Preconditioners for higher order edge finite element discretizations of Maxwell s equations

Size: px
Start display at page:

Download "Preconditioners for higher order edge finite element discretizations of Maxwell s equations"

Transcription

1 Science in Cina Series A: Matematics Aug., 2008, Vol. 51, No. 8, mat.scicina.com Preconditioners for iger order edge finite element discretizations of Maxwell s equations ZHONG LiuQiang 1,2,SHUSi 1,2, SUN DuDu 3 & TAN Lin 4 1 Scool of Matematical and Computational Sciences, Xiangtan University, Xiangtan , Cina 2 Hunan Key Laboratory for Computation and Simulation in Science and Engineering, Xiangtan , Cina 3 Institute of Computational Matematics and Scientific/Engineering Computing, Academy of Matematics and Systems Science, Graduate University of Cinese Academy of Sciences, Cinese Academy Sciences, P.O. Box 2719, Beijing , Cina 4 Department of Mat-Pysics, Nanua University, Hengyang , Cina ( zonglq@xtu.edu.cn, susi@xtu.edu.cn, sundudu@lsec.cc.ac.cn, tanlinboy@yaoo.com.cn) Abstract In tis paper, we are concerned wit te fast solvers for iger order edge finite element discretizations of Maxwell s equations. We present te preconditioners for te first family and second family of iger order Nédélec element equations, respectively. By combining te stable decompositions of two kinds of edge finite element spaces wit te abstract teory of auxiliary space preconditioning, we prove tat te corresponding condition numbers of our preconditioners are uniformly bounded on quasi-uniform grids. We also present some numerical experiments to demonstrate te teoretical results. Keywords: preconditioner, iger order edge finite element, stable decomposition MSC(2000): 65F10, 65N22 1 Introduction Nédélec edge finite elements in [1, 2] are most popular coices for discretizations of Maxwell s equations, but te resulting discrete systems are usually large and iger ill-condition, ence constructing te fast algoritms for te corresponding numerical solutions is necessary for realistic computational electromagnetism. Some literature concerning te fast solvers of te discrete Maxwell s variational problems can be found in [3 9]. Exploiting available efficient MG metods on auxiliary mes for te same bilinear form leading efficient auxiliary mes preconditioners to unstructured problems was sown in [6]. Bot te teory of auxiliary space preconditioning and te preconditioners of te first family were constructed in [7]. Te preconditioners of te second family of Nédélec edge finite element equations were presented in [8]. However, te researces mentioned above deal wit te discrete systems resulting from te low-order Nédélec edge finite elements, wereas, te ig-order edge finite element metods are superior and necessary under certain conditions over te low-order ones, for example, it can reduce te Received Marc 7, 2008; accepted May 8, 2008 DOI: /s Corresponding autor Tis work was partially supported by te National Natural Science Foundation of Cina (Grant Nos , ), te National Key Basic Researc Program of Cina (973 Program) (Grant No. 2005CB321702), te Key Project of Cinese Ministry of Education and Scientific Researc Fund of Hunan Provincial Education Department (Grant Nos , 07A068)

2 1538 ZHONG LiuQiang et al. numerical dispersion error, moreover, te construction of ig order finite element metods is a very active area nowadays in computational electromagnetism. Te construction of te base functions of H(curl)-conforming spaces on tetraedral can be found in [10]. In tis paper, we are concerned wit te preconditioned conjugate gradient (PCG) metods for iger order edge finite element equations, te key to wic is ow to construct ig efficient preconditioners. We will design and analyze two kinds of preconditioners for two families of te iger order edge finite element equations, respectively. Te main idea of te construction of preconditioners is to recursively translate te computation of te preconditioners for te iger order edge finite element equations into te one for oter edge or H 1 -conforming finite element equations wic possess less degrees of freedom witout costing too muc computational efforts. For te preconditioner for k +1 ordernédélec element equations of first kind, by using a Jacobi smooting (or Gauss-Seild smooting), we recursively translate te construction of its preconditioner into te one of te k order Nédélec element equations of second kind. For te preconditioner for k order Nédélec element equations of second kind, by solving an H 1 - conforming k + 1 order Lagrange finite element equations, we can translate it into te one for k order Nédélec element equations of first kind. Tere ave existed many works about teeffectivesolversforteh 1 -conforming ig order Lagrange finite element equations, for example, see [11], ence, we essentially translate te computation of iger order edge finite element equations into te one of te linear edge element equations of first kind by tis recursive metod. Furtermore, by using te abstract teory in [7], we prove tat te above condition numbers are uniformly bounded on quasi-uniform meses. We also present some numerical experiments to demonstrate te teoretical results. Te rest of te paper is organized as follows. In te next section, we introduce two kinds of ig order edge finite element equations, and present te corresponding frame of constructing preconditioner. We construct te preconditioners for two kinds of ig order Nédélec element equations, and prove tat teir corresponding condition number is uniformly bounded in Sections 3 and 4, respectively. We also present some numerical experiments in Section 5. 2 Edge finite element equations and preconditioner Let Ω be a simply connected polyedron in R 3 wit boundary Γ and unit outward normal ν 1). We now define some Sobolev functional spaces (see [12]): H0 1 (Ω) = {q L 2 (Ω) q (L 2 (Ω)) 3,u=0onΓ}, (2.1) H 0 (curl;ω)={u (L 2 (Ω)) 3 u (L 2 (Ω)) 3, ν u = 0 on Γ}, (2.2) H 0 (div; Ω) = {u (L 2 (Ω)) 3 u L 2 (Ω), ν u =0onΓ}. (2.3) For all u H 0 (curl;ω) and u H 0 (div; Ω), we define te norms u H(curl;Ω) = ( u /2 0) u 2, u H(div;Ω) = ( u /2 0) u 2, were 0 denotes te norm in (L 2 (Ω)) 3 or L 2 (Ω). In tis paper, we consider te following variational problem: Find u H 0 (curl;ω) suc tat a(u, v) =(f, v), v H 0 (curl;ω), (2.4) 1) In tis paper, vectors are distinguised from scalars by te use of bold type.

3 Preconditioners for iger order edge finite element discretizations of Maxwell s equations 1539 were f (L 2 (Ω)) 3 is a given data and a(u, v) = [( u) ( v)+τu v] dx, (f, v) = f vdx, (2.5) Ω Ω wit te constant τ>0. Te bilinear form a(, ) induces te energy norm v 2 A = a(v, v), v H 0(curl;Ω). (2.6) Variational problem of te form (2.4) arises in many simulations of electromagnetic fields, for instance, it can describe te eddy current model, and it is also a core task in te time-domain simulation of electrimagnetic fields if implicit timestepping is employed (see [3, 13]). Let T be a quasi-uniform tetraedron mes of Ω (see Figures 1 and 2), were is te maximum diameter of te tetraedra in T. Figure 1 Structured grids Figure 2 Unstructured grids For eac k>0, define polynomial spaces P k = {polynomials of maximum total degree k in x 1,x 2,x 3 }, P k = {omogeneous polynomials of maximum total degree exactly k in x 1,x 2,x 3 }. Now, we present te following two families of edge finite element spaces (see [12 14]): (i) k order Nédélec element of first kind V k,1 = {u k,1 H 0 (curl;ω) u k,1 K R k for all K T }, (2.7) were R k =(P k 1 ) 3 {p ( P k ) 3 p(x) x =0}. (ii) k order Nédélec element of second kind u k,l V k,2 = {u k,2 H 0(curl;Ω) u k,2 K (P k ) 3 for all K T }. (2.8) We are interested in te following finite element equations of variational problem (2.4): Find V k,l (k 1,l=1, 2) suc tat a(u k,l, vk,l )=(f, vk,l ), Teir algebraic systems can be described as vk,l V k,l. (2.9) A k,l U k,l = F k,l. (2.10)

