Size: px
Start display at page:

Download ""

Transcription

1 Chapter1 QuantumMechanicsinHilbert Spaces 1.1 Theessentialresultsinquantummechanicsaregiventhroughpurelyalgebraicrelations. TheAbstractHilbertSpace Specicresultscanbederived,e.g.,forvectors2`2andmatricesbeinglinearmaps; generalityandthenconsideraspecicrepresentationofthebasisvectorsofthehilbert however,thoseresultsareessentiallyindependentofthespecicrepresentationofthe operators.forthespecicresultsonlyalgebraicrelationsbetweenoperatorsandabstract propertiesofthehilbertspaceenter.thispointofviewallowstoconsiderproblemsinfull forwhichadditionandmultiplicationwithcomplexnumbersisdened I.TheabstractHilbertspace`2isgivenbyasetofelementsH=(j spaceandtheoperators(e.g.,matrices,dierentialoperators). j i+j'i=j +'i2h i;j'i;ji;), aj i=ja (1.1) togetherwithascalarproduct (1.2) Withrespectto(1.1)and(1.2),Hisalinearvectorspace,i.e., h'j i2c: (1.3) j i+j'i=j'i+j 2 i

2 (j i+j'i)+jxi=j j j i+j? i+j0i=j i=0 i+(j'i+jxi) i Thelasttworelationsstatetheexistenceofa0-vectorandtheexistenceofanegative vectorwithrespecttoj i. (1.4) a(bj 1j i)=(ab)j i=j i a(j (a+b)j i+j'i)=aj i=aj i+aj'i i+bj i i II.Withrespecttothescalarproduct,Hisaunitaryvectorspace (1.5) and h j i0 (1.6) h j i=0)j j'i i=0 (1.7) h'j h'ja 1+ 2i=h'j i=ah'j 1i+h'j i Becauseof(1.6)aNormcanbedened 2i (1.8) wherethespeciccharacteristicsofthenormdependonthevectorspace. k k=qh j i; (1.9) Onehas k'+ jh'j ijk'kk kk'k+k k and k (1.10) ha'j i=ah'j 3 i: (1.11)

3 Furtherpostulatesare: III.Hiscomplete. IV.Hisseparable. previousvectorj vectorj countable.letfj AHilbertspacebeingseparablemeansthatthereexistsasetofvectorsdenseinHand fj'1i;j'niginwhichtheoriginalsequenceiscontained.thesetoffj'nigisvia kifromthissequence,whichcanberepresentedaslinearcombinationofthe 1i;;j kigbeasequenceofvectorsinh.ifwetakeoutevery constructiondenseinh.onecanassumethatfj'nigisasetoforthogonalvectors(if not,usee.g.,gram-schmidtorthogonalization). k?1i,thenweobtainasetoflinearindependentvectors Iffj'nigisdenseinH,wecanexpandeveryarbitraryvectoraccordingtothisbasis h'mj'ni=mn: (1.12) andthus j i=1xn=1j'nian (1.13) fromwhichfollowthateachvectorcanberepresentedas h'mj i=1xn=1h'mj'nian=1xn=1mnan=am (1.14) Therelation(1.15)isonlyvalidiffj'nigisacompleteset,andwehavethecompleteness j i=1xn=1j'nih'nj i: Writingthecompletenessrelationas k k2=1xn=1jh'nj ij2: (1.16) allowstorepresenth j i=h j1j i=1xn=1h j'nih'nj i (1.17) 1=1Xn=1j'nih'nj; 4 (1.18)

4 scalarproduct whichiscalledthespectralrepresentationofthe1-operator.therepresentationofthe throughthecomponentsh'nj hxj iandh'njxiofthevectorsj i=1xn=1hxj'nih'nj i iandjxiwithrespectto (1.19) theorthonormalsystemfj'nigobtainedin(1.19)through"insertion"ofthe1-operator ofvectorsinorthonormalbasissystemsinaveryeconomicalway. asgivenin(1.18).thechosenbra-(h'j)andket-(j'i)notationallowstherepresentation 1.2 Arelation LinearOperatorsinH iscalledlinearoperatorinhifaj i=ja i=j 0i (1.20) assumethatda(therangeofa)isdenseinh. IngeneralAdoesnothavetobedenedonallvectors2H.Inthefollowingwewill A(aj i+bj'i)=aaj i+baj i: (1.21) AdjointoperatorAy: DAisdenseinH;Ayisuniquelydened. Ay,ingeneraldierentfromA,iscalledthetoAadjointoperatorif(1.22)isfullled.If h'jaj i=hay'j i If Hermitianoperators h'jaj i=ha'j 5 i (1.23)

5 Comparing(1.22)and(1.23)showsthatforahermitianoperatorAthereexistsalways thenaiscalledhermitian. anadjointoperatorwith atleastontherangedaofa.itcouldbethataydenedvia(1.22)existsonalarger rangedayda.ifthisisnotthecase,i.e.,day=da,thenaiscalledself-adjoint.it Ay=A (1.24) itisself-adjoint.sinceeigenvectors(oreigenfunctions)characterizequantummechanical shouldbementionedthatahermitianoperatoronlyhasacompletesetofeigenvectorsif systems,therequirementthatoperatorsareself-adjoint(notonlyhermitian),iftheyare supportedtocharacterizephysicalobservables,isquiterelevant. Foraself-adjointoperatorweobviouslyhave PropertiesofHermitianOperators:TheexpectationvalueofanobservableAinthe (Ay)y=A: (1.25) statej i(withk k=1)isgivenby Inorderfor(1.26)tobereal,onehastorequireAtobehermitian: hai =h h jaj j i i=h jaj i: (1.26) fromwhichfollowsthatforhermitianoperatorathequantityh h jaj i=ha j i=h ja i If jaj iisreal. thenj operatorsarereal. aiiscalledeigenvectortoawitheigenvaluea.theeigenvaluesofhermitian Aj ai=aj (1.27) orthogonal,i.e., Eigenvectorsj ai;j a0iofhermitianoperatorsatodierenteigenvaluesa6=a0are fora06=a. h a0j ai=0 (1.28) 6

6 Proof:From follows Aj ai=aj ai (1.29) and h a0jaj ai=ah a0j ai (1.30) andthus ha a0j ai=a0h a0j ai=ah a0j ai (1.31) Sinceaccordingtotheassumptiona06=a,i.e.,(a0?a)6=0,followsthath (a0?a)h a0j ai=0: a0j ai=0. (1.32) Self-adjointoperatorshaveaspecialroleinquantummechanics,sincetheyarerelatedto Isometricandunitaryoperators physicalobservables.formanytheoreticalconsiderationsoneneedsinadditionso-called isometricoperators,whicharedenedas Becauseof h'j i=h'j i: (1.33) theyobviouslyfulllh'jyj i=h'j i=h'j1j i; (1.34) forinnitedimensionalvectorspacesthisisingeneralnotthecase.ifanoperatoru Innitedimensionalvectorspaces,therelation(1.35)wouldimplyy=1.However, y=1: fullls itiscalledunitary.anotherdenitionforanoperatortobeunitarycanbewrittenas UyU=UUy=1 (1.36) Uy=U?1: 7 (1.37)

