CE 504 Computational Hydrology Introduction to Finite Difference Methods Fritz R. Fiedler

Size: px
Start display at page:

Download "CE 504 Computational Hydrology Introduction to Finite Difference Methods Fritz R. Fiedler"

Transcription

1 CE 504 Comptatoal Hydology Itodcto to Fte Dffeece Methods Ftz R. Fedle Itodcto a Taylo Sees Cosstecy, Covegece ad Stablty 3 Ital ad Boday Codtos 4 Methods ad Popetes a Classc Methods paabolc eqatos: fte dffeece eqatos paabolc eqatos: algothms hypebolc eqatos: fte dffeece eqatos v hypebolc eqatos: algothms 5 Mscellaeos Note: I apologze fo mg symbols ths veso of the otes. Ths s doe oly de to tme costas. Howeve, yo shold get sed to eadg/tepetg eqatos that se dffeet symbols ow, as eveyoe has the ow pefeece. Yo may wat to ty e-wtg the eqatos a cosstet symbol set that yo lke ode to avod cofso. Itodcto I the fte dffeece method, appomate dffeece qotets ae sed to appomate the devatves a dffeetal eqato, edcg the dffeetal eqato to a system of algebac eqatos. The defto of the patal devatves of a vaable =,t wth espect to space ad tme, espectvely, ae, t, t = lm 0, t t, t = lm t t 0 t As the ame fte dffeece mples, ad t ae take to be fte qattes the dffeece qotets athe tha allowg them to appoach zeo whe appomatg the dffeetals, t, t 3, t t, t 4 t t Eqatos 3 ad 4 ae appomatos to Eqatos ad, espectvely, ad the dffeece betwee the eact devatve ad the appomato s kow as the tcato eo. The Taylo sees s sed to assess the magtde of the tcato eo ad deve othe foms of fte dffeece appomatos. Gve a smooth fcto.e., a fcto that s cotos ad has cotos devatves, the Taylo sees appomates the vale of a fcto at oe pot based o the vale of the fcto ad ts devatves at aothe, eaby pot. I the dmeso, a Taylo sees s wtte

2 R q =! 3!! L 5 whee R s a emade tem that accots fo all tems to fty that s detemed sg! = R ξ 6 whee ξ s some vale betwee ad. Ths s based o the devatve mea vale theoem, whch essece states that thee mst at least oe pot betwee ad whee the fcto slope s eqal to the slope of a le og the vales of the fcto at ad. The vale of ξ s ot mpotat at ths tme. A fst-ode Taylo sees ca be wtte = ξ 7 ad solved fo the devatve to obta ξ = 8 whch coespods to Eqato 3 above. Theefoe, the tcato eo assocated wth the appomato s detemed by the last tem o the ght sde of Eqato 8. It shold be appaet that the tcato eo gets smalle as s edced, popoto to the fst powe of. Ths fte dffeece appomato s kow as the fowad dffeece fomla fo the fst devatve, ad s fst ode accate. Othe foms of fte dffeece appomatos ca be smlaly deved; fo eample, by epadg abot -, a fst-ode accate backwad dffeece fomla s obtaed. I ode to apply fte dffeece methods, t s ecessay to dscetze the doma to be modeled,.e., costct a gd the space-tme plae composed of dscete elemets. The eqatos ae the solved at the gd pots o odes the tesecto of gd les, o at the cetes of the cells fomed by the gd les. Methods based o the fome ae kow as odeceteed techqes, ad the latte as block-ceteed techqes. The focs hee s o the odeceteed method. A spescpt-sbscpt otato s ofte sed to de the vaables; fo eample, the tege sbscpts ad may efe to the ad y decto odes, ad the tege spescpt cold be sed as a tme de. Fo eample,,,, t y q = 9 ad,, O t y = 0 Hee the commoly sed otato O s sed to dcate that ths s a fst ode method. A patcla gd ode ca be efeeced, assmg a og at zeo, as,,,, t y t y = Gds ca be fom o o-fom both the space ad tme dmesos. The fowad ad backwad fte dffeece appomatos fo the tme devatve ca be wtte, espectvely, t O t t t = ad

3 , t = O t 3 t t Note that Eqato 3, the dffeece qotet ca be compted eplctly, as we pesmably kow the vale of fom the pevos tme step at all gd odes. Howeve, Eqato, t s ecessay to solve a system of eqatos fo at the tme level; ths s kow as a mplct method. Ths wll be dscssed moe detal below. Based o the above aalyss of tcato eo sg the Taylo sees, oe mght assme that a smple way to cease the accacy of the -decto fte dffeece appomato s to smply make as small as possble. Ths appoach, howeve, does ot always wok. By makg the space steps small, the comptatoal bde ceases, as the eqatos eed to be solved at evey gd ode. The allowable tme step s also ted to the space step eplct methods. Fally, od-off eo the eo assocated wth sg fte pecso compte epesetatos of eal mbes may become sgfcat fo vey small space ad tme steps. Table shows commo fte dffeece appomatos, sg a depedet vaable, a sbscpt fo the spatal de, ad a spescpt to epeset the tempoal de t s vey commo fo sbscpts to epeset spatal dees, ad spescpts to epeset tempoal dees, o matte what the symbols ae. Also, h s sed stead of, ad k stead of t. The otato Oh dcates a fst-ode accate spatal method, ad Oh dcates a secod-ode accate method; tempoal deftos ae aalagos. Also ote the defto of the opeato δ.

4 Table. Commo fte dffeece appomatos DChatea ad Zachma, 989 Cosstecy, Covegece ad Stablty I sg fte dffeece methods, we ae appomatg cotos patal dffeetal eqatos PDEs wth dscetzed, fte dffeece eqatos FDEs. Cosstecy efes to how well the FDE appomates the PDE as the gd spacg appoaches zeo. If the local tcato eo appoaches zeo wth the gd spacg, the fte dffeece scheme de cosdeato s sad to be cosstet. A patcla FDE s coveget f the solto to the FDE coveges to the solto of the PDE ove the ete doma. Ths s a mpotat dstcto a patcla FDE may be cosstet bt ot coveget. A FDE s stable f odg eos, todced each calclato step, ema boded. I a stable FDE scheme, small eos the tal codto eslt coespodgly small eos the solto. Ths, stablty efes to how eos ae popagated. Cosstecy, covegece, ad stablty ae elated to oe aothe thogh the La Eqvalece Theoem: Gve a well-posed lea tal-vale poblem o talboday-vale poblem ad a fte dffeece scheme cosstet wth t, stablty s both ecessay ad sffcet fo covegece.

5 A well-posed poblem essetally meas that a solto ests, the solto s qe, ad the solto depeds cotosly o the data. The data hee efes to the tal ad boday codtos, pls the coeffcets ad homogeeos soce tems of the PDE. Mathematcally povg cosstecy, covegece, ad stablty s beyod the scope of ths cose. The popetes of commoly sed fte dffeece schemes ae well-docmeted. Howeve, t s mpotat to have a sold destadg of stablty, as t s a ecessay ad sffcet codto fo covegece gve a cosstet scheme, whch we shold be sg. Let s cosde the geeal fte dffeece poblem defed by D[ ] = 0 =,,, I, =,,, N 4a 0 = f =,,, I 4b 0 = I = 0 =,,, N 4c whee D[ ] epesets a fte dffeece opeato o scheme fowad dffeece, backwad dffeece, etc. sed to appomate some PDE, the secod le s the tal codto, ad the thd le shows the boday codtos. Lettg ad f deote colm vectos of the depedet vaables ad tal codtos ove the spatal doma, the fte dffeece method s codtoally stable f fo ay t ad thee ests a depedet costat C sch that C f 0 t T 5 If t mst be fctoally elated to fo ths elatoshp to hold, the fte dffeece method s sad to be codtoally stable. The otato epesets the om of the vecto. Thee ae seveal types of oms, the smplest oe beg, fo eample f = ma f 6 I Eqato 5 dcates that as log as eos stay boded wth espect to the tal codto, the scheme s stable. Rodg ad tcato eos ae also todced evey comptatoal step. To ema stable, these eos mst ot accmlate ay faste tha f they wee smply added togethe. Thee ae seveal ways to aalyze a patcla fte dffeece scheme s stablty, bt we wll ot go to sch detal hee. Istead, we wll keep md the above dscsso, make some geealzatos abot stablty wth espect to eplct ad mplct methods, ad deal wth stablty of commoly sed schemes hydology o a case-by-case bass. Eplct schemes ae sally at best codtoally stable; some ae kow to be codtoally stable. The codtos elatoshps betwee allowable tme ad space steps deped ot oly o the fte dffeece scheme sed, bt also the eqato beg appomated ad soce tems. No-lea eqatos geeally have tghte stablty estctos. If the soce tems chage apdly wth espect to the depedet vaables o foce the depedet vaables themselves to chage apdly, the the soce tems wll dctate stablty eqemets. Eve f we wee to cove the detals of stablty aalyss, the methods sed ae typcally oly tactable fo lea poblems oe dmeso wth o soce tems, ths detemg the lmts of stablty fo a patcla poblem s ofte doe by tal ad eo. I geeal, eplct methods, as s edced say, fo eample, to bette esolve apdly chagg poblem geomety o depedet vaables, t mst also be edced. Implct fte dffeece methods ae geeally codtoally stable. So why bothe wth eplct methods at all? Fst, mplct methods eqe the solto of a system of eqatos at each tme step. The solto of systems of eqatos s ofte comptatoally tesve, ad