4 1540 ZHONG LiuQiang et al. Since A k,l is symmetric positive definite, we use PCG metods to solve algebraic systems (2.10). In tis paper, we will construct te preconditioners for te cases of ig order edge finite equations, and present some estimates of te corresponding condition numbers. For tis purpose, we need to introduce some auxiliary spaces and corresponding operators. Let V = V k,l wit inner product a(, ) given by (2.5). Let V 1,..., V J,J N, be Hilbert spaces endowed wit inner products ā j (, ),j =1,...,J. Te operators Āj : V j V j are isomorpisms induced by ā j(, ), namely ā j (ū j, v j )= Ājū j, v j, ū j, v j V j, ere we tag dual spaces by and use angle brackets for duality pairings. For eac V j, tere exist continuous transfer operators Π j : V j V. Ten we can construct te preconditioner for operator A k,l as follows: B = J Π j Bj Π j, (2.11) j=1 were B j : V j V j are given preconditioners for Āj, andπ j are adjoint operators of Π j. Te following teorem of an estimate for te spectral condition number of te preconditioner given by (2.11) was presented in [7]. Teorem 2.1. Assume tat tere exist constants c j, suc tat Π j ū j A c j ū j Āj, ū j V j, 1 j J, (2.12) and for u V,tereexistū j V j suc tat u = J j=1 Π jū j and ( J ) 1/2 ū j 2 Ā j c 0 u A, (2.13) j=1 ten for te preconditioner B given by (2.11), we ave te following estimate for te spectral condition number K(BA k,l ) max K( B j Ā j )c j J J c 2 j. (2.14) Te principal callenge confronted in te development of preconditioners by applying Teorem 2.1 is to construct some spaces and operators wic satisfy (2.12) and (2.13). In te following two sections, we present te corresponding spaces and operators for two kinds of Nédélec edge finite element spaces, respectively. 3 Preconditioner for edge element equations of second kind Let V = V k,2 and k order Lagrangian finite element space is j=1 S k = {p H 1 0 (Ω) p K P k for all K T }. We coose te following two auxiliary spaces and te corresponding transfer operator: (i) V 1 = V k,1 wit inner product ā 1 (, ) =a(, ) in te sense tat ā 1 (ū 1, v 1 ):= Ā1ū 1, v 1 = a(ū 1, v 1 ), ū 1, v 1 V 1,

5 Preconditioners for iger order edge finite element discretizations of Maxwell s equations 1541 wic concludes tat Ā1 = A k,1. Te corresponding transfer operator is Π 1 = Id. (ii) V 2 = S k+1 wit inner product ā 2 (ū 2, v 2 ):= Ā2ū 2, v 2 = τ( ū 2, v 2 ), ū 2, v 2 V 2. (3.1) Te corresponding transfer operator is Π 2 = grad. Ten by using (2.11), we obtain te auxiliary space preconditioner for A k,2 as follows: B k,2 = B k,1 + grad B 2 grad, (3.2) were B k,1 is te preconditioner of A k,1,and B 2 is te preconditioners of Ā2 given by (3.1). Especially, we adopt te new algebraic multigrid metods for te H 1 -conforming ig order Lagrange finite element equations as te preconditioner B 2, tis coice fulfils te following estimate (see [11]): K( B 2 Ā 2 ) C 1, (3.3) were te constant C 1 is independent of te mes parameters. It is easy to prove tat te above transfer operators satisfy te conditions (2.12). In fact, using te definitions of inner products and transfer operators in spaces V l (l =1, 2), we ave Π 1 v 1 A = v 1 A = v 1 Ā1, v 1 V 1, (3.4) Π 2 v 2 2 A = v 2 2 A = τ v = v 2 2 Ā 2, v 2 V 2, (3.5) namely, te conditions (2.12) of Teorem 2.1 old wit te constants c 1 = c 2 =1. In order to give te corresponding decomposition wic satisfies (2.13), we need to present some preliminary materials. We first introduce te following k order divergence conforming finite elements of first kind (R-T elements): W k,1 = {v k,1 H 0 (div; Ω) v k,1 K D k for all K T }, (3.6) were D k =(P k 1 ) 3 P k 1 x. Te Sobolev spaces (2.1) (2.3) and te corresponding finite element spaces possess te exceptional exact sequence properties (see [12, 14]): H 0 (curl0;ω):={u H 0 (curl;ω): u = 0} = H0 1 (Ω), (3.7) V k,l (curl0) :={vk,l V k,l : vk,l = 0} = Sk+l 1, l =1, 2, (3.8) W k,1 (div0) :={wk,1 W k,1 : w k,1 =0} = V k,1 = V k,2. (3.9) We suppose tat te bases B(k, 1) of V k,1 are L 2 stable in te sense tat 2) v = v b, v b span{b}, v b 2 0 v 2 0, vk,1 V k,1. (3.10) b B(k,1) b B(k,1) Assuming tat u as te necessary smootness, we can define two kinds of interpolants: Π k,1,curl and Π k,1,div, suc tat Πk,1,curl u V k,1 and Π k,1,div u W k,1 (for more details, refer to [1, 2, 12]). Especially, te interpolation Π k,1,curl is not defined for a general function in H 0 (curl; Ω). Here let us quote a sligtly simplified version (see Lemma 5.38 of [12]). 2) Trougout tis paper, a b is abbreviated to a Cb wit a mes-size being independent, generic constant C>0. Finally, a b is abbreviated to a b a.

6 1542 ZHONG LiuQiang et al. Lemma 3.1. Suppose tat tere are constants δ>0 and p>2 suc tat u (H 1/2+δ (K)) 3 and u (L p (K)) 3 for eac K in T.TenΠ k,1,curlu is well-defined and bounded. In te following, we present te error estimate wic is known for te interpolation of Π k,1,curl (see Teorem 5.41 of [12] and Lemma 4.6 of [14], respectively). Lemma 3.2. If u (H 1/2+δ (K)) 3, 0 <δ 1/2 and u K D k, ten we ave (Id Π k,1,curl )u 0,K 1/2+δ K u (H 1/2+δ (K)) 3 + K u 0,K, (3.11) wit a constant only depending on te sape regularity of T. Lemma 3.3. Te interpolation operator Π k,1,curl is bounded on {v (H0 1 (Ω)) 3, v W k,1 } (H1 0 (Ω))3 and satisfies 1 (Id Π k,1,curl )ψ 0 ψ (H 1 (Ω)) 3, ψ (H1 0 (Ω)) 3, ψ W k,1, (3.12) wit a constant only depending on te sape regularity of T. Furtermore, all above operators possess te following commuting diagram property (see [12]): curl Π k,1,curl =Πk,1,div curl. (3.13) We may apply te quasi-interpolation operators for Lagrangian finite element space introduced in [15] to te components of vector fields separately. Tis gives rise to te projectors Q :(H0 1 (Ω)) 3 (S 1)3, wic inerits te continuity Q Ψ (H 1 (Ω)) 3 Ψ (H 1 (Ω)) 3, Ψ (H1 0 (Ω)) 3, (3.14) and satisfies te local projection error esitmate 1 (Id Q )Ψ 0 Ψ (H1 (Ω)) 3, Ψ (H1 0 (Ω))3. (3.15) Now, we present te stable decomposition of V k,2. Lemma 3.4. For any u k,2 V k,2, tere are uk,1 V k,1 and p S k+1 suc tat and u k,2 = u k,1 + p, (3.16) ( u k,1 2 A + p 2 A) 1/2 c 0 u k,2 A, (3.17) were te constant c 0 only depends on Ω and te sape regularity of T. Proof. For any u k,2 V k,2, we can interpolate uk,2 by Lemma 3.1. Tus, using (3.13), we ave In view of (3.9), we ave Π k,1,curl uk,2 =Π k,1,div uk,2. (3.18) u k,2 W k,1. (3.19)

