Inner Product Spaces INNER PRODUCTS
|
|
- Gerard Bruce
- 6 years ago
- Views:
Transcription
1 MA4Hcdoc Ir Product Spcs INNER PRODCS Dto A r product o vctor spc V s ucto tht ssgs ubr spc V such wy tht th ollowg xos holds: P : w s rl ubr P : P : P 4 : P 5 : v, w = w, v v + w, u = u + w, u rv, w = r w v, v V w to vry pr w o vctor Dto A vctor spc V wth r product s clld r product spc Not, :V V R ( V, R,+, s vctor spc (, R, +,,, V s r product spc Expls h ollowg r r product spcs (, R, +,,, R, d X, Y = X Y th dot product ( C [, b], R, +,,,, d g = b ( x g( x (,,,,, A, B = tr AB R +, dx M, d ( hor Lt, b r product o spc V Lt u, w dot vctors V, r rl ubr h: u, v + w = u, v + u, w v, rw = r w v, = =, v 4 v, v = d oly v = hor I A s y postv dt trx, th X, Y = X AY, X, Y R ds r product o R, d vry r product o R, d vry r product o R rss ths wy Proo: Pg o 9
2 MA4Hcdoc X, Y = X AY s r product Ay, o R c b xprssd s X AY : = E b th stdrd bss o R h X = = Lt E { E,, } Morovr, Y = = X, Y y E = x E, y E = x y = = = E, E E, E E, E E, E [ x x ] = X AY A = A, = E, E y y x E d NORMS AND DISANCE Dto h or o v V s dd s or ( v v = v = (lgth Dto h dstc btw vctors w r product spc s ( w = v w d hor I v s y vctor r product spc, th ultpl o V v v ˆ = s th uqu ut vctor tht s postv v hor: Schwrz Iqulty I v d w r two vctors r product spc V, th d oly o o v or w s sclr ultpl o th othr w v w Morovr, qulty occurs Proo: Assu v = > d w = b > bv w = bv w, bv w = b( b w w b bv + w = bv + w, bv + w = b( b + w w b So b w b w b v w b =, d w Not: v w w or v w Pg o 9
3 MA4Hcdoc Expl Cosdr th vctor spc C [, b] o ll cotuous uctos o [, b] D g ( x g( x b b b h ( x g( x dx ( ( x dx ( g( x dx, dx = b hor: Proprts o Nors v v = oly v = rv = r v 4 v + w v + w (trgl qulty hor: Proprts o Dstc d(, w v d ( v, w = d oly v = w d ( v, w = d( w, v 4 d ( w d( u + d( u, w, u, w V ORHOGONAL SES OF VECORS Dto wo vctors w r product spc V r sd to b orthogol v, w = Dto A st o vctors {,, } ddto, s clld orthogol st ch d, =, I, =,, th th st s clld orthoorl st Expl = π π Cosdr { s x, cos x} C [ π,π ] wth g ( x g( x { x, cos x} s s orthogol st, dx h s x, cos x =, so hor: Pythgor hor s orthogol st o vctors, th + + = + + I {,, } hor Lt {,, } { r, } b orthogol st o vctors h:, r s lso orthogol or ll r R Pg o 9
4 MA4Hcdoc,, s orthoorl st,, s lrly dpdt { } hor: Expso hor Lt {,, } v = = b orthogol bss o r product spc V I v s y vctor V, th s th xpso o v s lr cobto o th bss vctors h cocts r clld Fourr cocts o v wth rspct to th orthogol bss {,, } L: Orthogol L Lt {,, } b orthogol st o vctors r product spc V, d lt v b y vctor ot sp{,, } D + = v =, th {, } s orthogol st o vctors,, + Gr-Schdt Orthogolzto Algorth Lt V b r product spc d { v,, } b y bss o V D vctors {,, } succssvly s ollows: = v v, = v v, v, = v v, = v = h {,, } s orthogol d { v,, v } sp{,, } v sp = V Dto h orthogol coplt o V s dd by = { v V u =, u } hor Lt b t dsol subspc o r product spc V h: s subspc o V d V = I d V =, th d + d = I d V =, th d = Proo o : s subspc o V bcus: Pg 4 o 9
5 MA4Hcdoc u + u V = bcus: Proo o : Sc Lt x, th x x, u =, u But x, x = sc x, so x = So = k bss { b,, } b d bss { b,, } b k + Assu sp{ b,, b, b+, bk } D v = v = b v b V, { b, b, b,, b, *}, k v k = * b So = k = + + s orthogol st V, so v *, = or =,, hs s V = + V = v* Cotrdcto! So V = sp{ b,, b, b+,, bk } d, so Proo o : = { v V ( u =, u } Dto ( v u d V = = d + d It s clr tht = pro : V V, pro = whr v = u + w or u, w W, V = W s clld th procto o wth krl W hor: Procto hor Lt b t dsol subspc o r product spc V d lt pro : V V s lr oprtor wth g d krl pro ( v d v ( v pro I {,, } s y orthogol bss o, th ( v = Proo o : pro : V V s lr oprtor bcus: v V pro = b h: Lt v = u + w d v = u + w pro ( v + v = pro ( u + u + w + w = u + u = pro ( v + pro ( v pro v = pro u + w = u = pro v ( ( ( ( pro = { pro ( v v V } k v u u ( pro = kr ( pro = { v V pro ( v = } pro ( v = pro ( u + u = u = kr ( = pro Proo o : pro ( v ollows ro dto Proo o : ( ( v pro v = u + u u = u =, h pro ( v = pro ( u u = So So Pg 5 o 9
