A Functionally Fitted 3-stage ESDIRK Method Kazufumi Ozawa Akita Prefectural University Honjo Akita , Japan

Size: px
Start display at page:

Download "A Functionally Fitted 3-stage ESDIRK Method Kazufumi Ozawa Akita Prefectural University Honjo Akita , Japan"

Transcription

1 A Functonally Ftted 3-stage ESDIRK Method Kazufum Ozawa Akta Prefectural Unversty Hono Akta , Japan Abstract A specal class of Runge-Kutta (-Nyström) methods called functonally ftted Runge-Kutta (FRK) methods has recently been proposed by the author. Ths class of methods s based on the exact ntegraton of a gven set of functons, whch s called the bass functons by the author, and as a result, the method s always exact, f the soluton of the ODE s expressed by a lnear combnaton of the bass functons. In ths paper, a 3-stage explct sngly dagonally mplct Runge-Kutta (ESDIRK) method n ths class s developed. It s shown that the global error of the proposed method behaves lke O(h 4 ), where h s the step-sze, even for the cases that the method s not ftted completely to the equaton. The method s extended to an embedded par. Several numercal experments show that the method ntegrates the ODE very accurately, for the case that a sutable set of the bass functons can be found, and produces reasonable accuracy even for general cases. Introducton Intal value problems of ODEs are very mportant tools n scence and technology. When solvng the problems, t s often the case, n practce, that a pror nformaton on the soluton and/or equaton, such as the perod of the soluton or the domnant egenvalue of the coeffcent matrx, s avalable. For ths case, f we could desgn a numercal method based on such nformaton, then the method would be very accurate for ths problem. For example, f the soluton of the ODE s a snusodal functon wth a small perturbaton, then the specal method whch s exact only for the trgonometrc functon wth the frequency wll be more accurate than general ODE methods. Many specal methods whch are exact for trgonometrc functons, exponental functons, or mxed-polynomals have been derved (see e.g. [], [7], [0], [], [3], [4]). As an extenson of these method, Ozawa has recently developed the Runge-Kutta (-Nyström) method that s exact on the lnear space of any gven functons ([8], [9]). The method s also an extenson of the collocaton Runge-Kutta methods, whch are the specal cases that the functons are polynomals. The method proposed by Ozawa, lke collocaton methods, s fully mplct, so that ts computatonal cost s extremely expensve compared wth explct methods, and expensve wth dagonally mplct Runge-Kutta (DIRK) methods. The purpose of ths work s to develop a computatonally cheap Runge-Kutta method whch are exact for the gven functons, usng the smlar technque used n Ozawa ([8] and [9]). Functonally ftted Runge-Kutta method Consder the ntal value problem dy(t) dt = f(y(t)), y(0) = y 0, t [0, T ], ()

2 and the s-stage Runge-Kutta method y n+ = y n + h Y = y n + h s b f(y ), = s a, f(y ), =,..., s, = () where h s a step-sze, and y n s the numercal approxmaton to y(nh). Almost all Runge-Kutta methods are desgned to ntegrate the equaton exactly, f the soluton of () s any polynomal of some degree or less. In our approach, however, the Runge-Kutta method s desgned to be exact not for polynomals but for the lnear space of gven functons {Φ m (t)} s m=. We call the functons {Φ m (t)} s m= the bass functons, and the resultng Runge-Kutta method and the functonally ftted Runge-Kutta (FRK) method. One way to get the coeffcents of the FRK s to gve the functons {Φ m (t)} s m=, and then solve the (s + ) smultaneous equatons s Φ m (t + c h) = Φ m (t) + h a, ϕ m (t + c h), m F, =,..., s, = (3) s Φ m (t + h) = Φ m (t) + h b ϕ m (t + c h), m F s+, = for the unknowns a, and b, where ϕ m (t) = Φ m (t), and F F {,,..., s} s the set of the subscrpts m of the functons Φ m (t) used n the th equaton. Here we assume that c s are constant and dfferent from each other. In [8] and [9], F s always F for each, that s, all the functons Φ m (t) (m =,..., s) are used to determne all the unknowns a, ( =,..., s), whch means the resultng method s necessarly a fully mplct one. In the present case, however, we frst gve s F coeffcents n accordance wth the sparsty pattern of a predetermned Butcher array, and then determne the remanng F coeffcents usng F dfferent functons of Φ (t),..., Φ s (t). It s clear from ths constructon that the method s always exact, f the soluton y(t) s an element of the space spanned by the Φ ν (t) s, where ν s+ = F. In Ozawa [8], the coeffcents of FRK gven by (3) are shown to be unquely determned for all h and t [0, T ], f the Wronskan matrx W (t) ϕ (t) ϕ s (t) (t) ϕ() s (t).. (t) ϕ s (s ) (t) ϕ () ϕ (s ) s nonsngular. Moreover, n [8], these coeffcents are shown to be analytc, f all of the functons {Φ m (t)} s m= are analytc n [0, T ]. In general, the coeffcents a, and b determned n ths way depend not only on h, but also on t. We shall consder, however, the case that these coeffcents depend only on h; f the bass functons Φ m (t) are polynomals, exponentals or snusodal functons, then ths s the case. By ths assumpton, we set t = 0 n (3) wthout loss of generalty. (4)

3 3 Local truncaton error of FRK method The numercal results gven by FRK wll have truncaton errors, except for the case that FRK s ftted to the problem () completely. Therefore, t s necessary to evaluate the error by some measure. In ths paper, we use the order of accuracy to evaluate the error, as n the case of conventonal numercal methods of ODEs. The defnton of the order of accuracy of FRK s the same. That s, f the numercal soluton by FRK satsfes y y(h) = O(h p+ ), y(0) = y 0, h 0, for any suffcently smooth functon y(t), then we shall call the nteger p the order of accuracy of FRK. However, unlke the conventonal case of constant coeffcent methods, we must consder the errors n the stuaton that the coeffcents a, and b also vary as functons of h, when h 0. In order to analyze the local truncaton error of FRK, let us ntroduce the followng quanttes: B(q) C (q) D(q) b c q q, (5) a, c q cq q, (6) b C (q). (7) Accordng to Ozawa [8], [9], these quanttes satsfy B(q) = O(h r s++ q ), q =,..., r s+, C (q) = O(h r + q ), q =,..., r, where we set r = F ( =,,..., s + ). We express the errors at the stages and step by usng B(q) and C (q). Frst we consder the resduals at the stages and step. Let y(t) be any suffcently smooth functon (not necessary the soluton of ()), then R y(0) + h R y(0) + h b y (c h) y(h) = q a, y (c h) y(c h) = q h q B(q) (q )! (y (0)) (q ), Note that y(t) = Φ m (t) these resduals vansh, whch mples h q B(q) (q )! (ϕ m(0)) (q ) = 0, m F s+ q q h q C (q) (q )! (ϕ m(0)) (q ) = 0, m F. h q C (q) (q )! (y (0)) (q ). On the other hand, f Φ m (t) are polynomals of some degree or less, then B(q) and C (q) vansh for the frst several q s, and ϕ (q ) m (t) = 0 for the other q s. From (8) and (9) we have R = O(h r+ ), R = O(h ρ+ ), () (8) (9) (0)

4 where ρ = mn{r }, r = r s+. Next we consder the relaton between the resduals and local errors of FRK method. Let y(t) be the soluton of y (t) = f(y(t)), then the errors at the stages are gven by therefore e Y y(c h) ( = y 0 + h a, f(y ) y 0 + h = h f y a, (e + O(e )) + R, e = ( a, h f y ) ( (h f y ) a, y (c h) R ) a, (e + O(e )) + R ) = O(h ρ+ ). () For the error at the step, we have E y y(h) = y 0 + h b f(y ) ( y 0 + h = h b (f(y ) f(y(c h))) + R = h f y b (Y y(c h) + O(e )) + R. b y (c h) R Before evaluatng E, we must evaluate b Y = b y 0 + h b a, f(y ),, b y(c h) = b y 0 + h b a, y (c h) T,, where we put T = For the order of T, f we assume then from () we have Thus E = (h f y ), = (h f y ), b R = q ) (3) h q D(q) (q )! (y (0)) (q ). (4) T = O(h τ+ ), (5) τ ρ = mn {r }. b a, (f(y ) y (c h)) + (h f y ) T + R + O(h ρ+3 ) b a, e + (h f y ) T + R + O(h ρ+3 ).