7 Preconditioners for iger order edge finite element discretizations of Maxwell s equations 1543 Making use of (3.19) and noting tat Π k,1,div W k,1 = Id in (3.18), we get Π k,1,curl uk,2 = u k,2, namely Note tat u k,2 tat (u k,2 Πk,1,curl uk,2 V k,2 Πk,1,curl uk,2 )=0. (3.20), ten by (3.8) and (3.20), tere exists p S k+1, suc u k,2 = u k,1 + p, (3.21) were u k,1 =Πk,1,curl uk,2, wic completes te proof of (3.16). Using (3.21), (3.11) wit δ =1/2, and te inverse estimate, we obtain p 0,K = u k,2 Πk,1,curl uk,2 0,K u k,2 (H 1 (K)) 3 uk,2 0,K. Squaring and summing over all te elements, we get p 2 0 = p 2 0,K u k,2 2 0,K = uk, (3.22) K T K T In view of (2.6) and (3.22), we find p 2 A = τ p 2 0 τ u k,2 2 0 u k,2 2 A. (3.23) Making use of (3.21), triangular inequality and (3.22), we ave A direct manipulation of (3.21) gives tat u k,1 0 u k,2 0 + p 0 u k, (3.24) u k,1 0 = u k,2 0. (3.25) A combination of (3.23), (3.24) and (3.25) concludes (3.17). As a direct consequence of Teorem 2.1, (3.4), (3.5) and Lemma 3.4, we ave Teorem 3.5. For B k,2 is given by (3.2), and B 2 satisfies te condition (3.3), we ave K(B k,2 Ak,2 ) K(Bk,1 Ak,1 wit a constant only depending on te constants c 0 and C 1. ), (3.26) 4 Preconditioner for edge element equations of first kind In tis case, we take V = V k+1,1 and coose anoter two auxiliary spaces and te corresponding transfer operator as follows. (i) V 1 = V k+1,1 wit inner product ā 1 (, ) wic is defined by ā 1 (ū 1, ū 1 ):= Ā 1 ū 1, v 1 = a(v b, v b ), were ū 1 = v b, v b span{b}. Te transfer operator is Π 1 = Id.

8 1544 ZHONG LiuQiang et al. (ii) V 2 = V k,2 wit inner product ā 1 (, ) =a(, ) in te sense tat ā 1 (ū 1, v 1 ):= Ā2ū 2, v 2 = a(ū 1, v 1 ), ū 1, v 1 V 1, wic concludes tat Ā2 = A k,2. Te transfer operator is Π 2 = Id. Making use of (2.11), te auxiliary space preconditioner for A k+1,1 reads were B k,2 is te preconditioner of A k,2 Noting tat Ā1 denotes te diagonal matrix of A k+1,1 B k+1,1 = B 1 + B k,2, (4.1), B1 is te preconditioners of Ā1., in te practical application, we will. Obviously, tis special take B 1 as te Jacobi (or Gauss-Seidel) smooting operator for A k+1,1 coose satisfies K( B 1 Ā 1 ) C 1, (4.2) were te constant C 1 is independent of te mes parameters. First, we prove tat te above transfer operators satisfy te condition (2.12). Due to te definitions of inner product and transfer operator in space V 1, for any given ū 1 = α bb V 1, were α b R, weave Π 1 ū 1 2 A = ū 1 2 A = M K T α b b 2 A = M α b b K T j=1 2 A,K α b 2 b 2 A,K = M ū 1 2 Ā 1, (4.3) were te constant M bounds te number of basis functions wose support overlaps wit a single element K. For any given ū 2 V 2, it is easy to obain Π 2 ū 2 A = ū 2 A = ū 2 Ā2. (4.4) Combining (4.3) wit (4.4), we conclude tat (2.12) olds wit te constants c 1 = M and c 2 =1. In te sequel, we present anoter lemma wic is devoted to a decomposition of V k+1,1. Here, we ave to use anoter metod wic is different from te proof of Lemma 3.4, since (Id Π k,2,curl )uk+1,1 does not belong to te space V k+1,1 (curl 0). In fact, it is te igfrequency contribution of u k+1,1. Lemma 4.1. For any u k+1,1 u k,2 V k,2, suc tat V k+1,1 u k+1,1 =,tereexist v b V k+1,1, v b Span{b}, v b + u k,2, (4.5) and ( ) 1/2 v b 2 A + u k,2 2 A c 0 u k+1,1 A, (4.6)

9 Preconditioners for iger order edge finite element discretizations of Maxwell s equations 1545 were te constant c 0 only depends on Ω and te sape regularity of T. Proof. For any given u k+1,1 V k+1,1, using te continuous Helmoltz decomposition, tere exist Ψ (H0 1(Ω))3,p H0 1 (Ω) suc tat and Ψ (H1 (Ω)) 3 u k+1,1 = Ψ + p, (4.7) uk+1,1 0, p H1 (Ω) u k+1,1 H(curl;Ω), (4.8) wit constants only depending on Ω. Taking te curl of bot sides of (4.7) and using (3.9), we get Ψ = u k+1,1 W k+1,1. OwingtoLemma3.1,Π k+1,1,curlψ is well defined. Furtermore, te commuting diagram property (3.13) implies Π k+1,1,curl Ψ =Πk+1,1,div Tis confirms tat te tird term in te splitting Ψ = Ψ (Id Πk+1,1,curl )Ψ =0. Ψ =Π k+1,1,curl (Id Q )Ψ +Π k+1,1,curl Q Ψ +(Id Π k+1,1,curl )Ψ (4.9) actually belongs to te kernel of curl. By (3.7), ten tere exists q H0 1 (Ω) suc tat Note tat Q Ψ (S 1)3 V k+1,1,wicleadsto Substituting (4.9), (4.10) and (4.11) into (4.7), we ave u k+1,1 (Id Π k+1,1,curl )Ψ = q. (4.10) Π k+1,1,curl Q Ψ = Q Ψ. (4.11) =Π k+1,1,curl (Id Q )Ψ + Q Ψ + (q + p). (4.12) Since u k+1,1, Π k+1,1,curl (Id Q )Ψ,Q Ψ V k+1,1,weobtain (q + p) V k+1,1 (4.12), ten observing (3.8), tere exists q S k+1, suc tat by using q = (q + p). (4.13) Let ũ k+1,1 =Π k+1,1,curl (Id Q )Ψ = v b, v b Span{b}, (4.14) u k,2 = Q Ψ + q. (4.15) It is easy to obtain u k,2 V k,2 by noting tat Q Ψ (S 1)3 V k,2 and q S k+1. Substituting (4.13), (4.14) and (4.15) into (4.12), we conclude V k,2 u k+1,1 = v b + u k,2, (4.16)

10 1546 ZHONG LiuQiang et al. wic completes te proof of (4.5). Using (4.14), triangular inequality, Lemma 3.3, (3.15) and (4.8), we ave wic leads to 1 ũ k+1,1 0 = 1 Π k+1,1,curl (Id Q )Ψ 0 1 (Id Π k+1,1,curl )(Id Q )Ψ (Id Q )Ψ 0 (Id Q )Ψ (H1 (Ω)) 3 + Ψ (H 1 (Ω)) 3 Ψ (H 1 (Ω)) 3 uk+1,1 0, ũ k+1,1 0 u k+1,1 0. (4.17) It follows readily from inverse estimate and (3.10) tat v b 2 A = ( v b τ v b 2 0) Using inverse estimate again yields ũ k+1,1 2 A b B(curl) ( 1 v b τ v b 2 0) ( 2 + τ) ũ k+1, (4.18) = ũk+1,1 By means of (4.17) and inverse estimate, we get ( 2 + τ ) ũ k+1, τ ũk+1,1 2 0 ( 2 + τ ) ũ k+1, (4.19) 2 0 ( 2 + τ ) 2 u k+1,1 2 0 u k+1, τ u k+1,1 2 0 = u k+1,1 2 A. (4.20) In view of (4.16), triangular inequality (4.18), (4.19) and (4.20), we ave v b 2 A + u k,2 2 A v b 2 A + ( u k+1,1 A + ũ k+1,1 ) 2 A ( 2 + τ ) ũ k+1,1 u k+1,1 2 A, uk+1,1 2 A wic completes te proof of (4.6). As a direct consequence of Teorem 2.1, (4.3), (4.4) and Lemma 4.1, we ave Teorem 4.2. For B k+1,1 is given by (4.1), and B 1 satisfies te condition (4.2), we ave K(B k+1,1 A k+1,1 ) K(B k,2 Ak,2 ), (4.21) wit a constant only depending on te constants c 0, C 1 and M. Combining Teorems 4.2 and 3.5, by using a Jacobi (or Guass-Seild) smooting, we can translate te construction of preconditioner for k + 1orderNédélec element equations of first kind into te one of te k order Nédélec element equations of second kind. Furtermore, by