6 MA4Hcdoc I {,, } s orthogol bss o, d {,, } th {,, +,, } s orthogol bss o V Sc, = v = +, v = = = + + s orthogol bss o = u + u, ( pro v = pro + = + = = = + = =, hor: Approxto hor Lt b t dsol subspc o r product spc V I tht s closst to v Closst s tht v pro ( v < v u, u, u pro ( v v V, th pro ( v s th vctor Expl Fd th polyol P tht bst pproxts th ucto ( x = x Assu [, ] = ( x g( x, g dx = {, x,x } B s orthogol bss o P x,x x, x, x pro ( x = + x + ( x = ( 5x + x P x 6 V d ORHOGONAL DIAGONALIZAION hor Lt : V V b lr oprtor o V h th ollowg codtos r quvlt: V hs bss o gvctors o M B s dgol hr xsts bss B o V such tht ( Proo: k B = {,, } bss o V h ( ( [ ( ( ] = λ M B B = λ λ hor Lt b lr oprtor o r product spc V I {,, } (, M ( = B Proo: Wrt ( [ ] M = B s orthogol bss o V, th Pg 6 o 9
7 MA4Hcdoc ( = + + C B ( ( Sc v = = = = or ll v V, ( = ( ( = = = = (, So Dto A lr oprtor s clld sytrc v ( w ( v, w, = holds or ll w V hor Lt V b t dsol r product spc h ollowg codtos r quvlt or lr oprtor : V V v, w = v, or ll w V ( ( w h trx o s sytrc wth rspct to vry orthoorl bss o V h trx o s sytrc wth rspct to so orthoorl bss o V 4 h s orthoorl bss {,, } o V such tht, ( (,, =, hor Lt : V V b sytrc lr oprtor o r product spc V, d lt b -vrt subspc o V h: h rstrcto o to s sytrc lr oprtor o s lso -vrt Proo: s tsl r product spc usg th s r product s V hus v, w = w, w, th, prtculr, t holds or w I ( ( V v d ( v u = ( v,, So v, u = hus u, th ( v u = ( u = u, u hor: Prcpl Axs hor h ollowg codtos r quvlt or lr oprtor o t dsol r product spc V s sytrc V hs orthogol bss cosstg o gvctor o Expl b gv by ( bx + cx = ( 8 b + c + ( + 5b + 4c x + ( + 4b + 5 c x Lt : P P + D + bx + cx, + b x + c x = + bb + cc Show tht sytrc d d orthoorl bss o P cosstg o gvctors Wt: s sytrc Pg 7 o 9
8 MA4Hcdoc k orthoorl bss o P {, x x } B =, 8 h M ( B = 5 4 So s sytrc 4 5 Wt: Orthoorl bss cosstg o gvctors W kow th gvlus o M ( d thus gvctors o ( {,, } =,, R, P such tht, W r lookg or { } I M B ( P M B ( P P P ( C ( C ( = s dgol, th [ ] [ ] B B = ( ( ( = So ( ( ( = + x x = + x + x = + x + x M to b ISOMERIES hor Lt : V V b lr oprtor o t dsol r product spc V h th ollowg codtos r quvlt: v = v, v ( prsrvs or ( V ( v ( v = v v v v V,, ( prsrvs dstc ( v, ( v = v, v V ( prsrvs r product 4 I {, } s y orthoorl bss V, th { (, ( } prsrvs bss Dto s lso orthoorl bss ( A lr oprtor s clld sotry t stss o o th codtos th prvous thor Corollry Evry sotry s soorphs h copost o two sotrs s sotry Expl Cosdr : d d A B tr( AB M M, = h ( A A = s sotry Pg 8 o 9
9 MA4Hcdoc hor Lt : V V b oprtor whr V s t dsol r product spc h th ollowg codtos r quvlt: s sotry M B s orthogol trx or vry orthoorl bss B ( M B ( s orthogol trx or so orthoorl bss B Proo: : Lt B = {,, } b orthoorl bss h th th colu o ( C Now C (, C =, sc C V R ( ( B d ( ( B ( ( ( ( ( B, =, sc : V V s sotry colus o M B ( r orthogol : Lt B {,, } = b th orthoorl bss h ( ( ( (, C ( ( { (,, ( } B M B s : s sotry,, =, =, so th,, =, B B = bcus M B ( s orthogol So, s orthoorl bss o V So s sotry Corollry I : V V s sotry whr V s t dsol r product spc, th dt = ± hor Lt : V V b sotry o two dsol r product spc V h thr r two possblts Ethr: cosθ s θ M B =, θ < (rotto s θ cosθ Or: M B =, θ < (rlcto hr s orthoorl bss B o V such tht ( π hr s orthoorl bss B o V such tht ( π L Lt : V V b sotry o t dsol r product spc V h: I s -vrt, th s lso -vrt I λ s coplx gvlus o, th λ = I hs o-rl gvlus, th V hs -dsol -vrt subspc Pg 9 o 9
Chapter 2 Reciprocal Lattice. An important concept for analyzing periodic structures
Chpt Rcpocl Lttc A mpott cocpt o lyzg podc stuctus Rsos o toducg cpocl lttc Thoy o cystl dcto o x-ys, utos, d lctos. Wh th dcto mxmum? Wht s th tsty? Abstct study o uctos wth th podcty o Bvs lttc Fou tsomto.