5 If the order of, b a, e s that of the mnmum of e s, then the order of accuracy p of the method s gven by p = mn {ρ +, τ +, r}. (6) 4 3-stage FESDIRK method Let us consder the 3-stage Runge-Kutta method gven by the Butcher array 0 0 c a, α c 3 a 3, a 3, α b b b 3 (7) Usually the methods of ths type are called explct SDIRK (ESDIRK) method when the coeffcents are constant, and we shall call t functonally ftted ESDIRK (FESDIRK) method, f the method s FRK. For the gven c s and the gven {Φ m (t)} 3 m=, we determne the coeffcents of (7) by Φ m (c h) = Φ m (0) + h (a, ϕ m (0) + α ϕ m (c h)), m =,, Φ m (c 3 h) = Φ m (0) + h (a 3, ϕ m (0) + a 3, ϕ m (c h) + α ϕ m (c 3 h)), m =,, Φ m (h) = Φ m (0) + h (b ϕ m (0) + b ϕ m (c h) + b 3 ϕ m (c 3 h)), m =,, 3, (8) where we assume that the Wronskan matrx ϕ (t) ϕ (t) ϕ 3 (t) W (t) = ϕ (t) ϕ() (t) ϕ() 3 (t) (9) ϕ () (t) ϕ () (t) ϕ () 3 (t) s nonsngular at t = 0. Ths method s exact for y(t) span{φ (t), Φ (t)}. For ths case, we have r = r 3 =, r 4 = 3, ρ =, τ, and B(q) = C (q) = 3 = 3 = b c q q = O(h4 q ), q =,, 3, a, c q cq q = O(h3 q ), q =,, whch leads to p = 3 from (6). We shall call the method FESDIRK3. When h 0, FESDIRK approaches a constant coeffcent method, whch has a key role n later consderatons. Here we consder the coeffcents of ths constant coeffcent method. Relaton (0) means that 3 = = (0) c q =, q =,, 3, () q a (0), cq = cq, q =,, () q

6 where a (0), and are the constant terms n the power seres expanson n h of the coeffcents. The relatons () and (), whch are the so-called smplfyng assumptons [], determne a (0), and b(0) unquely as functons of c. The results are: Note that a (0), and b(0) a (0), = c, a(0), = c (= α), a (0) 3, = 36 c4 0 c c 60 c c (3 c ), a (0) 3, = 4 c3 50 c + 36 c 9 8 c (3 c ), a (0) 3, 3 = α, = 6 c 6 c + 6 c (4 c 3), = 3 = 6 c (6 c 8 c + 3), (3 c ) 3 (4 c 3) (6 c 8 c + 3). gven above are ndependent of the choce of Φ m (t). 5 Fourth order FESDIRK method We have obtaned a 3-stage FESDIRK method, whch we call FESDIRK3, and have shown that the method s of order 3. In order to rase the order of the method up to 4 we assume two condtons. The frst condton s 0 t q t (t c ) (t c 3 ) dt { = 0, q =, 0, q. We wll consder later the case that the ntegral equals to 0 also for q. From ths assumpton we have c 3 = 4 c 3 (3 c ). (4) By assumng (4), we have from [8] B(q) = 3 = so that r = 4 n (), and we have, nstead of (), (3) b c q q = O(hmax{5 q, } ), q =,..., 4, (5) 3 = c q =, q =,..., 4. (6) q The second assumpton s a (0), = b(0) ( c ), =,, 3. (7)

7 It has been shown that ths condton together wth () and (6) s a suffcent condton for the method (a (0),, b(0), c ) to be of order 4 (see [], [4]). Next lemma proves that condtons (), (6) and (7) guarantee τ = 3 n (5). Lemma If condtons (), (6) and (7) hold, then D(q) = O(h 4 q ), q =,, 3, so that τ = 3 n (5). Proof. Let the power seres expanson of D(q) be D(q) = D (0) (q) + D () (q) h + D () (q) h + From the defnton of D(q) n (7) and the property of C (q) gven by (0), we have mmedately D() = O(h ), D() = O(h), or equvalently D (0) () = D () () = 0, D (0) () = 0. Next we show that several terms other than the above vansh. From (), (6) and (7), we have for q =,, 3 D (0) (q) = ( ) a (0), cq cq = ( c ) c q = 0. (8) q q (q + ) On the other hand, from (4) and (8) q h q D(q) (q )! (ϕ m(0)) (q ) = ν ( ν q= = 0, m =,. (ϕ m (0)) (q ) (q )! D (ν q) (q) ) h ν (9) Therefore, the condton that the coeffcent of h 3 n (9) s beng 0 can be expressed by ϕ (0) D () () + ϕ () D () () + ϕ() D (0) (3) = 0, ϕ (0) D () () + ϕ () D () () + ϕ() D (0) (3) = 0. Snce we have already had D (0) (3) = 0 n (8), and the submatrx ( ) ϕ ϕ ϕ () ϕ () of Wronskan matrx (9) s nonsngular by assumpton, then (30) (3) D () () = 0, D () () = 0.

8 Summarzng the results obtaned so far, we have D (0) () = D () () = D () () = 0, D (0) () = D () () = 0, D (0) (3) = 0. It s clear from the dscusson n ths lemma that any other terms of D (l) (q) never vansh. Thus we have proved ths lemma. Snce r = 4 has already been establshed, and τ = 3 has been proved by the above lemma, t s clear from (6) that p = 4. Thus we have the followng theorem: Theorem If the abscssae c and c 3 satsfy the two condtons (4) and (7), then FESDIRK wth the coeffcents gven by (3) s of order 4. Hereafter we call the (F)ESDIRK of order 4 (F)ESDIRK4. Next we must obtan the values of c for whch condton (7) s vald. Let d be d = a (0), b(0) ( c ), =,, 3, then from () and () we have d c q =, = q that s a (0), cq c q q + q + d + d + d 3 = 0, c d + c 3 d 3 = 0. ( c ) c q = 0, for q =,, Ths means that f we force one of d s to be 0, then the remanders become 0, provded that 0 < c c 3. Thus we put, for example, d = (3 c ) (3 c ) (c ) 6 c (4 c 3) whch leads to c = 3, 3,. Among these solutons, c = /3 s not allowed because of (4), so that we consder the remanng two solutons. Next we show the stablty regons of the ESDIRK4 s wth c = /3 and c =, and compare these regons wth that of the classcal Runge-Kutta method (RK4). = 0,

9 Fg.. Stablty regons of ESDIRK4 s wth c = 3 (sold), c = (dashed), and RK4 (dash-and-dotted). Fg. shows that the ESDIRK4 wth c = /3 s preferable to the ESDIRK4 wth c =, snce the former has broader stablty regon. Therefore we take c = /3 also for FESDIRK4, snce t s expected that FESDIRK has approxmately the same propertes as those of ESDIRK, when h s small. Here we show the Butcher array of the ESDIRK4 wth c = / Hereafter, we smply denote the ESDIRK4 and FESDIRK4 wth c = /3 by ESDIRK4 and FESDIRK4, respectvely. Fnally we nvestgate the attanable order wth the FESDIRK of the type (7). It s clear from the prevous dscusson that the FESDIRK and the ESDIRK have always the same order, snce the latter corresponds to the partcular case that Φ (t) = t, Φ (t) = t, Φ 3 (t) = t 3, and the dscusson s ndependent of the choce of the bass functons. Therefore, we wll consder the attanable order of the ESDIRK nstead of that of the FESDIRK. Any 3-stage method wth c = 0 cannot be of order 6, so that the attanable order of the ESDIRK of the type (7) wll be at most 5. If the order of the method s 5, then the condton (3) c q =, q =,..., 5 (33) q must be satsfed. The set of the abscssae satsfyng the condton s gven by whch s obtaned by solvng c = 6 6 0, c 3 = 6 + 6, 0 0 t q (t c ) (t c 3 ) dt = 0, q =,.

10 Substtutng the c and c 3 obtaned now nto (33), and solvng ths for, we have = 9, b(0) = 6 + 6, 3 = 6 6, and the values of a (0), s whch satsfy () wth these c s are gven a (0), = 6 6, a (0), 0 = 6 6, 0 a (0) 3, = , a(0) 3, = + 7 6, a (0) 3, 3 = Unfortunately, the set of the values lsted above does not satsfy some of the order condtons even for p = 4. For example, the order condton for the tallest tree of order 4, whch s gven by a (0), a(0),k a(0) k,l = 4,,,k,l s not satsfed wth these values; ths value becomes for the present set of the coeffcents. Thus we have the concluson that the attanable order wth the FESDIRK s 4. Ths means that FESDIRK4 s the hghest order method n the class of (7). 6 Numercal example In order to see how well FESDIRK4 s ftted to the specal problems for whch we can fnd the bass functons, and whether or not the global error of the method behaves lke O(h 4 ) for general problems, we shall some present numercal examples. The problems to be solved are: A. Ary equaton B. Bessel problem C. Constant coeffcent lnear equaton D. Duffng equaton Problem A s the one whose soluton oscllates wth varyng frequency. Problems B and D are perturbed oscllatons, and C s the problem whose soluton conssts of the two components: rapdly damped oscllatory component and decayng exponental component. In order to generate the coeffcents of FESDIRK4, we use snusodal bases for problems A, B and D, and exponental bases for problem C. In these experments, we compare the Eucldean norm of the errors wth those of the other methods. All the computatons are performed by the IEEE double precson arthmetc. Ary equaton Consder the Ary equaton y (t) t y(t) = 0, (34)