11 Preconditioners for iger order edge finite element discretizations of Maxwell s equations 1547 solving an H 1 -conforming k + 1 order Lagrange finite element equations, we can translate te preconditioner for k order Nédélec element equations of second kind into te one for k order Nédélec element equations of first kind. Since Hiptmair and Xu [7] ave constructed an efficient preconditioner B 1,1 for A 1,1, we can prove tat te spectral condition numbers K(B k,l Ak,l )(k>1,l =1, 2) are uniformly bounded and independent of mes size and te parameter τ by tis recursive form. In te next section, we will give some numerical experiments to verify te correctness of teories and sow te efficiency of te preconditioners. 5 Numerical experiments For variational problem (2.9), we construct two examples as follows: Example 5.1. Te computational domain is Ω = [0, 1] [0, 1] [0, 1] and te corresponding structured grids can be seen in Figure 1. For te convenience of computing te exact errors, we construct an exact solution u =(u 1,u 2,u 3 )as u 1 = xyz(x 1)(y 1)(z 1), u 2 =sin(πx)sin(πy)sin(πz), u 3 =(1 e x )(1 e x 1 )(1 e y )(1 e y 1 )(1 e z )(1 e z 1 ). Example 5.2. Te computational domain is te speres of radius 1, and te corresponding unstructured grids can be seen in Figure 2, te exact solution u =(u 1,u 2,u 3 )is u 1 = x 2 + y 2 + z 2 1, u 2 = x 2 + y 2 + z 2 1, u 3 = x 2 + y 2 + z 2 1. In te following, we will keep track of te number of PCG-iterations required to solve te discrete variational problems (2.9). A relative reduction of te Euklidean norm of te residual vector by a factor of 10 6 wasusedastermination criterion. Te numerical results for Example 5.1 in V 2,1 and V 2,2 are reported in Tables 1 and 2, respectively. Te numerical results for Example 5.2 in V 2,1 and V 2,2 are also presented in Tables 3 and 4, respectively. Table 1 No. of PCG-iterations for Example 5.1 in V 2,1 Level Cells τ Table 2 No. of PCG-iterations for Example 5.2 in V 2,1 τ Level Cells

12 1548 ZHONG LiuQiang et al. Table 3 No. of PCG-iterations for Example 5.1 in V 2,2 τ Level Cells Table 4 No. of PCG-iterations for Example 5.2 in V 2,2 τ Level Cells By te observation of te numerical results, we know tat te condition numbers do ardly deteriorate on successively finite structured grids and unstructured grids, and tey are almost independent of te parameter τ. Acknowledgements Te autors wis to tank Xu J, Department of Matematics, Pennsylvania State University, USA, for providing many constructive suggestions. References 1 Nédélec J C. Mixed finite elements in R 3. Numer Mat, 35: (1980) 2 Nédélec J C. A new family of mixed finite elements in R 3. Numer Mat, 50: (1986) 3 Hiptmair R. Multigrid metod for Maxwell s equations. SIAM J Numer Anal, 36: (1998) 4 Cen Z, Wang L, Zeng W. An adaptive multilevel metod for time-armonic Maxwell equations wit singularities. SIAM J Sci Comput, 29: (2007) 5 Arnold D, Falk R, Winter R. Multigrid in H(div) and H(curl). Numer Mat, 85: (2000) 6 Hiptmair R, Widmer G, Zou J. Auxiliary space preconditioning in H 0 (curl;ω). Numer Mat, 103(3): (2006) 7 Hiptmair R, Xu J. Nodal auxiliary space preconditioning in H(curl) andh(div) spaces. SIAM J Numer Anal, 45(6): (2007) 8 Zong L, Tan L, Wang J, Su S. A fast algoritm for a second family of Nédélec edge finite elements equations (in Cinese). Mat Numer Sin, (in press) 9 Tan L, Zong L, Su S. Fast iterative metod for solving first family quadratic edge finite element equations (in Cinese). Natur Sci J Xiangtan Univ, 30(1): (2008) 10 Ainswort M, Coyle J. Hierarcic finite element bases on unstructured tetraedral meses. Int J Num Met Eng, 58: (2003) 11 Su S, Sun D, Xu J. An algebraic multigrid metod for iger-order finite element discretizations, Computing, 77(4): (2006) 12 Monk P. Finite element metods for Maxwell s equations. Numerical Matematics and Scientific Computation. Oxford: Oxford University Press, Bossavit A. Computational Electromagnetizm. Variational Formulation, Complementarity, Edge Elments. Vol. 2 of Electromagnetizm Series. San Diego: Academic Press, Hiptmair R. Finite elements in computational electromagnetism. Acta Numerical, 11: (2002) 15 Scott L R, Zang S. Finite element interpolation of nonsmoot functions satisfying boundary conditions. Mat Comp, 54: (1990)

FINITE DIFFERENCE METHODS

FINITE DIFFERENCE METHODS FINITE DIFFERENCE METHODS LONG CHEN Te best known metods, finite difference, consists of replacing eac derivative by a difference quotient in te classic formulation. It is simple to code and economic to

More information

OPTIMAL DISCONTINUOUS GALERKIN METHODS FOR THE ACOUSTIC WAVE EQUATION IN HIGHER DIMENSIONS

OPTIMAL DISCONTINUOUS GALERKIN METHODS FOR THE ACOUSTIC WAVE EQUATION IN HIGHER DIMENSIONS OPTIMAL DISCONTINUOUS GALERKIN METHODS FOR THE ACOUSTIC WAVE EQUATION IN HIGHER DIMENSIONS ERIC T. CHUNG AND BJÖRN ENGQUIST Abstract. In tis paper, we developed and analyzed a new class of discontinuous

More information

In other words the graph of the polynomial should pass through the points

In other words the graph of the polynomial should pass through the points Capter 3 Interpolation Interpolation is te problem of fitting a smoot curve troug a given set of points, generally as te grap of a function. It is useful at least in data analysis (interpolation is a form

More information

Projective Geometry. Projective Geometry

Projective Geometry. Projective Geometry Euclidean versus Euclidean geometry describes sapes as tey are Properties of objects tat are uncanged by rigid motions» Lengts» Angles» Parallelism Projective geometry describes objects as tey appear Lengts,

More information

Lecture 10: What is a Function, definition, piecewise defined functions, difference quotient, domain of a function

Lecture 10: What is a Function, definition, piecewise defined functions, difference quotient, domain of a function Lecture 10: Wat is a Function, definition, piecewise defined functions, difference quotient, domain of a function A function arises wen one quantity depends on anoter. Many everyday relationsips between

More information

Geometric Stratification of Accounting Data

Geometric Stratification of Accounting Data Stratification of Accounting Data Patricia Gunning * Jane Mary Horgan ** William Yancey *** Abstract: We suggest a new procedure for defining te boundaries of te strata in igly skewed populations, usual

More information

A New Cement to Glue Nonconforming Grids with Robin Interface Conditions: The Finite Element Case

A New Cement to Glue Nonconforming Grids with Robin Interface Conditions: The Finite Element Case A New Cement to Glue Nonconforming Grids wit Robin Interface Conditions: Te Finite Element Case Martin J. Gander, Caroline Japet 2, Yvon Maday 3, and Frédéric Nataf 4 McGill University, Dept. of Matematics

More information

Verifying Numerical Convergence Rates

Verifying Numerical Convergence Rates 1 Order of accuracy Verifying Numerical Convergence Rates We consider a numerical approximation of an exact value u. Te approximation depends on a small parameter, suc as te grid size or time step, and

More information

Research on the Anti-perspective Correction Algorithm of QR Barcode

Research on the Anti-perspective Correction Algorithm of QR Barcode Researc on te Anti-perspective Correction Algoritm of QR Barcode Jianua Li, Yi-Wen Wang, YiJun Wang,Yi Cen, Guoceng Wang Key Laboratory of Electronic Tin Films and Integrated Devices University of Electronic

More information

ACT Math Facts & Formulas

ACT Math Facts & Formulas Numbers, Sequences, Factors Integers:..., -3, -2, -1, 0, 1, 2, 3,... Rationals: fractions, tat is, anyting expressable as a ratio of integers Reals: integers plus rationals plus special numbers suc as