More information3.4 Properties of the Stress Tensor
cto.4.4 Proprts of th trss sor.4. trss rasformato Lt th compots of th Cauchy strss tsor a coordat systm wth bas vctors b. h compots a scod coordat systm wth bas vctors j,, ar gv by th tsor trasformato
More informationSection 5.1/5.2: Areas and Distances the Definite Integral
Scto./.: Ars d Dstcs th Dt Itgrl Sgm Notto Prctc HW rom Stwrt Ttook ot to hd p. #,, 9 p. 6 #,, 9- odd, - odd Th sum o trms,,, s wrtt s, whr th d o summto Empl : Fd th sum. Soluto: Th Dt Itgrl Suppos w
More informationFOURIER SERIES. Series expansions are a ubiquitous tool of science and engineering. The kinds of
Do Bgyoko () FOURIER SERIES I. INTRODUCTION Srs psos r ubqutous too o scc d grg. Th kds o pso to utz dpd o () th proprts o th uctos to b studd d (b) th proprts or chrctrstcs o th systm udr vstgto. Powr
More informationLinear Prediction Analysis of Speech Sounds
Lr Prdcto Alyss of Sch Souds Brl Ch 4 frcs: X Hug t l So Lgug Procssg Chtrs 5 6 J Dllr t l Dscrt-T Procssg of Sch Sgls Chtrs 4-6 3 J W Pco Sgl odlg tchqus sch rcogto rocdgs of th I Stbr 993 5-47 Lr Prdctv
More informationHandout 11. Energy Bands in Graphene: Tight Binding and the Nearly Free Electron Approach
Hdout rg ds Grh: Tght dg d th Nrl Fr ltro roh I ths ltur ou wll lr: rg Th tght bdg thod (otd ) Th -bds grh FZ C 407 Srg 009 Frh R Corll Uvrst Grh d Crbo Notubs: ss Grh s two dsol sgl to lr o rbo tos rrgd
More informationLinear Prediction Analysis of
Lr Prdcto Alyss of Sch Souds Brl Ch Drtt of Coutr Scc & Iforto grg Ntol Tw Norl Uvrsty frcs: X Hug t l So Lgug g Procssg Chtrs 5 6 J Dllr t l Dscrt-T Procssg of Sch Sgls Chtrs 4-6 3 J W Pco Sgl odlg tchqus
More informationminimize c'x subject to subject to subject to
z ' sut to ' M ' M N uostrd N z ' sut to ' z ' sut to ' sl vrls vtor of : vrls surplus vtor of : uostrd s s s s s s z sut to whr : ut ost of :out of : out of ( ' gr of h food ( utrt : rqurt for h utrt
More informationNuclear Chemistry -- ANSWERS
Hoor Chstry Mr. Motro 5-6 Probl St Nuclar Chstry -- ANSWERS Clarly wrt aswrs o sparat shts. Show all work ad uts.. Wrt all th uclar quatos or th radoactv dcay srs o Urau-38 all th way to Lad-6. Th dcay
More informationComparisons of the Variance of Predictors with PPS sampling (update of c04ed26.doc) Ed Stanek
Coparo o th Varac o Prdctor wth PPS aplg (updat o c04d6doc Ed Sta troducto W copar prdctor o a PSU a or total bad o PPS aplg Th tratgy to ollow that o Sta ad Sgr (JASA, 004 whr w xpr th prdctor a a lar
More information1. Stefan-Boltzmann law states that the power emitted per unit area of the surface of a black
Stf-Boltzm lw stts tht th powr mttd pr ut r of th surfc of blck body s proportol to th fourth powr of th bsolut tmprtur: 4 S T whr T s th bsolut tmprtur d th Stf-Boltzm costt= 5 4 k B 3 5c h ( Clcult 5
More informationIFYFM002 Further Maths Appendix C Formula Booklet
Ittol Foudto Y (IFY) IFYFM00 Futh Mths Appd C Fomul Booklt Rltd Documts: IFY Futh Mthmtcs Syllbus 07/8 Cotts Mthmtcs Fomul L Equtos d Mtcs... Qudtc Equtos d Rmd Thom... Boml Epsos, Squcs d Ss... Idcs,
More informationCBSE , ˆj. cos CBSE_2015_SET-1. SECTION A 1. Given that a 2iˆ ˆj. We need to find. 3. Consider the vector equation of the plane.
CBSE CBSE SET- SECTION. Gv tht d W d to fd 7 7 Hc, 7 7 7. Lt,. W ow tht.. Thus,. Cosd th vcto quto of th pl.. z. - + z = - + z = Thus th Cts quto of th pl s - + z = Lt d th dstc tw th pot,, - to th pl.
More informationJOURNAL OF COLLEGE OF EDUCATION NO
NO.3...... 07 Ivrt S-bst Copproxmto -ormd Spcs Slw Slm bd Dprtmt of Mthmtcs Collg of ducto For Pur scc, Ib l-hthm, Uvrsty of Bghdd slwlbud@yhoo.com l Musddk Dlph Dprtmt of Mthmtcs,Collg of Bsc ducto, Uvrsty
More informationNORMAL POSITIVE LINEAR SYSTEMS AND ELECTRICAL CIRCUITS
EEKYK 8 Zzyt 5 o XIV duz KZOEK łyto Uvrty o chology NOM POSIIVE INE SYSEMS ND EEI IUIS Sury h oto o orl otv lctrcl crcut troducd d o thr cc rort r vtgtd Nw tt trc o otv lr yt d lctrcl crcut r rood d thr
More informationCBSE SAMPLE PAPER SOLUTIONS CLASS-XII MATHS SET-2 CBSE , ˆj. cos. SECTION A 1. Given that a 2iˆ ˆj. We need to find
BSE SMLE ER SOLUTONS LSS-X MTHS SET- BSE SETON Gv tht d W d to fd 7 7 Hc, 7 7 7 Lt, W ow tht Thus, osd th vcto quto of th pl z - + z = - + z = Thus th ts quto of th pl s - + z = Lt d th dstc tw th pot,,
More informationON THE RELATION BETWEEN THE CAUSAL BESSEL DERIVATIVE AND THE MARCEL RIESZ ELLIPTIC AND HYPERBOLIC KERNELS
ACENA Vo.. 03-08 005 03 ON THE RELATION BETWEEN THE CAUSAL BESSEL DERIVATIVE AND THE MARCEL RIESZ ELLIPTIC AND HYPERBOLIC KERNELS Rub A. CERUTTI RESUMEN: Cosrao os úcos Rsz coo casos artcuars úco causa
More informationReview of Linear Algebra
PGE 30: Forulto d Soluto Geosstes Egeerg Dr. Blhoff Sprg 0 Revew of Ler Alger Chpter 7 of Nuercl Methods wth MATLAB, Gerld Recktewld Vector s ordered set of rel (or cople) uers rrged s row or colu sclr
More informationA general N-dimensional vector consists of N values. They can be arranged as a column or a row and can be real or complex.
