CHAPTER 3: INVERSE METHODS BASED ON LENGTH. 3.1 Introduction. 3.2 Data Error and Model Parameter Vectors

Size: px
Start display at page:

Download "CHAPTER 3: INVERSE METHODS BASED ON LENGTH. 3.1 Introduction. 3.2 Data Error and Model Parameter Vectors"

Transcription

1 eoscences 567: CHAPER 3 (RR/Z) CHAPER 3: IVERSE EHODS BASED O EH 3. Inroucon s caper s concerne w nverse eos base on e leng of varous vecors a arse n a ypcal proble. e wo os coon vecors concerne are e aa-error or sf vecor an e oel paraeer vecor. eos base on e frs vecor gve rse o classc leas squares soluons. eos base on e secon vecor gve rse o wa are known as nu leng soluons. Iproveens over sple leas squares an nu leng soluons nclue e use of nforaon abou nose n e aa an a pror nforaon abou e oel paraeers, an are known as wege leas squares or wege nu leng soluons, respecvely. s caper wll en w aeral on ow o anle consrans an on varances of e esae oel paraeers. 3. Daa Error an oel Paraeer Vecors e aa error an oel paraeer vecors wll play an essenal role n e evelopen of nverse eos. ey are gven by an aa error vecor e obs pre (3.) oel paraeer vecor (3.) e enson of e error vecor e s, wle e enson of e oel paraeer vecor s, respecvely. In orer o ule ese vecors, we nex conser e noon of e se, or leng, of vecors. 3.3 easures of eng e nor of a vecor s a easure of s se, or leng. ere are any possble efnons for nors. e are os falar w e Caresan ( ) nor. Soe exaples of nors follow: e (3.3) 3

2 eoscences 567: CHAPER 3 (RR/Z) / e (3.4) e / (3.5) an fnally, ax e (3.6) Iporan oce! Inverse eos base on fferen nors can, an ofen o, gve fferen answers! e reason s a fferen nors gve fferen weg o oulers. For exaple, e nor gves all e weg o e larges sf. ow-orer nors gve ore equal weg o errors of fferen ses. e nor gves e falar Caresan leng of a vecor. Conser e oal sf E beween observe an prece aa. I as uns of leng square an can be foun eer as e square of e nor of e, e error vecor (Equaon 3.), or by nong a s also equvalen o e o (or nner) prouc of e w self, gven by e e E e e [ e e e ] e (3.7) e Inverse eos base on e nor are also closely e o e noon a errors n e aa ave aussan sascs. ey gve conserable weg o large errors, wc woul be consere unlkely f, n fac, e errors were srbue n a aussan fason. ow a we ave a way o quanfy e sf beween prece an observe aa, we are reay o efne a proceure for esang e value of e eleens n. e proceure s o ake e paral ervave of E w respec o eac eleen n an se e resulng equaons o ero. s wll prouce a syse of equaons a can be anpulae n suc a way a, n general, leas o a soluon for e eleens of. e nex secon wll sow ow s s one for e leas squares proble of fnng a bes f srag lne o a se of aa pons. 3

3 eoscences 567: CHAPER 3 (RR/Z) 3.4 nng e sf eas Squares 3.4. eas Squares Proble for a Srag ne Conser e fgure below (afer Fgure 3. fro enke, page 36): (a) (b) obs pre e. { (a) eas squares fng of a srag lne o (, ) pars. (b) e error e for eac observaon s e fference beween e observe an prece au: e obs pre. e prece au pre for e srag lne proble s gven by pre (3.8) were e wo unknowns, an, are e nercep an slope of e lne, respecvely, an s e value along e axs were e observaon s ae. For pons we ave a syse of suc equaons a can be wren n arx for as: Or, n e by now falar arx noaon, as (3.9) 33

4 eoscences 567: CHAPER 3 (RR/Z) (.3) ( ) ( ) ( ) e oal sf E s gven by E obs pre [ ] e e (3.) obs [ ( )] Droppng e obs n e noaon for e observe aa, we ave (3.) [ ] E (3.) en, akng e parals of E w respec o an, respecvely, an seng e o ero yels e followng equaons: an E (3.3) E (3.4) Rewrng Equaons (3.3) an (3.4) above yels (3.5) an (3.6) Cobnng e wo equaons n arx noaon n e for A b yels (3.7) or sply 34

5 eoscences 567: CHAPER 3 (RR/Z) A b (3.8) ( ) ( ) ( ) oe a by e above proceure we ave reuce e proble fro one w equaons n wo unknowns ( an ) n o one w wo equaons n e sae wo unknowns n A b. e arx equaon A b can also be rewren n ers of e orgnal an wen one noces a e arx A can be facore as ( ) ( ) ( ) ( ) (3.9) Also, b above can be rewren slarly as (3.) us, subsung Equaons (3.9) an (3.) no Equaon (3.7), one arrves a e so-calle noral equaons for e leas squares proble: e leas squares soluon S s en foun as assung a [ ] exss. (3.) S [ ] (3.) In suary, we use e forwar proble (Equaon 3.9) o gve us an explc relaonsp beween e oel paraeers ( an ) an a easure of e sf o e observe aa, E. en, we ne E by akng e paral ervaves of e sf funcon w respec o e unknown oel paraeers, seng e parals o ero, an solvng for e oel paraeers. 35

6 eoscences 567: CHAPER 3 (RR/Z) 3.4. Dervaon of e eneral eas Squares Soluon e sar w any syse of lnear equaons wc can be expresse n e for Agan, le E e e [ pre ] [ pre ] (.3) ( ) ( ) ( ) E [ ] [ ] (3.3) E j j j k k k (3.4) As before, e proceure s o wre ou e above equaon w all s cross ers, ake parals of E w respec o eac of e eleens n, an se e corresponng equaons o ero. For exaple, followng enke, page 4, Equaons (3.6) (3.9), we oban an expresson for e paral of E w respec o q : E q k qk k q (3.5) e can splfy s expresson by recallng Equaon (.4) fro e nroucory rearks on arx anpulaons n Caper : C j oe a e frs suaon on n Equaon (3.5) looks slar n for o Equaon (.4), bu e subscrps on e frs er are backwars. If we furer noe a nercangng e subscrps s equvalen o akng e ranspose of, we see a e suaon on gves e qk enry n : us, Equaon (3.5) reuces o k qk [ ] q k [ ] a k b kj qk (.4) (3.6) E q [ k ] qk k q (3.7) ow, we can furer splfy e frs suaon by recallng Equaon (.6) fro e sae secon j j j (.6) 36

7 eoscences 567: CHAPER 3 (RR/Z) o see s clearly, we rearrange e orer of ers n e frs su as follows: k k [ ] qk [ ] qk k [ k ] q (3.8) wc s e q enry n. oe a as enson ( )( )( ) ( ). a s, s an -ensonal vecor. In a slar fason, e secon suaon on can be reuce o a er n [ ] q, e q enry n an ( )( ) ( ) ensonal vecor. us, for e q equaon, we ave E [ ] q [ q ] q (3.9) Droppng e coon facor of an cobnng e q equaons no arx noaon, we arrve a e leas squares soluon for s us gven by e leas squares operaor, S, s us gven by (3.3) S [ ] (3.3) S [ ] (3.3) Recallng basc calculus, we noe a S above s e soluon a nes E, e oal sf. Suarng, seng e q paral ervaves of E w respec o e eleens n o ero leas o e leas squares soluon. e ave jus erve e leas squares soluon by akng e paral ervaves of E w respec o q an en cobnng e ers for q,,...,. An alernave, bu equvalen, forulaon begns w Equaon (3.) bu s wren ou as E [ ] [ ] (3.3) [ ][ ] (3.33) en, akng e paral ervave of E w respec o urns ou o be equvalen o wa was one n Equaons (3.5) (3.3) for q, naely 37

8 eoscences 567: CHAPER 3 (RR/Z) E/ (3.34) wc leas o (3.3) an S [ ] (3.3) I s also peraps neresng o noe a we coul ave obane e sae soluon wou akng parals. o see s, conser e followng four seps. Sep. e begn w (.3) Sep. e en preulply bo ses by (3.3) Sep 3. Preulply bo ses by [ ] [ ] [ ] (3.35) Sep 4. s reuces o S [ ] (3.3) as before. e pon s, owever, a s way oes no sow wy S s e soluon wc nes E, e sf beween e observe an prece aa. All of s assues a [ ] exss, of course. e wll reurn o e exsence an properes of [ ] laer. ex, we wll look a wo exaples of leas squares probles o sow a srkng slary a s no obvous a frs glance wo Exaples of eas Squares Probles Exaple. Bes-F Srag-ne Proble e ave, of course, alreay erve e soluon for s proble n e las secon. Brefly, en, for e syse of equaons 38

9 eoscences 567: CHAPER 3 (RR/Z) 39 (.3) gven by (3.9) we ave (3.36) an (3.37) us, e leas squares soluon s gven by S (3.38) Exaple. Bes-F Parabola Proble e prece au for a parabola s gven by 3 (3.39) were an ave e sae eanngs as n e srag lne proble, an 3 s e coeffcen of e quarac er. Agan, e proble can be wren n e for: (.3) were now we ave

10 eoscences 567: CHAPER 3 (RR/Z) 4 3 (3.4) an 4 3 3, (3.4) As before, we for e leas squares soluon as S [ ] (3.3) Aloug e forwar probles of precng aa for e srag lne an parabolc cases look very fferen, e leas squares soluon s fore n a way a epases e funaenal slary beween e wo probles. For exaple, noce ow e srag-lne proble s bure wn e parabola proble. e upper lef an par of n Equaon (3.4) s e sae as Equaon (3.36). Also, e frs wo enres n n Equaon (3.4) are e sae as Equaon (3.37). ex we conser a four-paraeer exaple Four-Paraeer oograpy Proble Fnally, le's conser a four-paraeer proble, bu s one base on e concep of oograpy. S R ) ( s s v v ) ( s s v v ) ( s s v v ) ( s s v v (3.4)

11 eoscences 567: CHAPER 3 (RR/Z) s s s s (3.43) or (.3) (3.44) (3.45) So, e noral equaons are (3.) s s s s (3.46) or s s s s (3.47) Exaple: s s s 3 s 4, ; en 3 4 By nspecon, s s s 3 s 4 s a soluon, bu so s s s 4, s s 3, or s s 4, s s 3.