6 sometmes t s dffclt to obta accate, stable soltos to these systems. Secod, fo steady poblems the tme step eqed to obta tme-accate soltos s lmted by how fast the depedet vaables chage; ths tme step s ofte smla to the tme steps eqed fo stable soltos sg eplct methods. Becase of the poblems assocated wth smltaeosly solvg lage systems of eqatos, eplct methods ae eve feqetly sed to obta steady state soltos to comptatoal fld dyamcs poblems. Theefoe, mplct methods ae ot the paacea they fst appea to be, bt ae vey sefl may cases. Ital ad Boday Codtos Ital ad boday codtos ae collectvely kow as alay codtos. These deteme, pat, f a patcla poblem s well- o ll-posed. If thee ae too few alay codtos, the solto wll ot be qe; f thee ae too may, the solto wll ot est; ad f they ae ot cosstet wth the PDE, the solto wll ot deped cotosly po the data. Ital codtos compse the vales of the depedet vaables sed to talze the solto at t = 0. These ae sometmes abtay e.g., costat moste cotet ad pesse head wth sol depth befoe a fltato evet, may epeset physcal ealty e.g., zeo ovelad flow depth po to afall, o they mght be deved fom edg codtos of a pevos smlato cold be sed to talze a ew smlato. I ay case, the tal codtos shold epeset a easoable solto to the eqatos beg solved, othewse the poblem may become ll-posed. Thee ae two boad types of boday codtos: Dchlet, whee solto vales ae specfed o the doma bodaes; ad Nema, whee a vale fo the dectoal devatve, omal to the boday, s specfed o the boday. Dchlet codtos ae faly easy to mplemet, ad may be steady o steady. Fo eample, Dchlet codtos may be wtte fo a oe-dmesoal poblem wth a doma legth of L 0, t = g t, L, t = h t, t > 0 7 Nema boday codtos ae typcally mplemeted sg a fte dffeece appomato. Fo eample, geealzed Nema codtos may be wtte 0, t = g t, L, t, t > 0 8 The fl of sol moste at the sol sface s a physcal eample of ths type of boday codto. We se fte dffeece appomatos to mplemet Nema boday codtos. Cosde a fom gd 0,,., I, I whee = 0 ad N = L. Fo the lowe boday, the followg fst-ode appomato to the devatve boday codto cold be sed 0 = g 9 ad the vale of at 0 ca be compted sce the fcto gt s kow at all tme levels. The choce of dffeece appomato depeds o whch dffeece method s beg sed fo the teo odes. Ths wll be dscssed moe detal sbseqetly. Thee ae othe types of boday codtos that cold be mplemeted. A feqetly sed boday codto hydology s specfcato of ctcal depth at a dowsteam boday. We wll eploe these o a case-by-case bass.

7 Methods ad Popetes I ths secto, we wll look at a few caocal foms of eqatos ad fte dffeece methods to llstate geeal techqes. Oly paabolc ad hypebolc eqatos ae coveed. Paabolc Eqatos: Fte Dffeece Eqatos The caocal paabolc eqato s wtte a = S, t 0 t whee a s a coeffcet ad S,t s the soce tem. A eplct fte dffeece method fo ths eqato s a = S t whee a fst-ode accate fowad dffeece s sed fo the tempoal devatve, ad a secodode accate cetal dffeece s sed fo the spatal devatve. Table lsts commo fte dffeece appomatos fo Eqato 0 ote the dffeeces vaables, as descbed pevosly; the ppe case s sed by these athos to dcate that the vale s appomate. Table. Fte dffeece appomatos fo the caocal paabolc eqato DChatea ad Zachma, 989. The fowad dffeece method show Table s eplct, ad stable oly whe s less tha o eqal to 0.5. The backwad dffeece ad Cak-Ncolso methods ae mplct, ad

8 codtoally stable. The Cak-Ncolso method s secod-ode accate both tme ad space. Both mplct methods eqe the solto of a system of eqatos. It s sefl to place these eqatos a mat fomat to see how the eqatos shold be solved ad how the boday codtos ae copoated. sg the symbols of Table, the fowad dffeece method mat fomat s = N N N q ks ks p ks M M O M Note that Eqato, Dchlet boday codtos ae sed, whee pt s the boday codto fcto at the lowe boday, ad qt s the boday codto mposed at the ppe boday. All of the vales o the ght sde of ths eqato ae kow, so the vales of at the tme level ca be compted eplctly. Compae ths to the backwad dffeece method mat fomat = N N N q ks ks p ks M M M O 3 Hee, a system of eqatos mst be solved. The mat s tdagoal, makg ths system faly easy to solve. Table 3, also take fom DChatea ad Zachma 989, smmazes the methods ad boday codtos fo the caocal paabolc eqato.

9 Table 3. Smmay of fte dffeece methods ad boday codtos sed fo the paabolc eqato DChatea ad Zachma, 989. O Table 3, the followg vectos ad mates ae defed:

10 Paabolc Eqatos: Algothms The easest of the methods descbed above to mplemet o a compte s the fowad dffeece method combed wth Dchlet boday codtos. sg the vaables as defed by DChatea ad Zachma 989, a basc algothm fo ths method s peseted sbseqetly. Pelmaes: defe vaables: eal a, dffsvty eal L, legth of doma eal k, tme step t eal h, space step tege ma, mbe of tme steps tege ma, mbe of gd pots defe fctos may call sbotes S,t, soce tem f, tal codto pt, Dchlet boday codto at =0 qt, Dchlet boday codto at =L ead pt data a, L, Defe gd: h = L / ma = a k/h Italzato V0 = p0 = f0 these shold be cosstet Vma = q0 = fma these shold be cosstet fo =,,, ma V = f edloop Tme loop fo =,,, ma fo =,,, ma = *V--**V*Vk*S,

11 edloop 0 = p ma = q Otpt Pepae fo et tme step fo = 0,,, ma V = edloop edloop I ode to mplemet ethe of the mplct methods, we mst solve a system of eqatos tdagoal fom ths type of system ases feqetly solvg PDEs, patclaly whe sg fte dffeece methods. I geeal tems, a tdagoal system s wtte b c d a b c d O M = M 4 an bn cn N d N a N bn N d N I the Thomas algothm, the mat defed Eqato 4 s coveted fom tdagoal to ppe bdagoal fom makg a coeffcets eqal to 0 ad solved sg backwad elmato. I backwad elmato, sce the last eqato epeseted the mat has oly oe kow, t ca be solved; the eslt s the sed the secod-to-last eqato, ad so o. Note that the ete mat does ot have to be stoed; athe, the mat s stoed as thee vectos: sbdagoal a, dagoal b, ad spedagoal c. The followg s the Thomas algothm: Pelmaes defe vaables Decomposto fo =, 3,, N a = a/b- b = b a*c- edloop Fowad Sbsttto fo =, 3,, N d = d a*d- edloop Backwad Sbsttto N = dn/bn fo = N-, N-,, = d c*/b edloop Ths algothm ca be sed wth a ote, fo eample, to solve the paabolc eqato wth Dchlet boday codtos sg the backwad fte dffeece method, as follows. Pelmaes: defe vaables:

12 eal a, dffsvty eal L, legth of doma eal k, tme step t eal h, space step tege ma, mbe of tme steps tege ma, mbe of gd pots defe fctos may call sbotes S,t, soce tem f, tal codto pt, Dchlet boday codto at =0 qt, Dchlet boday codto at =L ead pt data a, L, Defe gd: h = L / ma = a k/h Italzato 0 = p0 = f0 these shold be cosstet ma = q0 = fma these shold be cosstet fo =,,, ma = f edloop Tme loop fo =,,, ma fo =, ma a = - b = * c = - d = k*s, edloop d = d *p dma = dma *q call tdagoal solve ma, a, b, c, d, 0 = p ma = q edloop So fa, oly algothms employg Dchlet boday codtos have bee show. Eqato 9 shows a fst-ode accate dffeece appomato that cold be sed fo the Nema boday codto. Sce the teo odes ae solved sg a secod-ode accate spatal devatve, t makes sese to se a secod-ode accate spatal devatve to appomate the boday codto as well. Fo eample, the followg ceteed spatal dffeece appomato ca be sed at the lowe boday whth a backwad tempoal dffeece ths s the fomla sed fo the Nema codtos show the Table 3 0 = p 5 h whch ca be e-aaged = hp 6 0