Lnr lgr Vctors gnrl -dmnsonl ctor conssts of lus h cn rrngd s column or row nd cn rl or compl Rcll -dmnsonl ctor cn rprsnt poston, loct, or cclrton Lt & k,, unt ctors long,, & rspctl nd lt k h th componnts
More informationStats & Summary
Stts 443.3 & 85.3 Summr The Woodbur Theorem BCD B C D B D where the verses C C D B, d est. Block Mtrces Let the m mtr m q q m be rttoed to sub-mtrces,,,, Smlrl rtto the m k mtr B B B mk m B B l kl Product
More informationCOMPLEX NUMBERS AND ELEMENTARY FUNCTIONS OF COMPLEX VARIABLES
COMPLEX NUMBERS AND ELEMENTARY FUNCTIONS OF COMPLEX VARIABLES DEFINITION OF A COMPLEX NUMBER: A umbr of th form, whr = (, ad & ar ral umbrs s calld a compl umbr Th ral umbr, s calld ral part of whl s calld
More informationLet's revisit conditional probability, where the event M is expressed in terms of the random variable. P Ax x x = =
L's rvs codol rol whr h v M s rssd rs o h rdo vrl. L { M } rrr v such h { M } Assu. { } { A M} { A { } } M < { } { } A u { } { } { A} { A} ( A) ( A) { A} A A { A } hs llows us o cosdr h cs wh M { } [ (
More informationQuantum Circuits. School on Quantum Day 1, Lesson 5 16:00-17:00, March 22, 2005 Eisuke Abe
Qutum Crcuts School o Qutum Computg @Ygm D, Lsso 5 6:-7:, Mrch, 5 Esuk Ab Dprtmt of Appl Phscs Phsco-Iformtcs, CEST-JST, Ko vrst Outl Bloch sphr rprstto otto gts vrslt proof A rbtrr cotroll- gt c b mplmt
More informationMath Tricks. Basic Probability. x k. (Combination - number of ways to group r of n objects, order not important) (a is constant, 0 < r < 1)
Math Trcks r! Combato - umbr o was to group r o objcts, ordr ot mportat r! r! ar 0 a r a s costat, 0 < r < k k! k 0 EX E[XX-] + EX Basc Probablt 0 or d Pr[X > ] - Pr[X ] Pr[ X ] Pr[X ] - Pr[X ] Proprts
More informationLECTURE 6 TRANSFORMATION OF RANDOM VARIABLES
LECTURE 6 TRANSFORMATION OF RANDOM VARIABLES TRANSFORMATION OF FUNCTION OF A RANDOM VARIABLE UNIVARIATE TRANSFORMATIONS TRANSFORMATION OF RANDOM VARIABLES If s a rv wth cdf F th Y=g s also a rv. If w wrt
More informationERDOS-SMARANDACHE NUMBERS. Sabin Tabirca* Tatiana Tabirca**
ERDO-MARANDACHE NUMBER b Tbrc* Tt Tbrc** *Trslv Uvrsty of Brsov, Computr cc Dprtmt **Uvrsty of Mchstr, Computr cc Dprtmt Th strtg pot of ths rtcl s rprstd by rct work of Fch []. Bsd o two symptotc rsults
More informationFace Detection and Recognition. Linear Algebra and Face Recognition. Face Recognition. Face Recognition. Dimension reduction
F Dtto Roto Lr Alr F Roto C Y I Ursty O solto: tto o l trs s s ys os ot. Dlt to t to ltpl ws. F Roto Aotr ppro: ort y rry s tor o so E.. 56 56 > pot 6556- stol sp A st o s t ps to ollto o pots ts sp. F
More informationCONSTACYCLIC CODES OF LENGTH OVER A FINITE FIELD
Jorl o Algbr Nbr Tory: Ac Alco Vol 5 Nbr 6 Pg 4-64 Albl ://ccc.co. DOI: ://.o.org/.864/_753 ONSTAYLI ODES OF LENGTH OVER A FINITE FIELD AITA SAHNI POONA TRAA SEHGAL r or Ac Sy c Pb Ury gr 64 I -l: 5@gl.co
More informationLinear Algebra Existence of the determinant. Expansion according to a row.
Lir Algbr 2270 1 Existc of th dtrmit. Expsio ccordig to row. W dfi th dtrmit for 1 1 mtrics s dt([]) = (1) It is sy chck tht it stisfis D1)-D3). For y othr w dfi th dtrmit s follows. Assumig th dtrmit
More informationMatrix. Definition 1... a1 ... (i) where a. are real numbers. for i 1, 2,, m and j = 1, 2,, n (iii) A is called a square matrix if m n.
Mtrx Defto () s lled order of m mtrx, umer of rows ( 橫行 ) umer of olums ( 直列 ) m m m where j re rel umers () B j j for,,, m d j =,,, () s lled squre mtrx f m (v) s lled zero mtrx f (v) s lled detty mtrx
More informationThe University of Sydney MATH 2009
T Unvrsty o Syny MATH 2009 APH THEOY Tutorl 7 Solutons 2004 1. Lt t sonnt plnr rp sown. Drw ts ul, n t ul o t ul ( ). Sow tt s sonnt plnr rp, tn s onnt. Du tt ( ) s not somorp to. ( ) A onnt rp s on n
More informationChapter Unary Matrix Operations
Chpter 04.04 Ury trx Opertos After redg ths chpter, you should be ble to:. kow wht ury opertos mes,. fd the trspose of squre mtrx d t s reltoshp to symmetrc mtrces,. fd the trce of mtrx, d 4. fd the ermt
More informationTOPIC 5: INTEGRATION
TOPIC 5: INTEGRATION. Th indfinit intgrl In mny rspcts, th oprtion of intgrtion tht w r studying hr is th invrs oprtion of drivtion. Dfinition.. Th function F is n ntidrivtiv (or primitiv) of th function
More informationHandout 7. Properties of Bloch States and Electron Statistics in Energy Bands
Hdout 7 Popts of Bloch Stts d Elcto Sttstcs Eg Bds I ths lctu ou wll l: Popts of Bloch fuctos Podc boud codtos fo Bloch fuctos Dst of stts -spc Elcto occupto sttstcs g bds ECE 407 Spg 009 Fh R Coll Uvst
More informationOn the Existence and uniqueness for solution of system Fractional Differential Equations
OSR Jourl o Mhms OSR-JM SSN: 78-578. Volum 4 ssu 3 Nov. - D. PP -5 www.osrjourls.org O h Es d uquss or soluo o ssm rol Drl Equos Mh Ad Al-Wh Dprm o Appld S Uvrs o holog Bghdd- rq Asr: hs ppr w d horm o
More informationWeights Interpreting W and lnw What is β? Some Endnotes = n!ω if we neglect the zero point energy then ( )
Sprg Ch 35: Statstcal chacs ad Chcal Ktcs Wghts... 9 Itrprtg W ad lw... 3 What s?... 33 Lt s loo at... 34 So Edots... 35 Chaptr 3: Fudatal Prcpls of Stat ch fro a spl odl (drvato of oltza dstrbuto, also
More informationMASTER CLASS PROGRAM UNIT 4 SPECIALIST MATHEMATICS SEMESTER TWO 2014 WEEK 11 WRITTEN EXAMINATION 1 SOLUTIONS
MASTER CLASS PROGRAM UNIT SPECIALIST MATHEMATICS SEMESTER TWO WEEK WRITTEN EXAMINATION SOLUTIONS FOR ERRORS AND UPDATES, PLEASE VISIT WWW.TSFX.COM.AU/MC-UPDATES QUESTION () Lt p ( z) z z z If z i z ( is
More informationLecture 11 Waves in Periodic Potentials Today: Questions you should be able to address after today s lecture:
Lctur 11 Wvs in Priodic Potntils Tody: 1. Invrs lttic dfinition in 1D.. rphicl rprsnttion of priodic nd -priodic functions using th -xis nd invrs lttic vctors. 3. Sris solutions to th priodic potntil Hmiltonin
More informationIIT JEE MATHS MATRICES AND DETERMINANTS
IIT JEE MTHS MTRICES ND DETERMINNTS THIRUMURUGN.K PGT Mths IIT Trir 978757 Pg. Lt = 5, th () =, = () = -, = () =, = - (d) = -, = -. Lt sw smmtri mtri of odd th quls () () () - (d) o of ths. Th vlu of th
More informationLecture 1: Empirical economic relations
Ecoomcs 53 Lctur : Emprcal coomc rlatos What s coomtrcs? Ecoomtrcs s masurmt of coomc rlatos. W d to kow What s a coomc rlato? How do w masur such a rlato? Dfto: A coomc rlato s a rlato btw coomc varabls.