7 1.3 Ifonehas MatrixRepresentationofLinearOperators usestherepresentationofthe1-operator(1.18)andmultipliesfromtheleftwithh'mj, oneobtains Aj i=j 0i (1.38) Hereh'nj iarethefouriercomponentsoftheexpansion(1.15)ofj Xnh'mjAj'nih'nj i=h'mj 0i: iwithrespectto (1.39) acompleteorthonormalsystemfj'nig.ifonedenes then(1.39)canbewrittenash'mjaj'ni=amn XnAmnan=a0m (1.40) abstracthilbertspaceassignseachvectoracolumnvectoroffouriercoecients: whichistheformofalinearmaprepresentedbymatrices.introducingabasisinthe (1.41) j i?! 0 B@ h'1j. h'nj ica 1 0 B@ a a CA (1.42) andanoperatorcorrespondstothematrixrepresentationofalinearmap Hereallrulesderivedfromlinearalgebracanbeapplied. A?![h'mjAj'ni]=[Amn]: (1.43) Theeigenvectorsj'niofaself-adjointoperatorA 1.4 "A"-Representation Aj'ni=anj'ni 8 (1.44)

8 fordistincteigenvaluesandoingeneralnotformacompleteorthonormalsystem.ifthey doformacompleteorthonormalsystemfj vectorsforrepresentingotheroperators. Inthisparticularcaseonecanassigntoanarbitrarymap nig,thenonecanusethosevectorsasbasis therepresentation Xnh'mjBj'nih'nj Bj i=jb i=j 0i (1.45) i=h'mjb i=h'mj 0i: tation: Itiscalled"A"-Representation.Specically,theoperatorAisdiagonalinthisrepresen- (1.46) Thismeans:Alinearoperatorisdiagonalinitsownrepresentation.Ifoneusesthe eigenvectorsofthehamiltonianh,thenthisrepresentationiscalledh-orenergyrepresentation. Amn=h'mjAj'ni=mnan: (1.47) 1.5 Physicalobservablesareassignedtoself-adjointoperators,i.e.,onehasforpositionxand QuantumMechanicsinAbstractHilbertSpaces momentump x(t)?!x(t) p(t)?!p(t) whichobeythecommutationrelation (1.48) Allobservablesdependingonpandxcorrespondtoself-adjointoperators [P;X]=hi1: (1.49) e.g.,thehamiltonianoftheharmonicoscillatorisgivenas H=P2 A=A(P;X); (1.50) 2m+m2!2X2: 9 (1.51)

9 AquantummechanicalstateischaracterizedbyaHilbertspacevectorj tationvaluesofoperatorsinsuchastatearegivenas(providedk hai =h jaj i: k=1) i.theexpec- ThetimedependenceofanoperatorAisgiventhroughtheHamiltonianH(p;x)via (1.52) A=ih[H;A]: _ (1.53) Themeanvalue(expectationvalue)ofanobservableinagivenstatej 1.6 Root-Mean-SquareDeviation hai =h jaj i iisdenedas (withk themeanvalue(a?h k=1).theroot-meansquaredeviationfromthisexpectationvalueisgivenby jaj i2),i.e.,throughthenon-negativeexpression (1.54) (1.55)follows Itssquareroot(A) (A)2 iscalledroot-mean-squaredeviationorstandarddeviation.from =h j(a?h jaj i)2j i0: (1.55) andthus (A)2 =h ja2j i?2h jah jaj ij i+h jaj i2 (A)2 (1.56) Withthisdenition,oneprovesanessentialtheoreminquantummechanics =h ja2j i?h jaj i2: (1.57) zeroifj totheeigenvalue. Theroot-mean-squaredeviationofanobservableAinthestatej iiseigenvectorofa.theexpectationvalueinthisstatecorresponds iisexactly Letj aibeeigenvector,i.e., then Aj ai=aj ai; (1.58) h ajaj ai=a; 10 (1.59)

10 andthuswith(1.58):(a)2 Proofofinversedirection:Iftheroot-mean-squaredeviationofAiszeroforastatej a=h aj(a?a)2j ai=0: (1.60) then 0=(A)2 j(a?h jaj i)2j i i, =h(a?h =k(a?h jaj i) i) j(a?h k2 jaj i) i andthus (A?h jaj i)j i=0: (1.62) (1.61) Thismeansthatj valueofainthestatej Ifonepreparesanensembleofstatesexperimentallyinsuchawaythattheexpectation iiseigenvectorofawitheigenvalueh i. jaj i,i.e.,tothemean valueofastateisgivenwithzerodeviation(i.e.,sharp),whichmeansthatthisensemble BoundStates:Inquantummechanicsboundstateshavediscretevalues.Ifwedene Aj hasthesamevaluean,thentheensembleischaracterizedbytheeigenvectorj ni=anj ni. nivia boundstatej then,accordingtotheabovetheorem,onehas nivia Hj (H) ni=enj n=0; ni; (1.63) i.e.,boundstatesareobtainedbysolvingtheeigenvalueequationforthehamiltonianh. (1.64) LetAandBbehermitianoperators.WithSchwartz'inequalityfollows 1.7 UncertaintyPrinciple jh j(a?a)(b?b)j ij=jh(a?a) k(a?a) =A B kk(b?b) j(b?b) ik 11 (1.65)

11 HereAh jimh jaj j(a?a)(b?b)j i.theleft-handsideof(1.65)canbeestimatedfrombelowvia Onealsohas ijjh j(a?a)(b?b)j ij: (1.66) jimh j(a?a)(b?b)j j(a?a)(b?b)j ij j(a?a)(b?b)?(b?b)(a?a)j i?h(a?a)(b?b) ijj ij From(1.65),(1.66)and(1.67)followsthat =12jh jab?baj ij (1.67) Thus,ifAandBdonotcommute,theuncertaintyrelationgivesanestimateforthe 12jh root-mean-squaredeviationofaandb.thisisonlytrueifj j[a;b]j ij(a) (B): iisnotaneigenstateof (1.68) AorB.Ifj Becauseof(1.49)onehasforXandPoperators vanish,andtheequationwouldbemeaningless. iwouldbeaneigenstateofeitheroperator,thenbothsidesof(1.68)would h2=12h jhi1j ij= (P) 12jh (X) j[p;x]j ; ij i.e,independentof theproductofthedeviationsislimitedfrombelowas (1.69) AnimmediateconsequenceofthisrelationisthatneitherXnorPvanish.Thus, h2(p) accordingtothetheorem,noeigenvectorsexistforeitherpnorx. (X): (1.70) normalizeable.thismeanstheyarenotvectorsinahilbertspace. Remark:ItispossibletointroduceeigenvectorstoPandX;however,thosearenot 12

12 Chapter2 SymmetriesI 2.1 Accordingto(1.53)thetimedependenceofanoperatorAisgiventhroughtheHamiltonianH(P;X)via TheobservableAiscalledconstantofmotionofthesystem,if (i)aiscompatiblewithh,i.e. (2.1) ConstantsofMotion (2.3) (2.2) Condition(ii)statesthatAdoesnothaveanyexplicittimedependence.(Moreontime dependencelater.) Ingeneral,symmetriesorinvariancepropertiesleadtoconservationlaws.Therearetwo distinctkindsofsymmetries: onlyslightlyfromunity Letaninnitesimalunitarytransformationdependonarealparameter"andvary discretesymmetriesandcontinuoussymmetries. ^U"(^G)=1+i"^G; 13 (2.4)