12 eoscences 567: CHAPER 3 (RR/Z) Soluons are nonunque! ook back a. Are all of e coluns or rows nepenen? o! a oes a ply abou (an )? Rank < 4. a oes a ply abou ( )? I oes no exs. So oes S exs? o. Oer ways of sayng s: e vecors g o no span e space of. Or, e experenal se-up s no suffcen o unquely eerne e soluon. oe a s analyss can be one wou any aa, base srcly on e experenal esgn. Anoer way o look a : Are e coluns of nepenen? o. For exaple, coeffcens,,, an wll ake e equaons a o ero. a paern oes a sugges s no resolvable? ow a we ave erve e leas squares soluon, an consere soe exaples, we nex urn our aenon o soeng calle e eernancy of e syse of equaons gven by Equaon (.3): (.3) s wll begn o per us o classfy syses of equaons base on e naure of Inroucon 3.5 Deernancy of eas Squares Probles (See Pages 46 5, enke) e ave seen a e leas squares soluon o s gven by S [ ] (3.3) ere s no guaranee, as we saw n Secon 3.4.4, a e soluon even exss. I fals o exs wen e arx as no aeacal nverse. e noe a s square ( ), an s a leas aeacally possble o conser nverng. (.B. e enson of s, nepenen of e nuber of observaons ae). aeacally, we can say e as an nverse, an s unque, wen as rank. e rank of a arx was consere n Secon..3. Essenally, f as rank, en as enoug nforaon n o resolve ngs (n s case, oel paraeers). s appens wen all rows (or equvalenly, snce s square, all coluns) are nepenen. Recall also a nepenen eans you canno wre any row (or colun) as a lnear cobnaon of e oer rows (coluns). wll ave rank < f e nuber of observaons s less an. enke gves e exaple (pp ) of e srag-lne f o a sngle aa pon as an llusraon. If [ ] oes no exs, an nfne nuber of esaes wll all f e aa equally well. aeacally, as rank < f, were s e eernan of. 4

13 eoscences 567: CHAPER 3 (RR/Z) ow, le us nrouce enke s noenclaure base on e naure of an on e precon error. In all cases, e nuber of oel paraeers s an e nuber of observaons s Even-Deerne Probles: If a soluon exss, s unque. e precon error [ obs pre ] s encally ero. For exaple, 5 (3.48) for wc e soluon s [, 3] Overeerne Probles: ypcally, > ore observaons an unknowns, ypcally one canno f all e aa exacly. e leas squares proble falls n s caegory. Conser e followng exaple: 5 3 (3.49) s overeerne case consss of ang one equaon o Equaon (3.48) n e prevous exaple. e leas squares soluon s [.333, 4.833]. e aa can no longer be f exacly Unereerne Probles: ypcally, > ore unknowns an observaons, as no unque soluon. A specal case of e unereerne proble occurs wen you can f e aa exacly, wc s calle e purely unereerne case. e precon error for e purely unereerne case s exacly ero (.e., e aa can be f exacly). An exaple of suc a proble s [] [ ] (3.5) Possble soluons nclue [, ], [.5, ], [5, 9], [/3, /3] an [.4,.]. e soluon w e nu leng, n e nor sense, s [.4,.]. 43

14 eoscences 567: CHAPER 3 (RR/Z) e followng exaple, owever, s also unereerne, bu no coce of,, 3 wll prouce ero precon error. us, s no purely unereerne. 4 3 (3.5) (You g wan o verfy e above exaples. Can you nk of oers?) Aloug I ave sae a overeerne (unereerne) probles ypcally ave > ( < ), s poran o reale a s s no always e case. Conser e followng: (3.5) For s proble, s overeerne, (a s, no coce of can exacly f bo an unless appens o equal ), wle a e sae e an 3 are unereerne. s s e case even oug ere are wo equaons (.e., e las wo) n only wo unknowns (, 3 ). e wo equaons, owever, are no nepenen, snce wo es e nex o las row n equals e las row. us s proble s bo overeerne an unereerne a e sae e. For s reason, I a no very sasfe w enke s noenclaure. As we wll see laer, wen we eal w vecor spaces, e key wll be e sngle values (uc lke egenvalues) an assocae egenvecors for e arx. 3.6 nu eng Soluon e nu leng soluon arses fro e purely unereerne case ( <, an can f e aa exacly). In s secon, we wll evelop e nu leng operaor, usng agrange ulplers an borrowng on e basc eas of nng e leng of a vecor nrouce n Secon 3.4 on leas squares Backgroun Inforaon e begn w wo peces of nforaon:. Frs, [ ] oes no exs. erefore, we canno calculae e leas squares soluon S [ ]. 44

15 eoscences 567: CHAPER 3 (RR/Z). Secon, e precon error e obs pre s exacly equal o ero. o solve unereerne probles, we us a nforaon a s no alreay n. s s calle a pror nforaon. Exaples g nclue e consran a ensy be greaer an ero for rocks, or a v n, e sesc P-wave velocy a e oo falls wn e range 5 < v n < k/s, ec. Anoer a pror assupon s calle soluon splcy. One seeks soluons a are as sple as possble. By analogy o seekng a soluon w e sples sf o e aa (.e., e salles) n e leas squares proble, one can seek a soluon wc nes e oal leng of e oel paraeer vecor,. A frs glance, ere ay no see o be any reason o o s. I oes ake sense for soe cases, owever. Suppose, for exaple, a e unknown oel paraeers are e veloces of pons n a flu. A soluon a ne e leng of woul also ne e knec energy of e syse. us, woul be approprae n s case o ne. I also urns ou o be a nce propery wen one s ong nonlnear probles, an e a one s usng s acually a vecor of canges o e soluon a e prevous sep. en s nce o ave sall sep ses. e requreen of soluon splcy wll lea us, as sown laer, o e so-calle nu leng soluon agrange ulplers (See Page 5 an Appenx A., enke) agrange ulplers coe o n wenever one wses o solve a proble subjec o soe consrans. In e purely unereerne case, ese consrans are a e aa sf be ero. Before conserng e full purely unereerne case, conser e followng scusson of agrange ulplers, osly afer enke. agrange ulplers Unknowns an Consran Conser E(x, y), a funcon of wo varables. Suppose a we wan o ne E(x, y) subjec o soe consran of e for φ(x, y). e seps, usng agrange ulplers, are as follows (nex page): 45

16 eoscences 567: CHAPER 3 (RR/Z) Sep. A e nu n E, sall canges n x an y lea o no cange n E: E(x, (y consan)) E nu x E E E x y x y (3.53) Sep. e consran equaon, owever, says a x an y canno be vare nepenenly (snce e consran equaon s nepenen, or fferen, fro E). Snce φ(x, y) for all x, y, en so us φ(x, y). Bu, φ φ φ x y (3.54) x y Sep 3. For e wege su of (3.53) an (3.54) as E φ E φ E λ φ λ x λ y (3.55) x x y y were λ s a consan. oe a (3.55) ols for arbrary λ. Sep 4. If λ s cosen, owever, n suc a way a E x φ λ x (3.56) en follows a E y φ λ y (3.57) 46

17 eoscences 567: CHAPER 3 (RR/Z) snce a leas one of x, y (n s case, y) s arbrary (.e., y ay be cosen nonero). en λ as been cosen as ncae above, s calle e agrange ulpler. erefore, (3.55) above s equvalen o nng E λφ wou any consrans,.e., x E x φ x ( E λφ) λ (3.58) an y E y φ y ( E λφ) λ (3.59) Sep 5. Fnally, one us sll solve e consran equaon φ(x, y) (3.6) us, e soluon for (x, y) a nes E subjec o e consran a φ (x, y) s gven by (3.58), (3.59), an (3.6). a s, e proble as reuce o e followng ree equaons: E x φ λ x (3.56) an E φ λ y y (3.57) φ (x, y) (3.6) n e ree unknowns (x, y, λ). Exenng e Proble o Unknowns an Consrans e above proceure, use for a proble w wo varables an one consran, can be generale o unknowns n a vecor subjec o consrans φ (), j,...,. s leas o e followng syse of equaons,,..., : w consrans of e for E φ j λ j (3.6) j φ j () (3.6) 47

18 eoscences 567: CHAPER 3 (RR/Z) Applcaon o e Purely Unereerne Proble e backgroun we now ave n agrange ulplers, we are reay o reconser e purely unereerne proble. Frs, we pose e followng proble: fn suc a s ne subjec o e consrans a e aa sf be ero. a s, ne obs pre obs e j j,,..., (3.63) j ψ ( ) λ (3.64) e w respec o e eleens n. e can expan e ers n Equaon (3.64) an oban ψ ( ) k λ k j j j (3.65) en, we ave bu ψ q k j k λ (3.66) j k q j q k q δ kq j an δ jq (3.67) q were δ j s e Kronecker ela, gven by δ j,, j j us ψ q λ q q q,,..., (3.68) In arx noaon over all q, Equaon (3.68) can be wren as λ (3.69) 48

19 eoscences 567: CHAPER 3 (RR/Z) were λ s an vecor conanng e agrange ulplers λ,,...,. oe a λ as enson ( ) x ( ), as requre o be able o subrac fro. ow, solvng explcly for yels e consrans n s case are a e aa be f exacly. a s, Subsung (3.7) no (.3) gves wc ples λ (3.7) (.3) ( λ) (3.7) λ (3.7) were as enson ( ) ( ), or sply. Solvng for λ, wen [ ] exss, yels λ [ ] (3.73) e agrange ulplers are no ens n an of eselves. Bu, upon subsuon of Equaon (3.73) no (3.7), we oban Rearrangng, we arrve a e nu leng soluon, : λ {[ ] } (3.9) [ ] (3.74) were s an arx an e nu leng operaor,, s gven by [ ] (3.75) e above proceure, en, s one a eernes e soluon wc as e nu leng ( nor [ ] / ) aongs e nfne nuber of soluons a f e aa exacly. In pracce, one oes no acually calculae e values of e agrange ulplers, bu goes recly o (3.74) above. e above ervaon sows a e leng of s ne by e nu leng operaor. I ay ake ore sense o seek a soluon a evaes as lle as possble fro soe pror esae of e soluon, <>, raer an fro ero. e ero vecor s, n fac, e pror 49