More informationNational Quali cations
PRINT COPY OF BRAILLE Ntiol Quli ctios AH08 X747/77/ Mthmtics THURSDAY, MAY INSTRUCTIONS TO CANDIDATES Cdidts should tr thir surm, form(s), dt of birth, Scottish cdidt umbr d th m d Lvl of th subjct t
More informationPlanar convex hulls (I)
Covx Hu Covxty Gv st P o ots 2D, tr ovx u s t sst ovx oyo tt ots ots o P A oyo P s ovx or y, P, t st s try P. Pr ovx us (I) Coutto Gotry [s 3250] Lur To Bowo Co ovx o-ovx 1 2 3 Covx Hu Covx Hu Covx Hu
More informationAotomorphic Functions And Fermat s Last Theorem(4)
otomorphc Fuctos d Frmat s Last Thorm(4) Chu-Xua Jag P. O. Box 94 Bg 00854 P. R. Cha agchuxua@sohu.com bsract 67 Frmat wrot: It s mpossbl to sparat a cub to two cubs or a bquadrat to two bquadrats or gral
More informationDepartment of Mathematics and Statistics Indian Institute of Technology Kanpur MSO202A/MSO202 Assignment 3 Solutions Introduction To Complex Analysis
Dpartmt of Mathmatcs ad Statstcs Ida Isttut of Tchology Kapur MSOA/MSO Assgmt 3 Solutos Itroducto To omplx Aalyss Th problms markd (T) d a xplct dscusso th tutoral class. Othr problms ar for hacd practc..
More informationMM1. Introduction to State-Space Method
MM Itroductio to Stt-Spc Mthod Wht tt-pc thod? How to gt th tt-pc dcriptio? 3 Proprty Alyi Bd o SS Modl Rdig Mtril: FC: p469-49 C: p- /4/8 Modr Cotrol Wht th SttS tt-spc Mthod? I th tt-pc thod th dyic
More informationLinear System Review. Linear System Review. Descriptions of Linear Systems: 2008 Spring ME854 - GGZ Page 1
8 Sprg ME854 - Z Pg r Sym Rvw r Sym Rvw r Sym Rvw crpo of r Sym: p m R y R R y FT : & U Y Trfr Fco : y or : & : d y d r Sym Rvw orollbly d Obrvbly: fo 3.: FT dymc ym or h pr d o b corollbl f y l > d fl
More information1 4 6 is symmetric 3 SPECIAL MATRICES 3.1 SYMMETRIC MATRICES. Defn: A matrix A is symmetric if and only if A = A, i.e., a ij =a ji i, j. Example 3.1.
SPECIAL MATRICES SYMMETRIC MATRICES Def: A mtr A s symmetr f d oly f A A, e,, Emple A s symmetr Def: A mtr A s skew symmetr f d oly f A A, e,, Emple A s skew symmetr Remrks: If A s symmetr or skew symmetr,
More informationUnbalanced Panel Data Models
Ubalacd Pal Data odls Chaptr 9 from Baltag: Ecoomtrc Aalyss of Pal Data 5 by Adrás alascs 4448 troducto balacd or complt pals: a pal data st whr data/obsrvatos ar avalabl for all crosssctoal uts th tr
More informationVr Vr
F rt l Pr nt t r : xt rn l ppl t n : Pr nt rv nd PD RDT V t : t t : p bl ( ll R lt: 00.00 L n : n L t pd t : 0 6 20 8 :06: 6 pt (p bl Vr.2 8.0 20 8.0. 6 TH N PD PPL T N N RL http : h b. x v t h. p V l
More informationChapter 3 Fourier Series Representation of Periodic Signals
Chptr Fourir Sris Rprsttio of Priodic Sigls If ritrry sigl x(t or x[] is xprssd s lir comitio of som sic sigls th rspos of LI systm coms th sum of th idividul rsposs of thos sic sigls Such sic sigl must:
More informationLimits Indeterminate Forms and L Hospital s Rule
Limits Indtrmint Forms nd L Hospitl s Rul I Indtrmint Form o th Tp W hv prviousl studid its with th indtrmint orm s shown in th ollowin mpls: Empl : Empl : tn [Not: W us th ivn it ] Empl : 8 h 8 [Not:
More informationINTEGRALS. Chapter 7. d dx. 7.1 Overview Let d dx F (x) = f (x). Then, we write f ( x)
Chptr 7 INTEGRALS 7. Ovrviw 7.. Lt d d F () f (). Thn, w writ f ( ) d F () + C. Ths intgrls r clld indfinit intgrls or gnrl intgrls, C is clld constnt of intgrtion. All ths intgrls diffr y constnt. 7..