11 wth the ntal condton y ( 50) = A( 50) B( 50) = , y ( 50) = A ( 50) B ( 50) = , where A (t) and B (t) are Ary s A and B functons, whch are lnearly ndependent solutons of Eq. (34) (see [6]). The exact soluton of the problem s For ths problem, the bass functons y(t) = A(t) B(t). Φ (t) = t, Φ (t) = cos(ω t), Φ 3 (t) = sn(ω t), (35) wll be approprate, n whch case the Wronskan matrx assocated wth the functons s nonsngular. We ntegrate the equaton from t = 50 to 0, changng the angular frequency ω by the formula ω = t, at every nteger pont t = 50, 49,..., 0. The error of FESDIRK4 s compared favourably wth that of ESDIRK4 n Fg., although both of the methods are 4th order accurate. 0 log E log h Bessel problem [5] Next, let us consder the equaton wth the ntal condton Fg.. Errors E of FESDIRK4 (sold) and ESDIRK4 (dashed) versus step-sze h for Ary equaton (34). y (0.5) = 0.5 J 0 (5) y (t) + 00 y(t) = y(t) 4 t, (36) = y (0.5) = J 0(5) J (5) = The exact soluton of the problem s gven by y (t) = t J 0 (0 t),

12 where J ν (t) and Y ν (t) are the Bessel functons of the frst and second knd, respectvely. We ntegrate the equaton from t = 0.5 to 0 by the two methods: FESDIRK4 wth ω = 0 (fxed) and ESDIRK4. The results are shown n Fg.. From the result, we can observe that the two methods are of order 4 also n ths example, and that the accuracy of FESDIRK4 s remarkable compared wth the prevous example. 0 log E log h Fg.. Errors E of FESDIRK4 (sold) and ESDIRK4 (dashed) versus step-sze h for Bessel problem (36). Constant coeffcent lnear equaton The thrd problem to be consdered s the lnear homogeneous equaton where y (t) = P y(t), y(0) = (, 0, 0, 0) T, (37) P = The exact soluton of the problem s gven by e t + e 00 t sn t e y(t) = t ( + t) + e 00 t (cos t + sn t) e t + e 00 t (cos t + sn t). e 00 t sn t Ths soluton conssts of fast and slow modes. If the step-sze n the stablty regon s used, then the fast mode wll be damped out very soon and, as a result, the slow mode wll domnate the entre soluton. Hence, we ft the method to the slow mode, that s, we choose the followng bass functons: Φ (t) = t, Φ (t) = exp( t), Φ 3 (t) = t exp( t). (38) We ntegrate the equaton from t = 0 to by the FESDIRK4 wth bass functons (38), and compare the error wth those of the three Runge-Kutta methods: ESDIRK4, the

13 -stage Gauss (Gauss) and the classcal Runge-Kutta (RK4) methods, each of whch s of order 4. The results are shown n Table. Table. Errors of varous methods for lnear equaton (37). log h log E FESDIRK4 ESDIRK4 Gauss RK4.708e+0.95e+0-5.4e e e+0.73e e+0.53e e e e+0.68e e e e e e e e e e e e e e e+0-4.9e e e e e e e e e e+0-5.e e+0-5.3e e e e e e+0 E s the Eucldean norm of the error at t =. It can been seen that, although FESDIRK4 s less stable than the -stage Gauss Runge- Kutta method for large step-szes, ths method s ftted to the problem completely for moderately small step-szes; the values of order 50 or less n the second column of the table must be the accumulatons of round-off errors, snce the machne epslon of the IEEE double precson arthmetc s 53. On the other hand, the errors of the other methods decrease very slowly at the rate of O(h 4 ). Duffng equaton [3] The last one to be ntegrated by FESDIRK4 s a nonlnear equaton. Let us consder the Duffng equaton y (t) + µ y(t) = k (y(t) y(t) 3 ), (39) The exact soluton s gven by y(0) = 0, y (0) = µ. y(t) = sn (µ t; (k/µ) ), where sn( ; ) s the Jacoban ellptc functon. We ntegrate the equaton wth µ = and k = 0.03 from t = 0 to 00 by the FESDIRK4 wth the bass functons (35)(we set ω = ) and ESDIRK4. The result s shown n Fg. 3. For ths example, both of the methods are more accurate compared wth Problems A and B, n spte of the long term nterval of ntegraton. To summarze, FESDIRK4 s a very effcent scheme for the specal problems for whch we can fnd the bass functons successfully, and s reasonably accurate for general problems, unless the problem s very stff. Ths s due to the fact that the method has 4th order accuracy for general problems.

14 -0 log E log h Fg. 3. Errors E of FESDIRK4 (sold) and ESDIRK4 (dashed) versus step-sze h for Duffng equaton (39). 7 Embedded FRK method Snce we have solved Problems A, B, C, and D, next we must consder E (embedded) method. Let us consder the embedded par y n+ = y n + h (b f(y ) + b f(y ) + b 3 f(y 3 )), ȳ n+ = y n + h ( b f(y ) + b f(y ) + b 3 f(y 3 ) + b 4 f(y 4 )), (40) where Y = y n, Y = y n + h (a, f(y ) + α f(y )), Y 3 = y n + h (a 3, f(y ) + a 3, f(y ) + α f(y 3 )), Y 4 = y n + h (a 4, f(y ) + a 4, f(y ) + a 4, 3 f(y 3 ) + α f(y 4 )), and we assume c = /3 and c 3 =, as before. The Butcher array of the par s a, α 5 6 a 3, a 3, α a 4, a 4, a 4,3 α b b b 3 0 (4) b b b3 b4 In the array, we further assume b = a 4,, b = a 4,, b3 = a 4, 3, b4 = α, so that the method to calculate ȳ n+ becomes FSAL (frst same as last). The computatonal cost of ths method s approxmately the same as that of FESDIRK4, snce the number of LU decomposton to be performed n the step s stll one. Here we must determne the coeffcents of the method. We take the same a,, α and b as those of FESDIRK4, so that the order p of the method corresponds to b s 4. Wth

15 these coeffcents, f we choose the b such that the order of the method corresponds to b s 3. If we force then we have R y(0) + h B(q) 4 = b c q q = O(h4 q ), q =,, 3, (4) 4 b y (c h) y(h) = q = Therefore, f we set R = O(h r+ ), then r = 3 and h q B(q) (q )! (y (0)) (q ) = O(h 4 ). p mn {ρ +, τ +, r} = mn {4, 4, 3} = 3. (43) The coeffcents b, b and b 3 satsfyng (4) are gven by solvng the system of the equatons Φ m (h) = Φ m (0) + h ( b m ϕ m (0) + b ϕ m (c h) + b 3 ϕ m (c 3 h) + α ϕ m (h)), m =,, 3, for the gven {Φ m (t)} 3 m=. Thus, we have the 3rd order method embedded n the 4th order one. The step-sze strategy for ths par, whch controls the local truncaton error of the lower order method wthn a prescrbed tolerance T OL, s gven by ( ) /4 T OL h n+ = θ h n, ȳ n y n where θ s a safety factor, say θ = 0.9. Now, let us apply the embedded par to the two-body problem [5], [] wth the ntal condton y = y /r 3, y = y /r 3, r = y (0) = e, y (0) = 0, y (0) = 0, y (0) = + e e, y + y, (44) where e (0 e < ) s an eccentrcty. The exact soluton of ths problem s where u s the soluton of Kepler s equaton y (t) = cos u e, y (t) = e sn u, u = t + e sn u. The soluton of (44) s found to be π-perodc for any e. Hence, a natural choce of the bass functons s (35) wth ω =. By ths choce, the problem wth small e s expected to be accurately solved, snce the soluton s purely snusodal when e = 0. We ntegrate the problem wth e = from t = 0 to 50 π by the two embedded pars FESDIRK4(3) and the ESDIRK4(3). From the result of Table, we can see that the embedded method derved here controls the local truncaton error well, and as a result, the method ntegrates the equaton wth fewer steps compared wth ESDIRK4(3).

16 Table. Errors and the total steps for two-body problem (44) wth e = FESDIRK4(3) ESDIRK4(3) T OL error steps error steps 0.785e e e e e e e e e e e e e e e e e e T OL: Tolerance of the local error. error: Eucldean norms of the errors at t = 50 π. steps: Total number of tme steps (ncludng reected steps) h Error t t Fg. 5. Step-sze plot and error behavor of embedded par FESDIRK4(3). 8 Summary and future work We have presented a functonally ftted 3-stage ESDIRK method. Although the method s of order 4 for general cases, the method s always exact when the soluton of the ODE can be expressed by a lnear combnaton of the gven bass functons. Varous numercal examples show that the method has proved successful when a sutable set of the bass functons s found, and reasonably accurate, even f ths s not the case. The method s extended to an embedded par. The stablty analyss and the mplementaton ssue of FRK wll be future works. Acknowledgment. The author would greatly apprecate the conference organzers, Prof. S. Maeda, Prof. T. Ito and Prof. T. Mtsu, for ther efforts. The author also wshes to thank Mr. C. Hrota, the research assstant of Akta Prefectural Unversty. He provded much support for ths work.