20 eoscences 567: CHAPER 3 (RR/Z) esae <> for e nu leng soluon gven n Equaon (3.74). If we ws o explcly nclue <>, en Equaon (3.74) becoes <> [ ] [ <>] <> [ <>] [I ]<> (3.76) e noe eaely a Equaon (3.76) reuces o Equaon (3.74) wen <> Coparson of eas Squares an nu eng Soluons In closng s secon, s nsrucve o noe e slary n for beween e nu leng an leas squares soluons: eas Squares: S [ ] (3.3) w S [ ] (3.3) nu eng: <> [ ] [ <>] (3.76) w [ ] (3.75) e nu leng soluon exss wen [ ] exss. Snce s, s s e sae as sayng wen as rank. a s, wen e rows (or coluns) are nepenen. In s case, your ably o prec or calculae eac of e observaons s nepenen Exaple of nu eng Proble Recall e four-paraeer, four-observaon oograpy proble we nrouce n Secon A a e, we noe a e leas squares soluon no exs because [ ] oes no exs, snce oes no conan enoug nforaon o solve for 4 oel paraeers. In e sae way, oes no conan enoug nforaon o f an arbrary 4 observaons, an [ ] oes no exs eer for s exaple. e basc proble s a e four pas roug e srucure o no prove nepenen nforaon. However, f we elnae any one observaon (le s say e four), en we reuce e proble o one were e nu leng soluon exss. In s new case, we ave ree observaons an four unknown oel paraeers, an ence <., wc sll as enoug nforaon o eerne ree observaons unquely, s now gven by (3.77) 5

21 eoscences 567: CHAPER 3 (RR/Z) An s gven by (3.78) ow [ ] oes exs, an we ave [ ] (3.79) If we assue a rue oel gven by [.,.5,.5,.5], en e aa are gven by [.5,.,.5]. e nu leng soluon s gven by [ ] [.875,.65,.65,.375] (3.8) oe a e nu leng soluon s no e "rue" soluon. s s generally e case, snce e "rue" soluon s only one of an nfne nuber of soluons a f e aa exacly, an e nu leng soluon s e one of sores leng. e leng square of e "rue" soluon s.75, wle e leng square of e nu leng soluon s oe also a e nu leng soluon vares fro e "rue" soluon by [.5,.5,.5,.5]. s s e sae recon n oel space (.e., [,,, ] ) a represens e lnear cobnaon of e orgnal coluns of n e exaple n Secon a a o ero. e wll reurn o s subjec wen we ave nrouce sngular value ecoposon an e paronng of oel an aa space. 3.7 ege easures of eng 3.7. Inroucon One way o prove our esaes usng eer e leas squares soluon S [ ] (3.3) or e nu leng soluon <> [ ] [ <>] (3.76) s o use wege easures of e sf vecor 5

22 eoscences 567: CHAPER 3 (RR/Z) e obs pre (3.8) or e oel paraeer vecor, respecvely. e nex wo subsecons wll eal w ese wo approaces ege eas Squares ege easures of e sf Vecor e e saw n Secon 3.4 a e leas squares soluon S was e one a ne e oal sf beween prece an observe aa n e nor sense. a s, E n s ne. Conser a new E, efne as follows: e [ ] e E e e e e e e (3.7) e E e e e (3.8) an were e s an, as ye, unspecfe wegng arx. e can ake any for, bu one convenen coce s e [cov ] (3.83) were [cov ] s e nverse of e covarance arx for e aa. s coce for e wegng arx, aa w large varances are wege less an ones w sall varances. le s s rue n general, s easer o sow n e case were e s agonal. s appens wen [cov ] s agonal, wc ples a e errors n e aa are uncorrelae. e agonal enres n [cov ] are en gven by e recprocal of e agonal enres n [cov ]. a s, f en σ σ [cov] (3.84) O σ 5

23 eoscences 567: CHAPER 3 (RR/Z) σ σ [cov] (3.85) O σ s coce for e, e wege sf becoes E e e e e j je j (3.86) Bu, j δ j (3.87) σ were δ j s e Kronecker ela. us, we ave E σ e (3.88) If e varance σ s large, en e coponen of e error vecor n e recon, e, as lle nfluence on e se of E. s s no e case n e unwege leas squares proble, were an exanaon of Equaon (3.4) clearly sows a eac coponen of e error vecor conrbues equally o e oal sf. Obanng e ege eas Squares Soluon S If one uses E e e e as e wege easure of error, we wll see below a s leas o e wege leas squares soluon: w a wege leas squares operaor S gven by S [ e ] e (3.89) S [ e ] e (3.9) le s s rue n general, s easer o arrve a Equaon (3.89) n e case were e s a agonal arx an e forwar proble s gven by e leas squares proble for a bes-fng srag lne [see Equaon (3.9)]. 53

24 eoscences 567: CHAPER 3 (RR/Z) 54 Sep. j j j e e e e E e e (3.9) ( ) pre obs j j j (3.9) ( ) (3.93) Sep. en E (3.94) an E (3.95) s can be wren n arx for as (3.96) Sep 3. e lef-an se can be facore as O (3.97) or sply e (3.98) Slarly, e rg-an se can be facore as

25 eoscences 567: CHAPER 3 (RR/Z) O (3.99) or sply e (3.) Sep 4. erefore, usng Equaons (3.98) an (3.), Equaon (3.96) can be wren as e e (3.) e wege leas squares soluon, S fro Equaon (3.89) s us S [ e ] e (3.) assung a [ e ] exss, of course ege nu eng e evelopen of a wege nu leng soluon s slar o a of e wege leas squares proble. e seps are as follows. Frs, recall a e nu leng soluon nes. By analogy w wege leas squares, we can coose o ne nsea of. For exaple, f one wses o use en one us replace above w (3.3) [cov ] (3.4) <> (3.5) were <> s e expece, or a pror, esae for e paraeer values. e reason for s s a e varances us represen flucuaons abou ero. In e wege leas squares proble, s assue a e error vecor e wc s beng ne as a ean of ero. us, for e wege nu leng proble, we replace by s eparure fro e expece value <>. erefore, we nrouce a new funcon o be ne: 55

26 eoscences 567: CHAPER 3 (RR/Z) [ <>] [ <>] (3.6) If one en follows e proceure n Secon 3.6 w s new funcon, one evenually (as n I s lef o e suen as an exercse!! ) s le o e wege nu leng soluon gven by <> [ ] [ <>] (3.7) an e wege nu leng operaor,, s gven by [ ] (3.8) s expresson ffers fro Equaon (3.38), page 54 of enke, wc uses raer an. I beleve enke s equaon s wrong. oe a e soluon epens explcly on e expece, or a pror, esae of e oel paraeers <>. e secon er represens a eparure fro e a pror esae <>, base on e naequacy of e forwar proble <> o f e aa exacly. Oer coces for nclue:. D D, were D s a ervave arx (a easure of e flaness of ) of enson ( ) : D (3.9) O O. D D, were D s an ( ) rougness (secon ervave) arx gven by D (3.) O O O oe a for bo coces of D presene, D D s an arx of rank less an (for e frs-ervave case, s of rank, wle for e secon s of rank ). s eans a oes no ave a aeacal nverse. s can nrouce soe nonunqueness no e soluon, bu oes no preclue fnng a soluon. Fnally, noe a any coces for are possble. 56

27 eoscences 567: CHAPER 3 (RR/Z) ege Dape eas Squares In Secons 3.7. an we consere wege versons of e leas squares an nu leng soluons. Bo unwege an wege probles can be very unsable f e arces a ave o be nvere are nearly sngular. In e wege probles, ese are an e (3.) (3.) respecvely, for leas squares an nu leng probles. In s case, one can for a wege penaly, or cos funcon, gven by E ε (3.3) were E s fro Equaon (3.9) for wege leas squares an s fro Equaon (3.6) for e wege nu leng proble. One en goes roug e exercse of nng Equaon (3.3) w respec o e oel paraeers, an obans wa s known as e wege, ape leas squares soluon D. I s, n fac, a wege x of e wege leas squares an wege nu leng soluons. One fns a D s gven by eer or D <> [ e ε ] e [ <>] (3.4) D <> [ ε e ] [ <>] (3.5) were e wege, ape leas squares operaor, D, s gven by or D [ e ε ] e (3.6) D [ ε e ] (3.7) e wo fors for D can be sown o be equvalen. e ε er as e effec of apng e nsably. As we wll see laer n Caper 6 usng sngular-value ecoposon, e above proceure nes e effecs of sall sngular values n e or. In e nex secon we wll learn wo eos of nclung a pror nforaon an consrans n nverse probles. 57

28 eoscences 567: CHAPER 3 (RR/Z) 3.8 A Pror Inforaon an Consrans (See enke, Pages 55 57) 3.8. Inroucon Anoer coon ype of a pror nforaon akes e for of lnear equaly consrans: F (3.8) were F s a P arx, an P s e nuber of lnear consrans consere. As an exaple, conser e case for wc e ean of e oel paraeers s known. In s case w only one consran, we ave (3.9) en, Equaon (3.8) can be wren as F [ ] (3.) As anoer exaple, suppose a e j oel paraeer j s acually known n avance. a s, suppose en Equaon (3.8) akes e for j (3.) F [ ] j j colun (3.) oe a for s exaple woul be possble o reove j as an unknown, ereby reucng e syse of equaons by one. I s ofen preferable o use Equaon (3.), even n s case, raer an rewrng e forwar proble n a copuer coe. 58

29 eoscences 567: CHAPER 3 (RR/Z) 3.8. A Frs Approac o Inclung Consrans e wll conser wo basc approaces o nclung consrans n nverse probles. Eac as s srengs an weaknesses. e frs nclues e consran arx F n e forwar proble, an e secon uses agrange ulplers. e seps for e frs approac are as follows. Sep. Inclue F as rows n a new a operaes on e orgnal : F ( P) ( P) (3.3) Sep. e new ( P) sf vecor e becoes obs pre e pre (3.4) ( P) ( P) Perforng a leas squares nverson woul ne e new e e, base on Equaon (3.4). e fference pre (3.5) wc represens e sf o e consrans, ay be sall, bu s unlkely a woul vans, wc us f e consrans are o be sasfe. Sep 3. Inrouce a wege sf: were e s a agonal arx of e for e e e (3.6) 59

30 eoscences 567: CHAPER 3 (RR/Z) 6 _ (bg #) (bg #) (bg #) P e O O O O (3.7) a s, as relavely large values for e las P enres assocae w e consran equaons. Recallng e for of e wegng arx use n Equaon (3.83), one sees a Equaon (3.7) s equvalen o assgnng e consrans very sall varances. Hence, a wege leas squares approac n s case wll gve large weg o fng e consrans. e se of e bg nubers n e us be eerne eprcally. One seeks a nuber a leas o a soluon a sasfes e consrans accepably, bu oes no ake e arx n Equaon (3.) a us be nvere o oban e soluon oo poorly conone. arces w a large range of values n e en o be poorly conone. Conser e exaple of e soong consran ere, P : D (3.8) were e ensons of D ( ),, an ( ). e augene equaons are D (3.9) e's use e followng wegng arx: P P e I I θ θ θ O (3.3) were θ s a consan. s resuls n e followng, w e ensons of e ree arces n e frs se of brackes beng ( P), ( P) ( P), an ( P), respecvely:

31 eoscences 567: CHAPER 3 (RR/Z) 6 S I I D D I I D θ θ < > < > [ ] D θ [ ] D θ e lower arces avng ensons of ( P ) ( P). [ θ D D] [ ] (3.3) ] [ D D θ θ (3.3) e ree arces wn (3.3) ave ensons ( P), ( P), an, respecvely, wc prouce an arx wen evaluae. In s for we can see s s sply e S for e proble D θ (3.33) By varyng θ, we can rae off e sf an e sooness for e oel A Secon Approac o Inclung Consrans enever e subjec of consrans s rase, agrange ulplers coe o n! e seps for s approac are as follows. Sep. For a wege su of e sf an e consrans: φ() e e [F ] λ (3.34) wc can be expane as ) ( j j j P j j j F λ φ (3.35) were ncaes a fference fro Equaon (3.43) on page 56 n enke, an were ere are P lnear equaly consrans an were e facor of as been ae as a aer of convenence o ake e for of e fnal answer spler.