13 The followg algothm ses ths dffeece fomla to appomate Nema boday codtos at both eds of the doma to solve the oe-dmesoal caocal paabolc eqato. Note that the gd s defed a slghtly dffeet mae fo Nema boday codtos see Table 3. Pelmaes: defe vaables: eal a, dffsvty eal L, legth of doma eal k, tme step t eal h, space step tege ma, mbe of tme steps tege ma, mbe of gd pots defe fctos may call sbotes S,t, soce tem f, tal codto pt, Nema boday codto at =0 qt, Nema boday codto at =L ead pt data a, L, Defe gd: h = L / ma - = a k/h Italzato fo =,,, ma = f edloop Tme loop fo =,,, ma fo =,,, ma a = - b = * c = - d = k*s, edloop d = d **h*p dma = dma **h*q c = -* ama = -* call tdagoal solve ma, a, b, c, d, edloop Hypebolc Eqatos: Fte Dffeece Eqatos fo the IVP The caocal hypebolc wave eqato s wtte a = 0 7 t Note that ths s essetally the kematc wave eqato, ad ca be posed as a pe tal vale poblem,0 = f, - < < o as a tal-boday vale poblem,0 = f, 0 < < ad 0,t = gt, t > 0. Commo fte dffeece appomatos to ths eqato whe sed

14 a IVP ae show Table 4. Note that these ae all eplct methods, as mplct methods ae ot applcable to the pe IVP. Table 5 lsts the stablty eqemets of the methods lsted Table 4. Table 4. Commo fte dffeece appomatos fo the caocal hypebolc eqato wth o soce tem whe posed as a IVP DChatea ad Zachma, 989.

15 Table 5. Stablty of methods show Table 4. Method Stablty Accacy FTFS stable f a < 0 ad sa fst ode tme ad space FTBS stable f a > 0 ad sa fst ode tme ad space FTCS codtoally stable N/A La-Fedchs stable f sa fst ode tme ad space Leapfog stable f sa secod ode tme ad space La-Wedoff stable f sa secod ode tme ad space Hypebolc Eqatos: Chaactestcs ad the CFL Codto Eqato 7 descbes the movemet of a wave, whch does ot chage shape, oe dmeso. The method of chaactestcs s sed to edce ths eqato to oday dffeetal eqatos the -t plae. If we let C be a chaactestc cve descbed by = t, o C,t = t, t, ad dffeetatg alog C eslts the eqato d = 8 dt dt t If a s defed as the wave speed o the cve C, o d = a 9 dt the the chage of wth espect to t o C s zeo d = 0 30 dt Eqato 30 shows that the solto s costat o the chaactestc cve. Eqato 9 s a oday dffeetal eqato that ca be solved to obta = at 0 3 So whe a s costat, the chaactestc cve s a staght le. Gve Eqato 7 ad the tal codto,0 = f 3 fom Eqato 3 we ca see that the aalytcal solto s, t = at,0 = f at 33 povded that f s cotosly dffeetable. A ecessay, bt ot sffcet codto fo stablty of eplct fte dffeece methods fo hypebolc eqatos s the Coat-Fedchs-Lewy CFL codto, whch states that the mecal doma of depedece mst cota the aalytcal doma of depedece, whee the aalytcal doma of depedece s gve by the chaactestc cves. Sce a s the wave speed, o mecal method ca ot tasmt fomato the -t plae faste tha ths wave speed ad ema stable. Ths, the tme step mst be less tha the space step dvded by the wave speed, o that the tme step mst be less tha the tme fo the wave to tavel acoss oe space step t / a 34 Eqato 34 ca be e-aaged to gve the stablty codto show evey eplct method Table 5 ote that addtoal codtos eed to be met fo the FTBS ad FTFS methods.

16 Hypebolc Eqatos: Fte Dffeece Eqatos fo the IBVP I hydologc applcatos, we ae typcally coceed wth poblems defed o fte domas, ad mpose boday codtos o those domas. The eplct methods show Table 4 also apply to the IBVP, whee 0,t = gt s mposed at = 0. Fo eample, fo smlatg a hllslope, both the dschage ad wate depth ca be set to zeo at the hllslope dvde. The FTFS method does ot apply f the coeffcet a s take to be postve. Implct methods ae also applcable to the IBVP. Table 5 shows seveal mplct methods applcable whe a s postve ad a soce tem s clded; all ae codtoally stable. Note that sce the BTBS ad Wedoff mplct methods se adacet gd pots at the tme level, thee s o eed to solve a system of eqatos: to compte the methods have avalable both fom the last tme step ad fom the last space step. Table 5. Implct methods fo the hypebolc IBVP DChatea ad Zachma, 989. Hypebolc Eqatos: Algothms A algothm fo the La-Wedoff method to solve the pe IVP

17 a = s, t 0 < t < t ma, p < < q t 34,0 = f s peseted as follows. Note that the solto doma deceases by two gd pots each tme step, as thee ae o boday codtos ad the - ad vales ae sed to compte at the lowe ad ppe eds of the doma, espectvely. Theefoe, we mst beg comptatos o a doma *ma wde tha the desed solto doma. Also ote how the soce tem s compted, sg both the soce tem ad ts devatves wth espect to tme ad space. Pelmaes: Ipt: Itege p, lowe spatal de at t ma Itege q, ppe spatal de at t ma Itege ma, mbe of tme steps eal k, tme step eal h, space step eal a, coeffcet fcto f, tal codto fcto ss,t, soce tem fctos s, t, s, t s,t ad st,t, espectvely t Defe Gd: s = k/h check stablty Italze otpttg tal codto t = 0 m = p ma ma = q ma fo = m, m,, ma = *h V = f edloop otpt t fo = p, p,, q otpt V edloop Tme Loop: fo =,,, ma m = m ma = ma- fo = m, m,, ma = V-0.5*s*a*V-V- s***a**/*v--*vv k*ss*h,tk**/*st*h,t-a*s*h,t edloop t = t k fo = m, m,, ma V = edloop

18 otpt t fo = p, p,, q otpt V edloop edloop To llstate both how boday codtos ae hadled ad mplemetato of a mplct scheme, a algothm fo the Wedoff fte dffeece method appled to a IBVP s show below. The boday codto s 0,t = gt. Notce how the vale of fom the pevos space step at the tme level s sed the tme loop. Pelmaes: Ipt: Itege ma, mamm de Itege ma, mbe of tme steps eal k, tme step eal h, space step eal a, coeffcet fcto f, tal codto fcto gt, boday codto fcto ss,t, soce tem Defe Gd: s = k/h Italze otpttg tal codto t = 0 ma = ma*h fo = 0,,, ma = *h V = f edloop otpt t fo = p, p,, q otpt V edloop Tme Loop: fo =,,, ma t = t k 0 = gt q = -s*a/s*a fo = 0,,, ma- = Vq*V-q* *k*ssmah/, t-k//s*a edloop fo = 0,,, ma V = edloop otpt t fo = 0,,, ma

19 otpt edloop edloop Hypebolc Eqatos: Cosevato Law Fom Fom the geealzed kematc wave theoy, we have pevosly see that C F = 0 35 t whee F s the fl ad geeal s a fcto of both C, the cocetato, ad F = f C, 36 Eqato 35 ca be sbsttted to Eqato 34 to obta C F C = 0 37 t C A eplct fte dffeece method s sad to be cosevato law fom f t ca be cast the followg fom Q / Q / = 0 38 h k whee the Q vales ae mecal appomatos to the cotos fles acoss bodaes located at some pots below ad above the gd ode fo whch the dffeece eqato s wtte, ad the vales ae appomatos to the cocetatos. I geeal, the Q vales ae detemed as some fcto of the eghbog vales Q / = Q,, Q / = Q, 39 ad t s eqed that the mecal fles be cosstet wth the cotos fl. Eqato 37 ca be e-wtte h = h k Q / Q / 40 Wtg the cosevato law as F = 0 4 t the La-Fedchs method ca be wtte = s F F 4 wth the mecal fles defed as Q / = F / s 43 Q / = F / s ths method s a cosevato law dffeece eqato. Whe F = a ad a s costat, the cosevato fom of the La-Fedchs method edces to the fom show pevosly. Table 6. Fte Dffeece methods Cosevato Law fom DChatea ad Zachma, 989.

20 Refeeces DChatea, Pal, ad Davd Zachma, Appled Patal Dffeetal Eqatos, Hape & Row, Pblshes, Ic., New Yok, New Yok, 989.

= y and Normed Linear Spaces

= y and Normed Linear Spaces 304-50 LINER SYSTEMS Lectue 8: Solutos to = ad Nomed Lea Spaces 73 Fdg N To fd N, we eed to chaacteze all solutos to = 0 Recall that ow opeatos peseve N, so that = 0 = 0 We ca solve = 0 ecusvel backwads

More information

This may involve sweep, revolution, deformation, expansion and forming joints with other curves.

