of stregth Computatio -covariatio of X ad Y -uique variatio i X ad Y -measurig

More information

Properties of MLE: consistency, asymptotic normality. Fisher information.

Properties of MLE: consistency, asymptotic normality. Fisher information. Lecture 3 Properties of MLE: cosistecy, asymptotic ormality. Fisher iformatio. I this sectio we will try to uderstad why MLEs are good. Let us recall two facts from probability that we be used ofte throughout

More information

Lecture 5. Inner Product

Lecture 5. Inner Product Lecture 5 Inner Product Let us strt with the following problem. Given point P R nd line L R, how cn we find the point on the line closest to P? Answer: Drw line segment from P meeting the line in right

More information

.04. This means $1000 is multiplied by 1.02 five times, once for each of the remaining sixmonth

.04. This means $1000 is multiplied by 1.02 five times, once for each of the remaining sixmonth Questio 1: What is a ordiary auity? Let s look at a ordiary auity that is certai ad simple. By this, we mea a auity over a fixed term whose paymet period matches the iterest coversio period. Additioally,

More information

Appendix D: Completing the Square and the Quadratic Formula. In Appendix A, two special cases of expanding brackets were considered:

Appendix D: Completing the Square and the Quadratic Formula. In Appendix A, two special cases of expanding brackets were considered: Appendi D: Completing the Squre nd the Qudrtic Formul Fctoring qudrtic epressions such s: + 6 + 8 ws one of the topics introduced in Appendi C. Fctoring qudrtic epressions is useful skill tht cn help you

More information

MANUFACTURER-RETAILER CONTRACTING UNDER AN UNKNOWN DEMAND DISTRIBUTION

MANUFACTURER-RETAILER CONTRACTING UNDER AN UNKNOWN DEMAND DISTRIBUTION MANUFACTURER-RETAILER CONTRACTING UNDER AN UNKNOWN DEMAND DISTRIBUTION Mrti A. Lriviere Fuqu School of Busiess Duke Uiversity Ev L. Porteus Grdute School of Busiess Stford Uiversity Drft December, 995

More information

Building Blocks Problem Related to Harmonic Series

Building Blocks Problem Related to Harmonic Series TMME, vol3, o, p.76 Buildig Blocks Problem Related to Harmoic Series Yutaka Nishiyama Osaka Uiversity of Ecoomics, Japa Abstract: I this discussio I give a eplaatio of the divergece ad covergece of ifiite

More information

and thus, they are similar. If k = 3 then the Jordan form of both matrices is

and thus, they are similar. If k = 3 then the Jordan form of both matrices is Homework ssignment 11 Section 7. pp. 249-25 Exercise 1. Let N 1 nd N 2 be nilpotent mtrices over the field F. Prove tht N 1 nd N 2 re similr if nd only if they hve the sme miniml polynomil. Solution: If

More information

MATHEMATICAL ANALYSIS

MATHEMATICAL ANALYSIS Mri Predoi Trdfir Băl MATHEMATICAL ANALYSIS VOL II INTEGRAL CALCULUS Criov, 5 CONTENTS VOL II INTEGRAL CALCULUS Chpter V EXTENING THE EFINITE INTEGRAL V efiite itegrls with prmeters Problems V 5 V Improper

More information

Example A rectangular box without lid is to be made from a square cardboard of sides 18 cm by cutting equal squares from each corner and then folding

Example A rectangular box without lid is to be made from a square cardboard of sides 18 cm by cutting equal squares from each corner and then folding 1 Exmple A rectngulr box without lid is to be mde from squre crdbord of sides 18 cm by cutting equl squres from ech corner nd then folding up the sides. 1 Exmple A rectngulr box without lid is to be mde

More information

MATHEMATICAL INDUCTION

MATHEMATICAL INDUCTION MATHEMATICAL INDUCTION. Itroductio Mthemtics distiguishes itself from the other scieces i tht it is built upo set of xioms d defiitios, o which ll subsequet theorems rely. All theorems c be derived, or

More information

Operations with Polynomials

Operations with Polynomials 38 Chpter P Prerequisites P.4 Opertions with Polynomils Wht you should lern: Write polynomils in stndrd form nd identify the leding coefficients nd degrees of polynomils Add nd subtrct polynomils Multiply

More information

0.7 0.6 0.2 0 0 96 96.5 97 97.5 98 98.5 99 99.5 100 100.5 96.5 97 97.5 98 98.5 99 99.5 100 100.5

0.7 0.6 0.2 0 0 96 96.5 97 97.5 98 98.5 99 99.5 100 100.5 96.5 97 97.5 98 98.5 99 99.5 100 100.5 Sectio 13 Kolmogorov-Smirov test. Suppose that we have a i.i.d. sample X 1,..., X with some ukow distributio P ad we would like to test the hypothesis that P is equal to a particular distributio P 0, i.e.