32 eoscences 567: CHAPER 3 (RR/Z) Sep. One en akes e parals of Equaon (3.35) w respec o all e enres n an ses e o ero. a s, wc leas o φ( ) q,,..., q (3.36) jq j j q P λ F q,,..., (3.37) q were e frs wo ers are e sae as e leas squares case n Equaon (3.5) snce ey coe recly fro e e an e las er sows wy e facor of was ae n Equaon (3.35). Sep 3. Equaon (3.37) s no e coplee escrpon of e proble. o e equaons n Equaon (3.37), P consran equaons us also be ae. In arx for, s yels F F λ (3.38) Sep 4. ( P) ( P) ( P) ( P) e above syse of equaons can be solve as λ F F (3.39) As an exaple, conser consranng a srag lne o pass roug soe pon (', '). a s, for observaons, we ave subjec o e sngle consran en Equaon (3.8) as e for, (3.4) (3.4) F [ ] (3.4) e can en wre ou Equaon (3.39) explcly, an oban e followng: 6

33 eoscences 567: CHAPER 3 (RR/Z) 63 λ (3.43) oe e slary beween Equaons (3.43) an (3.36), e leas squares soluon o fng a srag lne o a se of pons wou any consrans: S (3.36) If you wane o ge e sae resul for e srag lne passng roug a pon usng e frs approac w e, you woul assgn,..., (3.44) an, bg # (3.45) wc s equvalen o assgnng a sall varance (relave o e unconsrane par of e proble) o e consran equaon. e soluon obane w Equaon (3.3) soul approac e soluon obane usng Equaon (3.43). oe a s easy o consran lnes o pass roug e orgn usng Equaon (3.43). In s case, we ave (3.46) an Equaon (3.43) becoes λ (3.47) e avanage of usng e agrange ulpler approac o consrans s a e consrans wll be sasfe exacly. I ofen appens, owever, a e consrans are only approxaely known, an usng agrange ulplers o f e consrans exacly ay no be approprae. An exaple g be a gravy nverson were ep o berock a one pon s known fro rllng. Consranng e ep o be exacly e rll ep ay be sleang f e ep n e oel s an average over soe area. en e exac ep a one pon ay no be e bes esae of e ep over e area n queson. A secon savanage of e agrange ulpler approac s a as one equaon o e syse of equaons n Equaon (3.43) for eac consran. s can a up quckly, akng e nverson conserably ore ffcul copuaonally.

34 eoscences 567: CHAPER 3 (RR/Z) An enrely fferen class of consrans are calle lnear nequaly consrans an ake e for F (3.48) ese can be solve usng lnear prograng ecnques, bu we wll no conser e furer n s class Sesc Recever Funcon Exaple e followng s an exaple of usng soong consrans n an nverse proble. Conser a general proble n e seres analyss, w a ela funcon npu. en e oupu fro e "oel" s e reens funcon of e syse. e nverse proble s s: ven e reens funcon, fn e paraeers of e oel. npu pulse oel reens Funcon In a lle ore concree for: 3. oel space c c c c 3... F() 3. aa space a c F. If s very nosy, en S wll ave a g-frequency coponen o ry o "f e nose," bu s wll no be real. How o we preven s? So far, we ave learne wo ways: use S f we know cov, or f no, we can place a soong consran on. An exaple of s approac usng recever funcon nversons can be foun n Aon, C. J.,. E. Ranall an. Zan, On e nonunqueness of recever funcon nversons, J. eopys. Res., 95, 5,33-5,38, 99. e poran pons are as follows: 64

35 eoscences 567: CHAPER 3 (RR/Z) s approac s use n e real worl. e forwar proble s wren j F j j,, 3... s s nonlnear, bu afer lnearaon (scusse n Caper 4), e equaons are e sae as scusse prevously (w nor fferences). oe e correlaon beween e rougness n e oel an e rougness n e aa. e way o coose e wegng paraeer, σ, s o plo e rae-off beween sooness an wavefor f. 3.9 Varances of oel Paraeers (See Pages 58 6, enke) 3.9. Inroucon Daa errors are appe no oel paraeer errors roug any ype of nverse. e noe n Caper [Equaons (.6) (.63)] a f es v (.6) an f [cov ] s e aa covarance arx wc escrbes e aa errors, en e a poseror oel covarance arx s gven by [cov ] [cov ] (.63) e covarance arx n Equaon (.63) s calle e a poseror oel covarance arx because s calculae afer e fac. I gves wa are soees calle e foral unceranes n e oel paraeers. I s fferen fro e a pror oel covarance arx of Equaon (3.85), wc s use o consran e unereerne proble. e a poseror covarance arx n Equaon (.63) sows explcly e appng of aa errors no unceranes n e oel paraeers. Aloug e appng wll be clearer once we conser e generale nverse n Caper 7, s nsrucve a s pon o conser applyng Equaon (.63) o e leas squares an nu leng probles Applcaon o eas Squares e can apply Equaon (.63) o e leas squares proble an oban 65

36 eoscences 567: CHAPER 3 (RR/Z) [cov ] {[ ] }[cov ]{[ ] } (3.49) Furer, f [cov ] s gven by [cov ] σ I (3.5) en [cov ] [ ] [σ I]{[ ] } σ [ ] {[ ] } σ {[ ] } σ [ ] (3.5) snce e ranspose of a syerc arx reurns e orgnal arx Applcaon o e nu eng Proble Applcaon of Equaon (.63) o e nu leng proble leas o e followng for e a poseror oel covarance arx: If e aa covarance arx s agan gven by [cov ] { [ ] }[cov ]{ [ ] } (3.5) we oban [cov ] σ I (3.53) [cov ] σ [ ] (3.54) were [ ] [ ] [ ] (3.55) eoercal Inerpreaon of Varance ere s anoer way o look a e varance of oel paraeer esaes for e leas squares proble a consers e precon error, or sf, o e aa. Recall a we efne e sf E as E e e [ pre ] [ pre ] [ ] [ ] (3.3) 66

37 eoscences 567: CHAPER 3 (RR/Z) wc explcly sows e epenence of E on e oel paraeers. a s, we ave E E() (3.56) If E() as a sarp, well-efne nu, en we can conclue a our soluon S s well consrane. Conversely, f E() as a broa, poorly efne nu, en we conclue a our soluon S s poorly consrane. Afer Fgure 3., page 59, of enke, we ave (nex page), (a) (b) E() E() E E es oel paraeer es oel paraeer (a) e bes esae es of oel paraeer occurs a e nu of E(). If e nu s relavely narrow, en rano flucuaons n E() lea o only sall errors n es. (b) If e nu s we, en large errors n can occur. One way o quanfy s qualave observaon s o reale a e w of e nu for E() s relae o e curvaure, or secon ervave, of E() a e nu. For e leas squares proble, we ave E [ ] S S (3.57) Evaluang e rg-an se, we ave for e q er E q q [ ] j (3.58) j q j ( ) j j (3.59) q q j 67

38 eoscences 567: CHAPER 3 (RR/Z) q jq (3.6) j q j Usng e sae seps as we n e ervaon of e leas squares soluon n Equaons (3.4) (3.9), s possble o see a Equaon (3.6) represens e q er n [ ]. Cobnng e q equaons no arx noaon yels [ ] { [ ] } (3.6) Evaluang e frs ervave on e rg-an se of Equaon (3.6), we ave for e q er jq (3.6) j { [ ] } q q q j j q ( ) (3.63) j q j q q (3.64) wc we recogne as e (q, q) enry n. erefore, we can wre e arx equaon as { [ ] } (3.65) Fro Equaons (3.5) (3.58) we can conclue a e secon ervave of E n e leas squares proble s proporonal o. a s, E S (consan) (3.66) Furerore, fro Equaon (3.5) we ave a [cov ] s proporonal o [ ]. erefore, we can assocae large values of e secon ervave of E, gven by (3.66) w () sarp curvaure for E, () narrow well for E, an (3) goo (.e., sall) oel varance. As enke pons ou, [cov ] can be nerpree as beng conrolle eer by () e varance of e aa es a easure of ow error n e aa s appe no oel paraeers or () a consan es e curvaure of e precon error a s nu. 68

39 eoscences 567: CHAPER 3 (RR/Z) I lke enke s suary for s caper (page 6) on s aeral very uc. Hence, I've reprouce s closng paragrap for you as follows: e eos of solvng nverse probles a ave been scusse n s caper epase e aa an oel paraeers eselves. e eo of leas squares esaes e oel paraeers w salles precon leng. e eo of nu leng esaes e sples oel paraeers. e eas of aa an oel paraeers are very concree an sragforwar, an e eos base on e are sple an easly unersoo. evereless, s vewpon ens o obscure an poran aspec of nverse probles. aely, a e naure of e proble epens ore on e relaonsp beween e aa an oel paraeers an on e aa or oel paraeers eselves. I soul, for nsance, be possble o ell a well-esgne experen fro a poorly esgne one wou knowng wa e nuercal values of e aa or oel paraeers are, or even e range n wc ey fall. Before conserng e relaonsps ple n e appng beween oel paraeers an aa n Caper 5, we exen wa we now know abou lnear nverse probles o nonlnear probles n e nex caper. 69

Periodic motions of a class of forced infinite lattices with nearest neighbor interaction