This may involve sweep, revolution, deformation, expansion and forming joints with other curves. 5--8 Shapes ae ceated by cves that a sface sch as ooftop of a ca o fselage of a acaft ca be ceated by the moto of cves space a specfed mae. Ths may volve sweep, evolto, defomato, expaso ad fomg jots wth

More information

Professor Wei Zhu. 1. Sampling from the Normal Population

Professor Wei Zhu. 1. Sampling from the Normal Population AMS570 Pofesso We Zhu. Samplg fom the Nomal Populato *Example: We wsh to estmate the dstbuto of heghts of adult US male. It s beleved that the heght of adult US male follows a omal dstbuto N(, ) Def. Smple

More information

( m is the length of columns of A ) spanned by the columns of A : . Select those columns of B that contain a pivot; say those are Bi

( m is the length of columns of A ) spanned by the columns of A : . Select those columns of B that contain a pivot; say those are Bi Assgmet /MATH 47/Wte Due: Thusday Jauay The poblems to solve ae umbeed [] to [] below Fst some explaatoy otes Fdg a bass of the colum-space of a max ad povg that the colum ak (dmeso of the colum space)

More information

ˆ SSE SSE q SST R SST R q R R q R R q

ˆ SSE SSE q SST R SST R q R R q R R q Bll Evas Spg 06 Sggested Aswes, Poblem Set 5 ECON 3033. a) The R meases the facto of the vaato Y eplaed by the model. I ths case, R =SSM/SST. Yo ae gve that SSM = 3.059 bt ot SST. Howeve, ote that SST=SSM+SSE

More information

Consumer theory. A. The preference ordering B. The feasible set C. The consumption decision. A. The preference ordering. Consumption bundle

Consumer theory. A. The preference ordering B. The feasible set C. The consumption decision. A. The preference ordering. Consumption bundle Thomas Soesso Mcoecoomcs Lecte Cosme theoy A. The efeece odeg B. The feasble set C. The cosmto decso A. The efeece odeg Cosmto bdle x ( 2 x, x,... x ) x Assmtos: Comleteess 2 Tastvty 3 Reflexvty 4 No-satato

More information

RECAPITULATION & CONDITIONAL PROBABILITY. Number of favourable events n E Total number of elementary events n S

RECAPITULATION & CONDITIONAL PROBABILITY. Number of favourable events n E Total number of elementary events n S Fomulae Fo u Pobablty By OP Gupta [Ida Awad We, +91-9650 350 480] Impotat Tems, Deftos & Fomulae 01 Bascs Of Pobablty: Let S ad E be the sample space ad a evet a expemet espectvely Numbe of favouable evets

More information

XII. Addition of many identical spins

XII. Addition of many identical spins XII. Addto of may detcal sps XII.. ymmetc goup ymmetc goup s the goup of all possble pemutatos of obects. I total! elemets cludg detty opeato. Each pemutato s a poduct of a ceta fte umbe of pawse taspostos.

More information

The Linear Probability Density Function of Continuous Random Variables in the Real Number Field and Its Existence Proof

The Linear Probability Density Function of Continuous Random Variables in the Real Number Field and Its Existence Proof MATEC Web of Cofeeces ICIEA 06 600 (06) DOI: 0.05/mateccof/0668600 The ea Pobablty Desty Fucto of Cotuous Radom Vaables the Real Numbe Feld ad Its Estece Poof Yya Che ad Ye Collee of Softwae, Taj Uvesty,

More information

Motion and Flow II. Structure from Motion. Passive Navigation and Structure from Motion. rot

Motion and Flow II. Structure from Motion. Passive Navigation and Structure from Motion. rot Moto ad Flow II Sce fom Moto Passve Navgato ad Sce fom Moto = + t, w F = zˆ t ( zˆ ( ([ ] =? hesystemmoveswth a gd moto wth aslat oal velocty t = ( U, V, W ad atoalvelocty w = ( α, β, γ. Scee pots R =

More information

u(x, t) = u 0 (x ct). This Riemann invariant u is constant along characteristics λ with x = x 0 +ct (u(x, t) = u 0 (x 0 )):

u(x, t) = u 0 (x ct). This Riemann invariant u is constant along characteristics λ with x = x 0 +ct (u(x, t) = u 0 (x 0 )): x, t, h x The Frst-Order Wave Eqato The frst-order wave advecto eqato s c > 0 t + c x = 0, x, t = 0 = 0x. The solto propagates the tal data 0 to the rght wth speed c: x, t = 0 x ct. Ths Rema varat s costat

More information

VECTOR MECHANICS FOR ENGINEERS: Vector Mechanics for Engineers: Dynamics. In the current chapter, you will study the motion of systems of particles.

VECTOR MECHANICS FOR ENGINEERS: Vector Mechanics for Engineers: Dynamics. In the current chapter, you will study the motion of systems of particles. Seeth Edto CHPTER 4 VECTOR MECHNICS FOR ENINEERS: DYNMICS Fedad P. ee E. Russell Johsto, J. Systems of Patcles Lectue Notes: J. Walt Ole Texas Tech Uesty 003 The Mcaw-Hll Compaes, Ic. ll ghts eseed. Seeth

More information

On EPr Bimatrices II. ON EP BIMATRICES A1 A Hence x. is said to be EP if it satisfies the condition ABx

On EPr Bimatrices II. ON EP BIMATRICES A1 A Hence x. is said to be EP if it satisfies the condition ABx Iteatoal Joual of Mathematcs ad Statstcs Iveto (IJMSI) E-ISSN: 3 4767 P-ISSN: 3-4759 www.jms.og Volume Issue 5 May. 4 PP-44-5 O EP matces.ramesh, N.baas ssocate Pofesso of Mathematcs, ovt. ts College(utoomous),Kumbakoam.

More information

Minimizing spherical aberrations Exploiting the existence of conjugate points in spherical lenses

Minimizing spherical aberrations Exploiting the existence of conjugate points in spherical lenses Mmzg sphecal abeatos Explotg the exstece of cojugate pots sphecal leses Let s ecall that whe usg asphecal leses, abeato fee magg occus oly fo a couple of, so called, cojugate pots ( ad the fgue below)

More information

Overview. Review Superposition Solution. Review Superposition. Review x and y Swap. Review General Superposition

Overview. Review Superposition Solution. Review Superposition. Review x and y Swap. Review General Superposition ylcal aplace Soltos ebay 6 9 aplace Eqato Soltos ylcal Geoety ay aetto Mechacal Egeeg 5B Sea Egeeg Aalyss ebay 6 9 Ovevew evew last class Speposto soltos tocto to aal cooates Atoal soltos of aplace s eqato

More information

χ be any function of X and Y then

χ be any function of X and Y then We have show that whe we ae gve Y g(), the [ ] [ g() ] g() f () Y o all g ()() f d fo dscete case Ths ca be eteded to clude fuctos of ay ube of ado vaables. Fo eaple, suppose ad Y ae.v. wth jot desty fucto,

More information

Fairing of Parametric Quintic Splines

Fairing of Parametric Quintic Splines ISSN 46-69 Eglad UK Joual of Ifomato ad omputg Scece Vol No 6 pp -8 Fag of Paametc Qutc Sples Yuau Wag Shagha Isttute of Spots Shagha 48 ha School of Mathematcal Scece Fuda Uvesty Shagha 4 ha { P t )}

More information

Non-axial symmetric loading on axial symmetric. Final Report of AFEM

Non-axial symmetric loading on axial symmetric. Final Report of AFEM No-axal symmetc loadg o axal symmetc body Fal Repot of AFEM Ths poject does hamoc aalyss of o-axal symmetc loadg o axal symmetc body. Shuagxg Da, Musket Kamtokat 5//009 No-axal symmetc loadg o axal symmetc

More information

MATHS FOR ENGINEERS ALGEBRA TUTORIAL 8 MATHEMATICAL PROGRESSIONS AND SERIES

MATHS FOR ENGINEERS ALGEBRA TUTORIAL 8 MATHEMATICAL PROGRESSIONS AND SERIES MATHS FOR ENGINEERS ALGEBRA TUTORIAL 8 MATHEMATICAL PROGRESSIONS AND SERIES O completio of this ttoial yo shold be able to do the followig. Eplai aithmetical ad geometic pogessios. Eplai factoial otatio

More information

such that for 1 From the definition of the k-fibonacci numbers, the firsts of them are presented in Table 1. Table 1: First k-fibonacci numbers F 1

such that for 1 From the definition of the k-fibonacci numbers, the firsts of them are presented in Table 1. Table 1: First k-fibonacci numbers F 1 Scholas Joual of Egeeg ad Techology (SJET) Sch. J. Eg. Tech. 0; (C):669-67 Scholas Academc ad Scetfc Publshe (A Iteatoal Publshe fo Academc ad Scetfc Resouces) www.saspublshe.com ISSN -X (Ole) ISSN 7-9

More information

u(x, t) = u 0 (x ct). This Riemann invariant u is constant along characteristics λ with x = x 0 +ct (u(x, t) = u 0 (x 0 )):

u(x, t) = u 0 (x ct). This Riemann invariant u is constant along characteristics λ with x = x 0 +ct (u(x, t) = u 0 (x 0 )): x, t ), h x The Frst-Order Wave Eqato The frst-order wave advecto) eqato s c > 0) t + c x = 0, x, t = 0) = 0x). The solto propagates the tal data 0 to the rght wth speed c: x, t) = 0 x ct). Ths Rema varat

More information

Lecture 2: The Simple Regression Model

Lecture 2: The Simple Regression Model Lectre Notes o Advaced coometrcs Lectre : The Smple Regresso Model Takash Yamao Fall Semester 5 I ths lectre we revew the smple bvarate lear regresso model. We focs o statstcal assmptos to obta based estmators.

