Explct Extenson Operators on Herarchcal Grds G. Haase y S.V. Nepomnyaschh z Abstract Extenson operators extend functons dened on the boundary of a dom

Size: px
Start display at page:

Download "Explct Extenson Operators on Herarchcal Grds G. Haase y S.V. Nepomnyaschh z Abstract Extenson operators extend functons dened on the boundary of a dom"

Transcription

1 JOHANNES KEPLER UNIVERSIT AT LINZ Explct Extenson Operators on Herarchcal Grds Gundolf Haase Department of Mathematcs Johannes Kepler Unversty, A{44 Lnz, Austra Sergej V. Nepomnyaschh omputng enter Sberan Branch of Russan Academy of Scences Novosbrs, 639, Russa Insttutsbercht Nr. 524 June 997 INSTITUT F UR MATHEMATIK A{44 LINZ, ALTENBERGERSTRASSE 69, AUSTRIA

2 Explct Extenson Operators on Herarchcal Grds G. Haase y S.V. Nepomnyaschh z Abstract Extenson operators extend functons dened on the boundary of a doman nto ts nteror. Ths paper presents explct extenson operators by means of multlevel decompostons on herarchcal grds. It s shown that the norm-preservng property of these operators holds for the 2D as well for the 3D case wth constants ndependent on dscretzaton and doman sze. These constants can be further mproved by an addtonal teraton scheme appled to the extenson operator. Some mplementaton of these technques s presented for a doman decomposton precondtoner and numercal experments are gven. Keywords : Boundary value problems, trace theory, multlevel methods, doman decomposton, precondtonng, nte element method. Introducton The purpose of ths paper s to dscuss the constructon of norm-preservng explct extenson operators of functons at a boundary nto the nteror of the doman. The theorems on traces of functons from Sobolev spaces play an mportant role n studyng boundary value problems of mathematcal physcs [2, 24, 3]. These theorems are commonly used for dervng a pror estmates of the stablty wth respect to boundary condtons. For the case The second author was apponted guest professor at the Faculty for Techncal and Natural Scences of the Unversty of Lnz for the perod from March untl June 997. Ths wor was partally supported by the Russan Basc Research Foundaton (RBRF) under the grant y Johannes Kepler Unversty Lnz, Inst. of Math., Altenberger Str. 69, A{44 Lnz, Austra, ghaase@numa.un-lnz.ac.at z omputng enter, Sberan Branch of Russan Academy of Scences, Novosbrs, 639, Russa, svnep@oapmg.sscc.ru

3 of grd functons the rst constructve analyss of ths problem seems to be carred out n [], where the case of rectangular grds was consdered. For a numercal soluton of non-homogeneous Drchlet problems t s mportant to have a good extenson of the Drchlet condtons nsde the doman. If the trval extenson,.e. extenson by zero at nteror nodes of the grd, s used then nstead of the numercal soluton of a boundary value problem wth a smooth soluton, we have to compute a soluton wth a huge gradent n the vcnty of the boundary. The use of norm-preservng explct extenson operators gves a "good" ntal guess for teraton processes and reduces the boundary value problems wth non-homogeneous Drchlet condtons to the boundary value problems wth homogeneous Drchlet condtons. The soluton of the last s unformly bounded wth respect to the grd sze. An other mportant applcaton of the explct extenson operators s connected wth doman decomposton methods [7, 8, 2]. Usng the explct extenson operators n doman decomposton methods, nstead of exact solvers n the subdomans, local precondtonng operators can be utlzed. Usng ths operators, optmal estmates of the convergence rate of the doman decomposton methods and optmal arthmetc cost are obtaned. The rst constructon of the norm-preservng explct extenson operators for unstructured grds seems to be suggested n [7] and used for the constructon of precondtonng operators n [7, 8, 9, 2]. For the case of herarchcal grds, near norm-preservng explct extenson operators has been suggested n [3] whch can be easy realzed. Its teratve mprovng was dscussed n [9, ]. In ths paper we follow [2]. To construct the explct extenson operators, functons at the boundary are decomposed nto seres of functons at herarchcal grds. Then, each component of ths decomposton wll be extended n a very smple way. To prove the trace theorem for herarchcal grds, a multlevel decomposton of the boundary has been used n [23]. In the suggested paper, we desgn the multlevel decomposton of functons on the boundary wth very cheap arthmetc costs and the arthmetc costs of the resultng explct extenson operators (as well as adjont operators) s proportonal to the number of grd nodes. The paper s organzed as follows. In Secton 2, the constructon of explct extenson operators s suggested. Realzaton aspects of these extensons are dscussed n Secton 3. The mprovement of the extensons by an teratve scheme s proposed n Secton 4. The applcaton to doman decomposton methods s consdered n Secton 5 and some numercal experments are presented n Secton 6. 2

4 2 onstructon of Extenson Operators Let be a bounded, polygonal doman and? be ts boundary. consder a coarse grd trangulaton of h = [ M () = ; dam( () ) = O() Let us whch wll be rened several tmes. Ths results n a sequence of nested trangulatons h ; h; : : : ; h J such that h = M [ = () ; = ; ; : : : ; J ; where the trangles (+) are generated by subdvdng trangles () nto four congruent subtrangles by connectng the mdponts of the edges. Introduce the spaces W and V of FE (nte element) functons. The space W conssts of real-valued functons whch are contnuous on and lnear on the trangles n h. The space V s the space of traces on? of functons from W : V = f' h j' h = u h j? ; wth u h 2 W g We consder W and V as the subspaces of the Sobolev spaces H () and H 2 (?), respectvely, wth correspondng norms [3]. The man goal s the constructon of some norm{preservng explct extenson operator t from V J to W J : t : V J! W J : Ths constructon s based on the dea from [3], but nstead of Yserentant's herarchcal decomposton [26, 27] of the space V J we use some analogous of the so{called BPX{decomposton of V J [6, 25]. Denote by ' () ; = ; 2; : : : ; N the nodal bass of V and by () the one{dmensonal subspace spanned by ths functon ' () wth the support (). Dene Q () : L 2 (?)! () the L 2 {le projecton from L 2 (?) onto () : h = d () ( h )' () Q () ; 3