Periodic motions of a class of forced infinite lattices with nearest neighbor interaction J. Mah. Anal. Appl. 34 28 44 52 www.elsever.co/locae/jaa Peroc oons of a class of force nfne laces wh neares neghbor neracon Chao Wang a,b,, Dngban Qan a a School of Maheacal Scence, Suzhou Unversy, Suzhou

More information

CHAPTER 5: MULTIVARIATE METHODS

CHAPTER 5: MULTIVARIATE METHODS CHAPER 5: MULIVARIAE MEHODS Mulvarae Daa 3 Mulple measuremens (sensors) npus/feaures/arbues: -varae N nsances/observaons/eamples Each row s an eample Each column represens a feaure X a b correspons o he

More information

Normal Random Variable and its discriminant functions

Normal Random Variable and its discriminant functions Noral Rando Varable and s dscrnan funcons Oulne Noral Rando Varable Properes Dscrnan funcons Why Noral Rando Varables? Analycally racable Works well when observaon coes for a corruped snle prooype 3 The

More information

THEORETICAL AUTOCORRELATIONS. ) if often denoted by γ. Note that

THEORETICAL AUTOCORRELATIONS. ) if often denoted by γ. Note that THEORETICAL AUTOCORRELATIONS Cov( y, y ) E( y E( y))( y E( y)) ρ = = Var( y) E( y E( y)) =,, L ρ = and Cov( y, y ) s ofen denoed by whle Var( y ) f ofen denoed by γ. Noe ha γ = γ and ρ = ρ and because

More information

Response of MDOF systems

Response of MDOF systems Response of MDOF syses Degree of freedo DOF: he nu nuber of ndependen coordnaes requred o deerne copleely he posons of all pars of a syse a any nsan of e. wo DOF syses hree DOF syses he noral ode analyss

More information

John Geweke a and Gianni Amisano b a Departments of Economics and Statistics, University of Iowa, USA b European Central Bank, Frankfurt, Germany

John Geweke a and Gianni Amisano b a Departments of Economics and Statistics, University of Iowa, USA b European Central Bank, Frankfurt, Germany Herarchcal Markov Normal Mxure models wh Applcaons o Fnancal Asse Reurns Appendx: Proofs of Theorems and Condonal Poseror Dsrbuons John Geweke a and Gann Amsano b a Deparmens of Economcs and Sascs, Unversy

More information

THE PUBLISHING HOUSE PROCEEDINGS OF THE ROMANIAN ACADEMY, Series A, OF THE ROMANIAN ACADEMY Volume 9, Number 1/2008, pp

THE PUBLISHING HOUSE PROCEEDINGS OF THE ROMANIAN ACADEMY, Series A, OF THE ROMANIAN ACADEMY Volume 9, Number 1/2008, pp THE PUBLISHING HOUSE PROCEEDINGS OF THE ROMNIN CDEMY, Seres, OF THE ROMNIN CDEMY Volue 9, Nuber /008, pp. 000 000 ON CIMMINO'S REFLECTION LGORITHM Consann POP Ovdus Unversy of Consana, Roana, E-al: cpopa@unv-ovdus.ro

More information

Midterm Exam. Thursday, April hour, 15 minutes

Midterm Exam. Thursday, April hour, 15 minutes Economcs of Grow, ECO560 San Francsco Sae Unvers Mcael Bar Sprng 04 Mderm Exam Tursda, prl 0 our, 5 mnues ame: Insrucons. Ts s closed boo, closed noes exam.. o calculaors of an nd are allowed. 3. Sow all

More information

Lecture 6: Learning for Control (Generalised Linear Regression)

Lecture 6: Learning for Control (Generalised Linear Regression) Lecure 6: Learnng for Conrol (Generalsed Lnear Regresson) Conens: Lnear Mehods for Regresson Leas Squares, Gauss Markov heorem Recursve Leas Squares Lecure 6: RLSC - Prof. Sehu Vjayakumar Lnear Regresson

More information

In the complete model, these slopes are ANALYSIS OF VARIANCE FOR THE COMPLETE TWO-WAY MODEL. (! i+1 -! i ) + [(!") i+1,q - [(!

In the complete model, these slopes are ANALYSIS OF VARIANCE FOR THE COMPLETE TWO-WAY MODEL. (! i+1 -! i ) + [(!) i+1,q - [(! ANALYSIS OF VARIANCE FOR THE COMPLETE TWO-WAY MODEL The frs hng o es n wo-way ANOVA: Is here neracon? "No neracon" means: The man effecs model would f. Ths n urn means: In he neracon plo (wh A on he horzonal

More information

Lecture VI Regression

Lecture VI Regression Lecure VI Regresson (Lnear Mehods for Regresson) Conens: Lnear Mehods for Regresson Leas Squares, Gauss Markov heorem Recursve Leas Squares Lecure VI: MLSC - Dr. Sehu Vjayakumar Lnear Regresson Model M

More information

Learning Objectives. Self Organization Map. Hamming Distance(1/5) Introduction. Hamming Distance(3/5) Hamming Distance(2/5) 15/04/2015

Learning Objectives. Self Organization Map. Hamming Distance(1/5) Introduction. Hamming Distance(3/5) Hamming Distance(2/5) 15/04/2015 /4/ Learnng Objecves Self Organzaon Map Learnng whou Exaples. Inroducon. MAXNET 3. Cluserng 4. Feaure Map. Self-organzng Feaure Map 6. Concluson 38 Inroducon. Learnng whou exaples. Daa are npu o he syse

More information

Lecture 18: The Laplace Transform (See Sections and 14.7 in Boas)

Lecture 18: The Laplace Transform (See Sections and 14.7 in Boas) Lecure 8: The Lalace Transform (See Secons 88- and 47 n Boas) Recall ha our bg-cure goal s he analyss of he dfferenal equaon, ax bx cx F, where we emloy varous exansons for he drvng funcon F deendng on

More information

Lecture Slides for INTRODUCTION TO. Machine Learning. ETHEM ALPAYDIN The MIT Press,

Lecture Slides for INTRODUCTION TO. Machine Learning. ETHEM ALPAYDIN The MIT Press, Lecure ldes for INRODUCION O Machne Learnng EHEM ALPAYDIN he MI Press, 004 alpaydn@boun.edu.r hp://.cpe.boun.edu.r/~ehe/l CHAPER 6: Densonaly Reducon Why Reduce Densonaly?. Reduces e copley: Less copuaon.

More information

CS434a/541a: Pattern Recognition Prof. Olga Veksler. Lecture 4

CS434a/541a: Pattern Recognition Prof. Olga Veksler. Lecture 4 CS434a/54a: Paern Recognon Prof. Olga Veksler Lecure 4 Oulne Normal Random Varable Properes Dscrmnan funcons Why Normal Random Varables? Analycally racable Works well when observaon comes form a corruped

More information

Solution in semi infinite diffusion couples (error function analysis)

Solution in semi infinite diffusion couples (error function analysis) Soluon n sem nfne dffuson couples (error funcon analyss) Le us consder now he sem nfne dffuson couple of wo blocks wh concenraon of and I means ha, n a A- bnary sysem, s bondng beween wo blocks made of

More information

Physics 3 (PHYF144) Chap 3: The Kinetic Theory of Gases - 1

Physics 3 (PHYF144) Chap 3: The Kinetic Theory of Gases - 1 Physcs (PYF44) ha : he nec heory of Gases -. Molecular Moel of an Ieal Gas he goal of he olecular oel of an eal gas s o unersan he acroscoc roeres (such as ressure an eeraure ) of gas n e of s croscoc

More information

( ) () we define the interaction representation by the unitary transformation () = ()

( ) () we define the interaction representation by the unitary transformation () = () Hgher Order Perurbaon Theory Mchael Fowler 3/7/6 The neracon Represenaon Recall ha n he frs par of hs course sequence, we dscussed he chrödnger and Hesenberg represenaons of quanum mechancs here n he chrödnger

More information

On Pfaff s solution of the Pfaff problem

On Pfaff s solution of the Pfaff problem Zur Pfaff scen Lösung des Pfaff scen Probles Mat. Ann. 7 (880) 53-530. On Pfaff s soluton of te Pfaff proble By A. MAYER n Lepzg Translated by D. H. Delpenc Te way tat Pfaff adopted for te ntegraton of

More information

Linear Response Theory: The connection between QFT and experiments

Linear Response Theory: The connection between QFT and experiments Phys540.nb 39 3 Lnear Response Theory: The connecon beween QFT and expermens 3.1. Basc conceps and deas Q: ow do we measure he conducvy of a meal? A: we frs nroduce a weak elecrc feld E, and hen measure

More information

The Single Particle Path Integral and Its Calculations. Lai Zhong Yuan

The Single Particle Path Integral and Its Calculations. Lai Zhong Yuan Te Sngle Parcle Pa Inegral and Is Calculaons La Zong Yuan Suary O Conens Inroducon and Movaon Soe Eaples n Calculang Pa Inegrals Te Free Parcle Te Haronc Oscllaor Perurbaon Epansons Inroducon and Movaon

More information

Notes on the stability of dynamic systems and the use of Eigen Values.

Notes on the stability of dynamic systems and the use of Eigen Values. Noes on he sabl of dnamc ssems and he use of Egen Values. Source: Macro II course noes, Dr. Davd Bessler s Tme Seres course noes, zarads (999) Ineremporal Macroeconomcs chaper 4 & Techncal ppend, and Hamlon

More information

THE PREDICTION OF COMPETITIVE ENVIRONMENT IN BUSINESS

THE PREDICTION OF COMPETITIVE ENVIRONMENT IN BUSINESS THE PREICTION OF COMPETITIVE ENVIRONMENT IN BUSINESS INTROUCTION The wo dmensonal paral dfferenal equaons of second order can be used for he smulaon of compeve envronmen n busness The arcle presens he

More information

Let s treat the problem of the response of a system to an applied external force. Again,

Let s treat the problem of the response of a system to an applied external force. Again, Page 33 QUANTUM LNEAR RESPONSE FUNCTON Le s rea he problem of he response of a sysem o an appled exernal force. Agan, H() H f () A H + V () Exernal agen acng on nernal varable Hamlonan for equlbrum sysem

More information

2/20/2013. EE 101 Midterm 2 Review

2/20/2013. EE 101 Midterm 2 Review //3 EE Mderm eew //3 Volage-mplfer Model The npu ressance s he equalen ressance see when lookng no he npu ermnals of he amplfer. o s he oupu ressance. I causes he oupu olage o decrease as he load ressance

More information

CH.3. COMPATIBILITY EQUATIONS. Continuum Mechanics Course (MMC) - ETSECCPB - UPC

CH.3. COMPATIBILITY EQUATIONS. Continuum Mechanics Course (MMC) - ETSECCPB - UPC CH.3. COMPATIBILITY EQUATIONS Connuum Mechancs Course (MMC) - ETSECCPB - UPC Overvew Compably Condons Compably Equaons of a Poenal Vecor Feld Compably Condons for Infnesmal Srans Inegraon of he Infnesmal

More information

Lecture 11 SVM cont

Lecture 11 SVM cont Lecure SVM con. 0 008 Wha we have done so far We have esalshed ha we wan o fnd a lnear decson oundary whose margn s he larges We know how o measure he margn of a lnear decson oundary Tha s: he mnmum geomerc

More information

Homework 2 Solutions

Homework 2 Solutions Mah 308 Differenial Equaions Fall 2002 & 2. See he las page. Hoework 2 Soluions 3a). Newon s secon law of oion says ha a = F, an we know a =, so we have = F. One par of he force is graviy, g. However,

More information

. The geometric multiplicity is dim[ker( λi. number of linearly independent eigenvectors associated with this eigenvalue.