More information

5 Boolean Decision Trees (February 11)

5 Boolean Decision Trees (February 11) 5 Boolea Decisio Trees (February 11) 5.1 Graph Coectivity Suppose we are give a udirected graph G, represeted as a boolea adjacecy matrix = (a ij ), where a ij = 1 if ad oly if vertices i ad j are coected

More information

EQUATIONS OF LINES AND PLANES

EQUATIONS OF LINES AND PLANES EQUATIONS OF LINES AND PLANES MATH 195, SECTION 59 (VIPUL NAIK) Corresponding mteril in the ook: Section 12.5. Wht students should definitely get: Prmetric eqution of line given in point-direction nd twopoint

More information

Modified Line Search Method for Global Optimization

Modified Line Search Method for Global Optimization Modified Lie Search Method for Global Optimizatio Cria Grosa ad Ajith Abraham Ceter of Excellece for Quatifiable Quality of Service Norwegia Uiversity of Sciece ad Techology Trodheim, Norway {cria, ajith}@q2s.tu.o

More information

4.11 Inner Product Spaces

4.11 Inner Product Spaces 314 CHAPTER 4 Vector Spces 9. A mtrix of the form 0 0 b c 0 d 0 0 e 0 f g 0 h 0 cnnot be invertible. 10. A mtrix of the form bc d e f ghi such tht e bd = 0 cnnot be invertible. 4.11 Inner Product Spces

More information

Integration by Substitution

Integration by Substitution Integrtion by Substitution Dr. Philippe B. Lvl Kennesw Stte University August, 8 Abstrct This hndout contins mteril on very importnt integrtion method clled integrtion by substitution. Substitution is

More information

The Velocity Factor of an Insulated Two-Wire Transmission Line

The Velocity Factor of an Insulated Two-Wire Transmission Line The Velocity Fctor of n Insulted Two-Wire Trnsmission Line Problem Kirk T. McDonld Joseph Henry Lbortories, Princeton University, Princeton, NJ 08544 Mrch 7, 008 Estimte the velocity fctor F = v/c nd the

More information

MODULE 3. 0, y = 0 for all y

MODULE 3. 0, y = 0 for all y Topics: Inner products MOULE 3 The inner product of two vectors: The inner product of two vectors x, y V, denoted by x, y is (in generl) complex vlued function which hs the following four properties: i)

More information

Lecture 4: Cheeger s Inequality

Lecture 4: Cheeger s Inequality Spectral Graph Theory ad Applicatios WS 0/0 Lecture 4: Cheeger s Iequality Lecturer: Thomas Sauerwald & He Su Statemet of Cheeger s Iequality I this lecture we assume for simplicity that G is a d-regular

More information

Use Geometry Expressions to create a more complex locus of points. Find evidence for equivalence using Geometry Expressions.

Use Geometry Expressions to create a more complex locus of points. Find evidence for equivalence using Geometry Expressions. Lerning Objectives Loci nd Conics Lesson 3: The Ellipse Level: Preclculus Time required: 120 minutes In this lesson, students will generlize their knowledge of the circle to the ellipse. The prmetric nd

More information

Vladimir N. Burkov, Dmitri A. Novikov MODELS AND METHODS OF MULTIPROJECTS MANAGEMENT

Vladimir N. Burkov, Dmitri A. Novikov MODELS AND METHODS OF MULTIPROJECTS MANAGEMENT Keywords: project maagemet, resource allocatio, etwork plaig Vladimir N Burkov, Dmitri A Novikov MODELS AND METHODS OF MULTIPROJECTS MANAGEMENT The paper deals with the problems of resource allocatio betwee

More information

SECTION 1.5 : SUMMATION NOTATION + WORK WITH SEQUENCES

SECTION 1.5 : SUMMATION NOTATION + WORK WITH SEQUENCES SECTION 1.5 : SUMMATION NOTATION + WORK WITH SEQUENCES Read Sectio 1.5 (pages 5 9) Overview I Sectio 1.5 we lear to work with summatio otatio ad formulas. We will also itroduce a brief overview of sequeces,

More information

Integration. 148 Chapter 7 Integration

Integration. 148 Chapter 7 Integration 48 Chpter 7 Integrtion 7 Integrtion t ech, by supposing tht during ech tenth of second the object is going t constnt speed Since the object initilly hs speed, we gin suppose it mintins this speed, but

More information

Asymptotic Growth of Functions

Asymptotic Growth of Functions CMPS Itroductio to Aalysis of Algorithms Fall 3 Asymptotic Growth of Fuctios We itroduce several types of asymptotic otatio which are used to compare the performace ad efficiecy of algorithms As we ll

More information

THE ARITHMETIC OF INTEGERS. - multiplication, exponentiation, division, addition, and subtraction