5 where d () = d () = ( h ; ' () (' () ) L2 (?) ; ) L2 (?) ; or (a) meas () ( h ; ) L2 ( () )) : (b) Denote by Q = N X = Q () ; = ; ; : : : ; J? : For = J, Q s dened as the L 2 {orthoprojecton from L 2 () onto V J. LEMMA. There exst postve constants c, c 2, ndependent of h, such that for any ' h 2 V J c ' h 2 H 2 (?) Q ' h 2 L 2 (?) + c 2 ' h 2 H 2 (?): = 2 (Q? Q (?) )' h 2 L 2 (?) PROOF The result above s a smple consequence of well-nown propertes of Q and a technque from [4, 8, 22, 23, 27]. However, for completeness we gve the proof. It s easy to see that Q s the lnear projecton onto V and there exsts a constant c 3, ndependent of h, such that Q ' L2 ( () c ) 3 ' L2 ( () ) 8 ' 2 L 2 (?) (2) holds, where () s the unon of all () j pont wth (). Also the followng approxmaton property of Q holds : '? Q ' 2 L 2 (?) = N X = N X = 2( + c 3 ) '? Q ' 2 L 2 ( () ) = N whch posses at least one common X = '? + Q (? ') 2 L 2 ( () ) N X = '? 2 L 2 ( () ) 4 '? +? Q ' 2 L 2 ( () ) :

6 Here s an arbtrary constant functon. Then the estmate '? Q ' 2 L 2 (?) c 4 2? j'j H (?) 8' 2 H (?) s vald wth some h-ndependent constant c 4. Accordng to [23] there exsts also an h-ndependent constant c 5 such that c 5 ' h 2 H =2 (?) nf h 2V JP h = 'h = = Q ' h 2 L 2 (?) + Q ' h 2 L 2 (?) h 2 L 2 (?) = = 2 (Q? Q? ) ' h 2 L 2 (?) 2 ' h? Q ' h 2 L 2 (?) : Let us estmate ' h? Q ' h L2 (?). For any 2 H (?) we have ' h? Q ' h L2 (?) = ' h? +? Q + Q? Q ' h L2 (?) ' h? L2 (?) +? Q L2 (?) + Q (? ' h ) L2 (?) ( + c 3)' h? L2 (?) + c 4 2? j j H (?) ; where the constant c 3 arses from (2) by summng of (). Tang the nmum over all 2 H (?), we obtan the K-functonal ' h? Q ' h L2 (?) c 6 nf 2H (?) ' h? + 2? j j H (?) = c 6 K (2? ; ' h ) ; where the constant c 6 s ndependent of h. Usng the equvalence of norms (see, e.g., [8, 23]) ' h 2 H =2 (?) 'h 2 L 2 (?) + one obtans the statement of Lemma. = 2 K (2? ; ' h ) ; Denote by x (), =,2, : : :,L the nodes of the trangulaton h (we assume that nodes x () are enumerated rst on? and then nsde ) and dene the extenson operator t n the followng way. For any ' h 2 V J set h = Q ' h ; (3a) h = (Q? Q? ) ' h ; = ; 2; : : : ; J : (3b) 5

7 Then ' h = h + h + : : : + h J : Dene the extenson u h 2 W of the functon h u h (x () ) = u h (x () ) = ( ( h (x () ) ; x () 2? ; x () 62? ; h (x () ) ; x () 2? ; ; x () 62? ; accordng to [3, 2]: (4a) (4b) For we choose ether the mean value of the boundary functon h or the soluton of the proper PDE on the coarsest grd wth Drchlet boundary condtons h. Now, we dene the extenson t ' h := u h u h + u h + : : : + u h J : (5) By settng t ' h := v J the denton above can be wrtten n a recursve way : v := u h (6a) v := v? + u h = ; : : : ; J : (6b) Note, that v 2 W s the extenson of Q ' h on level = ; : : : ; J. REMARK. We can use the L 2 {orthoprojecton from L 2 () onto V nstead of Q, = ; ; : : : ; J?. But n ths case the cost of the decomposton (3) s expensve (especally for three dmensonal problems). LEMMA 2. There exsts a postve constant c 4, ndependent of h, such that u h H () c 4 2 h L2 (?); = ; ; : : : ; J: PROOF The proof of ths lemma s obvous and was done n [3]. THEOREM. There exsts a postve constant c, ndependent of h, such that t' h H () c ' h H 2 (?) 8' h 2 V J : Here the operator t s dened n (5). 6

8 PROOF We have (see,e.g., [23]) t' h 2 H () = u h 2 H () c 2 nf w h 2W c 2 = JP 4 u h 2 L 2 () : w h = uh = = 4 w h 2 L 2 () Here c 2 s ndependent of h and the u h, = ; : : : ; J are dened n (4). Then t follows from Lemma, 2 and from the specal structure of the functons u h that = 4 u h 2 L 2 () c 3 = holds, where c 3, c 4 are ndependent of h. 2 h 2 L 2 (?) c 4 ' h 2 H =2 (?) REMARK 2. The constructon of the extenson operator t for three dmensonal problems can be done n the same way. The Theorem s vald too. If the orgnal doman s splt nto many subdomans n doman decomposton methods [8], then the dameters of the subdomans depend on some small parameter " and we need the extenson operator t such that the constant c from the Theorem s ndependent of ". To do ths, let us assume that by mang the change of varables x = " s; x 2 (7) the doman s transformed nto the doman wth the boundary? and that the propertes of are ndependent of ". From [8, 9] we have the followng. LEMMA 3. There exsts a postve constant c 2, ndependent of h and ", such that c 2 ' h H 2 " (?) u h H () for any functon u h 2 W J, where ' h 2 V J s the trace of u h at the boundary?. And there exsts a postve constant c 3, ndependent of h and ", such that for any ' h 2 V J there exsts u h 2 W J : u h (x) = ' h (x); x 2?; u h H () c 3 ' h H 2 " (?) 7 :

9 Here ' h 2 = "' h 2 L H 2 2 (?) + j' h j 2 " (?) H 2 (?) ' h 2 L 2 (?) = j' h j 2 H 2 (?) = Z? Z? (' h (x)) 2 dx ; Z? (' h (x)? ' h )y)) 2 jx? yj 2 dx dy : LEMMA 4. There exsts a postve constant c 4, ndependent of h and ", such that for any ' h 2 V J Here ' h 2 + H 2 (?) " 'h 2 L 2 (?) + j' h j 2 c H 2 4 ' h 2 (?) H The followng lemma s vald. ' h = Q ' h ; ' h = ' h? ' h : ; 2 " (?) : (8) LEMMA 5. There exsts a postve constant c 5, ndependent of h and ", such that ' h 2 H 2 " (?) + " Q ' h 2 L 2 (?) + Here ' h; 'h, are from (8). = PROOF Usng (7) and Lemma, we have 2 (Q? Q? )' h 2 L 2 (?)! c 5 ' h 2 H 2 (?) " 'h 2 L 2 (?) + j' h j 2 H 2 (?) = ' h 2 L 2 (? ) + j' h j 2 H 2 (? ) c (Q 'h 2 L 2 (? ) + = "c (Q ' h 2 L 2 (?) + = = 2 (Q? Q?)' h 2 L 2 (? )) 2 (Q? Q? )' h 2 L 2 (?) : Here Q s the projecton whch corresponds to Q wth the change of varables. 8