More information

Exponential Generating Functions - J. T. Butler

Exponential Generating Functions - J. T. Butler Epoetal Geeatg Fuctos - J. T. Butle Epoetal Geeatg Fuctos Geeatg fuctos fo pemutatos. Defto: a +a +a 2 2 + a + s the oday geeatg fucto fo the sequece of teges (a, a, a 2, a, ). Ep. Ge. Fuc.- J. T. Butle

More information

Lecture 10: Condensed matter systems

Lecture 10: Condensed matter systems Lectue 0: Codesed matte systems Itoducg matte ts codesed state.! Ams: " Idstgushable patcles ad the quatum atue of matte: # Cosequeces # Revew of deal gas etopy # Femos ad Bosos " Quatum statstcs. # Occupato

More information

GREEN S FUNCTION FOR HEAT CONDUCTION PROBLEMS IN A MULTI-LAYERED HOLLOW CYLINDER

GREEN S FUNCTION FOR HEAT CONDUCTION PROBLEMS IN A MULTI-LAYERED HOLLOW CYLINDER Joual of ppled Mathematcs ad Computatoal Mechacs 4, 3(3), 5- GREE S FUCTIO FOR HET CODUCTIO PROBLEMS I MULTI-LYERED HOLLOW CYLIDER Stasław Kukla, Uszula Sedlecka Isttute of Mathematcs, Czestochowa Uvesty

More information

DISTURBANCE TERMS. is a scalar and x i

DISTURBANCE TERMS. is a scalar and x i DISTURBANCE TERMS I a feld of research desg, we ofte have the qesto abot whether there s a relatoshp betwee a observed varable (sa, ) ad the other observed varables (sa, x ). To aswer the qesto, we ma

More information

2.1.1 The Art of Estimation Examples of Estimators Properties of Estimators Deriving Estimators Interval Estimators

2.1.1 The Art of Estimation Examples of Estimators Properties of Estimators Deriving Estimators Interval Estimators . ploatoy Statstcs. Itoducto to stmato.. The At of stmato.. amples of stmatos..3 Popetes of stmatos..4 Devg stmatos..5 Iteval stmatos . Itoducto to stmato Samplg - The samplg eecse ca be epeseted by a

More information

ON THE CONVERGENCE THEOREMS OF THE McSHANE INTEGRAL FOR RIESZ-SPACES-VALUED FUNCTIONS DEFINED ON REAL LINE

ON THE CONVERGENCE THEOREMS OF THE McSHANE INTEGRAL FOR RIESZ-SPACES-VALUED FUNCTIONS DEFINED ON REAL LINE O The Covegece Theoems... (Muslm Aso) ON THE CONVERGENCE THEOREMS OF THE McSHANE INTEGRAL FOR RIESZ-SPACES-VALUED FUNCTIONS DEFINED ON REAL LINE Muslm Aso, Yosephus D. Sumato, Nov Rustaa Dew 3 ) Mathematcs

More information

Objectives. Learning Outcome. 7.1 Centre of Gravity (C.G.) 7. Statics. Determine the C.G of a lamina (Experimental method)

Objectives. Learning Outcome. 7.1 Centre of Gravity (C.G.) 7. Statics. Determine the C.G of a lamina (Experimental method) Ojectves 7 Statcs 7. Cete of Gavty 7. Equlum of patcles 7.3 Equlum of g oes y Lew Sau oh Leag Outcome (a) efe cete of gavty () state the coto whch the cete of mass s the cete of gavty (c) state the coto

More information

Kinematics. Redundancy. Task Redundancy. Operational Coordinates. Generalized Coordinates. m task. Manipulator. Operational point

Kinematics. Redundancy. Task Redundancy. Operational Coordinates. Generalized Coordinates. m task. Manipulator. Operational point Mapulato smatc Jot Revolute Jot Kematcs Base Lks: movg lk fed lk Ed-Effecto Jots: Revolute ( DOF) smatc ( DOF) Geealzed Coodates Opeatoal Coodates O : Opeatoal pot 5 costats 6 paametes { postos oetatos

More information

FIBONACCI-LIKE SEQUENCE ASSOCIATED WITH K-PELL, K-PELL-LUCAS AND MODIFIED K-PELL SEQUENCES

FIBONACCI-LIKE SEQUENCE ASSOCIATED WITH K-PELL, K-PELL-LUCAS AND MODIFIED K-PELL SEQUENCES Joual of Appled Matheatcs ad Coputatoal Mechacs 7, 6(), 59-7 www.ac.pcz.pl p-issn 99-9965 DOI:.75/jac.7..3 e-issn 353-588 FIBONACCI-LIKE SEQUENCE ASSOCIATED WITH K-PELL, K-PELL-LUCAS AND MODIFIED K-PELL

More information

are positive, and the pair A, B is controllable. The uncertainty in is introduced to model control failures.

are positive, and the pair A, B is controllable. The uncertainty in is introduced to model control failures. Lectue 4 8. MRAC Desg fo Affe--Cotol MIMO Systes I ths secto, we cosde MRAC desg fo a class of ult-ut-ult-outut (MIMO) olea systes, whose lat dyacs ae lealy aaetezed, the ucetates satsfy the so-called

More information

Learning Bayesian belief networks

Learning Bayesian belief networks Lectue 6 Leag Bayesa belef etwoks Mlos Hauskecht mlos@cs.ptt.edu 5329 Seott Squae Admstato Mdtem: Wedesday, Mach 7, 2004 I class Closed book Mateal coveed by Spg beak, cludg ths lectue Last yea mdtem o

More information

At the end of this topic, students should be able to understand the meaning of finite and infinite sequences and series, and use the notation u

At the end of this topic, students should be able to understand the meaning of finite and infinite sequences and series, and use the notation u Natioal Jio College Mathematics Depatmet 00 Natioal Jio College 00 H Mathematics (Seio High ) Seqeces ad Seies (Lecte Notes) Topic : Seqeces ad Seies Objectives: At the ed of this topic, stdets shold be

More information

Application Of Alternating Group Explicit Method For Parabolic Equations

Application Of Alternating Group Explicit Method For Parabolic Equations WSEAS RANSACIONS o INFORMAION SCIENCE ad APPLICAIONS Qghua Feg Applcato Of Alteatg oup Explct Method Fo Paabolc Equatos Qghua Feg School of Scece Shadog uvesty of techology Zhagzhou Road # Zbo Shadog 09

More information

An Expansion of the Derivation of the Spline Smoothing Theory Alan Kaylor Cline

An Expansion of the Derivation of the Spline Smoothing Theory Alan Kaylor Cline A Epaso of the Derato of the Sple Smoothg heory Ala Kaylor Cle he classc paper "Smoothg by Sple Fctos", Nmersche Mathematk 0, 77-83 967) by Chrsta Resch showed that atral cbc sples were the soltos to a

More information

Trace of Positive Integer Power of Adjacency Matrix

Trace of Positive Integer Power of Adjacency Matrix Global Joual of Pue ad Appled Mathematcs. IN 097-78 Volume, Numbe 07), pp. 079-087 Reseach Ida Publcatos http://www.publcato.com Tace of Postve Itege Powe of Adacecy Matx Jagdsh Kuma Pahade * ad Mao Jha

More information

NUMERICAL SIMULATION OF TSUNAMI CURRENTS AROUND MOVING STRUCTURES

NUMERICAL SIMULATION OF TSUNAMI CURRENTS AROUND MOVING STRUCTURES NUMERICAL SIMULATION OF TSUNAMI CURRENTS AROUND MOVING STRUCTURES Ezo Nakaza 1, Tsuakyo Ibe ad Muhammad Abdu Rouf 1 The pape ams to smulate Tsuam cuets aoud movg ad fxed stuctues usg the movg-patcle semmplct