. The geometric multiplicity is dim[ker( λi. number of linearly independent eigenvectors associated with this eigenvalue. Lnear Algebra Lecure # Noes We connue wh he dscusson of egenvalues, egenvecors, and dagonalzably of marces We wan o know, n parcular wha condons wll assure ha a marx can be dagonalzed and wha he obsrucons

More information

A Modified Genetic Algorithm Comparable to Quantum GA

A Modified Genetic Algorithm Comparable to Quantum GA A Modfed Genec Algorh Coparable o Quanu GA Tahereh Kahookar Toos Ferdows Unversy of Mashhad _k_oos@wal.u.ac.r Habb Rajab Mashhad Ferdows Unversy of Mashhad h_rajab@ferdows.u.ac.r Absrac: Recenly, researchers

More information

On One Analytic Method of. Constructing Program Controls

On One Analytic Method of. Constructing Program Controls Appled Mahemacal Scences, Vol. 9, 05, no. 8, 409-407 HIKARI Ld, www.m-hkar.com hp://dx.do.org/0.988/ams.05.54349 On One Analyc Mehod of Consrucng Program Conrols A. N. Kvko, S. V. Chsyakov and Yu. E. Balyna

More information

January Examinations 2012

January Examinations 2012 Page of 5 EC79 January Examnaons No. of Pages: 5 No. of Quesons: 8 Subjec ECONOMICS (POSTGRADUATE) Tle of Paper EC79 QUANTITATIVE METHODS FOR BUSINESS AND FINANCE Tme Allowed Two Hours ( hours) Insrucons

More information

Comparison between the Discrete and Continuous Time Models

Comparison between the Discrete and Continuous Time Models Comparison beween e Discree and Coninuous Time Models D. Sulsky June 21, 2012 1 Discree o Coninuous Recall e discree ime model Î = AIS Ŝ = S Î. Tese equaions ell us ow e populaion canges from one day o

More information

Sklar: Sections (4.4.2 is not covered).

Sklar: Sections (4.4.2 is not covered). COSC 44: Dgal Councaons Insrucor: Dr. Ar Asf Deparen of Copuer Scence and Engneerng York Unversy Handou # 6: Bandpass Modulaon opcs:. Phasor Represenaon. Dgal Modulaon Schees: PSK FSK ASK APK ASK/FSK)

More information

NATIONAL UNIVERSITY OF SINGAPORE PC5202 ADVANCED STATISTICAL MECHANICS. (Semester II: AY ) Time Allowed: 2 Hours

NATIONAL UNIVERSITY OF SINGAPORE PC5202 ADVANCED STATISTICAL MECHANICS. (Semester II: AY ) Time Allowed: 2 Hours NATONAL UNVERSTY OF SNGAPORE PC5 ADVANCED STATSTCAL MECHANCS (Semeser : AY 1-13) Tme Allowed: Hours NSTRUCTONS TO CANDDATES 1. Ths examnaon paper conans 5 quesons and comprses 4 prned pages.. Answer all

More information

Density Matrix Description of NMR BCMB/CHEM 8190

Density Matrix Description of NMR BCMB/CHEM 8190 Densy Marx Descrpon of NMR BCMBCHEM 89 Operaors n Marx Noaon Alernae approach o second order specra: ask abou x magnezaon nsead of energes and ranson probables. If we say wh one bass se, properes vary

More information

. The geometric multiplicity is dim[ker( λi. A )], i.e. the number of linearly independent eigenvectors associated with this eigenvalue.

. The geometric multiplicity is dim[ker( λi. A )], i.e. the number of linearly independent eigenvectors associated with this eigenvalue. Mah E-b Lecure #0 Noes We connue wh he dscusson of egenvalues, egenvecors, and dagonalzably of marces We wan o know, n parcular wha condons wll assure ha a marx can be dagonalzed and wha he obsrucons are

More information

Graduate Macroeconomics 2 Problem set 5. - Solutions

Graduate Macroeconomics 2 Problem set 5. - Solutions Graduae Macroeconomcs 2 Problem se. - Soluons Queson 1 To answer hs queson we need he frms frs order condons and he equaon ha deermnes he number of frms n equlbrum. The frms frs order condons are: F K

More information

V.Abramov - FURTHER ANALYSIS OF CONFIDENCE INTERVALS FOR LARGE CLIENT/SERVER COMPUTER NETWORKS

V.Abramov - FURTHER ANALYSIS OF CONFIDENCE INTERVALS FOR LARGE CLIENT/SERVER COMPUTER NETWORKS R&RATA # Vol.) 8, March FURTHER AALYSIS OF COFIDECE ITERVALS FOR LARGE CLIET/SERVER COMPUTER ETWORKS Vyacheslav Abramov School of Mahemacal Scences, Monash Unversy, Buldng 8, Level 4, Clayon Campus, Wellngon

More information

Chapter 2 The Derivative Applied Calculus 107. We ll need a rule for finding the derivative of a product so we don t have to multiply everything out.

Chapter 2 The Derivative Applied Calculus 107. We ll need a rule for finding the derivative of a product so we don t have to multiply everything out. Chaper The Derivaive Applie Calculus 107 Secion 4: Prouc an Quoien Rules The basic rules will le us ackle simple funcions. Bu wha happens if we nee he erivaive of a combinaion of hese funcions? Eample

More information

Delay-Range-Dependent Stability Analysis for Continuous Linear System with Interval Delay

Delay-Range-Dependent Stability Analysis for Continuous Linear System with Interval Delay Inernaonal Journal of Emergng Engneerng esearch an echnology Volume 3, Issue 8, Augus 05, PP 70-76 ISSN 349-4395 (Prn) & ISSN 349-4409 (Onlne) Delay-ange-Depenen Sably Analyss for Connuous Lnear Sysem

More information

On Convergence Rate of Concave-Convex Procedure

On Convergence Rate of Concave-Convex Procedure On Converence Rae o Concave-Conve Proceure Ian E.H. Yen Nanun Pen Po-We Wan an Shou-De Ln Naonal awan Unvers OP 202 Oulne Derence o Conve Funcons.c. Prora Applcaons n SVM leraure Concave-Conve Proceure

More information

Chapter 6 DETECTION AND ESTIMATION: Model of digital communication system. Fundamental issues in digital communications are

Chapter 6 DETECTION AND ESTIMATION: Model of digital communication system. Fundamental issues in digital communications are Chaper 6 DCIO AD IMAIO: Fndaenal sses n dgal concaons are. Deecon and. saon Deecon heory: I deals wh he desgn and evalaon of decson ang processor ha observes he receved sgnal and gesses whch parclar sybol

More information

Transcription: Messenger RNA, mrna, is produced and transported to Ribosomes

Transcription: Messenger RNA, mrna, is produced and transported to Ribosomes Quanave Cenral Dogma I Reference hp//book.bonumbers.org Inaon ranscrpon RNA polymerase and ranscrpon Facor (F) s bnds o promoer regon of DNA ranscrpon Meenger RNA, mrna, s produced and ranspored o Rbosomes

More information

J i-1 i. J i i+1. Numerical integration of the diffusion equation (I) Finite difference method. Spatial Discretization. Internal nodes.

J i-1 i. J i i+1. Numerical integration of the diffusion equation (I) Finite difference method. Spatial Discretization. Internal nodes. umercal negraon of he dffuson equaon (I) Fne dfference mehod. Spaal screaon. Inernal nodes. R L V For hermal conducon le s dscree he spaal doman no small fne spans, =,,: Balance of parcles for an nernal

More information

From the Hamilton s Variational Principle to the Hamilton Jacobi Equation

From the Hamilton s Variational Principle to the Hamilton Jacobi Equation A. La osa Lecure Noes U-hyscs 4/5 ECE 598 I N T O D U C T I O N T O U A N T U M M E C A N I C ro he alon s Varaonal rncle o he alon Jacob Equaon ef: alean an Croer Theorecal Mechancs Wley 97. Ths s one

More information

Supporting information How to concatenate the local attractors of subnetworks in the HPFP

Supporting information How to concatenate the local attractors of subnetworks in the HPFP n Effcen lgorh for Idenfyng Prry Phenoype rcors of Lrge-Scle Boolen Newor Sng-Mo Choo nd Kwng-Hyun Cho Depren of Mhecs Unversy of Ulsn Ulsn 446 Republc of Kore Depren of Bo nd Brn Engneerng Kore dvnced

More information

Water Hammer in Pipes

Water Hammer in Pipes Waer Haer Hydraulcs and Hydraulc Machnes Waer Haer n Pes H Pressure wave A B If waer s flowng along a long e and s suddenly brough o res by he closng of a valve, or by any slar cause, here wll be a sudden

More information

Ordinary Differential Equations in Neuroscience with Matlab examples. Aim 1- Gain understanding of how to set up and solve ODE s

Ordinary Differential Equations in Neuroscience with Matlab examples. Aim 1- Gain understanding of how to set up and solve ODE s Ordnary Dfferenal Equaons n Neuroscence wh Malab eamples. Am - Gan undersandng of how o se up and solve ODE s Am Undersand how o se up an solve a smple eample of he Hebb rule n D Our goal a end of class

More information

Density Matrix Description of NMR BCMB/CHEM 8190

Density Matrix Description of NMR BCMB/CHEM 8190 Densy Marx Descrpon of NMR BCMBCHEM 89 Operaors n Marx Noaon If we say wh one bass se, properes vary only because of changes n he coeffcens weghng each bass se funcon x = h< Ix > - hs s how we calculae

More information

F-Tests and Analysis of Variance (ANOVA) in the Simple Linear Regression Model. 1. Introduction

F-Tests and Analysis of Variance (ANOVA) in the Simple Linear Regression Model. 1. Introduction ECOOMICS 35* -- OTE 9 ECO 35* -- OTE 9 F-Tess and Analyss of Varance (AOVA n he Smple Lnear Regresson Model Inroducon The smple lnear regresson model s gven by he followng populaon regresson equaon, or

More information

How about the more general "linear" scalar functions of scalars (i.e., a 1st degree polynomial of the following form with a constant term )?