10 THEOREM 2. There exsts a postve constant c 6, ndependent of h and ", such that t' h H () c 6 ' h Here the operator t s dened n (5). PROOF For ' h ; 'h For the functon ' h Q ' h 2 + H 2 " (?) from (8) we have = Q ' h 2 + H 2 " (?) + = H 2 " (?) 8' h 2 V J : 2 (Q? Q? )' h 2 L 2 (?) = 2 (Q? Q? )' h 2 L 2 (?) 2 (Q? Q? )' h 2 L 2 (?) : let us consder the followng decomposton: ' h = ' h ; + ' h ; ; R ' h ; = const = ' h meas(?) (x)dx? ' h ; = ' h? ' h : ; It s easy to see that (Q? Q? )' h ; = ; = ; 2; ; J : Then we can use the evdent trc from [3] wth the Poncare nequalty n H 2 (? ): = 2 (Q? Q? )' h 2 L 2 (?) = = " = = 2 (Q? Q? )' h ; 2 L 2 (?) 2 (Q? Q?)' h ; 2 L 2 (? ) c 2 "' h ; 2 H 2 (? ) c 7 "j' h ;j 2 H 2 (? ) = c 7 "j' h ;j 2 H 2 (?) = c 7 "j' h j 2 H 2 (?) Here c 7 s from the Poncare nequalty. It s easy to see that there exsts a postve constant c 8, ndependent of h and ", such that u h H () c 8 h H 2 " (?) where h = ' h = Q ' h, and u h 2 W s from (4). The rest of the estmates for Theorem 2 and Theorem s the same. ; : 9

11 3 Realzaton of the Extenson onsder the symmetrc, nd u 2 H () : Z (x) r T u(x) rv(x) dx = H {ellptc and H {bounded varatonal problem Z f(x) v(x) dx 8v 2 H () ; (9) arsng from the wea formulaton of a scalar second{order, symmetrc and unformly bounded ellptc boundary value problem gven n a plane bounded doman R 2 wth a pecewse smooth boundary? The materal coecents (x) > 8x 2 have to be restrcted for certan extenson technques. Dene the usual nte elements (FE) nodal bass = [ ; I ] = # J ; ; #J ; # J ; ; # J ; () N (J) N (J) + N (J) +N(J) I where the rst N (J) = N J bass functons on the nest level J correspond to nodes on?, the remanng bass functons are n the nteror. The proper nodes wll be denoted by \" and \I". Smlarly, N () and N () I represent the number of bass functons on the boundary and n the nteror on level. Then the FE somorphsm leads to the symmetrc and postve dente system of equatons on each level! K; K K u := I; u; f = ; =: f K I; u I; f ; () I; K I; where K I; s symmetrc, postve dente. In the followng, the matrces I ;, I I; denote the proper denttes on level and the matrces P +, ; P +, I; P + represent I; the usual lnear FE nterpolaton matrces (Fg. ) on the proper subsets of nodes. The multlevel extenson of a functon P h N = (J) ' (J) 2 V J represented by the vector 2 R N (J) P nto a functon u h N = (J) (J) +N I u # (J) 2 W J represented by the vector u 2 R N (J) I conssts of three steps : A 2 2 AAU A + v A A v 3 A A AK A 2 A 2 A A - v A coarse grd nodes new ne grd nodes 2 Fgure : 2 Lnear FE nterpolaton

12 . Determne the rectangular N () (J) N projecton matrx Q and dene the coecents of the projecton Q h n the FE nodal bass of level := Q = ; : : : ; J : (2) 2. Accordng to (3) splt the vectors nto the coecents of the multlevel nodal bass presentaton of h mne the coecent vectors = P J = P N () () ' () and deter- := (3a) :=??P ;? I ;? are deter- P N 3. The coecents v of the extensons v = () mned by v; I; v = := v I; B I; I; P ;? v = v; v I; := P I;? P I;? = ; : : : ; J : () +N I = v # () v ;? A : v I;? (3b) (4a) (4b) Denote by E the matrx representaton of the extenson t (5), then we set E' := v J. The matrx B I; can be chosen as N ()? N () I, mappng the N () mean value of the boundary data nto the nteror. Another approach s the dscrete harmonc extenson on the coarsest grd wth respect to the PDE,.e., B I; =?K? I; K I;. 4 Improvng the Extenson by an Iteraton Scheme Denote by z I; some extenson of boundary data ;, then the functonal J (z I; ) = K ; ; represents the square of the energy norm of z I; ; z I; that extenson. The extensons gven n (6) are just an approxmaton of the followng mnmzaton problem v I; = arg mn z I; J (z I; ) ; (5)

13 whch s equvalent to the system of equatons v ; = ; K I; v I; =?K I; ; = ; : : : ; J : (6) To mprove the qualty of those extensons gven n (6),.e., decrease the constants n Theorems and 2, we apply some teraton scheme on (6) v j I; By denng the teraton matrx M I; := Q? := vj?? I; j B I; K I; v j? + I; K I; ; : (7)? II;? j B I;K I; the teratons can be rewrtten to j= v I;? := M I; v I;? I I;? M I; K? I; K I; ; (8) wth some ntal guess v. To dene the precondtoner I; B I;, for nstance, we can splt K I; nto the strctly upper, dagonal and lower part,.e., K I; = L I; + D I; + U I;, then the Gau-Sedel teraton matrx s dened va M I; := (D I; + L I; )? U I;. There are two opportuntes to choose the ntal guess v I; and the boundary data ;. Frst, we start the teraton wth the zero ntal guess and the dscrete representaton of h,.e.,, on the boundary. Ths changes (4b) v ; A I ; P ;?? v I; )K?(I I;? M I; I; K I; P I;? P I;? AB@ v ;? v I;? A : (9) The second opportunty conssts n usng the actual extenson on level as ntal guess and the proper boundary data v ;. The relaton v ; = + P v ;? ;? leads drectly to the second reformulaton of v ; A I ; v I;?(I I;? M? )K I; I; K I; M I; I ; P ;? P I;? P I;? AB@ v ;? v I;? (2) In the followng, we omt the level ndex. Recallng the functonal J (z I ), the proper error functonal s E(z I ) = z I? w I 2 wth w K I I =?K? I K I as the exact dscrete extenson. A : 2