More information

M(0) = 1 M(1) = 2 M(h) = M(h 1) + M(h 2) + 1 (h > 1)

M(0) = 1 M(1) = 2 M(h) = M(h 1) + M(h 2) + 1 (h > 1) Insertion and Deletion in VL Trees Submitted in Partial Fulfillment of te Requirements for Dr. Eric Kaltofen s 66621: nalysis of lgoritms by Robert McCloskey December 14, 1984 1 ackground ccording to Knut

More information

Computer Science and Engineering, UCSD October 7, 1999 Goldreic-Levin Teorem Autor: Bellare Te Goldreic-Levin Teorem 1 Te problem We æx a an integer n for te lengt of te strings involved. If a is an n-bit

More information

SAT Subject Math Level 1 Facts & Formulas

SAT Subject Math Level 1 Facts & Formulas Numbers, Sequences, Factors Integers:..., -3, -2, -1, 0, 1, 2, 3,... Reals: integers plus fractions, decimals, and irrationals ( 2, 3, π, etc.) Order Of Operations: Aritmetic Sequences: PEMDAS (Parenteses

More information

New Vocabulary volume

New Vocabulary volume -. Plan Objectives To find te volume of a prism To find te volume of a cylinder Examples Finding Volume of a Rectangular Prism Finding Volume of a Triangular Prism 3 Finding Volume of a Cylinder Finding

More information

Math 113 HW #5 Solutions

Math 113 HW #5 Solutions Mat 3 HW #5 Solutions. Exercise.5.6. Suppose f is continuous on [, 5] and te only solutions of te equation f(x) = 6 are x = and x =. If f() = 8, explain wy f(3) > 6. Answer: Suppose we ad tat f(3) 6. Ten

More information

1.6. Analyse Optimum Volume and Surface Area. Maximum Volume for a Given Surface Area. Example 1. Solution

1.6. Analyse Optimum Volume and Surface Area. Maximum Volume for a Given Surface Area. Example 1. Solution 1.6 Analyse Optimum Volume and Surface Area Estimation and oter informal metods of optimizing measures suc as surface area and volume often lead to reasonable solutions suc as te design of te tent in tis

More information

Unemployment insurance/severance payments and informality in developing countries

Unemployment insurance/severance payments and informality in developing countries Unemployment insurance/severance payments and informality in developing countries David Bardey y and Fernando Jaramillo z First version: September 2011. Tis version: November 2011. Abstract We analyze

More information

Determine the perimeter of a triangle using algebra Find the area of a triangle using the formula

Determine the perimeter of a triangle using algebra Find the area of a triangle using the formula Student Name: Date: Contact Person Name: Pone Number: Lesson 0 Perimeter, Area, and Similarity of Triangles Objectives Determine te perimeter of a triangle using algebra Find te area of a triangle using

More information

A Multigrid Tutorial part two

A Multigrid Tutorial part two A Multigrid Tutorial part two William L. Briggs Department of Matematics University of Colorado at Denver Van Emden Henson Center for Applied Scientific Computing Lawrence Livermore National Laboratory

More information

ON LOCAL LIKELIHOOD DENSITY ESTIMATION WHEN THE BANDWIDTH IS LARGE

ON LOCAL LIKELIHOOD DENSITY ESTIMATION WHEN THE BANDWIDTH IS LARGE ON LOCAL LIKELIHOOD DENSITY ESTIMATION WHEN THE BANDWIDTH IS LARGE Byeong U. Park 1 and Young Kyung Lee 2 Department of Statistics, Seoul National University, Seoul, Korea Tae Yoon Kim 3 and Ceolyong Park

More information

Derivatives Math 120 Calculus I D Joyce, Fall 2013

Derivatives Math 120 Calculus I D Joyce, Fall 2013 Derivatives Mat 20 Calculus I D Joyce, Fall 203 Since we ave a good understanding of its, we can develop derivatives very quickly. Recall tat we defined te derivative f x of a function f at x to be te

More information

The EOQ Inventory Formula

The EOQ Inventory Formula Te EOQ Inventory Formula James M. Cargal Matematics Department Troy University Montgomery Campus A basic problem for businesses and manufacturers is, wen ordering supplies, to determine wat quantity of

More information

The finite element immersed boundary method: model, stability, and numerical results

The finite element immersed boundary method: model, stability, and numerical results Te finite element immersed boundary metod: model, stability, and numerical results Lucia Gastaldi Università di Brescia ttp://dm.ing.unibs.it/gastaldi/ INdAM Worksop, Cortona, September 18, 2006 Joint

More information

Training Robust Support Vector Regression via D. C. Program

Training Robust Support Vector Regression via D. C. Program Journal of Information & Computational Science 7: 12 (2010) 2385 2394 Available at ttp://www.joics.com Training Robust Support Vector Regression via D. C. Program Kuaini Wang, Ping Zong, Yaoong Zao College

More information

Note nine: Linear programming CSE 101. 1 Linear constraints and objective functions. 1.1 Introductory example. Copyright c Sanjoy Dasgupta 1

Note nine: Linear programming CSE 101. 1 Linear constraints and objective functions. 1.1 Introductory example. Copyright c Sanjoy Dasgupta 1 Copyrigt c Sanjoy Dasgupta Figure. (a) Te feasible region for a linear program wit two variables (see tet for details). (b) Contour lines of te objective function: for different values of (profit). Te

More information

TRADING AWAY WIDE BRANDS FOR CHEAP BRANDS. Swati Dhingra London School of Economics and CEP. Online Appendix

TRADING AWAY WIDE BRANDS FOR CHEAP BRANDS. Swati Dhingra London School of Economics and CEP. Online Appendix TRADING AWAY WIDE BRANDS FOR CHEAP BRANDS Swati Dingra London Scool of Economics and CEP Online Appendix APPENDIX A. THEORETICAL & EMPIRICAL RESULTS A.1. CES and Logit Preferences: Invariance of Innovation

More information

Høgskolen i Narvik Sivilingeniørutdanningen STE6237 ELEMENTMETODER. Oppgaver

Høgskolen i Narvik Sivilingeniørutdanningen STE6237 ELEMENTMETODER. Oppgaver Høgskolen i Narvik Sivilingeniørutdanningen STE637 ELEMENTMETODER Oppgaver Klasse: 4.ID, 4.IT Ekstern Professor: Gregory A. Chechkin e-mail: chechkin@mech.math.msu.su Narvik 6 PART I Task. Consider two-point

More information

Can a Lump-Sum Transfer Make Everyone Enjoy the Gains. from Free Trade?

Can a Lump-Sum Transfer Make Everyone Enjoy the Gains. from Free Trade? Can a Lump-Sum Transfer Make Everyone Enjoy te Gains from Free Trade? Yasukazu Icino Department of Economics, Konan University June 30, 2010 Abstract I examine lump-sum transfer rules to redistribute te

More information

Numerical Methods I Solving Linear Systems: Sparse Matrices, Iterative Methods and Non-Square Systems

Numerical Methods I Solving Linear Systems: Sparse Matrices, Iterative Methods and Non-Square Systems Numerical Methods I Solving Linear Systems: Sparse Matrices, Iterative Methods and Non-Square Systems Aleksandar Donev Courant Institute, NYU 1 donev@courant.nyu.edu 1 Course G63.2010.001 / G22.2420-001,

More information

Vector and Matrix Norms

Vector and Matrix Norms Chapter 1 Vector and Matrix Norms 11 Vector Spaces Let F be a field (such as the real numbers, R, or complex numbers, C) with elements called scalars A Vector Space, V, over the field F is a non-empty

More information

Catalogue no. 12-001-XIE. Survey Methodology. December 2004

Catalogue no. 12-001-XIE. Survey Methodology. December 2004 Catalogue no. 1-001-XIE Survey Metodology December 004 How to obtain more information Specific inquiries about tis product and related statistics or services sould be directed to: Business Survey Metods

More information

Instantaneous Rate of Change:

Instantaneous Rate of Change: Instantaneous Rate of Cange: Last section we discovered tat te average rate of cange in F(x) can also be interpreted as te slope of a scant line. Te average rate of cange involves te cange in F(x) over