How about the more general linear scalar functions of scalars (i.e., a 1st degree polynomial of the following form with a constant term )? lmcd Lnear ransformaon of a vecor he deas presened here are que general hey go beyond he radonal mar-vecor ype seen n lnear algebra Furhermore, hey do no deal wh bass and are equally vald for any se of

More information

AT&T Labs Research, Shannon Laboratory, 180 Park Avenue, Room A279, Florham Park, NJ , USA

AT&T Labs Research, Shannon Laboratory, 180 Park Avenue, Room A279, Florham Park, NJ , USA Machne Learnng, 43, 65 91, 001 c 001 Kluwer Acadec Publshers. Manufacured n The Neherlands. Drfng Gaes ROBERT E. SCHAPIRE schapre@research.a.co AT&T Labs Research, Shannon Laboraory, 180 Park Avenue, Roo

More information

LECTURE :FACTOR ANALYSIS

LECTURE :FACTOR ANALYSIS LCUR :FACOR ANALYSIS Rta Osadchy Based on Lecture Notes by A. Ng Motvaton Dstrbuton coes fro MoG Have suffcent aount of data: >>n denson Use M to ft Mture of Gaussans nu. of tranng ponts If

More information

An Adaptive Fuzzy Control Method for Spacecrafts Based on T-S Model

An Adaptive Fuzzy Control Method for Spacecrafts Based on T-S Model ELKOMNIKA, Vol., No., Noveber 20, pp. 6879~6888 e-issn: 2087-278X 6879 An Aapve Fuzzy Conrol Meho for Spacecrafs Base on -S Moel Wang Q*, Gao an 2, He He School of Elecronc Inforaon Engneerng, X an echnologcal

More information

Physics 3A: Linear Momentum. Physics 3A: Linear Momentum. Physics 3A: Linear Momentum. Physics 3A: Linear Momentum

Physics 3A: Linear Momentum. Physics 3A: Linear Momentum. Physics 3A: Linear Momentum. Physics 3A: Linear Momentum Recall that there was ore to oton than just spee A ore coplete escrpton of oton s the concept of lnear oentu: p v (8.) Beng a prouct of a scalar () an a vector (v), oentu s a vector: p v p y v y p z v

More information

Relative controllability of nonlinear systems with delays in control

Relative controllability of nonlinear systems with delays in control Relave conrollably o nonlnear sysems wh delays n conrol Jerzy Klamka Insue o Conrol Engneerng, Slesan Techncal Unversy, 44- Glwce, Poland. phone/ax : 48 32 37227, {jklamka}@a.polsl.glwce.pl Keywor: Conrollably.

More information

( ) [ ] MAP Decision Rule

( ) [ ] MAP Decision Rule Announcemens Bayes Decson Theory wh Normal Dsrbuons HW0 due oday HW o be assgned soon Proec descrpon posed Bomercs CSE 90 Lecure 4 CSE90, Sprng 04 CSE90, Sprng 04 Key Probables 4 ω class label X feaure

More information

FTCS Solution to the Heat Equation

FTCS Solution to the Heat Equation FTCS Soluon o he Hea Equaon ME 448/548 Noes Gerald Reckenwald Porland Sae Unversy Deparmen of Mechancal Engneerng gerry@pdxedu ME 448/548: FTCS Soluon o he Hea Equaon Overvew Use he forward fne d erence

More information

Viscous Damping Summary Sheet No Damping Case: Damped behaviour depends on the relative size of ω o and b/2m 3 Cases: 1.

Viscous Damping Summary Sheet No Damping Case: Damped behaviour depends on the relative size of ω o and b/2m 3 Cases: 1. Viscous Daping: && + & + ω Viscous Daping Suary Shee No Daping Case: & + ω solve A ( ω + α ) Daped ehaviour depends on he relaive size of ω o and / 3 Cases:. Criical Daping Wee 5 Lecure solve sae BC s

More information

M. Y. Adamu Mathematical Sciences Programme, AbubakarTafawaBalewa University, Bauchi, Nigeria

M. Y. Adamu Mathematical Sciences Programme, AbubakarTafawaBalewa University, Bauchi, Nigeria IOSR Journal of Mahemacs (IOSR-JM e-issn: 78-578, p-issn: 9-765X. Volume 0, Issue 4 Ver. IV (Jul-Aug. 04, PP 40-44 Mulple SolonSoluons for a (+-dmensonalhroa-sasuma shallow waer wave equaon UsngPanlevé-Bӓclund

More information

Mechanics Physics 151

Mechanics Physics 151 Mechancs Physcs 5 Lecure 9 Hamlonan Equaons of Moon (Chaper 8) Wha We Dd Las Tme Consruced Hamlonan formalsm H ( q, p, ) = q p L( q, q, ) H p = q H q = p H = L Equvalen o Lagrangan formalsm Smpler, bu

More information

GMM parameter estimation. Xiaoye Lu CMPS290c Final Project

GMM parameter estimation. Xiaoye Lu CMPS290c Final Project GMM paraeer esaon Xaoye Lu M290c Fnal rojec GMM nroducon Gaussan ure Model obnaon of several gaussan coponens Noaon: For each Gaussan dsrbuon:, s he ean and covarance ar. A GMM h ures(coponens): p ( 2π

More information

Variants of Pegasos. December 11, 2009

Variants of Pegasos. December 11, 2009 Inroducon Varans of Pegasos SooWoong Ryu bshboy@sanford.edu December, 009 Youngsoo Cho yc344@sanford.edu Developng a new SVM algorhm s ongong research opc. Among many exng SVM algorhms, we wll focus on

More information

Mechanics Physics 151

Mechanics Physics 151 Mechancs Physcs 5 Lecure 9 Hamlonan Equaons of Moon (Chaper 8) Wha We Dd Las Tme Consruced Hamlonan formalsm Hqp (,,) = qp Lqq (,,) H p = q H q = p H L = Equvalen o Lagrangan formalsm Smpler, bu wce as

More information

Mechanics Physics 151

Mechanics Physics 151 Mechancs Physcs 5 Lecure 0 Canoncal Transformaons (Chaper 9) Wha We Dd Las Tme Hamlon s Prncple n he Hamlonan formalsm Dervaon was smple δi δ Addonal end-pon consrans pq H( q, p, ) d 0 δ q ( ) δq ( ) δ

More information

Static Output-Feedback Simultaneous Stabilization of Interval Time-Delay Systems

Static Output-Feedback Simultaneous Stabilization of Interval Time-Delay Systems Sac Oupu-Feedback Sulaneous Sablzaon of Inerval e-delay Syses YUAN-CHANG CHANG SONG-SHYONG CHEN Deparen of Elecrcal Engneerng Lee-Mng Insue of echnology No. - Lee-Juan Road a-shan ape Couny 4305 AIWAN

More information

UNIVERSITAT AUTÒNOMA DE BARCELONA MARCH 2017 EXAMINATION

UNIVERSITAT AUTÒNOMA DE BARCELONA MARCH 2017 EXAMINATION INTERNATIONAL TRADE T. J. KEHOE UNIVERSITAT AUTÒNOMA DE BARCELONA MARCH 27 EXAMINATION Please answer wo of he hree quesons. You can consul class noes, workng papers, and arcles whle you are workng on he

More information

Econ107 Applied Econometrics Topic 5: Specification: Choosing Independent Variables (Studenmund, Chapter 6)

Econ107 Applied Econometrics Topic 5: Specification: Choosing Independent Variables (Studenmund, Chapter 6) Econ7 Appled Economercs Topc 5: Specfcaon: Choosng Independen Varables (Sudenmund, Chaper 6 Specfcaon errors ha we wll deal wh: wrong ndependen varable; wrong funconal form. Ths lecure deals wh wrong ndependen

More information

P R = P 0. The system is shown on the next figure:

P R = P 0. The system is shown on the next figure: TPG460 Reservor Smulaon 08 page of INTRODUCTION TO RESERVOIR SIMULATION Analycal and numercal soluons of smple one-dmensonal, one-phase flow equaons As an nroducon o reservor smulaon, we wll revew he smples

More information

Chapter Lagrangian Interpolation

Chapter Lagrangian Interpolation Chaper 5.4 agrangan Inerpolaon Afer readng hs chaper you should be able o:. dere agrangan mehod of nerpolaon. sole problems usng agrangan mehod of nerpolaon and. use agrangan nerpolans o fnd deraes and

More information

Should Exact Index Numbers have Standard Errors? Theory and Application to Asian Growth

Should Exact Index Numbers have Standard Errors? Theory and Application to Asian Growth Should Exac Index umbers have Sandard Errors? Theory and Applcaon o Asan Growh Rober C. Feensra Marshall B. Rensdorf ovember 003 Proof of Proposon APPEDIX () Frs, we wll derve he convenonal Sao-Vara prce

More information

02. MOTION. Questions and Answers

02. MOTION. Questions and Answers CLASS-09 02. MOTION Quesions and Answers PHYSICAL SCIENCE 1. Se moves a a consan speed in a consan direcion.. Reprase e same senence in fewer words using conceps relaed o moion. Se moves wi uniform velociy.

More information

LECTURE 8-9: THE BAKER-CAMPBELL-HAUSDORFF FORMULA

LECTURE 8-9: THE BAKER-CAMPBELL-HAUSDORFF FORMULA LECTURE 8-9: THE BAKER-CAMPBELL-HAUSDORFF FORMULA As we have seen, 1. Taylor s expanson on Le group, Y ] a(y ). So f G s an abelan group, then c(g) : G G s the entty ap for all g G. As a consequence, a()

More information

Additional Exercises for Chapter What is the slope-intercept form of the equation of the line given by 3x + 5y + 2 = 0?