14 LEMMA 6. Denote by w j I the j-th terate n teraton scheme (7), whch converges n the K I -energy norm,.e., there exsts a postve constant q < such that holds for all j >. Then the extenson wth some ntal guess w I. w j I? w I KI q w j? I? w I KI (2) w j I fullls J (w j I ) = J (w I) + E(w j I ) J (w I) + q 2j E(w I) PROOF Wth the dentons for J and E we wrte J (w j I ) = K ;? w j I w j I?? = K I w j I I ; wj + KI ; wi j + KI w j I ; {z }? =(w j I ;K I ) = K I w j I ; wj I + K I K? j I K I {z ; wi + } =?w I + (K I w I ; w I )? (K I w I ; w I ) + (K ; ) + (K ; ) I {z } =?w I w j I ; K I K? K I =? K I (w j I? w I); (w j I? w I)? (K I w I ; w I ) + (K ; ) = w j I? w I 2 K I {z } E(w j I ) 2? w I K I + 2 K {z } J (w I ) (2) q 2 w j? I? w I 2 K I +J (w I ) = q 2 E(w j? I ) + J (w I ) (2) q 2j w I? w I 2 K I +J (w I ) = q 2j E(w I) + J (w I ) : 5 Applcaton to a DD Precondtoner The doman wll be decomposed S nto p non-overlappng subdomans p s (s = ; : : : ; p) such that = s= s. The grd trangulatons h ; wll be dstrbuted analogously for all level = ; : : : ; J. Now, the submatrces n system () are changed nto blocmatrces, especally K I = dag (K I; ) =;::: ;p. Ths new system wll be solved by some 3

15 parallelzed precondtoned teratve method, e.g., G-method. As a precondtoner we use the ASM{DD precondtoner = I?B?T I O I O O I I O I?B I I I : (22) Ths precondtoner contans the three components I = dag ( I; ) =;::: ;p, and the bloc matrx B I, whch can be chosen freely n order to adapt the precondtoner to the partculars of the problem under consderaton. For the choce B I; =?B I; K I; see [2]. As precondtoner for the Schur complement S = K? K I K? I K I the BPS [5] s used. The precondtonng step w =? r can be rewrtten n the form Algorthm : The ASM-DD Precondtoner [2] w =? pp = A T ;? r; + B T I; r I; w I; =? I; r I; + B I; w ; ; = ; 2; : : : ; p A I;? A where A = ; A I; A I; denotes the subdoman connectvty matrx whch s used for a convenent notaton only. The subdoman FE assembly process whch s connected wth nearest neghbour communcaton stands behnd ths notaton. For further nvestgatons on DD precondtoners see [6, 2,, 5, 2, 9]. Assume postve, h-ndependent spectral equvalence constants ; ; I ; I fulllng the spectral equvalence nequaltes S and I I K I I I : If we have addtonally a constant c E so that v B I v K c E v S 8v 2 R Nc (23) holds then the upper and lower bounds of the condton number (? K) [2, 7] can be estmated as O(c 2 E) (? K) O(c 4 E) : (24) Estmate (23) represents the result from Theorem n a dscrete sense, so that B I can be chosen as the dscrete extenson operator dened n (4, 9, 2). Addtonally, Algorthm requres B I = B T I so that the transposed of 4

16 that extenson have to be appled. Whereas transposng (4) s qute smple one have to tae care when smoothng s ncluded. If we denote by M I; the adjont operator to M I; wth respect to the K I; nner product, then the transposed operaton to (9) can be wrtten v ;? v I;? A = M I; K? v ;? K I; I I;?? P T ;?? v; + PI;? T? vi; P I;? T vi; I; v I; A ; (25).e., we have to perform sweeps of the teraton procedure dened by M I; wth the rght hand sde v I; and a zero ntal guess. If M I; represents a lexcographcally forward Gau-Sedel teraton, then the adjont teraton s the lexcographcally bacward one. In the second case (2) we acheve v ;? v I;? A = I ; T P ;? P I;? P I;? T T B v ;?K I; M I; K? I I;? M I; T v I; The rst lne n the second matrx s smlar to (25). Notng that?? M T I; v I; = v I;? K I; K? I; v I; + MI; T v I; = v I;? K I; I I;? M I; K? I; v I; ; I; v I; A : the second lne s just a defect calculaton usng the result of the teraton n the rst lne. For algorthmc mprovements n combnaton wth the nner precondtoner I, see [, 9]. (26) 6 Numercal Experments In the numercal experments we used 2 smple and one challengng examples. Example :?4 u = n = [; ] [; :5] u = Example 2:?4 u = n = [; ] 2 u = For Example, the doman was subdvded nto 2 squares, the doman n 5

17 Example 2 was parttoned nto 6 squares. Example 3 (Electrcal machne): As a more challengng example we calculated the magnetc potental n an electrcal machne wth a rather complex geometry and large jumps n the coecents (see also [4]), for the decomposton of the doman nto 6 subdomans see Fg. 2. Fgure 2: Materal adapted decomposton and ntal mesh of Example 3 All calculatons were done on a 6 processor Parsytec Power-Xplorer wth 32 MByte memory per node. All examples were solved wth the precondtoned parallelzed G usng Algorthm as precondtong step untl an accuracy, measured n the K? K-energy norm, of?6 was acheved. As Schur complement precondtoner the BPS [5] was used. When not explaned further, the nner problem was solved exactly,.e., I = K I. For comparson we used n example 3 also a multgrd V-cycle wth one preand one post-smoothng sweep (V) for denng I. The teraton procedure (8) mplemented va (2) was appled, at most, one tme ( 2 f; g). In tables - 3 the projecton (a) was tested, the tables 4-6 present the results usng projecton (b). For measurng the qualty of the extensons we calculate aver = E' K '?K? I K I ' K, the rato between an approxmate extenson E' and the exact one n the energy norm. 6

18 proj. (a) J = J = J = 2 J = 3 J = 4 J = 5 J = 6 Iteratons aver Iteratons aver Table : # G-teratons for Example usng 2 processors proj. (a) J = J = J = 2 J = 3 J = 4 J = 5 J = 6 Iteratons aver Iteratons aver Table 2: # G-teratons for Example 2 ( d.o.f.) usng 6 processors proj. (a) J = J = J = 2 J = 3 J = 4 J = 5 # unnowns I exact: Iteratons Qualty aver I exact: Iteratons Qualty aver I - V: Iteratons solver n sec I - V: Iteratons solver n sec Table 3: # G-teratons for Example 3 usng 6 processors For all three examples, the behavor of the teraton counts reects the logarthmc grow of the condton number (? K) for the BPS Schur complement precondtoner,.e., the constant c E (23) seems to be h-ndependent. Also the qualty rato aver seems to be bounded wth growng level number J, especally n example 3. Both observatons conrm the result of Theorem. The addtonal teraton ( = ) for denng the extenson decreases the teraton count and really mproves the qualty of the extenson. The solver tmes n Table 3 ndcate that the addtonal teraton does not accelerate the soluton process for the example presented. 7