More information

The modelling of business rules for dashboard reporting using mutual information

The modelling of business rules for dashboard reporting using mutual information 8 t World IMACS / MODSIM Congress, Cairns, Australia 3-7 July 2009 ttp://mssanz.org.au/modsim09 Te modelling of business rules for dasboard reporting using mutual information Gregory Calbert Command, Control,

More information

Solutions by: KARATUĞ OZAN BiRCAN. PROBLEM 1 (20 points): Let D be a region, i.e., an open connected set in

Solutions by: KARATUĞ OZAN BiRCAN. PROBLEM 1 (20 points): Let D be a region, i.e., an open connected set in KOÇ UNIVERSITY, SPRING 2014 MATH 401, MIDTERM-1, MARCH 3 Instructor: BURAK OZBAGCI TIME: 75 Minutes Solutions by: KARATUĞ OZAN BiRCAN PROBLEM 1 (20 points): Let D be a region, i.e., an open connected set

More information

MixedÀ¾ нOptimization Problem via Lagrange Multiplier Theory

MixedÀ¾ нOptimization Problem via Lagrange Multiplier Theory MixedÀ¾ нOptimization Problem via Lagrange Multiplier Theory Jun WuÝ, Sheng ChenÞand Jian ChuÝ ÝNational Laboratory of Industrial Control Technology Institute of Advanced Process Control Zhejiang University,

More information

Optimized Data Indexing Algorithms for OLAP Systems

Optimized Data Indexing Algorithms for OLAP Systems Database Systems Journal vol. I, no. 2/200 7 Optimized Data Indexing Algoritms for OLAP Systems Lucian BORNAZ Faculty of Cybernetics, Statistics and Economic Informatics Academy of Economic Studies, Bucarest

More information

CHAPTER 7. Di erentiation

CHAPTER 7. Di erentiation CHAPTER 7 Di erentiation 1. Te Derivative at a Point Definition 7.1. Let f be a function defined on a neigborood of x 0. f is di erentiable at x 0, if te following it exists: f 0 fx 0 + ) fx 0 ) x 0 )=.

More information

Inner Product Spaces

Inner Product Spaces Math 571 Inner Product Spaces 1. Preliminaries An inner product space is a vector space V along with a function, called an inner product which associates each pair of vectors u, v with a scalar u, v, and

More information

Multigrid preconditioning for nonlinear (degenerate) parabolic equations with application to monument degradation

Multigrid preconditioning for nonlinear (degenerate) parabolic equations with application to monument degradation Multigrid preconditioning for nonlinear (degenerate) parabolic equations with application to monument degradation M. Donatelli 1 M. Semplice S. Serra-Capizzano 1 1 Department of Science and High Technology

More information

Schedulability Analysis under Graph Routing in WirelessHART Networks

Schedulability Analysis under Graph Routing in WirelessHART Networks Scedulability Analysis under Grap Routing in WirelessHART Networks Abusayeed Saifulla, Dolvara Gunatilaka, Paras Tiwari, Mo Sa, Cenyang Lu, Bo Li Cengjie Wu, and Yixin Cen Department of Computer Science,

More information

MATHEMATICS FOR ENGINEERING DIFFERENTIATION TUTORIAL 1 - BASIC DIFFERENTIATION

MATHEMATICS FOR ENGINEERING DIFFERENTIATION TUTORIAL 1 - BASIC DIFFERENTIATION MATHEMATICS FOR ENGINEERING DIFFERENTIATION TUTORIAL 1 - BASIC DIFFERENTIATION Tis tutorial is essential pre-requisite material for anyone stuing mecanical engineering. Tis tutorial uses te principle of

More information

An Additive Neumann-Neumann Method for Mortar Finite Element for 4th Order Problems

An Additive Neumann-Neumann Method for Mortar Finite Element for 4th Order Problems An Additive eumann-eumann Method for Mortar Finite Element for 4th Order Problems Leszek Marcinkowski Department of Mathematics, University of Warsaw, Banacha 2, 02-097 Warszawa, Poland, Leszek.Marcinkowski@mimuw.edu.pl

More information

f(a + h) f(a) f (a) = lim

f(a + h) f(a) f (a) = lim Lecture 7 : Derivative AS a Function In te previous section we defined te derivative of a function f at a number a (wen te function f is defined in an open interval containing a) to be f (a) 0 f(a + )

More information

Parallel Smoothers for Matrix-based Multigrid Methods on Unstructured Meshes Using Multicore CPUs and GPUs

Parallel Smoothers for Matrix-based Multigrid Methods on Unstructured Meshes Using Multicore CPUs and GPUs Parallel Smooters for Matrix-based Multigrid Metods on Unstructured Meses Using Multicore CPUs and GPUs Vincent Heuveline Dimitar Lukarski Nico Trost Jan-Pilipp Weiss No. 2-9 Preprint Series of te Engineering

More information

TESLA Report 2003-03

TESLA Report 2003-03 TESLA Report 23-3 A multigrid based 3D space-charge routine in the tracking code GPT Gisela Pöplau, Ursula van Rienen, Marieke de Loos and Bas van der Geer Institute of General Electrical Engineering,

More information

Metric Spaces. Chapter 7. 7.1. Metrics

Metric Spaces. Chapter 7. 7.1. Metrics Chapter 7 Metric Spaces A metric space is a set X that has a notion of the distance d(x, y) between every pair of points x, y X. The purpose of this chapter is to introduce metric spaces and give some

More information

Volumes of Pyramids and Cones. Use the Pythagorean Theorem to find the value of the variable. h 2 m. 1.5 m 12 in. 8 in. 2.5 m

Volumes of Pyramids and Cones. Use the Pythagorean Theorem to find the value of the variable. h 2 m. 1.5 m 12 in. 8 in. 2.5 m -5 Wat You ll Learn To find te volume of a pramid To find te volume of a cone... And W To find te volume of a structure in te sape of a pramid, as in Eample Volumes of Pramids and Cones Ceck Skills You

More information

Pre-trial Settlement with Imperfect Private Monitoring

Pre-trial Settlement with Imperfect Private Monitoring Pre-trial Settlement wit Imperfect Private Monitoring Mostafa Beskar University of New Hampsire Jee-Hyeong Park y Seoul National University July 2011 Incomplete, Do Not Circulate Abstract We model pretrial

More information

Section 3.3. Differentiation of Polynomials and Rational Functions. Difference Equations to Differential Equations

Section 3.3. Differentiation of Polynomials and Rational Functions. Difference Equations to Differential Equations Difference Equations to Differential Equations Section 3.3 Differentiation of Polynomials an Rational Functions In tis section we begin te task of iscovering rules for ifferentiating various classes of

More information

Distances in random graphs with infinite mean degrees

Distances in random graphs with infinite mean degrees Distances in random graps wit infinite mean degrees Henri van den Esker, Remco van der Hofstad, Gerard Hoogiemstra and Dmitri Znamenski April 26, 2005 Abstract We study random graps wit an i.i.d. degree

More information

Writing Mathematics Papers

Writing Mathematics Papers Writing Matematics Papers Tis essay is intended to elp your senior conference paper. It is a somewat astily produced amalgam of advice I ave given to students in my PDCs (Mat 4 and Mat 9), so it s not

More information

Warm medium, T H T T H T L. s Cold medium, T L

Warm medium, T H T T H T L. s Cold medium, T L Refrigeration Cycle Heat flows in direction of decreasing temperature, i.e., from ig-temperature to low temperature regions. Te transfer of eat from a low-temperature to ig-temperature requires a refrigerator

More information

MATHEMATICAL MODELS OF LIFE SUPPORT SYSTEMS Vol. I - Mathematical Models for Prediction of Climate - Dymnikov V.P.