More informationFibonacci and Lucas Numbers as Tridiagonal Matrix Determinants
Rochester Isttute of echology RI Scholr Wors Artcles 8-00 bocc d ucs Nubers s rdgol trx Deterts Nth D. Chll Est Kod Copy Drre Nry Rochester Isttute of echology ollow ths d ddtol wors t: http://scholrwors.rt.edu/rtcle
More informationCorrelation in tree The (ferromagnetic) Ising model
5/3/00 :\ jh\slf\nots.oc\7 Chaptr 7 Blf propagato corrlato tr Corrlato tr Th (frromagtc) Isg mol Th Isg mol s a graphcal mol or par ws raom Markov fl cosstg of a urct graph wth varabls assocat wth th vrtcs.
More informationQuantum Mechanics & Spectroscopy Prof. Jason Goodpaster. Problem Set #2 ANSWER KEY (5 questions, 10 points)
Chm 5 Problm St # ANSWER KEY 5 qustios, poits Qutum Mchics & Spctroscopy Prof. Jso Goodpstr Du ridy, b. 6 S th lst pgs for possibly usful costts, qutios d itgrls. Ths will lso b icludd o our futur ms..
More information(2) If we multiplied a row of B by λ, then the value is also multiplied by λ(here lambda could be 0). namely
. DETERMINANT.. Dtrminnt. Introution:I you think row vtor o mtrix s oorint o vtors in sp, thn th gomtri mning o th rnk o th mtrix is th imnsion o th prlllppi spnn y thm. But w r not only r out th imnsion,
More informationChapter 5 Special Discrete Distributions. Wen-Guey Tzeng Computer Science Department National Chiao University
Chatr 5 Scal Dscrt Dstrbutos W-Guy Tzg Comutr Scc Dartmt Natoal Chao Uvrsty Why study scal radom varabls Thy aar frqutly thory, alcatos, statstcs, scc, grg, fac, tc. For aml, Th umbr of customrs a rod
More informationMore Statistics tutorial at 1. Introduction to mathematical Statistics
Mor Sttstcs tutorl t wwwdumblttldoctorcom Itroducto to mthmtcl Sttstcs Fl Soluto A Gllup survy portrys US trprurs s " th mvrcks, drmrs, d lors whos rough dgs d ucompromsg d to do t thr ow wy st thm shrp
More informationCourse 10 Shading. 1. Basic Concepts: Radiance: the light energy. Light Source:
Cour 0 Shadg Cour 0 Shadg. Bac Coct: Lght Sourc: adac: th lght rg radatd from a ut ara of lght ourc or urfac a ut old agl. Sold agl: $ # r f lght ourc a ot ourc th ut ara omttd abov dfto. llumato: lght
More informationGraphs. Graphs. Graphs: Basic Terminology. Directed Graphs. Dr Papalaskari 1
CSC 00 Disrt Struturs : Introuon to Grph Thory Grphs Grphs CSC 00 Disrt Struturs Villnov Univrsity Grphs r isrt struturs onsisng o vrs n gs tht onnt ths vrs. Grphs n us to mol: omputr systms/ntworks mthml
More informationAsymptotic Dominance Problems. is not constant but for n 0, f ( n) 11. 0, so that for n N f
Asymptotc Domce Prolems Dsply ucto : N R tht s Ο( ) ut s ot costt 0 = 0 The ucto ( ) = > 0 s ot costt ut or 0, ( ) Dee the relto " " o uctos rom N to R y g d oly = Ο( g) Prove tht s relexve d trstve (Recll:
More informationME 501A Seminar in Engineering Analysis Page 1
Mtr Trsformtos usg Egevectors September 8, Mtr Trsformtos Usg Egevectors Lrry Cretto Mechcl Egeerg A Semr Egeerg Alyss September 8, Outle Revew lst lecture Trsformtos wth mtr of egevectors: = - A ermt
More informationDifferential Entropy 吳家麟教授
Deretl Etropy 吳家麟教授 Deto Let be rdom vrble wt cumultve dstrbuto ucto I F s cotuous te r.v. s sd to be cotuous. Let = F we te dervtve s deed. I te s clled te pd or. Te set were > 0 s clled te support set
More informationExtension Formulas of Lauricella s Functions by Applications of Dixon s Summation Theorem
Avll t http:pvu.u Appl. Appl. Mth. ISSN: 9-9466 Vol. 0 Issu Dr 05 pp. 007-08 Appltos Appl Mthts: A Itrtol Jourl AAM Etso oruls of Lurll s utos Appltos of Do s Suto Thor Ah Al Atsh Dprtt of Mthts A Uvrst
More informationCHAPTER 5 Vectors and Vector Space
HAPTE 5 Vetors d Vetor Spe 5. Alger d eometry of Vetors. Vetor A ordered trple,,, where,, re rel umers. Symol:, B,, A mgtude d dreto.. Norm of vetor,, Norm =,, = = mgtude. Slr multplto Produt of slr d
More informationa b c cat CAT A B C Aa Bb Cc cat cat Lesson 1 (Part 1) Verbal lesson: Capital Letters Make The Same Sound Lesson 1 (Part 1) continued...
Progrssiv Printing T.M. CPITLS g 4½+ Th sy, fun (n FR!) wy to tch cpitl lttrs. ook : C o - For Kinrgrtn or First Gr (not for pr-school). - Tchs tht cpitl lttrs mk th sm souns s th littl lttrs. - Tchs th
More information(4.1) D r v(t) ω(t, v(t))
1.4. Differentil inequlities. Let D r denote the right hnd derivtive of function. If ω(t, u) is sclr function of the sclrs t, u in some open connected set Ω, we sy tht function v(t), t < b, is solution
More information,. *â â > V>V. â ND * 828.