19 proj. (b) J = J = J = 2 J = 3 J = 4 J = 5 J = 6 Iteratons aver Iteratons aver Table 4: # G-teratons for Example usng 2 processors proj. (b) J = J = J = 2 J = 3 J = 4 J = 5 J = 6 Iteratons aver Iteratons aver Table 5: # G-teratons for Example 2 ( d.o.f.) usng 6 processors proj. (b) J = J = J = 2 J = 3 J = 4 J = 5 # unnowns Iteratons Qualty aver Iteratons Qualty aver I - V: Iteratons solver n sec I - V: Iteratons solver n sec Table 6: # G-teratons for Example 3 usng 6 processors Although the teraton counts n tables 4-6 are slghtly hgher, we can draw the same conclusons for projecton (b) as for the projecton accordng to (a). 7 onclusons The extenson technque (5) presented n Secton 2 s a fast and qualtatvely good approxmaton of the homogeneous extenson of Laplacan-le derental operators when usng the projectons (). The ndependence of the 8

20 constants n Theorems and 2 from the dscretzaton parameter h and the dameter " of the doman s stll vald n the 3D-case but has to be tested n future. In combnaton wth a proper teraton procedure (8), the extenson wors also wth respect to more complcated second order derental operators (see []). For the examples gven, the addtonal teraton step per level dd not result n an accelerated overall soluton tme. But ths property may change by the examples nvestgated. When usng the extensons n a DD precondtoner, the transposed of those extensons s needed. In combnaton wth a proper precondtoner I a sophstcated mplementaton reduces the tme per teraton (for more detals see [, 9]) sgncantly. Here, agan the 3D-case has to be nvestgated. Usng an ecent h-ndependent precondtoner I, e.g. multgrd, the asymptotc behavor of the condton number (? K) depends only on the asymptotc behavor of the Schur complement precondtoner. Therefore, the numercal eort of the whole parallel algorthm s nearly optmal (or logarthmcally optmal). References [] V. B. Andreev. The stablty of nte derence schemes for ellptc equatons wth respect to the Drchlet boundary condtons. Zh. Vychsl. Mat. Mat. Fz., 2:598{6, 972. [2] N. Aronszajn. Boundary value of functons wth nte Drchlet ntegral. In onference on partal derental equatons, Studes n egenvalue problems, 955. [3] O. V. Besov, V. P. Il'n, and S. M. Nol's. Integral presentatons of functons and embeddng theorems. Naua, Moscow, 975. n Russan. [4] F. A. Bornemann and H. Yserentant. A basc norm equvalence for the theory of multlevel methods. Numer. Math., 64:455{476, 993. [5] J. H. Bramble, J. E. Pasca, and A. H. Schatz. The constructon of precondtoners for ellptc problems by substructurng I { IV. Mathematcs of omputaton, 986, 987, 988, , 3{34, 49, {6, 5, 45{43, 53, {24. [6] J. H. Bramble, J. E. Pasca, and J. Xu. Parallel multlevel precondtoners. Mathematcs of omputaton, 55(9): { 22, 99. 9

21 [7] H. heng. Iteratve Soluton of Ellptc Fnte Element Problems on Partally Rened Meshes and the Eect of Usng Inexact Solvers. PhD thess, ourant Insttute of Mathematcal Scence, New Yor Unversty, 993. [8] W. Dahmen and A. Kunoth. Multlevel precondtonng. Numer. Math., 63:35{344, 992. [9] G. Haase. Multlevel extenson technques n doman decomposton precondtoners. In DD9 Proceedngs. John Wley and Sons Ltd., 997. [] G. Haase. Herarchcal extenson operators plus smoothng n doman decomposton precondtoners. Appled Numercal Mathematcs, 23(3):327{346, May 997. [] G. Haase and U. Langer. The non-overlappng doman decomposton multplcatve Schwarz method. Internatonal Journal of omputer Mathemathcs, 44:223{242, 992. [2] G. Haase, U. Langer, and A. Meyer. The approxmate drchlet doman decomposton method. Part I: An algebrac approach. Part II: Applcatons to 2nd-order ellptc boundary value problems. omputng, 47:37{5 (Part I), 53{67 (Part II), 99. [3] G. Haase, U. Langer, A. Meyer, and S. V. Nepomnyaschh. Herarchcal extenson operators and local multgrd methods n doman decomposton precondtoners. East-West Journal of Numercal Mathematcs, 2:73{93, 994. [4] B. Hese and M. Jung. Parallel solvers for nonlnear ellptc problems based on doman decomposton deas Submtted for publcaton. Avalable as Report 494, Insttute of Mathematcs, Unversty Lnz. [5] A. M. Matson and S. V. Nepomnyaschh. Norms n the space of traces of mesh functons. Sov. J. Numer. Anal. Math. Modellng, 3:99{26, 988. [6] A. Meyer. A parallel precondtoned conjugate gradent method usng doman decomposton and nexact solvers on each subdoman. omputng, 45:27{234, 99. [7] S. V. Nepomnyaschh. Doman Decomposton and Schwarz method n subspace for approxmate soluton of ellptc boundary value problems. PhD thess, Academy of Scence, Novosbrs, 986. n Russan. 2

22 [8] S. V. Nepomnyaschh. Doman Decomposton method for ellptc problems wth dscontnous coecents. In R. G. et al., edtor, Doman Decomposton methods for PDEs, pages 242{252. SIAM, 99. [9] S. V. Nepomnyaschh. Mesh theorems on traces, normalzaton of functon traces and and ther nverson. Sov. J. Numer. Anal. Math. Modellng, 6(3):223{242, 99. [2] S. V. Nepomnyaschh. Method of splttng nto subspaces for solvng ellptc boundary value problems n complex-form domans. Sov. J. Numer. Anal. Math. Modellng, 6(2), 99. [2] S. V. Nepomnyaschh. Optmal multlevel extenson operators. Report 95-3, TU hemntz, 995. [22] A. A. Oganesyan and L. A. Ruhovets. Varatonal derence methods for the soluton of ellptc equatons. Izdat. Aad. Nau Armanso SSR, Erevan, 979. (n Russan). [23] P. Oswald. Multlevel Fnte Element Approxmaton: Theory and Applcaton. Teubner, Stuttgart, 994. [24] L. N. S. V. M. Babch. On the boundness of the Drchlet ntegral. Dol. Aad. Nau SSSR, 6(4):64{67, 956. [25] J. Xu. Iteratve methods by space decomposton and subspace correcton: A unfyng approach. SIAM Revew, 34:58{63, 99. [26] H. Yserentant. On the mult-level splttng of nte element spaces. Numer. Math., 49(4):379{42, 986. [27] H. Yserentant. Two precondtoners based on the mult-level splttng of nte element spaces. Numer. Math., 58:63{84, 99. 2

Deriving the X-Z Identity from Auxiliary Space Method

Deriving the X-Z Identity from Auxiliary Space Method Dervng the X-Z Identty from Auxlary Space Method Long Chen Department of Mathematcs, Unversty of Calforna at Irvne, Irvne, CA 92697 chenlong@math.uc.edu 1 Iteratve Methods In ths paper we dscuss teratve

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

Robust Norm Equivalencies and Preconditioning

Robust Norm Equivalencies and Preconditioning Robust Norm Equvalences and Precondtonng Karl Scherer Insttut für Angewandte Mathematk, Unversty of Bonn, Wegelerstr. 6, 53115 Bonn, Germany Summary. In ths contrbuton we report on work done n contnuaton

More information

[7] R.S. Varga, Matrix Iterative Analysis, Prentice-Hall, Englewood Clis, New Jersey, (1962).