MATHEMATICAL MODELS OF LIFE SUPPORT SYSTEMS Vol. I - Mathematical Models for Prediction of Climate - Dymnikov V.P. MATHEMATICAL MODELS FOR PREDICTION OF CLIMATE Institute of Numerical Matematics, Russian Academy of Sciences, Moscow, Russia. Keywords: Modeling, climate system, climate, dynamic system, attractor, dimension,

More information

IRREDUCIBLE OPERATOR SEMIGROUPS SUCH THAT AB AND BA ARE PROPORTIONAL. 1. Introduction

IRREDUCIBLE OPERATOR SEMIGROUPS SUCH THAT AB AND BA ARE PROPORTIONAL. 1. Introduction IRREDUCIBLE OPERATOR SEMIGROUPS SUCH THAT AB AND BA ARE PROPORTIONAL R. DRNOVŠEK, T. KOŠIR Dedicated to Prof. Heydar Radjavi on the occasion of his seventieth birthday. Abstract. Let S be an irreducible

More information

Chapter 10: Refrigeration Cycles

Chapter 10: Refrigeration Cycles Capter 10: efrigeration Cycles Te vapor compression refrigeration cycle is a common metod for transferring eat from a low temperature to a ig temperature. Te above figure sows te objectives of refrigerators

More information

Mean value theorem, Taylors Theorem, Maxima and Minima.

Mean value theorem, Taylors Theorem, Maxima and Minima. MA 001 Preparatory Mathematics I. Complex numbers as ordered pairs. Argand s diagram. Triangle inequality. De Moivre s Theorem. Algebra: Quadratic equations and express-ions. Permutations and Combinations.

More information

Suggested solutions, FYS 500 Classical Mechanics and Field Theory 2014 fall

Suggested solutions, FYS 500 Classical Mechanics and Field Theory 2014 fall UNIVERSITETET I STAVANGER Institutt for matematikk og naturvitenskap Suggested solutions, FYS 500 Classical Mecanics and Field Teory 014 fall Set 11 for 17/18. November 014 Problem 59: Te Lagrangian for

More information

By choosing to view this document, you agree to all provisions of the copyright laws protecting it.

By choosing to view this document, you agree to all provisions of the copyright laws protecting it. This material is posted here with permission of the IEEE Such permission of the IEEE does not in any way imply IEEE endorsement of any of Helsinki University of Technology's products or services Internal

More information

Mathematics Course 111: Algebra I Part IV: Vector Spaces

Mathematics Course 111: Algebra I Part IV: Vector Spaces Mathematics Course 111: Algebra I Part IV: Vector Spaces D. R. Wilkins Academic Year 1996-7 9 Vector Spaces A vector space over some field K is an algebraic structure consisting of a set V on which are

More information

SAMPLE DESIGN FOR THE TERRORISM RISK INSURANCE PROGRAM SURVEY

SAMPLE DESIGN FOR THE TERRORISM RISK INSURANCE PROGRAM SURVEY ASA Section on Survey Researc Metods SAMPLE DESIG FOR TE TERRORISM RISK ISURACE PROGRAM SURVEY G. ussain Coudry, Westat; Mats yfjäll, Statisticon; and Marianne Winglee, Westat G. ussain Coudry, Westat,

More information

Chapter 7 Numerical Differentiation and Integration

Chapter 7 Numerical Differentiation and Integration 45 We ave a abit in writing articles publised in scientiþc journals to make te work as Þnised as possible, to cover up all te tracks, to not worry about te blind alleys or describe ow you ad te wrong idea

More information

Multivariate time series analysis: Some essential notions

Multivariate time series analysis: Some essential notions Capter 2 Multivariate time series analysis: Some essential notions An overview of a modeling and learning framework for multivariate time series was presented in Capter 1. In tis capter, some notions on

More information

Mean Value Coordinates

Mean Value Coordinates Mean Value Coordinates Michael S. Floater Abstract: We derive a generalization of barycentric coordinates which allows a vertex in a planar triangulation to be expressed as a convex combination of its

More information

2.23 Gambling Rehabilitation Services. Introduction

2.23 Gambling Rehabilitation Services. Introduction 2.23 Gambling Reabilitation Services Introduction Figure 1 Since 1995 provincial revenues from gambling activities ave increased over 56% from $69.2 million in 1995 to $108 million in 2004. Te majority

More information

SAT Math Must-Know Facts & Formulas

SAT Math Must-Know Facts & Formulas SAT Mat Must-Know Facts & Formuas Numbers, Sequences, Factors Integers:..., -3, -2, -1, 0, 1, 2, 3,... Rationas: fractions, tat is, anyting expressabe as a ratio of integers Reas: integers pus rationas

More information

Improved dynamic programs for some batcing problems involving te maximum lateness criterion A P M Wagelmans Econometric Institute Erasmus University Rotterdam PO Box 1738, 3000 DR Rotterdam Te Neterlands

More information

The cover SU(2) SO(3) and related topics

The cover SU(2) SO(3) and related topics The cover SU(2) SO(3) and related topics Iordan Ganev December 2011 Abstract The subgroup U of unit quaternions is isomorphic to SU(2) and is a double cover of SO(3). This allows a simple computation of

More information

- 1 - Handout #22 May 23, 2012 Huffman Encoding and Data Compression. CS106B Spring 2012. Handout by Julie Zelenski with minor edits by Keith Schwarz

- 1 - Handout #22 May 23, 2012 Huffman Encoding and Data Compression. CS106B Spring 2012. Handout by Julie Zelenski with minor edits by Keith Schwarz CS106B Spring 01 Handout # May 3, 01 Huffman Encoding and Data Compression Handout by Julie Zelenski wit minor edits by Keit Scwarz In te early 1980s, personal computers ad ard disks tat were no larger

More information

Fourth-Order Compact Schemes of a Heat Conduction Problem with Neumann Boundary Conditions

Fourth-Order Compact Schemes of a Heat Conduction Problem with Neumann Boundary Conditions Fourth-Order Compact Schemes of a Heat Conduction Problem with Neumann Boundary Conditions Jennifer Zhao, 1 Weizhong Dai, Tianchan Niu 1 Department of Mathematics and Statistics, University of Michigan-Dearborn,

More information

13 PERIMETER AND AREA OF 2D SHAPES

13 PERIMETER AND AREA OF 2D SHAPES 13 PERIMETER AND AREA OF D SHAPES 13.1 You can find te perimeter of sapes Key Points Te perimeter of a two-dimensional (D) sape is te total distance around te edge of te sape. l To work out te perimeter

More information

On a Satellite Coverage

On a Satellite Coverage I. INTRODUCTION On a Satellite Coverage Problem DANNY T. CHI Kodak Berkeley Researc Yu T. su National Ciao Tbng University Te eart coverage area for a satellite in an Eart syncronous orbit wit a nonzero

More information

Cyber Epidemic Models with Dependences

Cyber Epidemic Models with Dependences Cyber Epidemic Models wit Dependences Maocao Xu 1, Gaofeng Da 2 and Souuai Xu 3 1 Department of Matematics, Illinois State University mxu2@ilstu.edu 2 Institute for Cyber Security, University of Texas

More information

Multigrid computational methods are

Multigrid computational methods are M ULTIGRID C OMPUTING Wy Multigrid Metods Are So Efficient Originally introduced as a way to numerically solve elliptic boundary-value problems, multigrid metods, and teir various multiscale descendants,

More information

An inquiry into the multiplier process in IS-LM model

An inquiry into the multiplier process in IS-LM model An inquiry into te multiplier process in IS-LM model Autor: Li ziran Address: Li ziran, Room 409, Building 38#, Peing University, Beijing 00.87,PRC. Pone: (86) 00-62763074 Internet Address: jefferson@water.pu.edu.cn

More information

1 VECTOR SPACES AND SUBSPACES

1 VECTOR SPACES AND SUBSPACES 1 VECTOR SPACES AND SUBSPACES What is a vector? Many are familiar with the concept of a vector as: Something which has magnitude and direction. an ordered pair or triple. a description for quantities such

More information

POLYNOMIAL HISTOPOLATION, SUPERCONVERGENT DEGREES OF FREEDOM, AND PSEUDOSPECTRAL DISCRETE HODGE OPERATORS

POLYNOMIAL HISTOPOLATION, SUPERCONVERGENT DEGREES OF FREEDOM, AND PSEUDOSPECTRAL DISCRETE HODGE OPERATORS POLYNOMIAL HISTOPOLATION, SUPERCONVERGENT DEGREES OF FREEDOM, AND PSEUDOSPECTRAL DISCRETE HODGE OPERATORS N. ROBIDOUX Abstract. We show that, given a histogram with n bins possibly non-contiguous or consisting