THE ARITHMETIC OF INTEGERS. - multiplication, exponentiation, division, addition, and subtraction THE ARITHMETIC OF INTEGERS - multiplicatio, expoetiatio, divisio, additio, ad subtractio What to do ad what ot to do. THE INTEGERS Recall that a iteger is oe of the whole umbers, which may be either positive,

More information

Non-life insurance mathematics. Nils F. Haavardsson, University of Oslo and DNB Skadeforsikring

Non-life insurance mathematics. Nils F. Haavardsson, University of Oslo and DNB Skadeforsikring No-life isurace mathematics Nils F. Haavardsso, Uiversity of Oslo ad DNB Skadeforsikrig Mai issues so far Why does isurace work? How is risk premium defied ad why is it importat? How ca claim frequecy

More information

Incremental calculation of weighted mean and variance

Incremental calculation of weighted mean and variance Icremetal calculatio of weighted mea ad variace Toy Fich faf@cam.ac.uk dot@dotat.at Uiversity of Cambridge Computig Service February 009 Abstract I these otes I eplai how to derive formulae for umerically

More information

, a Wishart distribution with n -1 degrees of freedom and scale matrix.

, a Wishart distribution with n -1 degrees of freedom and scale matrix. UMEÅ UNIVERSITET Matematisk-statistiska istitutioe Multivariat dataaalys D MSTD79 PA TENTAMEN 004-0-9 LÖSNINGSFÖRSLAG TILL TENTAMEN I MATEMATISK STATISTIK Multivariat dataaalys D, 5 poäg.. Assume that

More information

9 CONTINUOUS DISTRIBUTIONS

9 CONTINUOUS DISTRIBUTIONS 9 CONTINUOUS DISTIBUTIONS A rndom vrible whose vlue my fll nywhere in rnge of vlues is continuous rndom vrible nd will be ssocited with some continuous distribution. Continuous distributions re to discrete

More information

MATH 150 HOMEWORK 4 SOLUTIONS

MATH 150 HOMEWORK 4 SOLUTIONS MATH 150 HOMEWORK 4 SOLUTIONS Section 1.8 Show tht the product of two of the numbers 65 1000 8 2001 + 3 177, 79 1212 9 2399 + 2 2001, nd 24 4493 5 8192 + 7 1777 is nonnegtive. Is your proof constructive

More information

Notes on exponential generating functions and structures.

Notes on exponential generating functions and structures. Notes o expoetial geeratig fuctios ad structures. 1. The cocept of a structure. Cosider the followig coutig problems: (1) to fid for each the umber of partitios of a -elemet set, (2) to fid for each the

More information

PHY 140A: Solid State Physics. Solution to Homework #2

PHY 140A: Solid State Physics. Solution to Homework #2 PHY 140A: Solid Stte Physics Solution to Homework # TA: Xun Ji 1 October 14, 006 1 Emil: jixun@physics.ucl.edu Problem #1 Prove tht the reciprocl lttice for the reciprocl lttice is the originl lttice.

More information

9.3. The Scalar Product. Introduction. Prerequisites. Learning Outcomes

9.3. The Scalar Product. Introduction. Prerequisites. Learning Outcomes The Sclr Product 9.3 Introduction There re two kinds of multipliction involving vectors. The first is known s the sclr product or dot product. This is so-clled becuse when the sclr product of two vectors

More information

Math C067 Sampling Distributions

Math C067 Sampling Distributions Math C067 Samplig Distributios Sample Mea ad Sample Proportio Richard Beigel Some time betwee April 16, 2007 ad April 16, 2007 Examples of Samplig A pollster may try to estimate the proportio of voters

More information

CS103X: Discrete Structures Homework 4 Solutions

CS103X: Discrete Structures Homework 4 Solutions CS103X: Discrete Structures Homewor 4 Solutios Due February 22, 2008 Exercise 1 10 poits. Silico Valley questios: a How may possible six-figure salaries i whole dollar amouts are there that cotai at least

More information

MARTINGALES AND A BASIC APPLICATION

MARTINGALES AND A BASIC APPLICATION MARTINGALES AND A BASIC APPLICATION TURNER SMITH Abstract. This paper will develop the measure-theoretic approach to probability i order to preset the defiitio of martigales. From there we will apply this

More information

Systems Design Project: Indoor Location of Wireless Devices

Systems Design Project: Indoor Location of Wireless Devices Systems Desig Project: Idoor Locatio of Wireless Devices Prepared By: Bria Murphy Seior Systems Sciece ad Egieerig Washigto Uiversity i St. Louis Phoe: (805) 698-5295 Email: bcm1@cec.wustl.edu Supervised