17 References [] J. Butcher, The Numercal Analyss of Ordnary Dfferental Equatons, Wley, 987. [] J.P. Coleman, Mxed nterpolaton methods wth arbtrary nodes, J. Comput. Appl. Math. 9 (998), [3] J.M. Franco, Embedded pars of explct ARKN methods for the numercal ntegraton of perturbed oscllators, Proceedngs of the 00 Conference on Computatonal and Mathematcal Methods on Scence and Engneerng CMMSE-00, (Sep. 00, at Alcante Span), Vol [4] E. Harer, S.P. Nørsett and G. Wanner, Solvng Ordnary Dfferental Equatons I, Sprnger-Verlag, Second Revsed Edton, 99. [5] T.E. Hull, W.H. Enrght, B.M. Fellen and A.E. Sedgwck, Comparng numercal methods for ordnary dfferental equatons, SIAM J. Numer. Anal., 9 (97), [6] N.N. Lebedev, Specal Functons & Ther Applcatons (Translated & edted by R.A. Slverman), Dover Publcatons, Inc. 97. [7] K. Ozawa, A four-stage mplct Runge-Kutta-Nyström method wth varable coeffcents for solvng perodc ntal value problems, Japan Journal of Industral and Appled Mathematcs, 6 (999), [8] K. Ozawa, Functonal fttng Runge-Kutta method wth varable coeffcents, Japan Journal of Industral and Appled Mathematcs 8 (00), [9] K. Ozawa, Functonal fttng Runge-Kutta-Nyström method wth varable coeffcents, Japan Journal of Industral and Appled Mathematcs 9 (00), [0] K. Ozawa, Functonally ftted lnear multstep method, Proceedngs of the 00 Conference on Computatonal and Mathematcal Methods on Scence and Engneerng CMMSE-00, (Sep. 00, at Alcante Span), Vol [] B. Paternoster, Runge-Kutta (-Nyström) methods for ODEs wth perodc solutons based on trgonometrc polynomals, Appled Numercal Mathematcs 8 (998), [] F.L. Shampne, Numercal Soluton of Ordnary Dfferental Equatons, Chapman & Hall, 994. [3] T.E. Smos, A fourth algebrac order exponentally-ftted Runge-Kutta method for the numercal soluton of the Schrödnger equaton, IMA J. Numer. Anal. (00), [4] G. Vanden Berghe, H. De Meyer, M. Van Daele and T. Van Hecke, Exponentallyftted Runge-Kutta methods, Journal of Computatonal and Appled Mathematcs, 5 (000), [5] P.J. Van Der Houwen and B.P. Sommeer, Dagonally mplct Runge-Kutta- Nyström methods for oscllatory problems, SIAM J. Numer. Anal. 6 (989),

n α j x j = 0 j=1 has a nontrivial solution. Here A is the n k matrix whose jth column is the vector for all t j=0

n α j x j = 0 j=1 has a nontrivial solution. Here A is the n k matrix whose jth column is the vector for all t j=0 MODULE 2 Topcs: Lnear ndependence, bass and dmenson We have seen that f n a set of vectors one vector s a lnear combnaton of the remanng vectors n the set then the span of the set s unchanged f that vector

More information

CHAPTER 5 NUMERICAL EVALUATION OF DYNAMIC RESPONSE

CHAPTER 5 NUMERICAL EVALUATION OF DYNAMIC RESPONSE CHAPTER 5 NUMERICAL EVALUATION OF DYNAMIC RESPONSE Analytcal soluton s usually not possble when exctaton vares arbtrarly wth tme or f the system s nonlnear. Such problems can be solved by numercal tmesteppng

More information

A new Approach for Solving Linear Ordinary Differential Equations

A new Approach for Solving Linear Ordinary Differential Equations , ISSN 974-57X (Onlne), ISSN 974-5718 (Prnt), Vol. ; Issue No. 1; Year 14, Copyrght 13-14 by CESER PUBLICATIONS A new Approach for Solvng Lnear Ordnary Dfferental Equatons Fawz Abdelwahd Department of

More information

Chapter 5. Solution of System of Linear Equations. Module No. 6. Solution of Inconsistent and Ill Conditioned Systems

Chapter 5. Solution of System of Linear Equations. Module No. 6. Solution of Inconsistent and Ill Conditioned Systems Numercal Analyss by Dr. Anta Pal Assstant Professor Department of Mathematcs Natonal Insttute of Technology Durgapur Durgapur-713209 emal: anta.bue@gmal.com 1 . Chapter 5 Soluton of System of Lnear Equatons

More information

Numerical Heat and Mass Transfer

Numerical Heat and Mass Transfer Master degree n Mechancal Engneerng Numercal Heat and Mass Transfer 06-Fnte-Dfference Method (One-dmensonal, steady state heat conducton) Fausto Arpno f.arpno@uncas.t Introducton Why we use models and

More information

Appendix B. The Finite Difference Scheme

Appendix B. The Finite Difference Scheme 140 APPENDIXES Appendx B. The Fnte Dfference Scheme In ths appendx we present numercal technques whch are used to approxmate solutons of system 3.1 3.3. A comprehensve treatment of theoretcal and mplementaton

More information

NON-CENTRAL 7-POINT FORMULA IN THE METHOD OF LINES FOR PARABOLIC AND BURGERS' EQUATIONS

NON-CENTRAL 7-POINT FORMULA IN THE METHOD OF LINES FOR PARABOLIC AND BURGERS' EQUATIONS IJRRAS 8 (3 September 011 www.arpapress.com/volumes/vol8issue3/ijrras_8_3_08.pdf NON-CENTRAL 7-POINT FORMULA IN THE METHOD OF LINES FOR PARABOLIC AND BURGERS' EQUATIONS H.O. Bakodah Dept. of Mathematc

More information

On a direct solver for linear least squares problems

On a direct solver for linear least squares problems ISSN 2066-6594 Ann. Acad. Rom. Sc. Ser. Math. Appl. Vol. 8, No. 2/2016 On a drect solver for lnear least squares problems Constantn Popa Abstract The Null Space (NS) algorthm s a drect solver for lnear

More information

Convexity preserving interpolation by splines of arbitrary degree

Convexity preserving interpolation by splines of arbitrary degree Computer Scence Journal of Moldova, vol.18, no.1(52), 2010 Convexty preservng nterpolaton by splnes of arbtrary degree Igor Verlan Abstract In the present paper an algorthm of C 2 nterpolaton of dscrete

More information

Transfer Functions. Convenient representation of a linear, dynamic model. A transfer function (TF) relates one input and one output: ( ) system

Transfer Functions. Convenient representation of a linear, dynamic model. A transfer function (TF) relates one input and one output: ( ) system Transfer Functons Convenent representaton of a lnear, dynamc model. A transfer functon (TF) relates one nput and one output: x t X s y t system Y s The followng termnology s used: x y nput output forcng

More information

Errors for Linear Systems

Errors for Linear Systems Errors for Lnear Systems When we solve a lnear system Ax b we often do not know A and b exactly, but have only approxmatons  and ˆb avalable. Then the best thng we can do s to solve ˆx ˆb exactly whch

More information

NUMERICAL DIFFERENTIATION

NUMERICAL DIFFERENTIATION NUMERICAL DIFFERENTIATION 1 Introducton Dfferentaton s a method to compute the rate at whch a dependent output y changes wth respect to the change n the ndependent nput x. Ths rate of change s called the

More information

Application of B-Spline to Numerical Solution of a System of Singularly Perturbed Problems

Application of B-Spline to Numerical Solution of a System of Singularly Perturbed Problems Mathematca Aeterna, Vol. 1, 011, no. 06, 405 415 Applcaton of B-Splne to Numercal Soluton of a System of Sngularly Perturbed Problems Yogesh Gupta Department of Mathematcs Unted College of Engneerng &

More information

Consistency & Convergence

Consistency & Convergence /9/007 CHE 374 Computatonal Methods n Engneerng Ordnary Dfferental Equatons Consstency, Convergence, Stablty, Stffness and Adaptve and Implct Methods ODE s n MATLAB, etc Consstency & Convergence Consstency

More information

APPENDIX A Some Linear Algebra

APPENDIX A Some Linear Algebra APPENDIX A Some Lnear Algebra The collecton of m, n matrces A.1 Matrces a 1,1,..., a 1,n A = a m,1,..., a m,n wth real elements a,j s denoted by R m,n. If n = 1 then A s called a column vector. Smlarly,