[7] R.S. Varga, Matrix Iterative Analysis, Prentice-Hall, Englewood Clis, New Jersey, (1962). [7] R.S. Varga, Matrx Iteratve Analyss, Prentce-Hall, Englewood ls, New Jersey, (962). [8] J. Zhang, Multgrd soluton of the convecton-duson equaton wth large Reynolds number, n Prelmnary Proceedngs of

More information

Relaxation Methods for Iterative Solution to Linear Systems of Equations

Relaxation Methods for Iterative Solution to Linear Systems of Equations Relaxaton Methods for Iteratve Soluton to Lnear Systems of Equatons Gerald Recktenwald Portland State Unversty Mechancal Engneerng Department gerry@pdx.edu Overvew Techncal topcs Basc Concepts Statonary

More information

1 GSW Iterative Techniques for y = Ax

1 GSW Iterative Techniques for y = Ax 1 for y = A I m gong to cheat here. here are a lot of teratve technques that can be used to solve the general case of a set of smultaneous equatons (wrtten n the matr form as y = A), but ths chapter sn

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

A New Refinement of Jacobi Method for Solution of Linear System Equations AX=b

A New Refinement of Jacobi Method for Solution of Linear System Equations AX=b Int J Contemp Math Scences, Vol 3, 28, no 17, 819-827 A New Refnement of Jacob Method for Soluton of Lnear System Equatons AX=b F Naem Dafchah Department of Mathematcs, Faculty of Scences Unversty of Gulan,

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

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

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

Hongyi Miao, College of Science, Nanjing Forestry University, Nanjing ,China. (Received 20 June 2013, accepted 11 March 2014) I)ϕ (k)

Hongyi Miao, College of Science, Nanjing Forestry University, Nanjing ,China. (Received 20 June 2013, accepted 11 March 2014) I)ϕ (k) ISSN 1749-3889 (prnt), 1749-3897 (onlne) Internatonal Journal of Nonlnear Scence Vol.17(2014) No.2,pp.188-192 Modfed Block Jacob-Davdson Method for Solvng Large Sparse Egenproblems Hongy Mao, College of

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

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

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

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

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

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

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

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

The Finite Element Method: A Short Introduction

The Finite Element Method: A Short Introduction Te Fnte Element Metod: A Sort ntroducton Wat s FEM? Te Fnte Element Metod (FEM) ntroduced by engneers n late 50 s and 60 s s a numercal tecnque for solvng problems wc are descrbed by Ordnary Dfferental

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

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

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

Singular Value Decomposition: Theory and Applications

Singular Value Decomposition: Theory and Applications Sngular Value Decomposton: Theory and Applcatons Danel Khashab Sprng 2015 Last Update: March 2, 2015 1 Introducton A = UDV where columns of U and V are orthonormal and matrx D s dagonal wth postve real

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

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

4DVAR, according to the name, is a four-dimensional variational method.

4DVAR, according to the name, is a four-dimensional variational method. 4D-Varatonal Data Assmlaton (4D-Var) 4DVAR, accordng to the name, s a four-dmensonal varatonal method. 4D-Var s actually a drect generalzaton of 3D-Var to handle observatons that are dstrbuted n tme. The

More information

Nonlinear Overlapping Domain Decomposition Methods

Nonlinear Overlapping Domain Decomposition Methods Nonlnear Overlappng Doman Decomposton Methods Xao-Chuan Ca 1 Department of Computer Scence, Unversty of Colorado at Boulder, Boulder, CO 80309, ca@cs.colorado.edu Summary. We dscuss some overlappng doman

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

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

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

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

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

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

Overlapping additive and multiplicative Schwarz iterations for H -matrices

Overlapping additive and multiplicative Schwarz iterations for H -matrices Lnear Algebra and ts Applcatons 393 (2004) 91 105 www.elsever.com/locate/laa Overlappng addtve and multplcatve Schwarz teratons for H -matrces Rafael Bru a,1, Francsco Pedroche a, Danel B. Szyld b,,2 a

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

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

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

Journal of Universal Computer Science, vol. 1, no. 7 (1995), submitted: 15/12/94, accepted: 26/6/95, appeared: 28/7/95 Springer Pub. Co.

Journal of Universal Computer Science, vol. 1, no. 7 (1995), submitted: 15/12/94, accepted: 26/6/95, appeared: 28/7/95 Springer Pub. Co. Journal of Unversal Computer Scence, vol. 1, no. 7 (1995), 469-483 submtted: 15/12/94, accepted: 26/6/95, appeared: 28/7/95 Sprnger Pub. Co. Round-o error propagaton n the soluton of the heat equaton by

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

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

New Method for Solving Poisson Equation. on Irregular Domains

New Method for Solving Poisson Equation. on Irregular Domains Appled Mathematcal Scences Vol. 6 01 no. 8 369 380 New Method for Solvng Posson Equaton on Irregular Domans J. Izadan and N. Karamooz Department of Mathematcs Facult of Scences Mashhad BranchIslamc Azad

More information

Lecture 10 Support Vector Machines II

Lecture 10 Support Vector Machines II Lecture 10 Support Vector Machnes II 22 February 2016 Taylor B. Arnold Yale Statstcs STAT 365/665 1/28 Notes: Problem 3 s posted and due ths upcomng Frday There was an early bug n the fake-test data; fxed

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

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

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

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

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

Foundations of Arithmetic

Foundations of Arithmetic Foundatons of Arthmetc Notaton We shall denote the sum and product of numbers n the usual notaton as a 2 + a 2 + a 3 + + a = a, a 1 a 2 a 3 a = a The notaton a b means a dvdes b,.e. ac = b where c s an

More information

EEE 241: Linear Systems

EEE 241: Linear Systems EEE : Lnear Systems Summary #: Backpropagaton BACKPROPAGATION The perceptron rule as well as the Wdrow Hoff learnng were desgned to tran sngle layer networks. They suffer from the same dsadvantage: they

More information

MATH 829: Introduction to Data Mining and Analysis The EM algorithm (part 2)

MATH 829: Introduction to Data Mining and Analysis The EM algorithm (part 2) 1/16 MATH 829: Introducton to Data Mnng and Analyss The EM algorthm (part 2) Domnque Gullot Departments of Mathematcal Scences Unversty of Delaware Aprl 20, 2016 Recall 2/16 We are gven ndependent observatons