BL D,. *â â > V>V Z V L. XX. J N R â J N, 828. LL BL D, D NB R H â ND T. D LL, TR ND, L ND N. * 828. n r t d n 20 2 2 0 : 0 T http: hdl.h ndl.n t 202 dp. 0 02802 68 Th N : l nd r.. N > R, L X. Fn r f,
More informationConvergence Theorems for Two Iterative Methods. A stationary iterative method for solving the linear system: (1.1)
Conrgnc Thors for Two Itrt Mthods A sttonry trt thod for solng th lnr syst: Ax = b (.) ploys n trton trx B nd constnt ctor c so tht for gn strtng stt x of x for = 2... x Bx c + = +. (.2) For such n trton
More informationSYSTEMS OF LINEAR EQUATIONS
SYSES OF INER EQUIONS Itroducto Emto thods Dcomposto thods tr Ivrs d Dtrmt Errors, Rsdus d Codto Numr Itrto thods Icompt d Rdudt Systms Chptr Systms of r Equtos /. Itroducto h systm of r qutos s formd
More informationTotal Prime Graph. Abstract: We introduce a new type of labeling known as Total Prime Labeling. Graphs which admit a Total Prime labeling are
Itratoal Joural Of Computatoal Egrg Rsarch (crol.com) Vol. Issu. 5 Total Prm Graph M.Rav (a) Ramasubramaa 1, R.Kala 1 Dpt.of Mathmatcs, Sr Shakth Isttut of Egrg & Tchology, Combator 641 06. Dpt. of Mathmatcs,
More informationGilbert the Green Tree Frog
Gbrt th Gr Tr Frog A org Kpr Kd book Wrtt by Stph Jk Iutrtd by T Eor ONCE UPON A TIME thr w frog md Gbrt. Gbrt w Gr Tr Frog. H fmy w gr. H hom w gr. Ad omtm v h food w gr. Gbrt w ck d trd of bg o gr th
More informationGraphs. CSC 1300 Discrete Structures Villanova University. Villanova CSC Dr Papalaskari
Grphs CSC 1300 Disrt Struturs Villnov Univrsity Grphs Grphs r isrt struturs onsis?ng of vr?s n gs tht onnt ths vr?s. Grphs n us to mol: omputr systms/ntworks mthm?l rl?ons logi iruit lyout jos/prosss f
More informationLinear Algebra Concepts
Ler Algebr Cocepts Nuo Vscocelos (Ke Kreutz-Delgdo) UCSD Vector spces Defto: vector spce s set H where ddto d sclr multplcto re defed d stsf: ) +( + ) = (+ )+ 5) H 2) + = + H 6) = 3) H, + = 7) ( ) = (
More informationBinary Choice. Multiple Choice. LPM logit logistic regresion probit. Multinomial Logit
(c Pogsa Porchawssul, Faculty of Ecoomcs, Chulalogor Uvrsty (c Pogsa Porchawssul, Faculty of Ecoomcs, Chulalogor Uvrsty 3 Bary Choc LPM logt logstc rgrso probt Multpl Choc Multomal Logt (c Pogsa Porchawssul,
More informationSOLVED EXAMPLES. Ex.1 If f(x) = , then. is equal to- Ex.5. f(x) equals - (A) 2 (B) 1/2 (C) 0 (D) 1 (A) 1 (B) 2. (D) Does not exist = [2(1 h)+1]= 3
SOLVED EXAMPLES E. If f() E.,,, th f() f() h h LHL RHL, so / / Lim f() quls - (D) Dos ot ist [( h)+] [(+h) + ] f(). LHL E. RHL h h h / h / h / h / h / h / h As.[C] (D) Dos ot ist LHL RHL, so giv it dos
More informationComplex Numbers. Prepared by: Prof. Sunil Department of Mathematics NIT Hamirpur (HP)
th Topc Compl Nmbrs Hyprbolc fctos ad Ivrs hyprbolc fctos, Rlato btw hyprbolc ad crclar fctos, Formla of hyprbolc fctos, Ivrs hyprbolc fctos Prpard by: Prof Sl Dpartmt of Mathmatcs NIT Hamrpr (HP) Hyprbolc
More informationb. How many ternary words of length 23 with eight 0 s, nine 1 s and six 2 s?
MATH 3012 Finl Exm, My 4, 2006, WTT Stunt Nm n ID Numr 1. All our prts o this prolm r onrn with trnry strings o lngth n, i.., wors o lngth n with lttrs rom th lpht {0, 1, 2}.. How mny trnry wors o lngth
More informationTheorem 1. An undirected graph is a tree if and only if there is a unique simple path between any two of its vertices.
Cptr 11: Trs 11.1 - Introuton to Trs Dnton 1 (Tr). A tr s onnt unrt rp wt no sp ruts. Tor 1. An unrt rp s tr n ony tr s unqu sp pt twn ny two o ts vrts. Dnton 2. A root tr s tr n w on vrtx s n snt s t
More informationAvailable online through
Avlble ole through wwwmfo FIXED POINTS FOR NON-SELF MAPPINGS ON CONEX ECTOR METRIC SPACES Susht Kumr Moht* Deprtmet of Mthemtcs West Begl Stte Uverst Brst 4 PrgsNorth) Kolt 76 West Begl Id E-ml: smwbes@yhoo
More informationMachine Learning. Principle Component Analysis. Prof. Dr. Volker Sperschneider
Mach Larg Prcpl Compot Aalyss Prof. Dr. Volkr Sprschdr AG Maschlls Lr ud Natürlchsprachlch Systm Isttut für Iformatk chsch Fakultät Albrt-Ludgs-Uvrstät Frburg sprschdr@formatk.u-frburg.d I. Archtctur II.
