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

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1 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, but use teratve technques) Gven Ax = b, wrte Mx = Nx + b and construct the teraton Subtractng these equatons, we obtan Mx (k+1) = Nx (k) + b M(x x (k+1) ) = N(x x (k) ) Therefore f we denote the error n x (k) by e (k) = x x (k), then e (k+1) = M 1 Ne (k) Be (k) Thus e (k) = B k e (0), whch suggests the followng theorem: Theorem e (k) 0 as k for all e (0) f and only f ρ(b) < 1 Convergence can stll occur f ρ(b) = 1, but n that case we must be careful n how we choose x (0) Note that from e (k) = B k e (0), t follows that e (k) e (0) B k The Jacob Method We now develop a smple teratve method If we rewrte Ax = b as n a x = b, = 1,, n, =1 then x = b a x, or x = 1 [ b a x ]

2 In other words, a 11 M =, a nn 0 a 1 a 1n N = a 1 a n1 a n,n 1 0 Our teraton s therefore x (k+1) = 1 [ b a x (k)], known as the Jacob method, wth a 0 1 a 11 M 1 a 1 N = a a n1 a a nn n,n 1 a nn 0 a 1n a 11 B J So, f M 1 N = max 1 n a < 1, e f B J s strctly dagonally domnant, then the teraton converges For example, suppose A = Then B J = 1, so the Jacob method converges rapdly On the other hand, f 1 1 A = 1, 1 whch arses from dscretzng the Laplacan, then B J = 1 A more subtle analyss can be used to show convergence, but t s slow Note that for these two examples, x (0) x (1) when all elements of x (1) have been computed Ths s a waste of storage; we need only n + elements of storage of A above Ths shows that the orderng of equatons s very mportant If we reorder the equatons n such a way that oddnumbered equatons and even-numbered equatons are grouped separately, then we obtan, for the

3 latter example, A = Then, we can solve for all odd ndces, then all even ndces, ndependently of each other Not only does ths approach save storage space but t also lends tself to parallelsm 3 The Gauss-Sedel Method In the Jacob method, we compute x (k+1) usng the elements of x (k), even though x (k+1) 1,, x (k+1) 1 are already known The Gauss-Sedel method s desgned to take advantage of the latest nformaton avalable about x: x (k+1) = 1 [ 1 n ] b a x (k+1) a x (k) =1 =+1 To derve ths method, we wrte A = L + D + U where 0 a 11 0 a 1 a 1n a L = 1, D =, U = an 1,n a n1 a n,n 1 0 a nn 0 Thus the Gauss-Sedel teraton can be wrtten as or whch yelds Dx (k+1) = b Lx (k+1) Ux (k), (D + L)x (k+1) = b Ux (k) x (k+1) = (D + L) 1 Ux (k) + (D + L) 1 b Thus the teraton matrx for the Gauss-Sedel method s B GS = (D + L) 1 U, as opposed to the teraton matrx for the Jacob method, B J = D 1 (L + U) In some cases, ρ(b GS ) = (ρ(b J )), so the Gauss-Sedel method converges twce as fast On the other hand, note that Gauss-Sedel s very sequental; e t does not lend tself to parallelsm 4 Posson s Equaton Consder the standard problem of solvng Posson s equaton on a doman R n two dmensons, u = f, (x, y) R, u = u xx + u yy, u = g, (x, y) R We take R to be the unt rectangle [0, 1] [0, 1] and dscretze the problem usng a unform grd wth spacng h = 1/(N + 1) n the x and y drectons, and grdponts x = h, = 0,, N + 1, 3