13 where^giscalledthegeneratoroftheinnitesimaltransformation. ^U"isunitaryonlyif"isrealand^GHermitian ^U"^Uy"=(1+i"^G)(1?i"^G)y onlyif =1+i"(^G^Gy)+O("2) ^G=^Gy: (2.6) (2.5) Applyonstatevector TransformationoftheoperatorA: ^U"j i=(1+i"^g)j i=j i+i"^g)j i=j i+j i (2.7) ThenA0=Aonlyif[^G;A]=0. A0=^U"A^Uy"=(1+i"(^G)A((1+i"(^G)y 'A+i"[^G;A] (2.8) Finiteunitarytransformationscanbebuiltfrominnitesimaltransformationsby successiveapplication Theoperator^Uisunitaryifisrealand^GHermitian.Then ^U(^G)=lim N!1NYk=1(1+iN^G)=lim N!1(1+i^G)N=ei^G: (2.9) ForthetransformationofanoperatorAoneobtains ei^gy=e?i^g=ei^g?1: ei^gae?i^g=a+i[^g;a]+(i)2 (2.10) onlyif[^g;a]=0. 2![^G;[^G;A]]+ (2.11) Considerthefollowingexamples: 1.TimeTranslation 14

14 Set ^G1hH andapplythistoastate ^Ut=1+ihtH "=t 1+ihtHj (2.12) onehas wheretheexplicitformoftheschrodingerequation,hj (t)ij (t?t)i; (t)iwasused.thus (2.13) wherehisthegeneratoroftimetranslations. ^Utj (t)i=1+ihthj(t)i=j(t?t)i; (2.14) Considerforsimplicationone-dimensionalspacetranslationsinx-direction.Then 2.SpaceTranslation Applicationonastatevectoryields^U"=1+ih"^Px ^G1h^Px 1+ih"^Pxj (2.15) (x)i=j j (x)i+ih"^pxj (x+"i; (x)i wherethecoordinatespacerepresentation^px=?ih@ (2.16) X0=^U"(^Px)X^U?1 "(^Px)=1+ih"^PxX1?ih"^Px X+ih"[^Px;X] Fornitetranslationsonehas =X+": (2.17) ^Ua(P) (r)=eihap 15(r)= (r+a); (2.18)

15 transformationexp(i^g).thenthehamiltoniantransformsas whereexp(ihap)isthegeneratorofnitetranslations. Ingeneral,symmetriesandconservationlawsarecloselyrelated.Considertheunitary H0=ei^GHe?i^G=H+i[^G;H]+(i)2 doesnotexplicitydependonthetime,i.e.^gisaconstantofmotion.thatis Onlyif[^G;H]=0followsH0=H.Wehaveshownbefore,thatif[^G;H]=0,then^G 2![^G;[^G;H]]+ (2.19) (2.20) Therstexampleofadiscretesymmetryisinversion. 2.2 Inversion Denition:Inversionisdenedastransformation whichmeansforcartesiancoordinates ~x!~x0=?~x; (2.21) intoleft-handedonesandviceversa. Thegeometricalinterpretationisthatinversionturnsright-handedcoordinatesystems (x;y;z)!(x0;y0;z0)=(?x;?y;?z) (2.22) Studyingthesymmetrypropertiesofasystemunderinversionmeansconsideringthe behaviorofthehamiltonianunderthetransformation ConsiderthekineticenergyoperatorP2 2m=?h2 H(~X;~P2)!H(?~X0;~P2)H0(~X0;~P2): 2m,whichisgivenas (2.23) 2mr2=?h writesthehamiltonianas (2.24) H(~X;~P2)=P2 162m+V(~X) (2.25)

16 oneonlyneedstoconsiderthetransformationpropertiesofv(~x).anycentralpotential V(~X)V(jj~Xjj)isinvariantunderinversion.Apotentialoftheform where~aisanarbitraryvectorisinvariantunderinversionprovidedv1=v2.ifhis invariantunderinversion,i.e.h(~x;~p2)=h(?~x;~p2); V(~X)V1(j~x+~aj)+V2(j~x?~aj); (2.26) howdowavefunctionsh~xji(~x)behave? (2.27) Deneanoperator(parityoperator)Psuchthat Applying~x!?~x0gives P(~x)=0(~x)=(?~x): (~x)=(~x0)=0(~x0): (2.28) Combining(2.28)and(2.29)gives (~x)=0(~x0)=p(~x0)=p(?~x)=p2(~x): (2.30) (2.29) Since(2.30)holdsforanyji,theoperatorPhastofulll Thus LetbetheeigenvalueoftheoperatorP,thenP2musthavetheeigenvalue2=1. P2=1: (2.31) fromwhichfollows P(~x)=(?~x); P(~x)=(~x) (?~x)=(~x); (2.33) (2.32) >FromP2=1follows i.e.parityeigenstateswitheigenvalue+1(?1)areeven(odd)functionsof~x. Thespecicchoiceof(~x)H(~x) P(~x)=PH(~x;::) (~x)gives (~x)=h(?~x;::) P?1=P (?~x) (2.34) =PH(~x;::)P?1P 17(~x)=PH(~x;::)P?1 (?~x): (2.35)

17 Since (~x)isarbitrary,onehastheoperatorequation whichleadsto PH(~x;::)P?1=H(?~x;::); [P;H(~x;::)]=0 (2.37) (2.36) Thus,iftheHamiltonianisinvariantunderinversion,itcommuteswiththeparityoperator,andparityisaconstantofmotion. Considerboundstateswithdiscreteeigenvaluesasgivenin(1.64):IfHisinvariantunder inversiononeobtainsfromh(~x;::) H(~x;::) n(~x)=en n(?~x)=en n(~x)whenapplyingp ifh(~x;::)isinvariantunderinversion. Twodierentcasesarise: n(?~x): (2.38) Then constantfactor~: (a)theeigenvalueenisnon-degenerate. (~x)and(?~x)areessentiallythesamefunction,andcandieratmostbya Applying~x!?~xgives (~x)=~ (?~x)=~ (?~x)=~2 (~x): (~x): (2.39) theparityoperatorp,andthus Thus,~2=1and~1,whichshowsthat~isthepreviouslyintroducedeigenvalueof (2.40) oddfunctionsof~x,havingeithereven(=+1)orodd(=?1)parity. Thismeansthattheeigenfunctionstoanon-degenerateeigenvalueEareeitherevenor (?~x)=(~x): (2.41) (b)theeigenvalueenisdegenerate. If(2.38)togetherwith(1.64)holds,thenanylinearcombination isalsoeigenfunctionofh(~x;::)tothesameeigenvalueen.onecanusethisfreedomto chooselinearcombinationswhichareevenoroddparitystates,i.e. a (~x)+b(?~x) (2.42) (~x) (?~x) (2.43) 18

18 2.3 TheHamiltonianoftheclassicalharmonicoscillatorisaquadraticfunctionofposition Ladder-OperatorsandtheU(1)Symmetry andmomenta.inthesimplestcase(providesm=!1),itreads whichcanbedecomposedas Hclass=12(p2+x2); (2.44) where Hclass=12(x?ip)(X+ip)=aa; a:=1p2(x+ip): (2.46) (2.45) InthequantummechanicsonedenesacorrespondingoperatorA A:=1 DuetotheoperatorcharacterofA,thefactorizationoftheHamiltonianisslightlymore p2(x+ip): (2.47) complicated,andoneobtainsanadditiveterm12; Thesamefactorizationcanbeappliedtothedierentialequationfortheharmonicoscillator wherethedierentialoperatorcanbedecomposedas d2x(t) H=12(P2+Q2)=AyA+121: (2.48) d2 dt2 +!2x(t)=0; (2.49) dt2+!2= ddt?i!! ddt+i!!: Thusoneobtainssolutionsto(2.49)ifonesolvesoneofthefollowingrst-orderdierential (2.50) equations: ddt?i!!x(t)=0 or ddt+i!!x(t)=0: (2.51) 19