More information

Lecture 12: Discrete Laplacian

Lecture 12: Discrete Laplacian Lecture 12: Dscrete Laplacan Scrbe: Tanye Lu Our goal s to come up wth a dscrete verson of Laplacan operator for trangulated surfaces, so that we can use t n practce to solve related problems We are mostly

More information

THE CHINESE REMAINDER THEOREM. We should thank the Chinese for their wonderful remainder theorem. Glenn Stevens

THE CHINESE REMAINDER THEOREM. We should thank the Chinese for their wonderful remainder theorem. Glenn Stevens THE CHINESE REMAINDER THEOREM KEITH CONRAD We should thank the Chnese for ther wonderful remander theorem. Glenn Stevens 1. Introducton The Chnese remander theorem says we can unquely solve any par of

More information

Difference Equations

Difference Equations Dfference Equatons c Jan Vrbk 1 Bascs Suppose a sequence of numbers, say a 0,a 1,a,a 3,... s defned by a certan general relatonshp between, say, three consecutve values of the sequence, e.g. a + +3a +1

More information

Lectures - Week 4 Matrix norms, Conditioning, Vector Spaces, Linear Independence, Spanning sets and Basis, Null space and Range of a Matrix

Lectures - Week 4 Matrix norms, Conditioning, Vector Spaces, Linear Independence, Spanning sets and Basis, Null space and Range of a Matrix Lectures - Week 4 Matrx norms, Condtonng, Vector Spaces, Lnear Independence, Spannng sets and Bass, Null space and Range of a Matrx Matrx Norms Now we turn to assocatng a number to each matrx. We could

More information

2.3 Nilpotent endomorphisms

2.3 Nilpotent endomorphisms s a block dagonal matrx, wth A Mat dm U (C) In fact, we can assume that B = B 1 B k, wth B an ordered bass of U, and that A = [f U ] B, where f U : U U s the restrcton of f to U 40 23 Nlpotent endomorphsms

More information

College of Computer & Information Science Fall 2009 Northeastern University 20 October 2009

College of Computer & Information Science Fall 2009 Northeastern University 20 October 2009 College of Computer & Informaton Scence Fall 2009 Northeastern Unversty 20 October 2009 CS7880: Algorthmc Power Tools Scrbe: Jan Wen and Laura Poplawsk Lecture Outlne: Prmal-dual schema Network Desgn:

More information

Inexact Newton Methods for Inverse Eigenvalue Problems

Inexact Newton Methods for Inverse Eigenvalue Problems Inexact Newton Methods for Inverse Egenvalue Problems Zheng-jan Ba Abstract In ths paper, we survey some of the latest development n usng nexact Newton-lke methods for solvng nverse egenvalue problems.

More information

Numerical Solution of Ordinary Differential Equations

Numerical Solution of Ordinary Differential Equations Numercal Methods (CENG 00) CHAPTER-VI Numercal Soluton of Ordnar Dfferental Equatons 6 Introducton Dfferental equatons are equatons composed of an unknown functon and ts dervatves The followng are examples

More information

DETERMINATION OF TEMPERATURE DISTRIBUTION FOR ANNULAR FINS WITH TEMPERATURE DEPENDENT THERMAL CONDUCTIVITY BY HPM

DETERMINATION OF TEMPERATURE DISTRIBUTION FOR ANNULAR FINS WITH TEMPERATURE DEPENDENT THERMAL CONDUCTIVITY BY HPM Ganj, Z. Z., et al.: Determnaton of Temperature Dstrbuton for S111 DETERMINATION OF TEMPERATURE DISTRIBUTION FOR ANNULAR FINS WITH TEMPERATURE DEPENDENT THERMAL CONDUCTIVITY BY HPM by Davood Domr GANJI

More information

High resolution entropy stable scheme for shallow water equations

High resolution entropy stable scheme for shallow water equations Internatonal Symposum on Computers & Informatcs (ISCI 05) Hgh resoluton entropy stable scheme for shallow water equatons Xaohan Cheng,a, Yufeng Ne,b, Department of Appled Mathematcs, Northwestern Polytechncal

More information

The Order Relation and Trace Inequalities for. Hermitian Operators

The Order Relation and Trace Inequalities for. Hermitian Operators Internatonal Mathematcal Forum, Vol 3, 08, no, 507-57 HIKARI Ltd, wwwm-hkarcom https://doorg/0988/mf088055 The Order Relaton and Trace Inequaltes for Hermtan Operators Y Huang School of Informaton Scence

More information

A Hybrid Variational Iteration Method for Blasius Equation

A Hybrid Variational Iteration Method for Blasius Equation Avalable at http://pvamu.edu/aam Appl. Appl. Math. ISSN: 1932-9466 Vol. 10, Issue 1 (June 2015), pp. 223-229 Applcatons and Appled Mathematcs: An Internatonal Journal (AAM) A Hybrd Varatonal Iteraton Method

More information

Some modelling aspects for the Matlab implementation of MMA

Some modelling aspects for the Matlab implementation of MMA Some modellng aspects for the Matlab mplementaton of MMA Krster Svanberg krlle@math.kth.se Optmzaton and Systems Theory Department of Mathematcs KTH, SE 10044 Stockholm September 2004 1. Consdered optmzaton

More information

Implicit Integration Henyey Method

Implicit Integration Henyey Method Implct Integraton Henyey Method In realstc stellar evoluton codes nstead of a drect ntegraton usng for example the Runge-Kutta method one employs an teratve mplct technque. Ths s because the structure

More information

Polynomial Regression Models

Polynomial Regression Models LINEAR REGRESSION ANALYSIS MODULE XII Lecture - 6 Polynomal Regresson Models Dr. Shalabh Department of Mathematcs and Statstcs Indan Insttute of Technology Kanpur Test of sgnfcance To test the sgnfcance

More information

Formal solvers of the RT equation

Formal solvers of the RT equation Formal solvers of the RT equaton Formal RT solvers Runge- Kutta (reference solver) Pskunov N.: 979, Master Thess Long characterstcs (Feautrer scheme) Cannon C.J.: 970, ApJ 6, 55 Short characterstcs (Hermtan

More information

5 The Rational Canonical Form

5 The Rational Canonical Form 5 The Ratonal Canoncal Form Here p s a monc rreducble factor of the mnmum polynomal m T and s not necessarly of degree one Let F p denote the feld constructed earler n the course, consstng of all matrces

More information

MMA and GCMMA two methods for nonlinear optimization

MMA and GCMMA two methods for nonlinear optimization MMA and GCMMA two methods for nonlnear optmzaton Krster Svanberg Optmzaton and Systems Theory, KTH, Stockholm, Sweden. krlle@math.kth.se Ths note descrbes the algorthms used n the author s 2007 mplementatons

More information

Research Article An Optimized Runge-Kutta Method for the Numerical Solution of the Radial Schrödinger Equation

Research Article An Optimized Runge-Kutta Method for the Numerical Solution of the Radial Schrödinger Equation Mathematcal Problems n Engneerng Volume 212, Artcle ID 867948, 12 pages do:1.1155/212/867948 Research Artcle An Optmzed Runge-Kutta Method for the Numercal Soluton of the Radal Schrödnger Equaton Qnghe

More information

Group Analysis of Ordinary Differential Equations of the Order n>2

Group Analysis of Ordinary Differential Equations of the Order n>2 Symmetry n Nonlnear Mathematcal Physcs 997, V., 64 7. Group Analyss of Ordnary Dfferental Equatons of the Order n> L.M. BERKOVICH and S.Y. POPOV Samara State Unversty, 4430, Samara, Russa E-mal: berk@nfo.ssu.samara.ru

More information

The equation of motion of a dynamical system is given by a set of differential equations. That is (1)

The equation of motion of a dynamical system is given by a set of differential equations. That is (1) Dynamcal Systems Many engneerng and natural systems are dynamcal systems. For example a pendulum s a dynamcal system. State l The state of the dynamcal system specfes t condtons. For a pendulum n the absence

More information

Yong Joon Ryang. 1. Introduction Consider the multicommodity transportation problem with convex quadratic cost function. 1 2 (x x0 ) T Q(x x 0 )

Yong Joon Ryang. 1. Introduction Consider the multicommodity transportation problem with convex quadratic cost function. 1 2 (x x0 ) T Q(x x 0 ) Kangweon-Kyungk Math. Jour. 4 1996), No. 1, pp. 7 16 AN ITERATIVE ROW-ACTION METHOD FOR MULTICOMMODITY TRANSPORTATION PROBLEMS Yong Joon Ryang Abstract. The optmzaton problems wth quadratc constrants often

More information

3.1 Expectation of Functions of Several Random Variables. )' be a k-dimensional discrete or continuous random vector, with joint PMF p (, E X E X1 E X