More information

φ (x,y,z) in the direction of a is given by

φ (x,y,z) in the direction of a is given by UNIT-II VECTOR CALCULUS Dectoal devatve The devatve o a pot ucto (scala o vecto) a patcula decto s called ts dectoal devatve alo the decto. The dectoal devatve o a scala pot ucto a ve decto s the ate o

More information

Probability. Stochastic Processes

Probability. Stochastic Processes Pobablty ad Stochastc Pocesses Weless Ifomato Tasmsso System Lab. Isttute of Commucatos Egeeg g Natoal Su Yat-se Uvesty Table of Cotets Pobablty Radom Vaables, Pobablty Dstbutos, ad Pobablty Destes Statstcal

More information

2.160 System Identification, Estimation, and Learning Lecture Notes No. 17 April 24, 2006

2.160 System Identification, Estimation, and Learning Lecture Notes No. 17 April 24, 2006 .6 System Idetfcato, Estmato, ad Learg Lectre Notes No. 7 Aprl 4, 6. Iformatve Expermets. Persstece of Exctato Iformatve data sets are closely related to Persstece of Exctato, a mportat cocept sed adaptve

More information

University of Pavia, Pavia, Italy. North Andover MA 01845, USA

University of Pavia, Pavia, Italy. North Andover MA 01845, USA Iteatoal Joual of Optmzato: heoy, Method ad Applcato 27-5565(Pt) 27-6839(Ole) wwwgph/otma 29 Global Ifomato Publhe (HK) Co, Ltd 29, Vol, No 2, 55-59 η -Peudoleaty ad Effcecy Gogo Gog, Noma G Rueda 2 *

More information

Alternating Direction Implicit Method

Alternating Direction Implicit Method Alteratg Drecto Implct Method Whle dealg wth Ellptc Eqatos the Implct form the mber of eqatos to be solved are N M whch are qte large mber. Thogh the coeffcet matrx has may zeros bt t s ot a baded system.

More information

Minimum Hyper-Wiener Index of Molecular Graph and Some Results on Szeged Related Index

Minimum Hyper-Wiener Index of Molecular Graph and Some Results on Szeged Related Index Joual of Multdscplay Egeeg Scece ad Techology (JMEST) ISSN: 359-0040 Vol Issue, Febuay - 05 Mmum Hype-Wee Idex of Molecula Gaph ad Some Results o eged Related Idex We Gao School of Ifomato Scece ad Techology,

More information

By the end of this section you will be able to prove the Chinese Remainder Theorem apply this theorem to solve simultaneous linear congruences

By the end of this section you will be able to prove the Chinese Remainder Theorem apply this theorem to solve simultaneous linear congruences Chapte : Theoy of Modula Aithmetic 8 Sectio D Chiese Remaide Theoem By the ed of this sectio you will be able to pove the Chiese Remaide Theoem apply this theoem to solve simultaeous liea cogueces The

More information

The Mathematical Appendix

The Mathematical Appendix The Mathematcal Appedx Defto A: If ( Λ, Ω, where ( λ λ λ whch the probablty dstrbutos,,..., Defto A. uppose that ( Λ,,..., s a expermet type, the σ-algebra o λ λ λ are defed s deoted by ( (,,...,, σ Ω.

More information

Chapter 7 Varying Probability Sampling

Chapter 7 Varying Probability Sampling Chapte 7 Vayg Pobablty Samplg The smple adom samplg scheme povdes a adom sample whee evey ut the populato has equal pobablty of selecto. Ude ceta ccumstaces, moe effcet estmatos ae obtaed by assgg uequal

More information

3. A Review of Some Existing AW (BT, CT) Algorithms

3. A Review of Some Existing AW (BT, CT) Algorithms 3. A Revew of Some Exstng AW (BT, CT) Algothms In ths secton, some typcal ant-wndp algothms wll be descbed. As the soltons fo bmpless and condtoned tansfe ae smla to those fo ant-wndp, the pesented algothms

More information

Distribution of Geometrically Weighted Sum of Bernoulli Random Variables

Distribution of Geometrically Weighted Sum of Bernoulli Random Variables Appled Mathematc, 0,, 8-86 do:046/am095 Publhed Ole Novembe 0 (http://wwwscrpog/oual/am) Dtbuto of Geometcally Weghted Sum of Beoull Radom Vaable Abtact Deepeh Bhat, Phazamle Kgo, Ragaath Naayaachaya Ratthall

More information

Legendre-coefficients Comparison Methods for the Numerical Solution of a Class of Ordinary Differential Equations

Legendre-coefficients Comparison Methods for the Numerical Solution of a Class of Ordinary Differential Equations IOSR Joual of Mathematcs (IOSRJM) ISS: 78-578 Volume, Issue (July-Aug 01), PP 14-19 Legede-coeffcets Compaso Methods fo the umecal Soluto of a Class of Oday Dffeetal Equatos Olaguju, A. S. ad Olaegu, D.G.

More information

Mu Sequences/Series Solutions National Convention 2014

Mu Sequences/Series Solutions National Convention 2014 Mu Sequeces/Seres Solutos Natoal Coveto 04 C 6 E A 6C A 6 B B 7 A D 7 D C 7 A B 8 A B 8 A C 8 E 4 B 9 B 4 E 9 B 4 C 9 E C 0 A A 0 D B 0 C C Usg basc propertes of arthmetc sequeces, we fd a ad bm m We eed

More information

RANDOM SYSTEMS WITH COMPLETE CONNECTIONS AND THE GAUSS PROBLEM FOR THE REGULAR CONTINUED FRACTIONS

RANDOM SYSTEMS WITH COMPLETE CONNECTIONS AND THE GAUSS PROBLEM FOR THE REGULAR CONTINUED FRACTIONS RNDOM SYSTEMS WTH COMPETE CONNECTONS ND THE GUSS PROBEM FOR THE REGUR CONTNUED FRCTONS BSTRCT Da ascu o Coltescu Naval cademy Mcea cel Bata Costata lascuda@gmalcom coltescu@yahoocom Ths pape peset the

More information

CHAPTER 4 RADICAL EXPRESSIONS

CHAPTER 4 RADICAL EXPRESSIONS 6 CHAPTER RADICAL EXPRESSIONS. The th Root of a Real Number A real umber a s called the th root of a real umber b f Thus, for example: s a square root of sce. s also a square root of sce ( ). s a cube

More information

Lecture 9 Multiple Class Models

Lecture 9 Multiple Class Models Lectue 9 Multple Class Models Multclass MVA Appoxmate MVA 8.4.2002 Copyght Teemu Keola 2002 1 Aval Theoem fo Multple Classes Wth jobs the system, a job class avg to ay seve sees the seve as equlbum wth

More information

Consider two masses m 1 at x = x 1 and m 2 at x 2.

Consider two masses m 1 at x = x 1 and m 2 at x 2. Chapte 09 Syste of Patcles Cete of ass: The cete of ass of a body o a syste of bodes s the pot that oes as f all of the ass ae cocetated thee ad all exteal foces ae appled thee. Note that HRW uses co but

More information

CE 561 Lecture Notes. Optimal Timing of Investment. Set 3. Case A- C is const. cost in 1 st yr, benefits start at the end of 1 st yr

CE 561 Lecture Notes. Optimal Timing of Investment. Set 3. Case A- C is const. cost in 1 st yr, benefits start at the end of 1 st yr CE 56 Letue otes Set 3 Optmal Tmg of Ivestmet Case A- C s ost. ost st y, beefts stat at the ed of st y C b b b3 0 3 Case B- Cost. s postpoed by oe yea C b b3 0 3 (B-A C s saved st yea C C, b b 0 3 Savg

More information

CHAPTER 5 : SERIES. 5.2 The Sum of a Series Sum of Power of n Positive Integers Sum of Series of Partial Fraction Difference Method

CHAPTER 5 : SERIES. 5.2 The Sum of a Series Sum of Power of n Positive Integers Sum of Series of Partial Fraction Difference Method CHAPTER 5 : SERIES 5.1 Seies 5. The Sum of a Seies 5..1 Sum of Powe of Positive Iteges 5.. Sum of Seies of Patial Factio 5..3 Diffeece Method 5.3 Test of covegece 5.3.1 Divegece Test 5.3. Itegal Test 5.3.3

More information

Inequalities for Dual Orlicz Mixed Quermassintegrals.

Inequalities for Dual Orlicz Mixed Quermassintegrals. Advaces Pue Mathematcs 206 6 894-902 http://wwwscpog/joual/apm IN Ole: 260-0384 IN Pt: 260-0368 Iequaltes fo Dual Olcz Mxed Quemasstegals jua u chool of Mathematcs ad Computatoal cece Hua Uvesty of cece

More information

1 Onto functions and bijections Applications to Counting

1 Onto functions and bijections Applications to Counting 1 Oto fuctos ad bectos Applcatos to Coutg Now we move o to a ew topc. Defto 1.1 (Surecto. A fucto f : A B s sad to be surectve or oto f for each b B there s some a A so that f(a B. What are examples of

More information

Suppose we have observed values t 1, t 2, t n of a random variable T.