19 Thesolutionsofthoseare Finally,thereisaspecicsymmetrypropertyin(2.44)ifoneconsidersthatinthe(x;p) x(t)=x0ei!t and x(t)=x0e?i!t: (2.52) changeifonecarriesoutarotationinthephasespacegivenby phasespacehclassdescribesthe"length"ofavectorinthatspace.thus,hclassdoesnot x0=xcos+psin ThisrotationleavesHclassinvariant,i.e.,Hclass(x0;p0)=Hclass(x;p).Forthequantities p0=?xsin+pcos: (2.53) afrom(2.45),thecorrespondingsymmetrytransformationisgivenby Theanalogoussymmetrycarriesoverintoquantummechanics,sinceN=AyAisinvariant a0=eia and a0=e?ia: (2.54) under Accordingtothegeneralsymmetryprincipleinquantummechanics,thissymmetryoperationmustbegeneratedbyaunitaryoperator.Thus,aunitaryoperatorU()must exist,whichcarriedaintoa0,a0=u()yau(): U()canbeexpressedasexponentialofaHamiltonianoperator (2.56) A0=eiA and Ay=e?iAy: (2.55) andonehastriviallyfor=0 U()=eiX() (2.57) Importantisthatthetransformationintroducedin(2.56)formsagroup,i.e.,carrying U(0)=1 and X(0)=0: (2.58) outtransformationsofthiskindgivesanotheroneofthesamekind, Especially,theinverseelementexists: U()U()=U(+): (2.59) U(?)=U?1(): 20 (2.60)

20 Thegeometricinterpretationofthisgroupisobviousifoneconsidersthe(x;p)formofthe d=1;u(1). thecomplexformof(2.55),theunderlyinggroupistheunitarygroupwithdimension transformationinphasespace.theoperatoru()describesarotationinthe(x;p)plane andthegroupiscalledo(2)orthogonalgroupintwodimensions.ifoneconsiders Oneobtainsimportantinsightintothequantumtheoryifoneexplicitlyconstructsthe valuesoftheparameter,i.e.,so-calledinnitesimaltransformations. Becauseof(2.58),onecanexpand operatoru().thisprocedurefollowsthegeneralrulethatoneconsidersrstsmall Applyingtheseto(2.56)gives X()=Y+ and ()=1+iY+ (2.61) Thus A0=(1?iY)A(1+iY)A+i(AY?YA): (2.62) Thismeansthattheinnitesimaltransformationisdeterminedbythecommutator[A;Y]. Thus,Yiscalledtheinnitesimalgeneratorofthegroup(hereU(1)).Uptonowwe A0=A+i[A;Y]+::: (2.63) havenotusedanyspecicpropertiesofthegroupu(1).thiscomesintoplaywhenwe expand(2.55)inpowersofa0=eiaa+ia+::: Comparing(2.64)with(2.63)leadsto[A;Y]=A (2.64) whichisanequationfory,whichcanbesolvedbyusing (2.65) Thesolutionis(uptoanadditivec-number) [N;A]=?A: (2.66) Thus,thenumberoperatorNistheinnitesimalgeneratorofthegroupU(1). Y=N: (2.67) 21

21 Thus,onehasalinearapproximationX()N.Becauseof(2.59)X()depends linearlyon,sothatx()=?nisexact.therefore, isanexactrepresentation. U()=e?iN (2.68) Theresultsderivedfortheharmonicoscillatorhavenumerousapplicationsinmodern physics,sincetransformationsoftheform(2.56)andoperatorswiththepropertiesof charge,baryonnumber,strangeness,etc.inallcases,thequantitiesare"quantized," Nappearinmanyareasoftheoreticalphysics.Examplesareparticlenumber,electric understandingquantumnumbers. i.e.,havediscretevalues,whichare,ingeneral,integers.thus,wehaveaparadigmfor 22

Factoring. Factoring Polynomial Equations. Special Factoring Patterns. Factoring. Special Factoring Patterns. Special Factoring Patterns

Factoring. Factoring Polynomial Equations. Special Factoring Patterns. Factoring. Special Factoring Patterns. Special Factoring Patterns Factoring Factoring Polynomial Equations Ms. Laster Earlier, you learned to factor several types of quadratic expressions: General trinomial - 2x 2-5x-12 = (2x + 3)(x - 4) Perfect Square Trinomial - x

More information

TechnischeUniversitatChemnitz-Zwickau

TechnischeUniversitatChemnitz-Zwickau TechnischeUniversitatChemnitz-Zwickau DFG-Forschergruppe\SPC"FakultatfurMathematik DomainDecompositionand PreconditioningOperators MultilevelTechniquesfor SergejV.Nepomnyaschikh SiberianBranchofRussianAcademyofSciences

More information

Lecture 4: Partitioned Matrices and Determinants

Lecture 4: Partitioned Matrices and Determinants Lecture 4: Partitioned Matrices and Determinants 1 Elementary row operations Recall the elementary operations on the rows of a matrix, equivalent to premultiplying by an elementary matrix E: (1) multiplying

More information

Linear Threshold Units

Linear Threshold Units Linear Threshold Units w x hx (... w n x n w We assume that each feature x j and each weight w j is a real number (we will relax this later) We will study three different algorithms for learning linear

More information

Lecture 2 Matrix Operations

Lecture 2 Matrix Operations Lecture 2 Matrix Operations transpose, sum & difference, scalar multiplication matrix multiplication, matrix-vector product matrix inverse 2 1 Matrix transpose transpose of m n matrix A, denoted A T or

More information

1 Introduction to Matrices

1 Introduction to Matrices 1 Introduction to Matrices In this section, important definitions and results from matrix algebra that are useful in regression analysis are introduced. While all statements below regarding the columns

More information

Iterative Solvers for Linear Systems

Iterative Solvers for Linear Systems 9th SimLab Course on Parallel Numerical Simulation, 4.10 8.10.2010 Iterative Solvers for Linear Systems Bernhard Gatzhammer Chair of Scientific Computing in Computer Science Technische Universität München

More information

Derivative Approximation by Finite Differences

Derivative Approximation by Finite Differences Derivative Approximation by Finite Differences David Eberly Geometric Tools, LLC http://wwwgeometrictoolscom/ Copyright c 998-26 All Rights Reserved Created: May 3, 2 Last Modified: April 25, 25 Contents

More information

Solutions to TOPICS IN ALGEBRA I.N. HERSTEIN. Part II: Group Theory

Solutions to TOPICS IN ALGEBRA I.N. HERSTEIN. Part II: Group Theory Solutions to TOPICS IN ALGEBRA I.N. HERSTEIN Part II: Group Theory No rights reserved. Any part of this work can be reproduced or transmitted in any form or by any means. Version: 1.1 Release: Jan 2013

More information

1.5. Factorisation. Introduction. Prerequisites. Learning Outcomes. Learning Style

1.5. Factorisation. Introduction. Prerequisites. Learning Outcomes. Learning Style Factorisation 1.5 Introduction In Block 4 we showed the way in which brackets were removed from algebraic expressions. Factorisation, which can be considered as the reverse of this process, is dealt with

More information

AP Calculus BC Exam. The Calculus BC Exam. At a Glance. Section I. SECTION I: Multiple-Choice Questions. Instructions. About Guessing.