Additional Exercises for Chapter What is the slope-intercept form of the equation of the line given by 3x + 5y + 2 = 0? ddiional Eercises for Caper 5 bou Lines, Slopes, and Tangen Lines 39. Find an equaion for e line roug e wo poins (, 7) and (5, ). 4. Wa is e slope-inercep form of e equaion of e line given by 3 + 5y +

More information

CS286.2 Lecture 14: Quantum de Finetti Theorems II

CS286.2 Lecture 14: Quantum de Finetti Theorems II CS286.2 Lecure 14: Quanum de Fne Theorems II Scrbe: Mara Okounkova 1 Saemen of he heorem Recall he las saemen of he quanum de Fne heorem from he prevous lecure. Theorem 1 Quanum de Fne). Le ρ Dens C 2

More information

GENERATING CERTAIN QUINTIC IRREDUCIBLE POLYNOMIALS OVER FINITE FIELDS. Youngwoo Ahn and Kitae Kim

GENERATING CERTAIN QUINTIC IRREDUCIBLE POLYNOMIALS OVER FINITE FIELDS. Youngwoo Ahn and Kitae Kim Korean J. Mah. 19 (2011), No. 3, pp. 263 272 GENERATING CERTAIN QUINTIC IRREDUCIBLE POLYNOMIALS OVER FINITE FIELDS Youngwoo Ahn and Kae Km Absrac. In he paper [1], an explc correspondence beween ceran

More information

Supplementary Online Material

Supplementary Online Material Suppleenary Onlne Maeral In he followng secons, we presen our approach o calculang yapunov exponens. We derve our cenral resul Λ= τ n n pτλ ( A pbt λ( = τ, = A ( drecly fro he growh equaon x ( = AE x (

More information

Opening Shock and Shape of the Drag-vs-Time Curve

Opening Shock and Shape of the Drag-vs-Time Curve Openng Shock and Shape o he Drag-vs-Te Curve Jean Povn Physcs Deparen, San Lous Unversy, S. Lous MO Conac: povnj@slu.edu 314-977-8424 Talk presened a he 19 h AIAA Aerodynac Deceleraor Syses Conerence Wllasburg,

More information

Practice Problems - Week #4 Higher-Order DEs, Applications Solutions

Practice Problems - Week #4 Higher-Order DEs, Applications Solutions Pracice Probles - Wee #4 Higher-Orer DEs, Applicaions Soluions 1. Solve he iniial value proble where y y = 0, y0 = 0, y 0 = 1, an y 0 =. r r = rr 1 = rr 1r + 1, so he general soluion is C 1 + C e x + C

More information

CHAPTER II AC POWER CALCULATIONS

CHAPTER II AC POWER CALCULATIONS CHAE AC OWE CACUAON Conens nroducon nsananeous and Aerage ower Effece or M alue Apparen ower Coplex ower Conseraon of AC ower ower Facor and ower Facor Correcon Maxu Aerage ower ransfer Applcaons 3 nroducon

More information

Homework 8: Rigid Body Dynamics Due Friday April 21, 2017

Homework 8: Rigid Body Dynamics Due Friday April 21, 2017 EN40: Dynacs and Vbraons Hoework 8: gd Body Dynacs Due Frday Aprl 1, 017 School of Engneerng Brown Unversy 1. The earh s roaon rae has been esaed o decrease so as o ncrease he lengh of a day a a rae of

More information

The Quantum Theory of Atoms and Molecules: The Schrodinger equation. Hilary Term 2008 Dr Grant Ritchie

The Quantum Theory of Atoms and Molecules: The Schrodinger equation. Hilary Term 2008 Dr Grant Ritchie e Quanum eory of Aoms and Molecules: e Scrodinger equaion Hilary erm 008 Dr Gran Ricie An equaion for maer waves? De Broglie posulaed a every paricles as an associaed wave of waveleng: / p Wave naure of

More information

1 Widrow-Hoff Algorithm

1 Widrow-Hoff Algorithm COS 511: heoreical Machine Learning Lecurer: Rob Schapire Lecure # 18 Scribe: Shaoqing Yang April 10, 014 1 Widrow-Hoff Algorih Firs le s review he Widrow-Hoff algorih ha was covered fro las lecure: Algorih

More information

Approximate Analytic Solution of (2+1) - Dimensional Zakharov-Kuznetsov(Zk) Equations Using Homotopy

Approximate Analytic Solution of (2+1) - Dimensional Zakharov-Kuznetsov(Zk) Equations Using Homotopy Arcle Inernaonal Journal of Modern Mahemacal Scences, 4, (): - Inernaonal Journal of Modern Mahemacal Scences Journal homepage: www.modernscenfcpress.com/journals/jmms.aspx ISSN: 66-86X Florda, USA Approxmae

More information

Fall 2010 Graduate Course on Dynamic Learning

Fall 2010 Graduate Course on Dynamic Learning Fall 200 Graduae Course on Dynamc Learnng Chaper 4: Parcle Flers Sepember 27, 200 Byoung-Tak Zhang School of Compuer Scence and Engneerng & Cognve Scence and Bran Scence Programs Seoul aonal Unversy hp://b.snu.ac.kr/~bzhang/

More information

Pendulum Dynamics. = Ft tangential direction (2) radial direction (1)

Pendulum Dynamics. = Ft tangential direction (2) radial direction (1) Pendulum Dynams Consder a smple pendulum wh a massless arm of lengh L and a pon mass, m, a he end of he arm. Assumng ha he fron n he sysem s proporonal o he negave of he angenal veloy, Newon s seond law

More information

Testing a new idea to solve the P = NP problem with mathematical induction

Testing a new idea to solve the P = NP problem with mathematical induction Tesng a new dea o solve he P = NP problem wh mahemacal nducon Bacground P and NP are wo classes (ses) of languages n Compuer Scence An open problem s wheher P = NP Ths paper ess a new dea o compare he

More information

Tight results for Next Fit and Worst Fit with resource augmentation

Tight results for Next Fit and Worst Fit with resource augmentation Tgh resuls for Nex F and Wors F wh resource augmenaon Joan Boyar Leah Epsen Asaf Levn Asrac I s well known ha he wo smple algorhms for he classc n packng prolem, NF and WF oh have an approxmaon rao of

More information

Bayesian Learning based Negotiation Agents for Supporting Negotiation with Incomplete Information

Bayesian Learning based Negotiation Agents for Supporting Negotiation with Incomplete Information ayesan Learnng base Negoaon Agens for upporng Negoaon wh Incomplee Informaon Jeonghwan Gwak an Kwang Mong m Absrac An opmal negoaon agen shoul have capably for mamzng s uly even for negoaon wh ncomplee

More information

Chapters 2 Kinematics. Position, Distance, Displacement

Chapters 2 Kinematics. Position, Distance, Displacement Chapers Knemacs Poson, Dsance, Dsplacemen Mechancs: Knemacs and Dynamcs. Knemacs deals wh moon, bu s no concerned wh he cause o moon. Dynamcs deals wh he relaonshp beween orce and moon. The word dsplacemen

More information

M x t = K x F t x t = A x M 1 F t. M x t = K x cos t G 0. x t = A x cos t F 0

M x t = K x F t x t = A x M 1 F t. M x t = K x cos t G 0. x t = A x cos t F 0 Forced oscillaions (sill undaped): If he forcing is sinusoidal, M = K F = A M F M = K cos G wih F = M G = A cos F Fro he fundaenal heore for linear ransforaions we now ha he general soluion o his inhoogeneous

More information

HEAT CONDUCTION PROBLEM IN A TWO-LAYERED HOLLOW CYLINDER BY USING THE GREEN S FUNCTION METHOD

HEAT CONDUCTION PROBLEM IN A TWO-LAYERED HOLLOW CYLINDER BY USING THE GREEN S FUNCTION METHOD Journal of Appled Mahemacs and Compuaonal Mechancs 3, (), 45-5 HEAT CONDUCTION PROBLEM IN A TWO-LAYERED HOLLOW CYLINDER BY USING THE GREEN S FUNCTION METHOD Sansław Kukla, Urszula Sedlecka Insue of Mahemacs,

More information

Chapter Three Systems of Linear Differential Equations

Chapter Three Systems of Linear Differential Equations Chaper Three Sysems of Linear Differenial Equaions In his chaper we are going o consier sysems of firs orer orinary ifferenial equaions. These are sysems of he form x a x a x a n x n x a x a x a n x n

More information

Volatility Interpolation

Volatility Interpolation Volaly Inerpolaon Prelmnary Verson March 00 Jesper Andreasen and Bran Huge Danse Mares, Copenhagen wan.daddy@danseban.com brno@danseban.com Elecronc copy avalable a: hp://ssrn.com/absrac=69497 Inro Local

More information

Thus the force is proportional but opposite to the displacement away from equilibrium.

Thus the force is proportional but opposite to the displacement away from equilibrium. Chaper 3 : Siple Haronic Moion Hooe s law saes ha he force (F) eered by an ideal spring is proporional o is elongaion l F= l where is he spring consan. Consider a ass hanging on a he spring. In equilibriu

More information

A DECOMPOSITION METHOD FOR SOLVING DIFFUSION EQUATIONS VIA LOCAL FRACTIONAL TIME DERIVATIVE

A DECOMPOSITION METHOD FOR SOLVING DIFFUSION EQUATIONS VIA LOCAL FRACTIONAL TIME DERIVATIVE S13 A DECOMPOSITION METHOD FOR SOLVING DIFFUSION EQUATIONS VIA LOCAL FRACTIONAL TIME DERIVATIVE by Hossen JAFARI a,b, Haleh TAJADODI c, and Sarah Jane JOHNSTON a a Deparen of Maheacal Scences, Unversy

More information

( t) Outline of program: BGC1: Survival and event history analysis Oslo, March-May Recapitulation. The additive regression model

( t) Outline of program: BGC1: Survival and event history analysis Oslo, March-May Recapitulation. The additive regression model BGC1: Survval and even hsory analyss Oslo, March-May 212 Monday May 7h and Tuesday May 8h The addve regresson model Ørnulf Borgan Deparmen of Mahemacs Unversy of Oslo Oulne of program: Recapulaon Counng

More information

Revision: June 12, E Main Suite D Pullman, WA (509) Voice and Fax

Revision: June 12, E Main Suite D Pullman, WA (509) Voice and Fax .: apacors Reson: June, 5 E Man Sue D Pullman, WA 9963 59 334 636 Voce an Fax Oerew We begn our suy of energy sorage elemens wh a scusson of capacors. apacors, lke ressors, are passe wo-ermnal crcu elemens.

More information

Advanced time-series analysis (University of Lund, Economic History Department)

Advanced time-series analysis (University of Lund, Economic History Department) Advanced me-seres analss (Unvers of Lund, Economc Hsor Dearmen) 3 Jan-3 Februar and 6-3 March Lecure 4 Economerc echnues for saonar seres : Unvarae sochasc models wh Box- Jenns mehodolog, smle forecasng

More information