Suppose we have observed values t 1, t 2, t n of a random variable T. Sppose we have obseved vales, 2, of a adom vaable T. The dsbo of T s ow o belog o a cea ype (e.g., expoeal, omal, ec.) b he veco θ ( θ, θ2, θp ) of ow paamees assocaed wh s ow (whee p s he mbe of ow paamees).

More information

ESS Line Fitting

ESS Line Fitting ESS 5 014 17. Le Fttg A very commo problem data aalyss s lookg for relatoshpetwee dfferet parameters ad fttg les or surfaces to data. The smplest example s fttg a straght le ad we wll dscuss that here

More information

Generalized Duality for a Nondifferentiable Control Problem

Generalized Duality for a Nondifferentiable Control Problem Aeca Joal of Appled Matheatcs ad Statstcs, 4, Vol., No. 4, 93- Avalable ole at http://pbs.scepb.co/aas//4/3 Scece ad Edcato Pblsh DO:.69/aas--4-3 Geealzed Dalty fo a Nodffeetable Cotol Poble. Hsa,*, Vkas

More information

Probability and Stochastic Processes

Probability and Stochastic Processes Pobablty ad Stochastc Pocesses Weless Ifomato Tasmsso System Lab. Isttute of Commucatos Egeeg Natoal Su Yat-se Uvesty Table of Cotets Pobablty Radom Vaables, Pobablty Dstbutos, ad Pobablty Destes Statstcal

More information

19 th INTERNATIONAL CONGRESS ON ACOUSTICS MADRID, 2-7 SEPTEMBER 2007 DETERMINATION OF THE SOUND RADIATION OF TURBULENT FLAMES USING AN INTEGRAL METHOD

19 th INTERNATIONAL CONGRESS ON ACOUSTICS MADRID, 2-7 SEPTEMBER 2007 DETERMINATION OF THE SOUND RADIATION OF TURBULENT FLAMES USING AN INTEGRAL METHOD 9 th NTERNATONAL ONGRE ON AOUT MADRD, -7 EPTEMBER 7 DETERMNATON OF THE OUND RADATON OF TURBULENT FLAME UNG AN NTEGRAL METHOD PA: 43..Rz Pscoya, Rafael; Ochma, Mat Uvesty of Aled ceces; Lembe t., 3353 Bel,

More information

B-spline curves. 1. Properties of the B-spline curve. control of the curve shape as opposed to global control by using a special set of blending

B-spline curves. 1. Properties of the B-spline curve. control of the curve shape as opposed to global control by using a special set of blending B-sple crve Copyrght@, YZU Optmal Desg Laboratory. All rghts reserved. Last pdated: Yeh-Lag Hs (--9). ote: Ths s the corse materal for ME Geometrc modelg ad compter graphcs, Ya Ze Uversty. art of ths materal

More information

SOME REMARKS ON HORIZONTAL, SLANT, PARABOLIC AND POLYNOMIAL ASYMPTOTE

SOME REMARKS ON HORIZONTAL, SLANT, PARABOLIC AND POLYNOMIAL ASYMPTOTE D I D A C T I C S O F A T H E A T I C S No (4) 3 SOE REARKS ON HORIZONTAL, SLANT, PARABOLIC AND POLYNOIAL ASYPTOTE Tdeusz Jszk Abstct I the techg o clculus, we cosde hozotl d slt symptote I ths ppe the

More information

APPROXIMATE ANALYTIC WAVE FUNCTION METHOD IN ELECTRON ATOM SCATTERING CALCULATIONS. Budi Santoso

APPROXIMATE ANALYTIC WAVE FUNCTION METHOD IN ELECTRON ATOM SCATTERING CALCULATIONS. Budi Santoso APPROXIMATE ANALYTIC WAVE FUNCTION METHOD IN ELECTRON ATOM SCATTERING CALCULATIONS Bud Satoso ABSTRACT APPROXIMATE ANALYTIC WAVE FUNCTION METHOD IN ELECTRON ATOM SCATTERING CALCULATIONS. Appoxmate aalytc

More information

A. Thicknesses and Densities

A. Thicknesses and Densities 10 Lab0 The Eath s Shells A. Thcknesses and Denstes Any theoy of the nteo of the Eath must be consstent wth the fact that ts aggegate densty s 5.5 g/cm (ecall we calculated ths densty last tme). In othe

More information

Beam Warming Second-Order Upwind Method

Beam Warming Second-Order Upwind Method Beam Warmg Secod-Order Upwd Method Petr Valeta Jauary 6, 015 Ths documet s a part of the assessmet work for the subject 1DRP Dfferetal Equatos o Computer lectured o FNSPE CTU Prague. Abstract Ths documet

More information

SOME GEOMETRIC ASPECTS OF VARIATIONAL PROBLEMS IN FIBRED MANIFOLDS

SOME GEOMETRIC ASPECTS OF VARIATIONAL PROBLEMS IN FIBRED MANIFOLDS Electoc tascptos Mathematcal Isttute Slesa Uvesty Opava Czech Republc August Ths tet s a electoc tascpto of the ogal eseach pape D Kupa Some Geometc Aspects of Vaatoal Poblems Fbed Mafolds Fola Fac Sc

More information

The Geometric Proof of the Hecke Conjecture

The Geometric Proof of the Hecke Conjecture The Geometc Poof of the Hecke Cojectue Kada Sh Depatmet of Mathematc Zhejag Ocea Uvety Zhouha Cty 6 Zhejag Povce Cha Atact Begg fom the eoluto of Dchlet fucto ug the e poduct fomula of two fte-dmeoal vecto

More information

Chapter 2: Descriptive Statistics

Chapter 2: Descriptive Statistics Chapte : Decptve Stattc Peequte: Chapte. Revew of Uvaate Stattc The cetal teecy of a oe o le yetc tbuto of a et of teval, o hghe, cale coe, ofte uaze by the athetc ea, whch efe a We ca ue the ea to ceate

More information

Conditional Convergence of Infinite Products

Conditional Convergence of Infinite Products Coditioal Covegece of Ifiite Poducts William F. Tech Ameica Mathematical Mothly 106 1999), 646-651 I this aticle we evisit the classical subject of ifiite poducts. Fo stadad defiitios ad theoems o this

More information

Chapter 2 Probability and Stochastic Processes

Chapter 2 Probability and Stochastic Processes Chapte Pobablty ad Stochastc Pocesses Table of Cotets Pobablty Radom Vaables, Pobablty Dstbutos, ad Pobablty Destes Fuctos of Radom Vaables Statstcal Aveages of Radom Vaables Some Useful Pobablty Dstbutos

More information

PGE 310: Formulation and Solution in Geosystems Engineering. Dr. Balhoff. Interpolation

PGE 310: Formulation and Solution in Geosystems Engineering. Dr. Balhoff. Interpolation PGE 30: Formulato ad Soluto Geosystems Egeerg Dr. Balhoff Iterpolato Numercal Methods wth MATLAB, Recktewald, Chapter 0 ad Numercal Methods for Egeers, Chapra ad Caale, 5 th Ed., Part Fve, Chapter 8 ad

More information

Harmonic Curvatures in Lorentzian Space

Harmonic Curvatures in Lorentzian Space BULLETIN of the Bull Malaya Math Sc Soc Secod See 7-79 MALAYSIAN MATEMATICAL SCIENCES SOCIETY amoc Cuvatue Loetza Space NEJAT EKMEKÇI ILMI ACISALIOĞLU AND KĀZIM İLARSLAN Aaa Uvety Faculty of Scece Depatmet

More information

( ) ( ) ( ( )) ( ) ( ) ( ) ( ) ( ) = ( ) ( ) + ( ) ( ) = ( ( )) ( ) + ( ( )) ( ) Review. Second Derivatives for f : y R. Let A be an m n matrix.

( ) ( ) ( ( )) ( ) ( ) ( ) ( ) ( ) = ( ) ( ) + ( ) ( ) = ( ( )) ( ) + ( ( )) ( ) Review. Second Derivatives for f : y R. Let A be an m n matrix. Revew + v, + y = v, + v, + y, + y, Cato! v, + y, + v, + y geeral Let A be a atr Let f,g : Ω R ( ) ( ) R y R Ω R h( ) f ( ) g ( ) ( ) ( ) ( ( )) ( ) dh = f dg + g df A, y y A Ay = = r= c= =, : Ω R he Proof

More information

Chapter 5 Properties of a Random Sample

Chapter 5 Properties of a Random Sample Lecture 6 o BST 63: Statstcal Theory I Ku Zhag, /0/008 Revew for the prevous lecture Cocepts: t-dstrbuto, F-dstrbuto Theorems: Dstrbutos of sample mea ad sample varace, relatoshp betwee sample mea ad sample

More information

Lecture 24: Observability and Constructibility

Lecture 24: Observability and Constructibility ectue 24: Obsevability ad Costuctibility 7 Obsevability ad Costuctibility Motivatio: State feedback laws deped o a kowledge of the cuet state. I some systems, xt () ca be measued diectly, e.g., positio

More information

Atomic units The atomic units have been chosen such that the fundamental electron properties are all equal to one atomic unit.