AP Calculus BC Exam. The Calculus BC Exam. At a Glance. Section I. SECTION I: Multiple-Choice Questions. Instructions. About Guessing. The Calculus BC Exam AP Calculus BC Exam SECTION I: Multiple-Choice Questions At a Glance Total Time 1 hour, 45 minutes Number of Questions 45 Percent of Total Grade 50% Writing Instrument Pencil required

More information

Solutions to Homework 6

Solutions to Homework 6 Solutions to Homework 6 Debasish Das EECS Department, Northwestern University [email protected] 1 Problem 5.24 We want to find light spanning trees with certain special properties. Given is one example

More information

On Cyclotomic Polynomials with1 Coefficients

On Cyclotomic Polynomials with1 Coefficients On Cyclotomic Polynomials with1 Coefficients Peter Borwein and Kwok-Kwong Stephen Choi CONTENTS 1. Introduction 2. Cyclotomic Polynomials with Odd Coefficients 3. Cyclotomic Littlewood Polynomials 4. Cyclotomic

More information

SF2940: Probability theory Lecture 8: Multivariate Normal Distribution

SF2940: Probability theory Lecture 8: Multivariate Normal Distribution SF2940: Probability theory Lecture 8: Multivariate Normal Distribution Timo Koski 24.09.2014 Timo Koski () Mathematisk statistik 24.09.2014 1 / 75 Learning outcomes Random vectors, mean vector, covariance

More information

Question 1a of 14 ( 2 Identifying the roots of a polynomial and their importance 91008 )

Question 1a of 14 ( 2 Identifying the roots of a polynomial and their importance 91008 ) Quiz: Factoring by Graphing Question 1a of 14 ( 2 Identifying the roots of a polynomial and their importance 91008 ) (x-3)(x-6), (x-6)(x-3), (1x-3)(1x-6), (1x-6)(1x-3), (x-3)*(x-6), (x-6)*(x-3), (1x- 3)*(1x-6),

More information

How To Pay For An Ambulance Ride

How To Pay For An Ambulance Ride Chapter 9Ambulance 9 9.1 Enrollment........................................................ 9-2 9.2 Emergency Ground Ambulance Transportation.............................. 9-2 9.2.1 Benefits, Limitations,

More information

SF2940: Probability theory Lecture 8: Multivariate Normal Distribution

SF2940: Probability theory Lecture 8: Multivariate Normal Distribution SF2940: Probability theory Lecture 8: Multivariate Normal Distribution Timo Koski 24.09.2015 Timo Koski Matematisk statistik 24.09.2015 1 / 1 Learning outcomes Random vectors, mean vector, covariance matrix,

More information

Stability criteria for large-scale time-delay systems: the LMI approach and the Genetic Algorithms

Stability criteria for large-scale time-delay systems: the LMI approach and the Genetic Algorithms Control and Cybernetics vol 35 (2006) No 2 Stability criteria for large-scale time-delay systems: the LMI approach and the Genetic Algorithms by Jenq-Der Chen Department of Electronic Engineering National

More information

Using a Balanced Scorecard to Tie the Results Act to Your Day-to-Day Operational Priorities

Using a Balanced Scorecard to Tie the Results Act to Your Day-to-Day Operational Priorities Using a Balanced Scorecard to Tie the Results Act to Your Day-to-Day Operational Priorities September 2004 Institute for the Study of Public Policy Implementation Overview Background Behind the Results

More information

What time is it right now? (They will have to enter the time) What is the subject code? (they will have to enter this in)

What time is it right now? (They will have to enter the time) What is the subject code? (they will have to enter this in) What time is it right now? (They will have to enter the time) What is the subject code? (they will have to enter this in) What is your interviewer code? (they will have to enter this in) 1. First, you

More information

Scalar Valued Functions of Several Variables; the Gradient Vector

Scalar Valued Functions of Several Variables; the Gradient Vector Scalar Valued Functions of Several Variables; the Gradient Vector Scalar Valued Functions vector valued function of n variables: Let us consider a scalar (i.e., numerical, rather than y = φ(x = φ(x 1,

More information

Notes on the SHARP EL-738 calculator

Notes on the SHARP EL-738 calculator Chapter 1 Notes on the SHARP EL-738 calculator General The SHARP EL-738 calculator is recommended for this module. The advantage of this calculator is that it can do basic calculations, financial calculations

More information

The Inversion Transformation

The Inversion Transformation The Inversion Transformation A non-linear transformation The transformations of the Euclidean plane that we have studied so far have all had the property that lines have been mapped to lines. Transformations

More information

Find all of the real numbers x that satisfy the algebraic equation:

Find all of the real numbers x that satisfy the algebraic equation: Appendix C: Factoring Algebraic Expressions Factoring algebraic equations is the reverse of expanding algebraic expressions discussed in Appendix B. Factoring algebraic equations can be a great help when

More information

Systems of Linear Equations

Systems of Linear Equations Systems of Linear Equations Beifang Chen Systems of linear equations Linear systems A linear equation in variables x, x,, x n is an equation of the form a x + a x + + a n x n = b, where a, a,, a n and

More information

Matrix Algebra. Some Basic Matrix Laws. Before reading the text or the following notes glance at the following list of basic matrix algebra laws.

Matrix Algebra. Some Basic Matrix Laws. Before reading the text or the following notes glance at the following list of basic matrix algebra laws. Matrix Algebra A. Doerr Before reading the text or the following notes glance at the following list of basic matrix algebra laws. Some Basic Matrix Laws Assume the orders of the matrices are such that

More information

Mechanical Vibrations Overview of Experimental Modal Analysis Peter Avitabile Mechanical Engineering Department University of Massachusetts Lowell

Mechanical Vibrations Overview of Experimental Modal Analysis Peter Avitabile Mechanical Engineering Department University of Massachusetts Lowell Mechanical Vibrations Overview of Experimental Modal Analysis Peter Avitabile Mechanical Engineering Department University of Massachusetts Lowell.457 Mechanical Vibrations - Experimental Modal Analysis

More information

The Structure of Galois Algebras

The Structure of Galois Algebras The Structure of Galois Algebras George Szeto Department of Mathematics, Bradley University Peoria, Illinois 61625 { U.S.A. Email: [email protected] and Lianyong Xue Department of Mathematics,

More information

T ( a i x i ) = a i T (x i ).

T ( a i x i ) = a i T (x i ). Chapter 2 Defn 1. (p. 65) Let V and W be vector spaces (over F ). We call a function T : V W a linear transformation form V to W if, for all x, y V and c F, we have (a) T (x + y) = T (x) + T (y) and (b)

More information

Sections 2.11 and 5.8

Sections 2.11 and 5.8 Sections 211 and 58 Timothy Hanson Department of Statistics, University of South Carolina Stat 704: Data Analysis I 1/25 Gesell data Let X be the age in in months a child speaks his/her first word and

More information

A Nice Theorem on Mixtilinear Incircles

A Nice Theorem on Mixtilinear Incircles A Nice Theorem on Mixtilinear Incircles Khakimboy Egamberganov Abstract There are three mixtilinear incircles and three mixtilinear excircles in an arbitrary triangle. In this paper, we will present many

More information

DATING YOUR GUILD 1952-1960

DATING YOUR GUILD 1952-1960 DATING YOUR GUILD 1952-1960 YEAR APPROXIMATE LAST SERIAL NUMBER PRODUCED 1953 1000-1500 1954 1500-2200 1955 2200-3000 1956 3000-4000 1957 4000-5700 1958 5700-8300 1959 12035 1960-1969 This chart displays

More information

UNIVERSITY OF ILUNOхS LIBRARY AT URBANA-CHAMPA1GN AGR1CULT-"'J?'-

UNIVERSITY OF ILUNOхS LIBRARY AT URBANA-CHAMPA1GN AGR1CULT-'J?'- ' UNVRSTY F NхS LBRARY AT URBANA-HAMPA1GN AGR1ULT-"'J?'- igitied by the nternet Arhive 2012 ith fndg frm University f llis Urbn-hmpign http://.rhive.rg/detils/illismmeri1982med s 8 h U p m UU t g 5. -