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

2.29 Numerical Fluid Mechanics Fall 2011 Lecture 12

2.29 Numerical Fluid Mechanics Fall 2011 Lecture 12 REVIEW Lecture 11: 2.29 Numercal Flud Mechancs Fall 2011 Lecture 12 End of (Lnear) Algebrac Systems Gradent Methods Krylov Subspace Methods Precondtonng of Ax=b FINITE DIFFERENCES Classfcaton of Partal

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

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

12 MATH 101A: ALGEBRA I, PART C: MULTILINEAR ALGEBRA. 4. Tensor product

12 MATH 101A: ALGEBRA I, PART C: MULTILINEAR ALGEBRA. 4. Tensor product 12 MATH 101A: ALGEBRA I, PART C: MULTILINEAR ALGEBRA Here s an outlne of what I dd: (1) categorcal defnton (2) constructon (3) lst of basc propertes (4) dstrbutve property (5) rght exactness (6) localzaton

More information

Additional Codes using Finite Difference Method. 1 HJB Equation for Consumption-Saving Problem Without Uncertainty

Additional Codes using Finite Difference Method. 1 HJB Equation for Consumption-Saving Problem Without Uncertainty Addtonal Codes usng Fnte Dfference Method Benamn Moll 1 HJB Equaton for Consumpton-Savng Problem Wthout Uncertanty Before consderng the case wth stochastc ncome n http://www.prnceton.edu/~moll/ HACTproect/HACT_Numercal_Appendx.pdf,

More information

Stanford University CS359G: Graph Partitioning and Expanders Handout 4 Luca Trevisan January 13, 2011

Stanford University CS359G: Graph Partitioning and Expanders Handout 4 Luca Trevisan January 13, 2011 Stanford Unversty CS359G: Graph Parttonng and Expanders Handout 4 Luca Trevsan January 3, 0 Lecture 4 In whch we prove the dffcult drecton of Cheeger s nequalty. As n the past lectures, consder an undrected

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

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

Winter 2008 CS567 Stochastic Linear/Integer Programming Guest Lecturer: Xu, Huan

Winter 2008 CS567 Stochastic Linear/Integer Programming Guest Lecturer: Xu, Huan Wnter 2008 CS567 Stochastc Lnear/Integer Programmng Guest Lecturer: Xu, Huan Class 2: More Modelng Examples 1 Capacty Expanson Capacty expanson models optmal choces of the tmng and levels of nvestments

More information

Lecture 20: Lift and Project, SDP Duality. Today we will study the Lift and Project method. Then we will prove the SDP duality theorem.

Lecture 20: Lift and Project, SDP Duality. Today we will study the Lift and Project method. Then we will prove the SDP duality theorem. prnceton u. sp 02 cos 598B: algorthms and complexty Lecture 20: Lft and Project, SDP Dualty Lecturer: Sanjeev Arora Scrbe:Yury Makarychev Today we wll study the Lft and Project method. Then we wll prove

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

On the Interval Zoro Symmetric Single-step Procedure for Simultaneous Finding of Polynomial Zeros

On the Interval Zoro Symmetric Single-step Procedure for Simultaneous Finding of Polynomial Zeros Appled Mathematcal Scences, Vol. 5, 2011, no. 75, 3693-3706 On the Interval Zoro Symmetrc Sngle-step Procedure for Smultaneous Fndng of Polynomal Zeros S. F. M. Rusl, M. Mons, M. A. Hassan and W. J. Leong

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

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

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

partial dierential equations by iteratively solving subproblems dened on and localized treatment of complex and irregular geometries, singularities

partial dierential equations by iteratively solving subproblems dened on and localized treatment of complex and irregular geometries, singularities Acta Numerca (1994), pp. 61{143 Doman decomposton algorthms Tony F. Chan Department of Mathematcs, Unversty of Calforna at Los Angeles, Los Angeles, CA 90024, USA Emal: chan@math.ucla.edu. Tarek P. Mathew

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

Additive Schwarz Method for DG Discretization of Anisotropic Elliptic Problems

Additive Schwarz Method for DG Discretization of Anisotropic Elliptic Problems Addtve Schwarz Method for DG Dscretzaton of Ansotropc Ellptc Problems Maksymlan Dryja 1, Potr Krzyżanowsk 1, and Marcus Sarks 2 1 Introducton In the paper we consder a second order ellptc problem wth dscontnuous

More information

Grover s Algorithm + Quantum Zeno Effect + Vaidman

Grover s Algorithm + Quantum Zeno Effect + Vaidman Grover s Algorthm + Quantum Zeno Effect + Vadman CS 294-2 Bomb 10/12/04 Fall 2004 Lecture 11 Grover s algorthm Recall that Grover s algorthm for searchng over a space of sze wors as follows: consder the

More information

VARIATION OF CONSTANT SUM CONSTRAINT FOR INTEGER MODEL WITH NON UNIFORM VARIABLES

VARIATION OF CONSTANT SUM CONSTRAINT FOR INTEGER MODEL WITH NON UNIFORM VARIABLES VARIATION OF CONSTANT SUM CONSTRAINT FOR INTEGER MODEL WITH NON UNIFORM VARIABLES BÂRZĂ, Slvu Faculty of Mathematcs-Informatcs Spru Haret Unversty barza_slvu@yahoo.com Abstract Ths paper wants to contnue

More information

Chat eld, C. and A.J.Collins, Introduction to multivariate analysis. Chapman & Hall, 1980

Chat eld, C. and A.J.Collins, Introduction to multivariate analysis. Chapman & Hall, 1980 MT07: Multvarate Statstcal Methods Mke Tso: emal mke.tso@manchester.ac.uk Webpage for notes: http://www.maths.manchester.ac.uk/~mkt/new_teachng.htm. Introducton to multvarate data. Books Chat eld, C. and

More information

Preconditioning techniques in Chebyshev collocation method for elliptic equations

Preconditioning techniques in Chebyshev collocation method for elliptic equations Precondtonng technques n Chebyshev collocaton method for ellptc equatons Zh-We Fang Je Shen Ha-We Sun (n memory of late Professor Benyu Guo Abstract When one approxmates ellptc equatons by the spectral

More information

Randić Energy and Randić Estrada Index of a Graph

Randić Energy and Randić Estrada Index of a Graph EUROPEAN JOURNAL OF PURE AND APPLIED MATHEMATICS Vol. 5, No., 202, 88-96 ISSN 307-5543 www.ejpam.com SPECIAL ISSUE FOR THE INTERNATIONAL CONFERENCE ON APPLIED ANALYSIS AND ALGEBRA 29 JUNE -02JULY 20, ISTANBUL

More information

RESTRICTED ADDITIVE SCHWARZ METHOD WITH HARMONIC OVERLAP

RESTRICTED ADDITIVE SCHWARZ METHOD WITH HARMONIC OVERLAP RESTRICTED ADDITIVE SCHWARZ METHOD WITH HARMONIC OVERLAP XIAO-CHUAN CAI, MAKSYMILIAN DRYJA, AND MARCUS SARKIS Abstract. In ths paper, we ntroduce a new Schwarz precondtoner and wth a new coarse space.