Atomic units The atomic units have been chosen such that the fundamental electron properties are all equal to one atomic unit. tomc uts The atomc uts have bee chose such that the fudametal electo popetes ae all equal to oe atomc ut. m e, e, h/, a o, ad the potetal eegy the hydoge atom e /a o. D3.33564 0-30 Cm The use of atomc

More information

Lecture 12: Spiral: Domain Specific HLS. Housekeeping

Lecture 12: Spiral: Domain Specific HLS. Housekeeping 8 643 ectue : Spal: Doma Specfc HS James C. Hoe Depatmet of ECE Caege Mello Uvesty 8 643 F7 S, James C. Hoe, CMU/ECE/CACM, 7 Houseeepg You goal today: see a eample of eally hghlevel sythess (ths lectue

More information

= 5! 3! 2! = 5! 3! (5 3)!. In general, the number of different groups of r items out of n items (when the order is ignored) is given by n!

= 5! 3! 2! = 5! 3! (5 3)!. In general, the number of different groups of r items out of n items (when the order is ignored) is given by n! 0 Combiatoial Aalysis Copyight by Deiz Kalı 4 Combiatios Questio 4 What is the diffeece betwee the followig questio i How may 3-lette wods ca you wite usig the lettes A, B, C, D, E ii How may 3-elemet

More information

STATISTICAL PROPERTIES OF LEAST SQUARES ESTIMATORS. x, where. = y - ˆ " 1

STATISTICAL PROPERTIES OF LEAST SQUARES ESTIMATORS. x, where. = y - ˆ  1 STATISTICAL PROPERTIES OF LEAST SQUARES ESTIMATORS Recall Assumpto E(Y x) η 0 + η x (lear codtoal mea fucto) Data (x, y ), (x 2, y 2 ),, (x, y ) Least squares estmator ˆ E (Y x) ˆ " 0 + ˆ " x, where ˆ

More information

Progression. CATsyllabus.com. CATsyllabus.com. Sequence & Series. Arithmetic Progression (A.P.) n th term of an A.P.

Progression. CATsyllabus.com. CATsyllabus.com. Sequence & Series. Arithmetic Progression (A.P.) n th term of an A.P. Pogessio Sequece & Seies A set of umbes whose domai is a eal umbe is called a SEQUENCE ad sum of the sequece is called a SERIES. If a, a, a, a 4,., a, is a sequece, the the expessio a + a + a + a 4 + a

More information

International Journal of Scientific & Engineering Research, Volume 5, Issue 9, September ISSN

International Journal of Scientific & Engineering Research, Volume 5, Issue 9, September ISSN Iteratoal Joral o Scetc & Egeerg Research, Volme 5, Isse 9, September-4 5 ISSN 9-558 Nmercal Implemetato o BD va Method o Les or Tme Depedet Nolear Brgers Eqato VjthaMkda, Ashsh Awasth Departmet o Mathematcs,

More information

Fundamentals of Regression Analysis

Fundamentals of Regression Analysis Fdametals of Regresso Aalyss Regresso aalyss s cocered wth the stdy of the depedece of oe varable, the depedet varable, o oe or more other varables, the explaatory varables, wth a vew of estmatg ad/or

More information

The number of observed cases The number of parameters. ith case of the dichotomous dependent variable. the ith case of the jth parameter

The number of observed cases The number of parameters. ith case of the dichotomous dependent variable. the ith case of the jth parameter LOGISTIC REGRESSION Notato Model Logstc regresso regresses a dchotomous depedet varable o a set of depedet varables. Several methods are mplemeted for selectg the depedet varables. The followg otato s

More information

n -dimensional vectors follow naturally from the one

n -dimensional vectors follow naturally from the one B. Vectors ad sets B. Vectors Ecoomsts study ecoomc pheomea by buldg hghly stylzed models. Uderstadg ad makg use of almost all such models requres a hgh comfort level wth some key mathematcal sklls. I

More information

Mean is only appropriate for interval or ratio scales, not ordinal or nominal.

Mean is only appropriate for interval or ratio scales, not ordinal or nominal. Mea Same as ordary average Sum all the data values ad dvde by the sample sze. x = ( x + x +... + x Usg summato otato, we wrte ths as x = x = x = = ) x Mea s oly approprate for terval or rato scales, ot

More information

a) The average (mean) of the two fractions is halfway between them: b) The answer is yes. Assume without loss of generality that p < r.

a) The average (mean) of the two fractions is halfway between them: b) The answer is yes. Assume without loss of generality that p < r. Solutios to MAML Olympiad Level 00. Factioated a) The aveage (mea) of the two factios is halfway betwee them: p ps+ q ps+ q + q s qs qs b) The aswe is yes. Assume without loss of geeality that p

More information

DERIVATION OF THE BASIC LAWS OF GEOMETRIC OPTICS

DERIVATION OF THE BASIC LAWS OF GEOMETRIC OPTICS DERIVATION OF THE BASIC LAWS OF GEOMETRIC OPTICS It s well kow that a lght ay eflectg off of a suface has ts agle of eflecto equal to ts agle of cdece ad that f ths ay passes fom oe medum to aothe that

More information

A DATA DRIVEN PARAMETER ESTIMATION FOR THE THREE- PARAMETER WEIBULL POPULATION FROM CENSORED SAMPLES

A DATA DRIVEN PARAMETER ESTIMATION FOR THE THREE- PARAMETER WEIBULL POPULATION FROM CENSORED SAMPLES Mathematcal ad Computatoal Applcatos, Vol. 3, No., pp. 9-36 008. Assocato fo Scetfc Reseach A DATA DRIVEN PARAMETER ESTIMATION FOR THE THREE- PARAMETER WEIBULL POPULATION FROM CENSORED SAMPLES Ahmed M.

More information

Permutations that Decompose in Cycles of Length 2 and are Given by Monomials

Permutations that Decompose in Cycles of Length 2 and are Given by Monomials Poceedgs of The Natoa Cofeece O Udegaduate Reseach (NCUR) 00 The Uvesty of Noth Caoa at Asheve Asheve, Noth Caoa Ap -, 00 Pemutatos that Decompose Cyces of Legth ad ae Gve y Moomas Lous J Cuz Depatmet

More information

Lecture 07: Poles and Zeros

Lecture 07: Poles and Zeros Lecture 07: Poles ad Zeros Defto of poles ad zeros The trasfer fucto provdes a bass for determg mportat system respose characterstcs wthout solvg the complete dfferetal equato. As defed, the trasfer fucto

More information

Johns Hopkins University Department of Biostatistics Math Review for Introductory Courses

Johns Hopkins University Department of Biostatistics Math Review for Introductory Courses Johs Hopks Uverst Departmet of Bostatstcs Math Revew for Itroductor Courses Ratoale Bostatstcs courses wll rel o some fudametal mathematcal relatoshps, fuctos ad otato. The purpose of ths Math Revew s

More information

Johns Hopkins University Department of Biostatistics Math Review for Introductory Courses

Johns Hopkins University Department of Biostatistics Math Review for Introductory Courses Johs Hopks Uverst Departmet of Bostatstcs Math Revew for Itroductor Courses Ratoale Bostatstcs courses wll rel o some fudametal mathematcal relatoshps, fuctos ad otato. The purpose of ths Math Revew s

More information

Introduction to local (nonparametric) density estimation. methods

Introduction to local (nonparametric) density estimation. methods Itroducto to local (oparametrc) desty estmato methods A slecture by Yu Lu for ECE 66 Sprg 014 1. Itroducto Ths slecture troduces two local desty estmato methods whch are Parze desty estmato ad k-earest

More information

Multivector Functions

Multivector Functions I: J. Math. Aal. ad Appl., ol. 24, No. 3, c Academic Pess (968) 467 473. Multivecto Fuctios David Hestees I a pevious pape [], the fudametals of diffeetial ad itegal calculus o Euclidea -space wee expessed

More information

MATH Midterm Solutions

MATH Midterm Solutions MATH 2113 - Midtem Solutios Febuay 18 1. A bag of mables cotais 4 which ae ed, 4 which ae blue ad 4 which ae gee. a How may mables must be chose fom the bag to guaatee that thee ae the same colou? We ca

More information

Econometric Methods. Review of Estimation

Econometric Methods. Review of Estimation Ecoometrc Methods Revew of Estmato Estmatg the populato mea Radom samplg Pot ad terval estmators Lear estmators Ubased estmators Lear Ubased Estmators (LUEs) Effcecy (mmum varace) ad Best Lear Ubased Estmators

More information