More information

INFINITE SERIES KEITH CONRAD

INFINITE SERIES KEITH CONRAD INFINITE SERIES KEITH CONRAD. Itroductio The two basic cocepts of calculus, differetiatio ad itegratio, are defied i terms of limits (Newto quotiets ad Riema sums). I additio to these is a third fudametal

More information

Hypothesis testing. Null and alternative hypotheses

Hypothesis testing. Null and alternative hypotheses Hypothesis testig Aother importat use of samplig distributios is to test hypotheses about populatio parameters, e.g. mea, proportio, regressio coefficiets, etc. For example, it is possible to stipulate

More information

Determining the sample size

Determining the sample size Determiig the sample size Oe of the most commo questios ay statisticia gets asked is How large a sample size do I eed? Researchers are ofte surprised to fid out that the aswer depeds o a umber of factors

More information

A probabilistic proof of a binomial identity

A probabilistic proof of a binomial identity A probabilistic proof of a biomial idetity Joatho Peterso Abstract We give a elemetary probabilistic proof of a biomial idetity. The proof is obtaied by computig the probability of a certai evet i two

More information

Trigonometric Form of a Complex Number. The Complex Plane. axis. ( 2, 1) or 2 i FIGURE 6.44. The absolute value of the complex number z a bi is

Trigonometric Form of a Complex Number. The Complex Plane. axis. ( 2, 1) or 2 i FIGURE 6.44. The absolute value of the complex number z a bi is 0_0605.qxd /5/05 0:45 AM Page 470 470 Chapter 6 Additioal Topics i Trigoometry 6.5 Trigoometric Form of a Complex Number What you should lear Plot complex umbers i the complex plae ad fid absolute values

More information

Our aim is to show that under reasonable assumptions a given 2π-periodic function f can be represented as convergent series

Our aim is to show that under reasonable assumptions a given 2π-periodic function f can be represented as convergent series 8 Fourier Series Our aim is to show that uder reasoable assumptios a give -periodic fuctio f ca be represeted as coverget series f(x) = a + (a cos x + b si x). (8.) By defiitio, the covergece of the series

More information

Week 3 Conditional probabilities, Bayes formula, WEEK 3 page 1 Expected value of a random variable

Week 3 Conditional probabilities, Bayes formula, WEEK 3 page 1 Expected value of a random variable Week 3 Coditioal probabilities, Bayes formula, WEEK 3 page 1 Expected value of a radom variable We recall our discussio of 5 card poker hads. Example 13 : a) What is the probability of evet A that a 5

More information

1. MATHEMATICAL INDUCTION

1. MATHEMATICAL INDUCTION 1. MATHEMATICAL INDUCTION EXAMPLE 1: Prove that for ay iteger 1. Proof: 1 + 2 + 3 +... + ( + 1 2 (1.1 STEP 1: For 1 (1.1 is true, sice 1 1(1 + 1. 2 STEP 2: Suppose (1.1 is true for some k 1, that is 1

More information

Econ 4721 Money and Banking Problem Set 2 Answer Key

Econ 4721 Money and Banking Problem Set 2 Answer Key Econ 472 Money nd Bnking Problem Set 2 Answer Key Problem (35 points) Consider n overlpping genertions model in which consumers live for two periods. The number of people born in ech genertion grows in

More information

Basic Analysis of Autarky and Free Trade Models

Basic Analysis of Autarky and Free Trade Models Bsic Anlysis of Autrky nd Free Trde Models AUTARKY Autrky condition in prticulr commodity mrket refers to sitution in which country does not engge in ny trde in tht commodity with other countries. Consequently

More information

Convexity, Inequalities, and Norms

Convexity, Inequalities, and Norms Covexity, Iequalities, ad Norms Covex Fuctios You are probably familiar with the otio of cocavity of fuctios. Give a twicedifferetiable fuctio ϕ: R R, We say that ϕ is covex (or cocave up) if ϕ (x) 0 for

More information

5: Introduction to Estimation

5: Introduction to Estimation 5: Itroductio to Estimatio Cotets Acroyms ad symbols... 1 Statistical iferece... Estimatig µ with cofidece... 3 Samplig distributio of the mea... 3 Cofidece Iterval for μ whe σ is kow before had... 4 Sample

More information

Lecture 7: Stationary Perturbation Theory

Lecture 7: Stationary Perturbation Theory Lecture 7: Statioary Perturbatio Theory I most practical applicatios the time idepedet Schrödiger equatio Hψ = Eψ (1) caot be solved exactly ad oe has to resort to some scheme of fidig approximate solutios,

More information

Chapter 6: Variance, the law of large numbers and the Monte-Carlo method

Chapter 6: Variance, the law of large numbers and the Monte-Carlo method Chapter 6: Variace, the law of large umbers ad the Mote-Carlo method Expected value, variace, ad Chebyshev iequality. If X is a radom variable recall that the expected value of X, E[X] is the average value

More information