More information

Chapter. CPT only copyright 2009 American Medical Association. All rights reserved. 9Ambulance

Chapter. CPT only copyright 2009 American Medical Association. All rights reserved. 9Ambulance Chapter 9Ambulance 9 9.1 Enrollment........................................................ 9-2 9.2 Emergency Ground Ambulance Transportation.............................. 9-2 9.2.1 Benefits, Limitations,

More information

Qualitative vs Quantitative research & Multilevel methods

Qualitative vs Quantitative research & Multilevel methods Qualitative vs Quantitative research & Multilevel methods How to include context in your research April 2005 Marjolein Deunk Content What is qualitative analysis and how does it differ from quantitative

More information

Currency Options (2): Hedging and Valuation

Currency Options (2): Hedging and Valuation Overview Chapter 9 (2): Hedging and Overview Overview The Replication Approach The Hedging Approach The Risk-adjusted Probabilities Notation Discussion Binomial Option Pricing Backward Pricing, Dynamic

More information

INTERACTIVE MAP EGYPT

INTERACTIVE MAP EGYPT Multi-criteria Analysis for lanning Renewable Energy (MapRE) INTERACTIVE MA EGYT Of Southern and Eastern Africa Renewable Energy Zones (SEAREZs) This interactive DF map contains locations of high quality

More information

MyOWNMcMaster Degree Pathway: Diploma in Business Administration & Bachelor of Arts in History

MyOWNMcMaster Degree Pathway: Diploma in Business Administration & Bachelor of Arts in History MyOWNMcMaster Degree Pathway: Diploma in Business Administration & Bachelor of Arts in History Requirements The MyOWNMcMaster degree pathway has three parts: diploma, elective and undergraduate courses.

More information

Requirements The MyOWNMcMaster degree pathway has three parts: diploma, elective and undergraduate courses.

Requirements The MyOWNMcMaster degree pathway has three parts: diploma, elective and undergraduate courses. MyOWNMcMaster Degree Pathway: Diploma in Business Administration with a Concentration in Marketing & Bachelor of Arts in History Requirements The MyOWNMcMaster degree pathway has three parts: diploma,

More information

Chapter 7. Matrices. Definition. An m n matrix is an array of numbers set out in m rows and n columns. Examples. ( 1 1 5 2 0 6

Chapter 7. Matrices. Definition. An m n matrix is an array of numbers set out in m rows and n columns. Examples. ( 1 1 5 2 0 6 Chapter 7 Matrices Definition An m n matrix is an array of numbers set out in m rows and n columns Examples (i ( 1 1 5 2 0 6 has 2 rows and 3 columns and so it is a 2 3 matrix (ii 1 0 7 1 2 3 3 1 is a

More information

SEATTLE CENTRAL COMMUNITY COLLEGE DIVISION OF SCIENCE AND MATHEMATICS. Oxidation-Reduction

SEATTLE CENTRAL COMMUNITY COLLEGE DIVISION OF SCIENCE AND MATHEMATICS. Oxidation-Reduction SEATTLE CENTRAL COMMUNITY COLLEGE DIVISION OF SCIENCE AND MATHEMATICS OxidationReduction Oxidation is loss of electrons. (Oxygen is EN enough to grab e away from most elements, so the term originally meant

More information

The MyOWNMcMaster degree pathway has three parts: diploma, elective and undergraduate courses.

The MyOWNMcMaster degree pathway has three parts: diploma, elective and undergraduate courses. MyOWNMcMaster Degree Pathway: Diploma in Human Resources Management & Bachelor of Arts in History Requirements The MyOWNMcMaster degree pathway has three parts: diploma, elective and undergraduate courses.

More information

First mean return time in decoherent quantum walks

First mean return time in decoherent quantum walks First mean return time in decoherent quantum walks Péter Sinkovicz, János K. Asbóth, Tamás Kiss Wigner Research Centre for Physics Hungarian Academy of Sciences, April 0. Problem statement Example: N=,,,...

More information

SOLVING LINEAR SYSTEMS

SOLVING LINEAR SYSTEMS SOLVING LINEAR SYSTEMS Linear systems Ax = b occur widely in applied mathematics They occur as direct formulations of real world problems; but more often, they occur as a part of the numerical analysis

More information

Math 312 Homework 1 Solutions

Math 312 Homework 1 Solutions Math 31 Homework 1 Solutions Last modified: July 15, 01 This homework is due on Thursday, July 1th, 01 at 1:10pm Please turn it in during class, or in my mailbox in the main math office (next to 4W1) Please

More information

A linear algebraic method for pricing temporary life annuities

A linear algebraic method for pricing temporary life annuities A linear algebraic method for pricing temporary life annuities P. Date (joint work with R. Mamon, L. Jalen and I.C. Wang) Department of Mathematical Sciences, Brunel University, London Outline Introduction

More information

Cryptosystem. Diploma Thesis. Mol Petros. July 17, 2006. Supervisor: Stathis Zachos

Cryptosystem. Diploma Thesis. Mol Petros. July 17, 2006. Supervisor: Stathis Zachos s and s and Diploma Thesis Department of Electrical and Computer Engineering, National Technical University of Athens July 17, 2006 Supervisor: Stathis Zachos ol Petros (Department of Electrical and Computer

More information

m Future of learning Zehn J a hr e N et A c a d ei n E r f o l g s p r o g r a m Cisco E x p o 2 0 0 7 2 6. J u n i 2 0 0 7, M e sse W ie n C. D or n in g e r, b m u k k 1/ 12 P r e n t t z d e r p u t

More information

N 1. (q k+1 q k ) 2 + α 3. k=0

N 1. (q k+1 q k ) 2 + α 3. k=0 Teoretisk Fysik Hand-in problem B, SI1142, Spring 2010 In 1955 Fermi, Pasta and Ulam 1 numerically studied a simple model for a one dimensional chain of non-linear oscillators to see how the energy distribution

More information

FINITE ELEMENT : MATRIX FORMULATION. Georges Cailletaud Ecole des Mines de Paris, Centre des Matériaux UMR CNRS 7633

FINITE ELEMENT : MATRIX FORMULATION. Georges Cailletaud Ecole des Mines de Paris, Centre des Matériaux UMR CNRS 7633 FINITE ELEMENT : MATRIX FORMULATION Georges Cailletaud Ecole des Mines de Paris, Centre des Matériaux UMR CNRS 76 FINITE ELEMENT : MATRIX FORMULATION Discrete vs continuous Element type Polynomial approximation

More information

Nice Cubic Polynomials, Pythagorean Triples, and the Law of Cosines

Nice Cubic Polynomials, Pythagorean Triples, and the Law of Cosines 244 MATHEMATICS MAGAZINE Nice Cubic Polynomials, Pythagorean Triples, and the Law of Cosines JIM BUDDENHAGEN Southwestern Bell Telephone Co. Saint Louis, MO 63101 CHARLES FORD MIKE MAY, 5.1. Saint Louis

More information

Mathematics 205 HWK 6 Solutions Section 13.3 p627. Note: Remember that boldface is being used here, rather than overhead arrows, to indicate vectors.