3.1 Expectation of Functions of Several Random Variables. )' be a k-dimensional discrete or continuous random vector, with joint PMF p (, E X E X1 E X Statstcs 1: Probablty Theory II 37 3 EPECTATION OF SEVERAL RANDOM VARIABLES As n Probablty Theory I, the nterest n most stuatons les not on the actual dstrbuton of a random vector, but rather on a number

More information

Asymptotics of the Solution of a Boundary Value. Problem for One-Characteristic Differential. Equation Degenerating into a Parabolic Equation

Asymptotics of the Solution of a Boundary Value. Problem for One-Characteristic Differential. Equation Degenerating into a Parabolic Equation Nonl. Analyss and Dfferental Equatons, ol., 4, no., 5 - HIKARI Ltd, www.m-har.com http://dx.do.org/.988/nade.4.456 Asymptotcs of the Soluton of a Boundary alue Problem for One-Characterstc Dfferental Equaton

More information

The Exact Formulation of the Inverse of the Tridiagonal Matrix for Solving the 1D Poisson Equation with the Finite Difference Method

The Exact Formulation of the Inverse of the Tridiagonal Matrix for Solving the 1D Poisson Equation with the Finite Difference Method Journal of Electromagnetc Analyss and Applcatons, 04, 6, 0-08 Publshed Onlne September 04 n ScRes. http://www.scrp.org/journal/jemaa http://dx.do.org/0.46/jemaa.04.6000 The Exact Formulaton of the Inverse

More information

Salmon: Lectures on partial differential equations. Consider the general linear, second-order PDE in the form. ,x 2

Salmon: Lectures on partial differential equations. Consider the general linear, second-order PDE in the form. ,x 2 Salmon: Lectures on partal dfferental equatons 5. Classfcaton of second-order equatons There are general methods for classfyng hgher-order partal dfferental equatons. One s very general (applyng even to

More information

ON A DETERMINATION OF THE INITIAL FUNCTIONS FROM THE OBSERVED VALUES OF THE BOUNDARY FUNCTIONS FOR THE SECOND-ORDER HYPERBOLIC EQUATION

ON A DETERMINATION OF THE INITIAL FUNCTIONS FROM THE OBSERVED VALUES OF THE BOUNDARY FUNCTIONS FOR THE SECOND-ORDER HYPERBOLIC EQUATION Advanced Mathematcal Models & Applcatons Vol.3, No.3, 2018, pp.215-222 ON A DETERMINATION OF THE INITIAL FUNCTIONS FROM THE OBSERVED VALUES OF THE BOUNDARY FUNCTIONS FOR THE SECOND-ORDER HYPERBOLIC EUATION

More information

THE STURM-LIOUVILLE EIGENVALUE PROBLEM - A NUMERICAL SOLUTION USING THE CONTROL VOLUME METHOD

THE STURM-LIOUVILLE EIGENVALUE PROBLEM - A NUMERICAL SOLUTION USING THE CONTROL VOLUME METHOD Journal of Appled Mathematcs and Computatonal Mechancs 06, 5(), 7-36 www.amcm.pcz.pl p-iss 99-9965 DOI: 0.75/jamcm.06..4 e-iss 353-0588 THE STURM-LIOUVILLE EIGEVALUE PROBLEM - A UMERICAL SOLUTIO USIG THE

More information

The Quadratic Trigonometric Bézier Curve with Single Shape Parameter

The Quadratic Trigonometric Bézier Curve with Single Shape Parameter J. Basc. Appl. Sc. Res., (3541-546, 01 01, TextRoad Publcaton ISSN 090-4304 Journal of Basc and Appled Scentfc Research www.textroad.com The Quadratc Trgonometrc Bézer Curve wth Sngle Shape Parameter Uzma

More information

Lecture 21: Numerical methods for pricing American type derivatives

Lecture 21: Numerical methods for pricing American type derivatives Lecture 21: Numercal methods for prcng Amercan type dervatves Xaoguang Wang STAT 598W Aprl 10th, 2014 (STAT 598W) Lecture 21 1 / 26 Outlne 1 Fnte Dfference Method Explct Method Penalty Method (STAT 598W)

More information

Linear Approximation with Regularization and Moving Least Squares

Linear Approximation with Regularization and Moving Least Squares Lnear Approxmaton wth Regularzaton and Movng Least Squares Igor Grešovn May 007 Revson 4.6 (Revson : March 004). 5 4 3 0.5 3 3.5 4 Contents: Lnear Fttng...4. Weghted Least Squares n Functon Approxmaton...

More information

6.3.4 Modified Euler s method of integration

6.3.4 Modified Euler s method of integration 6.3.4 Modfed Euler s method of ntegraton Before dscussng the applcaton of Euler s method for solvng the swng equatons, let us frst revew the basc Euler s method of numercal ntegraton. Let the general from

More information

Inner Product. Euclidean Space. Orthonormal Basis. Orthogonal

Inner Product. Euclidean Space. Orthonormal Basis. Orthogonal Inner Product Defnton 1 () A Eucldean space s a fnte-dmensonal vector space over the reals R, wth an nner product,. Defnton 2 (Inner Product) An nner product, on a real vector space X s a symmetrc, blnear,

More information

LINEAR REGRESSION ANALYSIS. MODULE IX Lecture Multicollinearity

LINEAR REGRESSION ANALYSIS. MODULE IX Lecture Multicollinearity LINEAR REGRESSION ANALYSIS MODULE IX Lecture - 30 Multcollnearty Dr. Shalabh Department of Mathematcs and Statstcs Indan Insttute of Technology Kanpur 2 Remedes for multcollnearty Varous technques have

More information

Global Sensitivity. Tuesday 20 th February, 2018

Global Sensitivity. Tuesday 20 th February, 2018 Global Senstvty Tuesday 2 th February, 28 ) Local Senstvty Most senstvty analyses [] are based on local estmates of senstvty, typcally by expandng the response n a Taylor seres about some specfc values

More information

( ) = ( ) + ( 0) ) ( )

( ) = ( ) + ( 0) ) ( ) EETOMAGNETI OMPATIBIITY HANDBOOK 1 hapter 9: Transent Behavor n the Tme Doman 9.1 Desgn a crcut usng reasonable values for the components that s capable of provdng a tme delay of 100 ms to a dgtal sgnal.

More information

PHYS 705: Classical Mechanics. Canonical Transformation II

PHYS 705: Classical Mechanics. Canonical Transformation II 1 PHYS 705: Classcal Mechancs Canoncal Transformaton II Example: Harmonc Oscllator f ( x) x m 0 x U( x) x mx x LT U m Defne or L p p mx x x m mx x H px L px p m p x m m H p 1 x m p m 1 m H x p m x m m

More information

STAT 309: MATHEMATICAL COMPUTATIONS I FALL 2018 LECTURE 16

STAT 309: MATHEMATICAL COMPUTATIONS I FALL 2018 LECTURE 16 STAT 39: MATHEMATICAL COMPUTATIONS I FALL 218 LECTURE 16 1 why teratve methods f we have a lnear system Ax = b where A s very, very large but s ether sparse or structured (eg, banded, Toepltz, banded plus

More information

Introduction to Vapor/Liquid Equilibrium, part 2. Raoult s Law:

Introduction to Vapor/Liquid Equilibrium, part 2. Raoult s Law: CE304, Sprng 2004 Lecture 4 Introducton to Vapor/Lqud Equlbrum, part 2 Raoult s Law: The smplest model that allows us do VLE calculatons s obtaned when we assume that the vapor phase s an deal gas, and

More information

8.4 COMPLEX VECTOR SPACES AND INNER PRODUCTS

8.4 COMPLEX VECTOR SPACES AND INNER PRODUCTS SECTION 8.4 COMPLEX VECTOR SPACES AND INNER PRODUCTS 493 8.4 COMPLEX VECTOR SPACES AND INNER PRODUCTS All the vector spaces you have studed thus far n the text are real vector spaces because the scalars

More information

Irregular vibrations in multi-mass discrete-continuous systems torsionally deformed

Irregular vibrations in multi-mass discrete-continuous systems torsionally deformed (2) 4 48 Irregular vbratons n mult-mass dscrete-contnuous systems torsonally deformed Abstract In the paper rregular vbratons of dscrete-contnuous systems consstng of an arbtrary number rgd bodes connected

More information

U.C. Berkeley CS294: Spectral Methods and Expanders Handout 8 Luca Trevisan February 17, 2016

U.C. Berkeley CS294: Spectral Methods and Expanders Handout 8 Luca Trevisan February 17, 2016 U.C. Berkeley CS94: Spectral Methods and Expanders Handout 8 Luca Trevsan February 7, 06 Lecture 8: Spectral Algorthms Wrap-up In whch we talk about even more generalzatons of Cheeger s nequaltes, and

More information

Report on Image warping

Report on Image warping Report on Image warpng Xuan Ne, Dec. 20, 2004 Ths document summarzed the algorthms of our mage warpng soluton for further study, and there s a detaled descrpton about the mplementaton of these algorthms.