More information1- I. M. ALGHROUZ: A New Approach To Fractional Derivatives, J. AOU, V. 10, (2007), pp
Jourl o Al-Qus Op Uvrsy or Rsrch Sus - No.4 - Ocobr 8 Rrcs: - I. M. ALGHROUZ: A Nw Approch To Frcol Drvvs, J. AOU, V., 7, pp. 4-47 - K.S. Mllr: Drvvs o or orr: Mh M., V 68, 995 pp. 83-9. 3- I. PODLUBNY:
More informationVectors. Vectors and the scalar multiplication and vector addition operations:
Vectors Vectors and the scalar multiplication and vector addition operations: x 1 x 1 y 1 2x 1 + 3y 1 x x n 1 = 2 x R n, 2 2 y + 3 2 2x = 2 + 3y 2............ x n x n y n 2x n + 3y n I ll use the two terms
More informationME 501A Seminar in Engineering Analysis Page 1
St Ssts o Ordar Drtal Equatos Novbr 7 St Ssts o Ordar Drtal Equatos Larr Cartto Mcacal Er 5A Sar Er Aalss Novbr 7 Outl Mr Rsults Rvw last class Stablt o urcal solutos Stp sz varato or rror cotrol Multstp
More informationChapter 3. LQ, LQG and Control System Design. Dutch Institute of Systems and Control
Chapter 3 LQ, LQG and Control System H 2 Design Overview LQ optimization state feedback LQG optimization output feedback H 2 optimization non-stochastic version of LQG Application to feedback system design
More information4 4 N v b r t, 20 xpr n f th ll f th p p l t n p pr d. H ndr d nd th nd f t v L th n n f th pr v n f V ln, r dn nd l r thr n nt pr n, h r th ff r d nd
n r t d n 20 20 0 : 0 T P bl D n, l d t z d http:.h th tr t. r pd l 4 4 N v b r t, 20 xpr n f th ll f th p p l t n p pr d. H ndr d nd th nd f t v L th n n f th pr v n f V ln, r dn nd l r thr n nt pr n,
More informationD t r l f r th n t d t t pr p r d b th t ff f th l t tt n N tr t n nd H n N d, n t d t t n t. n t d t t. h n t n :.. vt. Pr nt. ff.,. http://hdl.handle.net/2027/uiug.30112023368936 P bl D n, l d t z d
More informationFourier Transforms. Convolutions. Capturing what s important. Last Time. Linear Image Transformation. Invertible Transforms.
orr Trasors Rq rad: Captr 7 92 &P Adso Soc ad ra (adot o) Opt rad: Hor 7 & 8 P 8 Last T Cooto trs: a/box tr Gassa tr t drc tr Lapaca o Gassa tr Ed Dtcto Cootos Cooto s coptatoay costy ad a copx oprato
More informationAssignment 7/MATH 247/Winter, 2010 Due: Friday, March 19. Powers of a square matrix
Assgmet 7/MATH 47/Wter, 00 Due: Frday, March 9 Powers o a square matrx Gve a square matrx A, ts powers A or large, or eve arbtrary, teger expoets ca be calculated by dagoalzg A -- that s possble (!) Namely,
More informationX ε ) = 0, or equivalently, lim
Revew for the prevous lecture Cocepts: order statstcs Theorems: Dstrbutos of order statstcs Examples: How to get the dstrbuto of order statstcs Chapter 5 Propertes of a Radom Sample Secto 55 Covergece
More informationx, x, e are not periodic. Properties of periodic function: 1. For any integer n,
Chpr Fourir Sri, Igrl, d Tror. Fourir Sri A uio i lld priodi i hr i o poiiv ur p uh h p, p i lld priod o R i,, r priodi uio.,, r o priodi. Propri o priodi uio:. For y igr, p. I d g hv priod p, h h g lo
More informationLog1 Contest Round 2 Theta Complex Numbers. 4 points each. 5 points each
01 Log1 Cotest Roud Theta Complex Numbers 1 Wrte a b Wrte a b form: 1 5 form: 1 5 4 pots each Wrte a b form: 65 4 4 Evaluate: 65 5 Determe f the followg statemet s always, sometmes, or ever true (you may
More informationTh n nt T p n n th V ll f x Th r h l l r r h nd xpl r t n rr d nt ff t b Pr f r ll N v n d r n th r 8 l t p t, n z n l n n th n rth t rn p rt n f th v
Th n nt T p n n th V ll f x Th r h l l r r h nd xpl r t n rr d nt ff t b Pr f r ll N v n d r n th r 8 l t p t, n z n l n n th n rth t rn p rt n f th v ll f x, h v nd d pr v n t fr tf l t th f nt r n r
More information22 t b r 2, 20 h r, th xp t d bl n nd t fr th b rd r t t. f r r z r t l n l th h r t rl T l t n b rd n n l h d, nd n nh rd f pp t t f r n. H v v d n f
n r t d n 20 2 : 6 T P bl D n, l d t z d http:.h th tr t. r pd l 22 t b r 2, 20 h r, th xp t d bl n nd t fr th b rd r t t. f r r z r t l n l th h r t rl T l t n b rd n n l h d, nd n nh rd f pp t t f r
More informationChapter 6. pn-junction diode: I-V characteristics
Chatr 6. -jucto dod: -V charactrstcs Tocs: stady stat rsos of th jucto dod udr ald d.c. voltag. ucto udr bas qualtatv dscusso dal dod quato Dvatos from th dal dod Charg-cotrol aroach Prof. Yo-S M Elctroc
More information828.^ 2 F r, Br n, nd t h. n, v n lth h th n l nd h d n r d t n v l l n th f v r x t p th l ft. n ll n n n f lt ll th t p n nt r f d pp nt nt nd, th t
2Â F b. Th h ph rd l nd r. l X. TH H PH RD L ND R. L X. F r, Br n, nd t h. B th ttr h ph rd. n th l f p t r l l nd, t t d t, n n t n, nt r rl r th n th n r l t f th f th th r l, nd d r b t t f nn r r pr
More informationThe Theory of Small Reflections
Jim Stils Th Univ. of Knss Dt. of EECS 4//9 Th Thory of Smll Rflctions /9 Th Thory of Smll Rflctions Rcll tht w nlyzd qurtr-wv trnsformr usg th multil rflction viw ot. V ( z) = + β ( z + ) V ( z) = = R
More informationChapter 3. Vector Spaces
3.4 Liner Trnsformtions 1 Chpter 3. Vector Spces 3.4 Liner Trnsformtions Note. We hve lredy studied liner trnsformtions from R n into R m. Now we look t liner trnsformtions from one generl vector spce
More informationIn Calculus I you learned an approximation method using a Riemann sum. Recall that the Riemann sum is
Mth Sprg 08 L Approxmtg Dete Itegrls I Itroducto We hve studed severl methods tht llow us to d the exct vlues o dete tegrls However, there re some cses whch t s ot possle to evlute dete tegrl exctly I
More informationGreen s Theorem. Fundamental Theorem for Conservative Vector Fields
Assignment - Mathematics 4(Model Answer) onservative vector field and Green theorem onservative Vector Fields If F = φ, for some differentiable function φ in a domaind, then we say that F is conservative
More information