Introduction to Krylov subspace methods - IMAGe

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jx j − b kMinimizationjm∑•j=1Simplest way to find j=1 a minimum?∇g = Ax − b = 0 ⇐⇒•∇g = Ax − b = 0 ⇐⇒ AxStart with a guess ∗ Ax ∗ = b,= b,solution• and search directionx 1 = x 0 + αp• controlled g(x 1 ) = g(x 0 by + αp) a step sizej=1∴ (∇g) k ∑ j − b k= 1 ∑= ∴ 1 (∇g) a kja kj x j + 1 x∑j + 1 2 k = aa2kj x ja ik −xb ik− b kj2 j=1 ∇g 2 = Axik xi− bj=1bk= 0 ⇐⇒ Ax ∗ = b,Ax − b = 0 ⇐⇒ It means Ax ∗ = that ∴ b, (∇g) the xm∑∗ k = : solution solution a kj x j −of bof k Ax Ax = = b is b also thx ∗ : solution of Ax = bolutionNow, what’sof Axthe=best Now, b isand also what’s simplestthemethod minimum best to and of g. simplest methodx ∗ xfind ∗ : solutiona minimum?of Ax = beminimum Starting with a ”guess”best and simplest of Starting g.x 0 ∈ Rmethod with m and a non-zero: solutionvectora to ”guess” find axminimum?0 ofp ∈Ax∈ R R m =withbstep lengthand a non-zeItdetermined It meansmeans thatin thatthe a solution waythetosolutionminimizeof Ax = b is also the of g.of Ax = b is also the minimum of g.to ess” find Now,Now,x 0 what’s ∈a what’sRminimum?the m determined besttheand andbesta simplestand simplestnon-zeroinmethod a way methodvector findto minimizeafindpminimum?a minimum?∈ R m with step leny to minimizeodetermined vector determined pa way in ∈a Rto way minimizem towith minimizestep length αWex 1 get:= x 0 + αpx 1 ) = g(x 0 + αp)2 i,j2∑ m∑ i,j ∑ i=1∇g = Ax − b = 0 ⇐⇒ Ax ∗ = b,It means that the solution∴ of (∇g) Ax = k b=is also a kj the x j −minimum b k of g.StartingStartingwithwitha ”guess”a ”guess”x 0 ∈ Rx 0 m ∈andR m aandnon-zeroa non-zerovectorvectorp ∈ R m pwith∈ R m stepwithlengthstepαlengx 1 = x 0 + αpg(x 1 ) = g(x 0 + αp)= 1 2 〈A(x0 + αp), (x 0 += 1 2 〈A(x0 dg(x 1 dg(x ) + 1 αp), (x 0 dg(x αp)〉 1 )− 〈b, x 0 + αp〉dα= α〈Ap, )But:p〉 + 〈Ax0 − b, p〉 =dα = 0 and α〈Ap, then we p〉 find + ”α”dα= α〈Ap, p〉 + 〈Ax0 〈Ap, − b, p〉 p〉 = 0 and then we find ”α”〈Ax0 − b, p〉Leads to optimal step size: ∴ α = 〈r0 , p〉ix 1 = x= 1 1 0 + = αp x 0 + αp2 〈A(x0 + αp), (x 0 + αp)〉 − 〈b, x 0 + αp〉g(x 1 ) g(x = g(x 1 ) = 0 + g(x αp)0 + αp)dg(x 1 )dα= = α〈Ap, 1 2 〈A(x0 = p〉 1 + αp), (x 0 + αp)〉 − 〈b, x 0 + αp〉2 〈A(x0 〈Ax0 − b, p〉+ αp), (x 0 = 0 and then we+ αp)〉 − 〈b, x 0 find ”α”+ αp〉α = −〈Ax0 − b, p〉p)〉 − 〈b, x 0 α = −〈Ax0 − b, p〉α = −〈Ax0 − b, p〉+ αp〉 0〈Ap, p〉〈Ap, p〉〈Ap, p〉x ∗ : solution of Ax = b012
SUBSPACE METHODSKRYLOV SUBSPACE METHODS 2Minimizationdon’t decide the p yet. Problem if r 0 ⊥ p ?cide the p yet. Problem if r 0 ⊥ p ?ice thatWhere division is ensured because of SPDnessWhat is p ?∣d 2 g ∣∣∣xdα 2 0 +αpGradient Methodo known as(AKA) ”Steepest Descent(SD)”= 〈Ap, p〉 = p T Ap > 0x 1 = x 0 + 〈r0 , p〉〈Ap, p〉 p (5.4• What is a good search direction?m (5.4), taking p = r 0 ,∣d 2 g ∣∣∣xdα 2 0 +αp= 〈Ap, p〉 = p T Ap > 0THODS 23blem if If r 0 ⊥ p ?p ?dient Methodx 1 = x 0 + 〈r0 , p〉〈Ap, p〉 pthe method “stalls”p = −∇g = −(Ax − b) = b − Ax = r= 〈Ap, p〉 = p T Ap > 013
Page 1 and 2: Introduction to Krylovsubspace meth
Page 3 and 4: Why iterative methods?• Most disc
Page 5 and 6: et r n = f − Au n ,Let r n = f
Page 7 and 8: Gauss-Seidel A = L + D + U ; M = L
Page 11: For a SPD matrix A, considerg(x) :=
Page 15 and 16: Steepest descent (SD)• Homework:
Page 17 and 18: 〈Ap, p〉r k 〉 = 〈r k , r k
Page 19 and 20: ∴α i = − 〈di , e i 〉 A〈d
Page 21 and 22: 〈r , p〉 = 〈r , p〉 − α
Page 23 and 24: iFrom:Suppose u i = r i (cheap!)p i
Page 25 and 26: e A-conjugate GS,ThenConjugatee i =
Page 27 and 28: α i−1 〈Ap i−1 , p i−1 〉
Page 29 and 30: (√w understand that CG/SDesc./Ric
Page 31 and 32: Projection methods5 KRYLOV SUBSPACE
Page 33 and 34: TakingV T m AV m y = H m r 0 = H m
Page 35 and 36: 5 KRYLOV SUBSPACE METHODSRemarks :F
Page 37 and 38: GMRES5 KRYLOV SUBSPACE METHODS 36r
Page 39: Thank you!39

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