Mathematics 205 HWK 6 Solutions Section 13.3 p627. Note: Remember that boldface is being used here, rather than overhead arrows, to indicate vectors. Mathematics 205 HWK 6 Solutions Section 13.3 p627 Note: Remember that boldface is being used here, rather than overhead arrows, to indicate vectors. Problem 5, 13.3, p627. Given a = 2j + k or a = (0,2,

More information

1 Lecture: Integration of rational functions by decomposition

1 Lecture: Integration of rational functions by decomposition Lecture: Integration of rational functions by decomposition into partial fractions Recognize and integrate basic rational functions, except when the denominator is a power of an irreducible quadratic.

More information

Algebra (Expansion and Factorisation)

Algebra (Expansion and Factorisation) Chapter10 Algebra (Expansion and Factorisation) Contents: A B C D E F The distributive law Siplifying algebraic expressions Brackets with negative coefficients The product (a + b)(c + d) Geoetric applications

More information

vector calculus 2 Learning outcomes

vector calculus 2 Learning outcomes 29 ontents vector calculus 2 1. Line integrals involving vectors 2. Surface and volume integrals 3. Integral vector theorems Learning outcomes In this Workbook you will learn how to integrate functions

More information

LIST OF RANK AND FILE CLASSES IN BU 9 As of December 1, 2011

LIST OF RANK AND FILE CLASSES IN BU 9 As of December 1, 2011 IB50 3812 AIR POLLUTION RESEARCH SPECIALIST $7,472.00 $9,082.00 E IB75 3887 AIR POLLUTION SPECIALIST A $4,204.00 $4,867.00 2 IB75 3887 AIR POLLUTION SPECIALIST B $5,034.00 $6,117.00 2 IB75 3887 AIR POLLUTION

More information

MAT188H1S Lec0101 Burbulla

MAT188H1S Lec0101 Burbulla Winter 206 Linear Transformations A linear transformation T : R m R n is a function that takes vectors in R m to vectors in R n such that and T (u + v) T (u) + T (v) T (k v) k T (v), for all vectors u

More information

SCALE Driver. Rth. Rth

SCALE Driver. Rth. Rth ,K= 5+) -,HELAH 5,!#)1!! B H0= B>HE@CA1/*6IKFJ!!8,AI?HEFJE 6DA 5+) - @HELAHI BH + +-26 =HA >=IA@ =?DEF IAJ JD=J M=I @ALA FA@ IFA?EBE?= O B H JDA HA E=> A@HELE C= @I=BA FAH=JE B1/*6I= @F MAH 5.-6I 6DA =

More information

Ź Ź ł ź Ź ś ź ł ź Ś ę ż ż ł ż ż Ż Ś ę Ż Ż ę ś ź ł Ź ł ł ż ż ź ż ż Ś ę ż ż Ź Ł Ż Ż Ą ż ż ę ź Ń Ź ś ł ź ż ł ś ź ź Ą ć ś ś Ź Ś ę ę ć ż Ź Ą Ń Ą ł ć ć ł ł ź ę Ś ę ś ę ł ś ć ź ś ł ś ł ł ł ł ć ć Ś ł ź Ś ł

More information

A SIMPLE PROCEDURE FOR EXTRACTING QUADRATICS FROM A GIVEN ALGEBRAIC POLYNOMIAL.

A SIMPLE PROCEDURE FOR EXTRACTING QUADRATICS FROM A GIVEN ALGEBRAIC POLYNOMIAL. A SIMPLE PROCEDURE FOR EXTRACTING QUADRATICS FROM A GIVEN ALGEBRAIC POLYNOMIAL. S.N.SIVANANDAM Professor and Head: Department of CSE PSG College of Technology Coimbatore, TamilNadu, India 641 004. [email protected],

More information

Lagrangian representation of microphysics in numerical models. Formulation and application to cloud geo-engineering problem

Lagrangian representation of microphysics in numerical models. Formulation and application to cloud geo-engineering problem Lagrangian representation of microphysics in numerical models. Formulation and application to cloud geo-engineering problem M. Andrejczuk and A. Gadian University of Oxford University of Leeds Outline

More information

i=(1,0), j=(0,1) in R 2 i=(1,0,0), j=(0,1,0), k=(0,0,1) in R 3 e 1 =(1,0,..,0), e 2 =(0,1,,0),,e n =(0,0,,1) in R n.

i=(1,0), j=(0,1) in R 2 i=(1,0,0), j=(0,1,0), k=(0,0,1) in R 3 e 1 =(1,0,..,0), e 2 =(0,1,,0),,e n =(0,0,,1) in R n. Length, norm, magnitude of a vector v=(v 1,,v n ) is v = (v 12 +v 22 + +v n2 ) 1/2. Examples v=(1,1,,1) v =n 1/2. Unit vectors u=v/ v corresponds to directions. Standard unit vectors i=(1,0), j=(0,1) in

More information

Inner Product Spaces and Orthogonality

Inner Product Spaces and Orthogonality Inner Product Spaces and Orthogonality week 3-4 Fall 2006 Dot product of R n The inner product or dot product of R n is a function, defined by u, v a b + a 2 b 2 + + a n b n for u a, a 2,, a n T, v b,

More information

14.451 Lecture Notes 10

14.451 Lecture Notes 10 14.451 Lecture Notes 1 Guido Lorenzoni Fall 29 1 Continuous time: nite horizon Time goes from to T. Instantaneous payo : f (t; x (t) ; y (t)) ; (the time dependence includes discounting), where x (t) 2

More information

Manpower Codes Lookup

Manpower Codes Lookup 00 NO PREFERENCE RECORDED 01 NO RELIGIOUS PREFERENCE 02 SEVENTH-DAY ADVENTIST 04 ASSEMBLIES OF GOD 05 GRACE GOSPEL FELLOWSHIP 06 AMERICAN BAPTIST CHURCHES 07 INDEPENDENT BAPTIST BIBLE MISSION 08 SOUTHERN

More information

PERFECT SQUARES AND FACTORING EXAMPLES

PERFECT SQUARES AND FACTORING EXAMPLES PERFECT SQUARES AND FACTORING EXAMPLES 1. Ask the students what is meant by identical. Get their responses and then explain that when we have two factors that are identical, we call them perfect squares.

More information

Background: State Estimation

Background: State Estimation State Estimation Cyber Security of the Smart Grid Dr. Deepa Kundur Background: State Estimation University of Toronto Dr. Deepa Kundur (University of Toronto) Cyber Security of the Smart Grid 1 / 81 Dr.

More information

Limits and Continuity

Limits and Continuity Math 20C Multivariable Calculus Lecture Limits and Continuity Slide Review of Limit. Side limits and squeeze theorem. Continuous functions of 2,3 variables. Review: Limits Slide 2 Definition Given a function

More information

Polynomials. Key Terms. quadratic equation parabola conjugates trinomial. polynomial coefficient degree monomial binomial GCF

Polynomials. Key Terms. quadratic equation parabola conjugates trinomial. polynomial coefficient degree monomial binomial GCF Polynomials 5 5.1 Addition and Subtraction of Polynomials and Polynomial Functions 5.2 Multiplication of Polynomials 5.3 Division of Polynomials Problem Recognition Exercises Operations on Polynomials

More information

Basics of binary quadratic forms and Gauss composition

Basics of binary quadratic forms and Gauss composition Basics of binary quadratic forms and Gauss composition Andrew Granville Université de Montréal SMS summer school: Counting arithmetic objects Monday June 3rd, 014, 3:30-5:00 pm 0 Sums of two squares 1

More information

PRODUCTION PLANNING AND SCHEDULING Part 1

PRODUCTION PLANNING AND SCHEDULING Part 1 PRODUCTION PLANNING AND SCHEDULING Part Andrew Kusiak 9 Seamans Center Iowa City, Iowa - 7 Tel: 9-9 Fax: 9-669 [email protected] http://www.icaen.uiowa.edu/~ankusiak Forecasting Planning Hierarchy

More information

Samknows Broadband Report

Samknows Broadband Report Samknows Broadband Report London Borough of Enfield This report describes the current broadband landscape for the London Borough of Enfield both in terms of general availability and choice, and in terms

More information

British Columbia Institute of Technology Calculus for Business and Economics Lecture Notes

British Columbia Institute of Technology Calculus for Business and Economics Lecture Notes British Columbia Institute of Technology Calculus for Business and Economics Lecture Notes Kevin Wainwright PhD August 17, 2012 Contents 1 Matrix Algebra 4 1.1 Matrices and Vectors.........................