More information

Generalized Linear Methods

Generalized Linear Methods Generalzed Lnear Methods 1 Introducton In the Ensemble Methods the general dea s that usng a combnaton of several weak learner one could make a better learner. More formally, assume that we have a set

More information

Finite Element Modelling of truss/cable structures

Finite Element Modelling of truss/cable structures Pet Schreurs Endhoven Unversty of echnology Department of Mechancal Engneerng Materals echnology November 3, 214 Fnte Element Modellng of truss/cable structures 1 Fnte Element Analyss of prestressed structures

More information

ALGORITHM FOR THE CALCULATION OF THE TWO VARIABLES CUBIC SPLINE FUNCTION

ALGORITHM FOR THE CALCULATION OF THE TWO VARIABLES CUBIC SPLINE FUNCTION ANALELE ŞTIINŢIFICE ALE UNIVERSITĂŢII AL.I. CUZA DIN IAŞI (S.N.) MATEMATICĂ, Tomul LIX, 013, f.1 DOI: 10.478/v10157-01-00-y ALGORITHM FOR THE CALCULATION OF THE TWO VARIABLES CUBIC SPLINE FUNCTION BY ION

More information

Physics 5153 Classical Mechanics. D Alembert s Principle and The Lagrangian-1

Physics 5153 Classical Mechanics. D Alembert s Principle and The Lagrangian-1 P. Guterrez Physcs 5153 Classcal Mechancs D Alembert s Prncple and The Lagrangan 1 Introducton The prncple of vrtual work provdes a method of solvng problems of statc equlbrum wthout havng to consder the

More information

arxiv: v1 [math.co] 12 Sep 2014

arxiv: v1 [math.co] 12 Sep 2014 arxv:1409.3707v1 [math.co] 12 Sep 2014 On the bnomal sums of Horadam sequence Nazmye Ylmaz and Necat Taskara Department of Mathematcs, Scence Faculty, Selcuk Unversty, 42075, Campus, Konya, Turkey March

More information

One-sided finite-difference approximations suitable for use with Richardson extrapolation

One-sided finite-difference approximations suitable for use with Richardson extrapolation Journal of Computatonal Physcs 219 (2006) 13 20 Short note One-sded fnte-dfference approxmatons sutable for use wth Rchardson extrapolaton Kumar Rahul, S.N. Bhattacharyya * Department of Mechancal Engneerng,

More information

Problem Set 9 Solutions

Problem Set 9 Solutions Desgn and Analyss of Algorthms May 4, 2015 Massachusetts Insttute of Technology 6.046J/18.410J Profs. Erk Demane, Srn Devadas, and Nancy Lynch Problem Set 9 Solutons Problem Set 9 Solutons Ths problem

More information

Econ107 Applied Econometrics Topic 3: Classical Model (Studenmund, Chapter 4)

Econ107 Applied Econometrics Topic 3: Classical Model (Studenmund, Chapter 4) I. Classcal Assumptons Econ7 Appled Econometrcs Topc 3: Classcal Model (Studenmund, Chapter 4) We have defned OLS and studed some algebrac propertes of OLS. In ths topc we wll study statstcal propertes

More information

Perron Vectors of an Irreducible Nonnegative Interval Matrix

Perron Vectors of an Irreducible Nonnegative Interval Matrix Perron Vectors of an Irreducble Nonnegatve Interval Matrx Jr Rohn August 4 2005 Abstract As s well known an rreducble nonnegatve matrx possesses a unquely determned Perron vector. As the man result of

More information

LINEAR REGRESSION ANALYSIS. MODULE IX Lecture Multicollinearity

LINEAR REGRESSION ANALYSIS. MODULE IX Lecture Multicollinearity LINEAR REGRESSION ANALYSIS MODULE IX Lecture - 31 Multcollnearty Dr. Shalabh Department of Mathematcs and Statstcs Indan Insttute of Technology Kanpur 6. Rdge regresson The OLSE s the best lnear unbased

More information

Workshop: Approximating energies and wave functions Quantum aspects of physical chemistry

Workshop: Approximating energies and wave functions Quantum aspects of physical chemistry Workshop: Approxmatng energes and wave functons Quantum aspects of physcal chemstry http://quantum.bu.edu/pltl/6/6.pdf Last updated Thursday, November 7, 25 7:9:5-5: Copyrght 25 Dan Dll (dan@bu.edu) Department

More information

C/CS/Phy191 Problem Set 3 Solutions Out: Oct 1, 2008., where ( 00. ), so the overall state of the system is ) ( ( ( ( 00 ± 11 ), Φ ± = 1

C/CS/Phy191 Problem Set 3 Solutions Out: Oct 1, 2008., where ( 00. ), so the overall state of the system is ) ( ( ( ( 00 ± 11 ), Φ ± = 1 C/CS/Phy9 Problem Set 3 Solutons Out: Oct, 8 Suppose you have two qubts n some arbtrary entangled state ψ You apply the teleportaton protocol to each of the qubts separately What s the resultng state obtaned

More information

ME 501A Seminar in Engineering Analysis Page 1

ME 501A Seminar in Engineering Analysis Page 1 umercal Solutons of oundary-value Problems n Os ovember 7, 7 umercal Solutons of oundary- Value Problems n Os Larry aretto Mechancal ngneerng 5 Semnar n ngneerng nalyss ovember 7, 7 Outlne Revew stff equaton

More information

On Finite Rank Perturbation of Diagonalizable Operators

On Finite Rank Perturbation of Diagonalizable Operators Functonal Analyss, Approxmaton and Computaton 6 (1) (2014), 49 53 Publshed by Faculty of Scences and Mathematcs, Unversty of Nš, Serba Avalable at: http://wwwpmfnacrs/faac On Fnte Rank Perturbaton of Dagonalzable

More information

Restricted divisor sums

Restricted divisor sums ACTA ARITHMETICA 02 2002) Restrcted dvsor sums by Kevn A Broughan Hamlton) Introducton There s a body of work n the lterature on varous restrcted sums of the number of dvsors of an nteger functon ncludng

More information

SIO 224. m(r) =(ρ(r),k s (r),µ(r))

SIO 224. m(r) =(ρ(r),k s (r),µ(r)) SIO 224 1. A bref look at resoluton analyss Here s some background for the Masters and Gubbns resoluton paper. Global Earth models are usually found teratvely by assumng a startng model and fndng small

More information

Module 3 LOSSY IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur

Module 3 LOSSY IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur Module 3 LOSSY IMAGE COMPRESSION SYSTEMS Verson ECE IIT, Kharagpur Lesson 6 Theory of Quantzaton Verson ECE IIT, Kharagpur Instructonal Objectves At the end of ths lesson, the students should be able to:

More information

The Finite Element Method

The Finite Element Method The Fnte Element Method GENERAL INTRODUCTION Read: Chapters 1 and 2 CONTENTS Engneerng and analyss Smulaton of a physcal process Examples mathematcal model development Approxmate solutons and methods of

More information

Some Comments on Accelerating Convergence of Iterative Sequences Using Direct Inversion of the Iterative Subspace (DIIS)

Some Comments on Accelerating Convergence of Iterative Sequences Using Direct Inversion of the Iterative Subspace (DIIS) Some Comments on Acceleratng Convergence of Iteratve Sequences Usng Drect Inverson of the Iteratve Subspace (DIIS) C. Davd Sherrll School of Chemstry and Bochemstry Georga Insttute of Technology May 1998

More information

Vector Norms. Chapter 7 Iterative Techniques in Matrix Algebra. Cauchy-Bunyakovsky-Schwarz Inequality for Sums. Distances. Convergence.

