A Jacobi-Davidson method for two real parameter nonlinear eigenvalue problems arising from delay differential equations

Transcription

1 A Jacobi-Davidson method for two real parameter nonlinear eigenvalue problems arising from delay differential equations Heinrich Voss Joint work with Karl Meerbergen (KU Leuven) and Christian Schröder (TU Berlin) Hamburg University of Technology Institute of Mathematics TUHH Heinrich Voss Delay JD Beijing, May / 41

2 Outline 1 Problem definition 2 small critical delay problems 3 Iterative projection methods for nonlinear eigenproblems 4 A Jacobi-Davidson-type method for two-real-param. EVP 5 Numerical Experience TUHH Heinrich Voss Delay JD Beijing, May / 41

3 Outline Problem definition 1 Problem definition 2 small critical delay problems 3 Iterative projection methods for nonlinear eigenproblems 4 A Jacobi-Davidson-type method for two-real-param. EVP 5 Numerical Experience TUHH Heinrich Voss Delay JD Beijing, May / 41

4 Problem Problem definition Consider the linear time-invariant delay differential equation Mẋ(t) + Ax(t) + Bx(t τ) = 0 with given M, A, B C n n ; τ 0 is the delay TUHH Heinrich Voss Delay JD Beijing, May / 41

5 Problem Problem definition Consider the linear time-invariant delay differential equation Mẋ(t) + Ax(t) + Bx(t τ) = 0 with given M, A, B C n n ; τ 0 is the delay This system is (asymptotically) stable if, for every bounded initial condition, it holds that x(t) 0 as t TUHH Heinrich Voss Delay JD Beijing, May / 41

6 Problem Problem definition Consider the linear time-invariant delay differential equation Mẋ(t) + Ax(t) + Bx(t τ) = 0 with given M, A, B C n n ; τ 0 is the delay This system is (asymptotically) stable if, for every bounded initial condition, it holds that x(t) 0 as t Necessary and sufficient is that the spectrum of the nonlinear eigenvalue problem λmu + Au + e τλ Bu = 0 is contained in the open left half plane. TUHH Heinrich Voss Delay JD Beijing, May / 41

7 Problem cnt. Problem definition Approach at hand: homotopy, i.e. follow eigenvalues close to imaginary axis for changing τ TUHH Heinrich Voss Delay JD Beijing, May / 41

8 Problem cnt. Problem definition Approach at hand: homotopy, i.e. follow eigenvalues close to imaginary axis for changing τ Problem expensive and unreliable; a different eigenvalue curve may have crossed the imaginary axis before the followed one arrives it. TUHH Heinrich Voss Delay JD Beijing, May / 41

9 Problem cnt. Problem definition Approach at hand: homotopy, i.e. follow eigenvalues close to imaginary axis for changing τ Problem expensive and unreliable; a different eigenvalue curve may have crossed the imaginary axis before the followed one arrives it. Wanted: critical delays τ where system changes stability necessary: system has a purely imaginary eigenvalue TUHH Heinrich Voss Delay JD Beijing, May / 41

10 Outline small critical delay problems 1 Problem definition 2 small critical delay problems 3 Iterative projection methods for nonlinear eigenproblems 4 A Jacobi-Davidson-type method for two-real-param. EVP 5 Numerical Experience TUHH Heinrich Voss Delay JD Beijing, May / 41

11 small critical delay problems Solving small critical delay problems Recall the DEVP (iωm + A + e iωτ B)u = 0 where M, A, B C n n. We are interested in solutions (ω, τ, u) R R C n, u 0. TUHH Heinrich Voss Delay JD Beijing, May / 41

12 small critical delay problems Solving small critical delay problems Recall the DEVP where M, A, B C n n. (iωm + A + e iωτ B)u = 0 We are interested in solutions (ω, τ, u) R R C n, u 0. Introducing the parameter µ = e iωτ translates the problem to the two-parameter problem iωmu + Au + µbu = 0. TUHH Heinrich Voss Delay JD Beijing, May / 41

13 small critical delay problems Solving small critical delay problems Recall the DEVP where M, A, B C n n. (iωm + A + e iωτ B)u = 0 We are interested in solutions (ω, τ, u) R R C n, u 0. Introducing the parameter µ = e iωτ translates the problem to the two-parameter problem iωmu + Au + µbu = 0. Note that µ lies on the unit circle, thus µ = µ 1. TUHH Heinrich Voss Delay JD Beijing, May / 41