More information

Moving Least Squares Approximation

Moving Least Squares Approximation Chapter 7 Moving Least Squares Approimation An alternative to radial basis function interpolation and approimation is the so-called moving least squares method. As we will see below, in this method the

More information

Overview of Component Search System SPARS-J

Overview of Component Search System SPARS-J Overview of omponent Searc System Tetsuo Yamamoto*,Makoto Matsusita**, Katsuro Inoue** *Japan Science and Tecnology gency **Osaka University ac part nalysis part xperiment onclusion and Future work Motivation

More information

Equilibria in sequential bargaining games as solutions to systems of equations

Equilibria in sequential bargaining games as solutions to systems of equations Economics Letters 84 (2004) 407 411 www.elsevier.com/locate/econbase Equilibria in sequential bargaining games as solutions to systems of equations Tasos Kalandrakis* Department of Political Science, Yale

More information

Inner Product Spaces and Orthogonality

Inner Product Spaces and Orthogonality Inner Product Spaces and Orthogonality week 3-4 Fall 2006 Dot product of R n The inner product or dot product of R n is a function, defined by u, v a b + a 2 b 2 + + a n b n for u a, a 2,, a n T, v b,

More information

2.12 Student Transportation. Introduction

2.12 Student Transportation. Introduction Introduction Figure 1 At 31 Marc 2003, tere were approximately 84,000 students enrolled in scools in te Province of Newfoundland and Labrador, of wic an estimated 57,000 were transported by scool buses.

More information

Iterative Solvers for Linear Systems

Iterative Solvers for Linear Systems 9th SimLab Course on Parallel Numerical Simulation, 4.10 8.10.2010 Iterative Solvers for Linear Systems Bernhard Gatzhammer Chair of Scientific Computing in Computer Science Technische Universität München

More information

Applied Linear Algebra I Review page 1

Applied Linear Algebra I Review page 1 Applied Linear Algebra Review 1 I. Determinants A. Definition of a determinant 1. Using sum a. Permutations i. Sign of a permutation ii. Cycle 2. Uniqueness of the determinant function in terms of properties

More information

Strategic trading in a dynamic noisy market. Dimitri Vayanos

Strategic trading in a dynamic noisy market. Dimitri Vayanos LSE Researc Online Article (refereed) Strategic trading in a dynamic noisy market Dimitri Vayanos LSE as developed LSE Researc Online so tat users may access researc output of te Scool. Copyrigt and Moral

More information

How To Prove The Dirichlet Unit Theorem

How To Prove The Dirichlet Unit Theorem Chapter 6 The Dirichlet Unit Theorem As usual, we will be working in the ring B of algebraic integers of a number field L. Two factorizations of an element of B are regarded as essentially the same if

More information

COUNTING INDEPENDENT SETS IN SOME CLASSES OF (ALMOST) REGULAR GRAPHS

COUNTING INDEPENDENT SETS IN SOME CLASSES OF (ALMOST) REGULAR GRAPHS COUNTING INDEPENDENT SETS IN SOME CLASSES OF (ALMOST) REGULAR GRAPHS Alexander Burstein Department of Mathematics Howard University Washington, DC 259, USA aburstein@howard.edu Sergey Kitaev Mathematics

More information

Code Verification and Numerical Accuracy Assessment for Finite Volume CFD Codes. Subrahmanya P. Veluri

Code Verification and Numerical Accuracy Assessment for Finite Volume CFD Codes. Subrahmanya P. Veluri Code Verification and Numerical Accuracy Assessment for Finite Volume CFD Codes Subramanya P. Veluri Dissertation submitted to te faculty of te Virginia Polytecnic Institute and State University in partial

More information

f(x + h) f(x) h as representing the slope of a secant line. As h goes to 0, the slope of the secant line approaches the slope of the tangent line.

f(x + h) f(x) h as representing the slope of a secant line. As h goes to 0, the slope of the secant line approaches the slope of the tangent line. Derivative of f(z) Dr. E. Jacobs Te erivative of a function is efine as a limit: f (x) 0 f(x + ) f(x) We can visualize te expression f(x+) f(x) as representing te slope of a secant line. As goes to 0,

More information

1 Sets and Set Notation.

1 Sets and Set Notation. LINEAR ALGEBRA MATH 27.6 SPRING 23 (COHEN) LECTURE NOTES Sets and Set Notation. Definition (Naive Definition of a Set). A set is any collection of objects, called the elements of that set. We will most

More information

The Dynamics of Movie Purchase and Rental Decisions: Customer Relationship Implications to Movie Studios

The Dynamics of Movie Purchase and Rental Decisions: Customer Relationship Implications to Movie Studios Te Dynamics of Movie Purcase and Rental Decisions: Customer Relationsip Implications to Movie Studios Eddie Ree Associate Professor Business Administration Stoneill College 320 Wasington St Easton, MA

More information

Part II: Finite Difference/Volume Discretisation for CFD

Part II: Finite Difference/Volume Discretisation for CFD Part II: Finite Difference/Volume Discretisation for CFD Finite Volume Metod of te Advection-Diffusion Equation A Finite Difference/Volume Metod for te Incompressible Navier-Stokes Equations Marker-and-Cell

More information

How High a Degree is High Enough for High Order Finite Elements?

How High a Degree is High Enough for High Order Finite Elements? This space is reserved for the Procedia header, do not use it How High a Degree is High Enough for High Order Finite Elements? William F. National Institute of Standards and Technology, Gaithersburg, Maryland,

More information

Comparison between two approaches to overload control in a Real Server: local or hybrid solutions?

Comparison between two approaches to overload control in a Real Server: local or hybrid solutions? Comparison between two approaces to overload control in a Real Server: local or ybrid solutions? S. Montagna and M. Pignolo Researc and Development Italtel S.p.A. Settimo Milanese, ITALY Abstract Tis wor

More information

5.3 The Cross Product in R 3

5.3 The Cross Product in R 3 53 The Cross Product in R 3 Definition 531 Let u = [u 1, u 2, u 3 ] and v = [v 1, v 2, v 3 ] Then the vector given by [u 2 v 3 u 3 v 2, u 3 v 1 u 1 v 3, u 1 v 2 u 2 v 1 ] is called the cross product (or

More information

Theoretical calculation of the heat capacity

Theoretical calculation of the heat capacity eoretical calculation of te eat capacity Principle of equipartition of energy Heat capacity of ideal and real gases Heat capacity of solids: Dulong-Petit, Einstein, Debye models Heat capacity of metals

More information

Lecture 13 Linear quadratic Lyapunov theory

Lecture 13 Linear quadratic Lyapunov theory EE363 Winter 28-9 Lecture 13 Linear quadratic Lyapunov theory the Lyapunov equation Lyapunov stability conditions the Lyapunov operator and integral evaluating quadratic integrals analysis of ARE discrete-time

More information

Perimeter, Area and Volume of Regular Shapes

Perimeter, Area and Volume of Regular Shapes Perimeter, Area and Volume of Regular Sapes Perimeter of Regular Polygons Perimeter means te total lengt of all sides, or distance around te edge of a polygon. For a polygon wit straigt sides tis is te

More information

Math Test Sections. The College Board: Expanding College Opportunity

Math Test Sections. The College Board: Expanding College Opportunity Taking te SAT I: Reasoning Test Mat Test Sections Te materials in tese files are intended for individual use by students getting ready to take an SAT Program test; permission for any oter use must be sougt

More information

SWITCH T F T F SELECT. (b) local schedule of two branches. (a) if-then-else construct A & B MUX. one iteration cycle

SWITCH T F T F SELECT. (b) local schedule of two branches. (a) if-then-else construct A & B MUX. one iteration cycle 768 IEEE RANSACIONS ON COMPUERS, VOL. 46, NO. 7, JULY 997 Compile-ime Sceduling of Dynamic Constructs in Dataæow Program Graps Soonoi Ha, Member, IEEE and Edward A. Lee, Fellow, IEEE Abstract Sceduling

More information