4 and y = h, = 0,, N + 1 Then, for, = 1,, N, we replace the dfferental equaton by a dfference approxmaton u 1, + u u +1, h + u,+1 + u u,+1 h = f, where u = u(x, y ) and f = f(x, y ) From the boundary condtons, we have u 0 = g(x 0, y ), = 1,,, N, and smlar condtons for the other grdponts along the boundary Let u = [u 1,, u N ] Then where u 1 + T u u +1 = f h f 1 + g(x 0, ) = 1 T = 1, [ f ] = h f =,, N 1 h f N + g(x N, ) = N 14 Thus we can solve the problem on the entre doman by solvng Au = f where T I I T I A = I I T We say that A s a block trdagonal matrx A s also a band matrx, but the band s sparse and Gaussan elmnaton may fll-n the whole band However, the equatons can be re-ordered to avod fll-n Department of Computer Scence, Gates Buldng B, Room 80, Stanford, CA E-mal address: golub@stanfordedu 4

5 CME 30: NUMERICAL LINEAR ALGEBRA FALL 005/06 LECTURE 15 GENE H GOLUB 1 Convergence of Iteratve Methods Recall the basc teratve methods based on the splttng A = D + L + U, the Jacob method and the Gauss-Sedel method Dx (k+1) = (L + U)x (k) + b (D + L)x (k+1) = Ux (k) + b These are examples of one-step statonary method, whch s an teraton of the form Mx (k+1) = Nx (k) + b, where A = M N Let B = M 1 N, and defne e (k) = x x (k) Then e (k+1) = Be (k) = B k+1 e (0) Recall that f ρ(b k ) < 1 then e (k) 0 for all choces of x (0) Also, recall that for all consstent norms, ρ(b) B Therefore, a suffcent condton for convergence of the Jacob method s B < 1 where, b = 0 = Note that f B s dagonally domnant Now, defne Then we have the followng result: B = max r = a, a < 1 r = max r Theorem If r < 1, then ρ(b GS ) < 1 In other words, the Gauss-Sedel teraton converges f A s dagonally domnant Proof The proof proceeds usng nducton on the elements of e (k) We have whch can be wrtten as =1 a e (k+1) (D + L)e (k+1) = Ue (k), = =+1 a e (k), = 1,, N

6 Thus For = 1, we have e (k+1) = Assume that for p = 1,, 1, Then, Therefore from whch t follows that snce r < 1 =+1 e (k+1) 1 a e (k) = 1 =1 a e(k) a e (k+1), = 1,, N e (k) r 1 e (k+1) p e (k) r p r e (k) e (k+1) 1 =1 a e(k+1) 1 r e (k) + =1 e (k) = r e (k) r e (k) a a =+1 + e a e(k) =+1 e (k+1) r e (k) r k+1 e (0), lm k e(k) = 0 We see that the Jacob method and the Gauss-Sedel method both converge f A s dagonally domnant, but convergence can be slow n some cases For example, f 1 1 A = 1 1 s of sze N N then 0 1/ D 1 1/ (L + U) = 1/ 1/ 0 and therefore π ρ(b J ) = cos N + 1 = cos πh 1 π h + whch s approxmately 1 for small h = 1 N+1 We would lke to develop a method where ρ(b) 1 ch Now, suppose B = B Then e (k) e (0) B k = ρ(b) k a

7 We want e (k) / e (0) ɛ, so f we let ρ k = ɛ, then k = log ɛ log ρ s the number of teratons necessary for convergence The quantty log ρ s called the rate of convergence The SOR Method The method of successve overrelaxaton (SOR) s the teraton = ω 1 [b a x (k+1) x (k+1) =1 =+1 ] a x (k) + (1 ω)x (k) The quantty ω s called the relaxaton parameter If ω = 1, then the SOR method reduces to the Gauss-Sedel method In matrx form, the teraton can be wrtten as whch can be rearranged to obtan or Defne Then x (k+1) = Dx (k+1) = ω(b Lx (k+1) Ux (k) ) + (1 ω)dx (k) (D + ωl)x (k+1) = ωb + [(1 ω)d ωu]x (k) ( ) 1 1 [( ) ] ( ) ω D + L ω 1 D U x (k) + ω D + L b L ω = ( ) 1 1 [( ) ] 1 ω D + L ω 1 D U ( ) 1 1 [( 1 det L ω = det ω D + L det = 1 det ( 1 det ω D + L) ω n = n =1 = (1 ω) n [( 1 ω 1 (1 ω) n n =1 ω n ) ω 1 ) ] D U ] D U Therefore, n =1 λ = (1 ω) n where λ 1,, λ n are the egenvalues of L ω, wth λ 1 λ n Therefore λ 1 n (1 ω) n Snce we must have λ 1 < 1 for convergence, t follows that a necessary condton for convergence of SOR s 0 < ω < 3 Block Methods Recall that n solvng Posson s equaton on a rectangle, we needed to solve systems of the form Ths can be accomplshed usng an teraton v + T v v +1 = g T v (k+1) = g + v (k) 1 + v(k) +1, 3