Vector Norms. Chapter 7 Iterative Techniques in Matrix Algebra. Cauchy-Bunyakovsky-Schwarz Inequality for Sums. Distances. Convergence. Vector Norms Chapter 7 Iteratve Technques n Matrx Algebra Per-Olof Persson persson@berkeley.edu Department of Mathematcs Unversty of Calforna, Berkeley Math 128B Numercal Analyss Defnton A vector norm

More information

The lower and upper bounds on Perron root of nonnegative irreducible matrices

The lower and upper bounds on Perron root of nonnegative irreducible matrices Journal of Computatonal Appled Mathematcs 217 (2008) 259 267 wwwelsevercom/locate/cam The lower upper bounds on Perron root of nonnegatve rreducble matrces Guang-Xn Huang a,, Feng Yn b,keguo a a College

More information

COEFFICIENT DIAGRAM: A NOVEL TOOL IN POLYNOMIAL CONTROLLER DESIGN

COEFFICIENT DIAGRAM: A NOVEL TOOL IN POLYNOMIAL CONTROLLER DESIGN Int. J. Chem. Sc.: (4), 04, 645654 ISSN 097768X www.sadgurupublcatons.com COEFFICIENT DIAGRAM: A NOVEL TOOL IN POLYNOMIAL CONTROLLER DESIGN R. GOVINDARASU a, R. PARTHIBAN a and P. K. BHABA b* a Department

More information

Projective change between two Special (α, β)- Finsler Metrics

Projective change between two Special (α, β)- Finsler Metrics Internatonal Journal of Trend n Research and Development, Volume 2(6), ISSN 2394-9333 www.jtrd.com Projectve change between two Specal (, β)- Fnsler Metrcs Gayathr.K 1 and Narasmhamurthy.S.K 2 1 Assstant

More information

Solving Singularly Perturbed Differential Difference Equations via Fitted Method

Solving Singularly Perturbed Differential Difference Equations via Fitted Method Avalable at ttp://pvamu.edu/aam Appl. Appl. Mat. ISSN: 193-9466 Vol. 8, Issue 1 (June 013), pp. 318-33 Applcatons and Appled Matematcs: An Internatonal Journal (AAM) Solvng Sngularly Perturbed Dfferental

More information

Georgia Tech PHYS 6124 Mathematical Methods of Physics I

Georgia Tech PHYS 6124 Mathematical Methods of Physics I Georga Tech PHYS 624 Mathematcal Methods of Physcs I Instructor: Predrag Cvtanovć Fall semester 202 Homework Set #7 due October 30 202 == show all your work for maxmum credt == put labels ttle legends

More information

CSci 6974 and ECSE 6966 Math. Tech. for Vision, Graphics and Robotics Lecture 21, April 17, 2006 Estimating A Plane Homography

CSci 6974 and ECSE 6966 Math. Tech. for Vision, Graphics and Robotics Lecture 21, April 17, 2006 Estimating A Plane Homography CSc 6974 and ECSE 6966 Math. Tech. for Vson, Graphcs and Robotcs Lecture 21, Aprl 17, 2006 Estmatng A Plane Homography Overvew We contnue wth a dscusson of the major ssues, usng estmaton of plane projectve

More information

Snce h( q^; q) = hq ~ and h( p^ ; p) = hp, one can wrte ~ h hq hp = hq ~hp ~ (7) the uncertanty relaton for an arbtrary state. The states that mnmze t

Snce h( q^; q) = hq ~ and h( p^ ; p) = hp, one can wrte ~ h hq hp = hq ~hp ~ (7) the uncertanty relaton for an arbtrary state. The states that mnmze t 8.5: Many-body phenomena n condensed matter and atomc physcs Last moded: September, 003 Lecture. Squeezed States In ths lecture we shall contnue the dscusson of coherent states, focusng on ther propertes

More information

Chapter 3 Differentiation and Integration

Chapter 3 Differentiation and Integration MEE07 Computer Modelng Technques n Engneerng Chapter Derentaton and Integraton Reerence: An Introducton to Numercal Computatons, nd edton, S. yakowtz and F. zdarovsky, Mawell/Macmllan, 990. Derentaton

More information

CME 302: NUMERICAL LINEAR ALGEBRA FALL 2005/06 LECTURE 13

CME 302: NUMERICAL LINEAR ALGEBRA FALL 2005/06 LECTURE 13 CME 30: NUMERICAL LINEAR ALGEBRA FALL 005/06 LECTURE 13 GENE H GOLUB 1 Iteratve Methods Very large problems (naturally sparse, from applcatons): teratve methods Structured matrces (even sometmes dense,

More information

4 Analysis of Variance (ANOVA) 5 ANOVA. 5.1 Introduction. 5.2 Fixed Effects ANOVA

4 Analysis of Variance (ANOVA) 5 ANOVA. 5.1 Introduction. 5.2 Fixed Effects ANOVA 4 Analyss of Varance (ANOVA) 5 ANOVA 51 Introducton ANOVA ANOVA s a way to estmate and test the means of multple populatons We wll start wth one-way ANOVA If the populatons ncluded n the study are selected

More information

CHAPTER 14 GENERAL PERTURBATION THEORY

CHAPTER 14 GENERAL PERTURBATION THEORY CHAPTER 4 GENERAL PERTURBATION THEORY 4 Introducton A partcle n orbt around a pont mass or a sphercally symmetrc mass dstrbuton s movng n a gravtatonal potental of the form GM / r In ths potental t moves

More information

COMPARISON OF SOME RELIABILITY CHARACTERISTICS BETWEEN REDUNDANT SYSTEMS REQUIRING SUPPORTING UNITS FOR THEIR OPERATIONS

COMPARISON OF SOME RELIABILITY CHARACTERISTICS BETWEEN REDUNDANT SYSTEMS REQUIRING SUPPORTING UNITS FOR THEIR OPERATIONS Avalable onlne at http://sck.org J. Math. Comput. Sc. 3 (3), No., 6-3 ISSN: 97-537 COMPARISON OF SOME RELIABILITY CHARACTERISTICS BETWEEN REDUNDANT SYSTEMS REQUIRING SUPPORTING UNITS FOR THEIR OPERATIONS

More information

Using T.O.M to Estimate Parameter of distributions that have not Single Exponential Family

Using T.O.M to Estimate Parameter of distributions that have not Single Exponential Family IOSR Journal of Mathematcs IOSR-JM) ISSN: 2278-5728. Volume 3, Issue 3 Sep-Oct. 202), PP 44-48 www.osrjournals.org Usng T.O.M to Estmate Parameter of dstrbutons that have not Sngle Exponental Famly Jubran

More information

FREQUENCY DISTRIBUTIONS Page 1 of The idea of a frequency distribution for sets of observations will be introduced,

FREQUENCY DISTRIBUTIONS Page 1 of The idea of a frequency distribution for sets of observations will be introduced, FREQUENCY DISTRIBUTIONS Page 1 of 6 I. Introducton 1. The dea of a frequency dstrbuton for sets of observatons wll be ntroduced, together wth some of the mechancs for constructng dstrbutons of data. Then

More information

SL n (F ) Equals its Own Derived Group

SL n (F ) Equals its Own Derived Group Internatonal Journal of Algebra, Vol. 2, 2008, no. 12, 585-594 SL n (F ) Equals ts Own Derved Group Jorge Macel BMCC-The Cty Unversty of New York, CUNY 199 Chambers street, New York, NY 10007, USA macel@cms.nyu.edu

More information

The Minimum Universal Cost Flow in an Infeasible Flow Network

The Minimum Universal Cost Flow in an Infeasible Flow Network Journal of Scences, Islamc Republc of Iran 17(2): 175-180 (2006) Unversty of Tehran, ISSN 1016-1104 http://jscencesutacr The Mnmum Unversal Cost Flow n an Infeasble Flow Network H Saleh Fathabad * M Bagheran

More information

Canonical transformations

Canonical transformations Canoncal transformatons November 23, 2014 Recall that we have defned a symplectc transformaton to be any lnear transformaton M A B leavng the symplectc form nvarant, Ω AB M A CM B DΩ CD Coordnate transformatons,

More information

Norms, Condition Numbers, Eigenvalues and Eigenvectors

Norms, Condition Numbers, Eigenvalues and Eigenvectors Norms, Condton Numbers, Egenvalues and Egenvectors 1 Norms A norm s a measure of the sze of a matrx or a vector For vectors the common norms are: N a 2 = ( x 2 1/2 the Eucldean Norm (1a b 1 = =1 N x (1b

More information

Solving Fractional Nonlinear Fredholm Integro-differential Equations via Hybrid of Rationalized Haar Functions

Solving Fractional Nonlinear Fredholm Integro-differential Equations via Hybrid of Rationalized Haar Functions ISSN 746-7659 England UK Journal of Informaton and Computng Scence Vol. 9 No. 3 4 pp. 69-8 Solvng Fractonal Nonlnear Fredholm Integro-dfferental Equatons va Hybrd of Ratonalzed Haar Functons Yadollah Ordokhan

More information

More metrics on cartesian products

More metrics on cartesian products More metrcs on cartesan products If (X, d ) are metrc spaces for 1 n, then n Secton II4 of the lecture notes we defned three metrcs on X whose underlyng topologes are the product topology The purpose of

More information

Some new additive Runge Kutta methods and their applications

Some new additive Runge Kutta methods and their applications Journal of Computatonal and Appled Mathematcs 9 (6) 74 98 www.elsever.com/locate/cam Some new addtve Runge Kutta methods and ther applcatons Hongyu Lu,, Jun Zou Department of Mathematcs, The Chnese Unversty

More information

Handout # 6 (MEEN 617) Numerical Integration to Find Time Response of SDOF mechanical system. and write EOM (1) as two first-order Eqs.

Handout # 6 (MEEN 617) Numerical Integration to Find Time Response of SDOF mechanical system. and write EOM (1) as two first-order Eqs. Handout # 6 (MEEN 67) Numercal Integraton to Fnd Tme Response of SDOF mechancal system State Space Method The EOM for a lnear system s M X + DX + K X = F() t () t = X = X X = X = V wth ntal condtons, at

More information