More information

Numerical Methods for Solving Systems of Nonlinear Equations

Numerical Methods for Solving Systems of Nonlinear Equations Numerical Methods for Solving Systems of Nonlinear Equations by Courtney Remani A project submitted to the Department of Mathematical Sciences in conformity with the requirements for Math 4301 Honour s

More information

Pair-copula constructions of multiple dependence

Pair-copula constructions of multiple dependence Pair-copula constructions of multiple dependence Kjersti Aas The Norwegian Computing Center, Oslo, Norway Claudia Czado Technische Universität, München, Germany Arnoldo Frigessi University of Oslo and

More information

Factoring Methods. Example 1: 2x + 2 2 * x + 2 * 1 2(x + 1)

Factoring Methods. Example 1: 2x + 2 2 * x + 2 * 1 2(x + 1) Factoring Methods When you are trying to factor a polynomial, there are three general steps you want to follow: 1. See if there is a Greatest Common Factor 2. See if you can Factor by Grouping 3. See if

More information

GCE Mathematics (6360) Further Pure unit 4 (MFP4) Textbook

GCE Mathematics (6360) Further Pure unit 4 (MFP4) Textbook Version 36 klm GCE Mathematics (636) Further Pure unit 4 (MFP4) Textbook The Assessment and Qualifications Alliance (AQA) is a company limited by guarantee registered in England and Wales 364473 and a

More information

LECTURE 4. Last time: Lecture outline

LECTURE 4. Last time: Lecture outline LECTURE 4 Last time: Types of convergence Weak Law of Large Numbers Strong Law of Large Numbers Asymptotic Equipartition Property Lecture outline Stochastic processes Markov chains Entropy rate Random

More information

Dr.Web anti-viruses Visual standards

Dr.Web anti-viruses Visual standards Dr.Web anti-viruses Visual standards Contents Who are we? The main Dr.Web logo Logos of Dr.Web products Registered trademarks Typography 1 Who are we? Doctor Web is a Russian developer of information security

More information

December 4, 2013 MATH 171 BASIC LINEAR ALGEBRA B. KITCHENS

December 4, 2013 MATH 171 BASIC LINEAR ALGEBRA B. KITCHENS December 4, 2013 MATH 171 BASIC LINEAR ALGEBRA B KITCHENS The equation 1 Lines in two-dimensional space (1) 2x y = 3 describes a line in two-dimensional space The coefficients of x and y in the equation

More information

1. Introduction to multivariate data

1. Introduction to multivariate data . Introduction to multivariate data. Books Chat eld, C. and A.J.Collins, Introduction to multivariate analysis. Chapman & Hall Krzanowski, W.J. Principles of multivariate analysis. Oxford.000 Johnson,

More information

Chapter 13: Basic ring theory

Chapter 13: Basic ring theory Chapter 3: Basic ring theory Matthew Macauley Department of Mathematical Sciences Clemson University http://www.math.clemson.edu/~macaule/ Math 42, Spring 24 M. Macauley (Clemson) Chapter 3: Basic ring

More information

How To Understand The Theory Of Algebraic Functions

How To Understand The Theory Of Algebraic Functions Homework 4 3.4,. Show that x x cos x x holds for x 0. Solution: Since cos x, multiply all three parts by x > 0, we get: x x cos x x, and since x 0 x x 0 ( x ) = 0, then by Sandwich theorem, we get: x 0

More information

CM2202: Scientific Computing and Multimedia Applications General Maths: 2. Algebra - Factorisation

CM2202: Scientific Computing and Multimedia Applications General Maths: 2. Algebra - Factorisation CM2202: Scientific Computing and Multimedia Applications General Maths: 2. Algebra - Factorisation Prof. David Marshall School of Computer Science & Informatics Factorisation Factorisation is a way of

More information

Portfolio Distribution Modelling and Computation. Harry Zheng Department of Mathematics Imperial College [email protected]

Portfolio Distribution Modelling and Computation. Harry Zheng Department of Mathematics Imperial College h.zheng@imperial.ac.uk Portfolio Distribution Modelling and Computation Harry Zheng Department of Mathematics Imperial College [email protected] Workshop on Fast Financial Algorithms Tanaka Business School Imperial College

More information

Matrix Differentiation

Matrix Differentiation 1 Introduction Matrix Differentiation ( and some other stuff ) Randal J. Barnes Department of Civil Engineering, University of Minnesota Minneapolis, Minnesota, USA Throughout this presentation I have

More information

Summary: Transformations. Lecture 14 Parameter Estimation Readings T&V Sec 5.1-5.3. Parameter Estimation: Fitting Geometric Models

Summary: Transformations. Lecture 14 Parameter Estimation Readings T&V Sec 5.1-5.3. Parameter Estimation: Fitting Geometric Models Summary: Transformations Lecture 14 Parameter Estimation eadings T&V Sec 5.1-5.3 Euclidean similarity affine projective Parameter Estimation We will talk about estimating parameters of 1) Geometric models

More information

College of the Holy Cross, Spring 2009 Math 373, Partial Differential Equations Midterm 1 Practice Questions

College of the Holy Cross, Spring 2009 Math 373, Partial Differential Equations Midterm 1 Practice Questions College of the Holy Cross, Spring 29 Math 373, Partial Differential Equations Midterm 1 Practice Questions 1. (a) Find a solution of u x + u y + u = xy. Hint: Try a polynomial of degree 2. Solution. Use

More information

BOOLEAN CONSENSUS FOR SOCIETIES OF ROBOTS

BOOLEAN CONSENSUS FOR SOCIETIES OF ROBOTS Workshop on New frontiers of Robotics - Interdep. Research Center E. Piaggio June 2-22, 22 - Pisa (Italy) BOOLEAN CONSENSUS FOR SOCIETIES OF ROBOTS Adriano Fagiolini DIEETCAM, College of Engineering, University

More information

Regression Analysis. Regression Analysis MIT 18.S096. Dr. Kempthorne. Fall 2013

Regression Analysis. Regression Analysis MIT 18.S096. Dr. Kempthorne. Fall 2013 Lecture 6: Regression Analysis MIT 18.S096 Dr. Kempthorne Fall 2013 MIT 18.S096 Regression Analysis 1 Outline Regression Analysis 1 Regression Analysis MIT 18.S096 Regression Analysis 2 Multiple Linear

More information

Bias in the Estimation of Mean Reversion in Continuous-Time Lévy Processes

Bias in the Estimation of Mean Reversion in Continuous-Time Lévy Processes Bias in the Estimation of Mean Reversion in Continuous-Time Lévy Processes Yong Bao a, Aman Ullah b, Yun Wang c, and Jun Yu d a Purdue University, IN, USA b University of California, Riverside, CA, USA

More information