8 whch s an example of a block Jacob teraton, snce t nvolves solvng the system Au = g by applyng the Jacob method to A, except each block of sze N N s treated as a sngle element Smlarly, we can use the block Gauss-Sedel teraton Consder the teraton T v (k+1) = g + v (k+1) 1 + v (k) 4 Rchardson Method x (k+1) = (I αa)x (k) + αb = x (k) + α(b Ax (k) ) = x (k) + αr (k) Ths s known as the Rchardson method If we defne the error e (k) = x x (k), then e (k+1) = B α e (k) where B α = I αa; we want to choose the parameter α a pror so as to mnmze B α Suppose A s symmetrc postve defnte, wth egenvalues µ 1 µ µ n > 0 Snce B = I αa, λ = 1 αµ We want to choose α so that B α s mnmzed; e mn α The optmal parameter ˆα s found by solvng max λ (α) = mn max 1 αµ 1 n α 1 n 1 ˆαµ n = (1 ˆαµ 1 ) whch yelds ˆα = µ 1 + µ n Note that When 1 αµ n = 1 that the teraton dverges, from whch t follows that the method converges for 0 < α < /µ n However, ths teraton s senstve to perturbaton, and therefore bad numercally For example, f µ 1 = 10 and µ n = 10 4, then the optmal α s /( , but ths s close to a value of α for whch the teraton dverges, α = /10 Also, note that and smlarly, λ 1 (ˆα) = 1 λ n (ˆα) = µ 1 µ n µ 1 + µ n = µ 1 + µ n µ 1 = µ n µ 1 µ 1 + µ n, µ 1 µ n 1 µ 1 µ n + 1 = κ(a) 1 κ(a) + 1 Therefore the convergence rate depends on κ(a) For example, consder the Helmholtz equaton on a rectangle R, u (k+1) + σ(x, y)u (k) = f, u = g, Usng a fnte dfference approxmaton for gves T I I A = I I T and thus the teraton has the form Au (k+1) + h Σu (k) = f (x, y) R (x, y) R 4

9 where Σ = σ 11 σ nn, σ = σ(x, y ) We wsh to determne the rate of convergence We defne the error operator by Therefore But and Therefore e (k+1) = (h A 1 Σ)e (k) e (k+1) h A 1 Σ e (k) Σ = max σ, λ mn = 4 4 cos πh = 4(1 cos πh) ( ) πh = 8 sn e (k+1) max, σ ) e max, σ π e (k) ( sn xh/ h/ and thus the sze of the problem mesh has dsappeared, and the method converges f max, σ 0 The rate of convergence s essentally ndependent of h, whch s very desrable Department of Computer Scence, Gates Buldng B, Room 80, Stanford, CA E-mal address: golub@stanfordedu 5

10 Problem 1 A = gallery( posson, n) returns the block trdagonal matrx A of order n resultng from dscretzng Posson s equaton wth the 5-pont operator on an n- by-n mesh Choose b so that Ax = b has soluton vector x of all 1 s For n =5, 10, approxmate x by usng the Jacob, Gauss-Sedel, and SOR wth optmal ω Problem Do the same procedure for Hlbert matrx H of sze n =50, 100