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

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

CSCE 790S Background Results

CSCE 790S Background Results CSCE 790S Background Results Stephen A. Fenner September 8, 011 Abstract These results are background to the course CSCE 790S/CSCE 790B, Quantum Computaton and Informaton (Sprng 007 and Fall 011). Each

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

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

Chapter 12. Ordinary Differential Equation Boundary Value (BV) Problems

Chapter 12. Ordinary Differential Equation Boundary Value (BV) Problems Chapter. Ordnar Dfferental Equaton Boundar Value (BV) Problems In ths chapter we wll learn how to solve ODE boundar value problem. BV ODE s usuall gven wth x beng the ndependent space varable. p( x) q(

More information

Inductance Calculation for Conductors of Arbitrary Shape

Inductance Calculation for Conductors of Arbitrary Shape CRYO/02/028 Aprl 5, 2002 Inductance Calculaton for Conductors of Arbtrary Shape L. Bottura Dstrbuton: Internal Summary In ths note we descrbe a method for the numercal calculaton of nductances among conductors

More information

Approximate D-optimal designs of experiments on the convex hull of a finite set of information matrices

Approximate D-optimal designs of experiments on the convex hull of a finite set of information matrices Approxmate D-optmal desgns of experments on the convex hull of a fnte set of nformaton matrces Radoslav Harman, Mára Trnovská Department of Appled Mathematcs and Statstcs Faculty of Mathematcs, Physcs

More information

General viscosity iterative method for a sequence of quasi-nonexpansive mappings

General viscosity iterative method for a sequence of quasi-nonexpansive mappings Avalable onlne at www.tjnsa.com J. Nonlnear Sc. Appl. 9 (2016), 5672 5682 Research Artcle General vscosty teratve method for a sequence of quas-nonexpansve mappngs Cuje Zhang, Ynan Wang College of Scence,

More information

A MODIFIED METHOD FOR SOLVING SYSTEM OF NONLINEAR EQUATIONS

A MODIFIED METHOD FOR SOLVING SYSTEM OF NONLINEAR EQUATIONS Journal of Mathematcs and Statstcs 9 (1): 4-8, 1 ISSN 1549-644 1 Scence Publcatons do:1.844/jmssp.1.4.8 Publshed Onlne 9 (1) 1 (http://www.thescpub.com/jmss.toc) A MODIFIED METHOD FOR SOLVING SYSTEM OF

More information

LOW BIAS INTEGRATED PATH ESTIMATORS. James M. Calvin

LOW BIAS INTEGRATED PATH ESTIMATORS. James M. Calvin Proceedngs of the 007 Wnter Smulaton Conference S G Henderson, B Bller, M-H Hseh, J Shortle, J D Tew, and R R Barton, eds LOW BIAS INTEGRATED PATH ESTIMATORS James M Calvn Department of Computer Scence

More information

An Explicit Construction of an Expander Family (Margulis-Gaber-Galil)

An Explicit Construction of an Expander Family (Margulis-Gaber-Galil) An Explct Constructon of an Expander Famly Marguls-Gaber-Gall) Orr Paradse July 4th 08 Prepared for a Theorst's Toolkt, a course taught by Irt Dnur at the Wezmann Insttute of Scence. The purpose of these

More information

Module 3: Element Properties Lecture 1: Natural Coordinates

Module 3: Element Properties Lecture 1: Natural Coordinates Module 3: Element Propertes Lecture : Natural Coordnates Natural coordnate system s bascally a local coordnate system whch allows the specfcaton of a pont wthn the element by a set of dmensonless numbers

More information

Matrix Approximation via Sampling, Subspace Embedding. 1 Solving Linear Systems Using SVD

Matrix Approximation via Sampling, Subspace Embedding. 1 Solving Linear Systems Using SVD Matrx Approxmaton va Samplng, Subspace Embeddng Lecturer: Anup Rao Scrbe: Rashth Sharma, Peng Zhang 0/01/016 1 Solvng Lnear Systems Usng SVD Two applcatons of SVD have been covered so far. Today we loo

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

Technische Universitat Chemnitz-Zwickau. Sonderforschungsbereich 393. Numerische Simulation auf massiv parallelen Rechnern.

Technische Universitat Chemnitz-Zwickau. Sonderforschungsbereich 393. Numerische Simulation auf massiv parallelen Rechnern. Technsche Unverstat Chemntz-Zwckau Sonderforschungsberech 393 Numersche Smulaton auf massv parallelen Rechnern Thomas Apel Interpolaton of non-smooth functons on ansotropc nte element meshes Preprnt SFB393/97-06

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

= = = (a) Use the MATLAB command rref to solve the system. (b) Let A be the coefficient matrix and B be the right-hand side of the system.

= = = (a) Use the MATLAB command rref to solve the system. (b) Let A be the coefficient matrix and B be the right-hand side of the system. Chapter Matlab Exercses Chapter Matlab Exercses. Consder the lnear system of Example n Secton.. x x x y z y y z (a) Use the MATLAB command rref to solve the system. (b) Let A be the coeffcent matrx and

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

On the Multicriteria Integer Network Flow Problem

On the Multicriteria Integer Network Flow Problem BULGARIAN ACADEMY OF SCIENCES CYBERNETICS AND INFORMATION TECHNOLOGIES Volume 5, No 2 Sofa 2005 On the Multcrtera Integer Network Flow Problem Vassl Vasslev, Marana Nkolova, Maryana Vassleva Insttute of

More information

FTCS Solution to the Heat Equation

FTCS Solution to the Heat Equation FTCS Soluton to the Heat Equaton ME 448/548 Notes Gerald Recktenwald Portland State Unversty Department of Mechancal Engneerng gerry@pdx.edu ME 448/548: FTCS Soluton to the Heat Equaton Overvew 1. Use

More information

Lecture 3. Ax x i a i. i i

Lecture 3. Ax x i a i. i i 18.409 The Behavor of Algorthms n Practce 2/14/2 Lecturer: Dan Spelman Lecture 3 Scrbe: Arvnd Sankar 1 Largest sngular value In order to bound the condton number, we need an upper bound on the largest

More information

An (almost) unbiased estimator for the S-Gini index

An (almost) unbiased estimator for the S-Gini index An (almost unbased estmator for the S-Gn ndex Thomas Demuynck February 25, 2009 Abstract Ths note provdes an unbased estmator for the absolute S-Gn and an almost unbased estmator for the relatve S-Gn for

More information