14 small critical delay problems Solving small critical delay problems Recall the DEVP where M, A, B C n n. (iωm + A + e iωτ B)u = 0 We are interested in solutions (ω, τ, u) R R C n, u 0. Introducing the parameter µ = e iωτ translates the problem to the two-parameter problem iωmu + Au + µbu = 0. Note that µ lies on the unit circle, thus µ = µ 1. Hence the complex conjugate equation reads iω Mū + Āū + µ 1 Bū = 0. TUHH Heinrich Voss Delay JD Beijing, May / 41

15 small critical delay problems Solving small critical delay problems Using Kronecker products we eliminate ω TUHH Heinrich Voss Delay JD Beijing, May / 41

16 small critical delay problems Solving small critical delay problems Using Kronecker products we eliminate ω 0 = (iωmu) Mū Mu ( iω Mū) = (Au + µbu) Mū + Mu (Āū + µ 1 Bū) = ((A + µb) M + M (Ā + µ 1 B))(u ū). and arrive at a rational eigenvalue problem. TUHH Heinrich Voss Delay JD Beijing, May / 41

17 small critical delay problems Solving small critical delay problems Using Kronecker products we eliminate ω 0 = (iωmu) Mū Mu ( iω Mū) = (Au + µbu) Mū + Mu (Āū + µ 1 Bū) = ((A + µb) M + M (Ā + µ 1 B))(u ū). and arrive at a rational eigenvalue problem. Expanding and multiplication by µ yields the quadratic eigenvalue problem µ 2 (B M)z + µ(a M + M Ā)z + (M B)z = 0. TUHH Heinrich Voss Delay JD Beijing, May / 41

18 small critical delay problems Solving small critical delay problems Using Kronecker products we eliminate ω 0 = (iωmu) Mū Mu ( iω Mū) = (Au + µbu) Mū + Mu (Āū + µ 1 Bū) = ((A + µb) M + M (Ā + µ 1 B))(u ū). and arrive at a rational eigenvalue problem. Expanding and multiplication by µ yields the quadratic eigenvalue problem µ 2 (B M)z + µ(a M + M Ā)z + (M B)z = 0. We have thus shown that if (ω, τ, u) is a solution of the DEVP and µ = e iωτ is on the unit circle, then (µ, u ū) is an eigenpair of the quadratic eigenvalue problem (QEVP) above. TUHH Heinrich Voss Delay JD Beijing, May / 41

19 small critical delay problems Solving small critical delay problems The converse is also true: TUHH Heinrich Voss Delay JD Beijing, May / 41

20 small critical delay problems Solving small critical delay problems The converse is also true: Theorem Let M, A, B C n n with M nonsingular. Then any eigenvector z C n2 of QEVP corresponding to a simple eigenvalue µ C can be written as z = αu 1 u 2 for some vectors u 1, u 2 C n and some α C. TUHH Heinrich Voss Delay JD Beijing, May / 41

21 small critical delay problems Solving small critical delay problems The converse is also true: Theorem Let M, A, B C n n with M nonsingular. Then any eigenvector z C n2 of QEVP corresponding to a simple eigenvalue µ C can be written as z = αu 1 u 2 for some vectors u 1, u 2 C n and some α C. Moreover, if µ = 1 then u 1 = ū 2 = u and there is an ω R such that DEVP holds. TUHH Heinrich Voss Delay JD Beijing, May / 41

22 small critical delay problems Solving small critical delay problems The converse is also true: Theorem Let M, A, B C n n with M nonsingular. Then any eigenvector z C n2 of QEVP corresponding to a simple eigenvalue µ C can be written as z = αu 1 u 2 for some vectors u 1, u 2 C n and some α C. Moreover, if µ = 1 then u 1 = ū 2 = u and there is an ω R such that DEVP holds. We have thus transformed the problem of finding solutions of the DEVP to the problem of finding eigenvalues of modulus one of a QEVP. TUHH Heinrich Voss Delay JD Beijing, May / 41

23 small critical delay problems Solving small critical delay problems The converse is also true: Theorem Let M, A, B C n n with M nonsingular. Then any eigenvector z C n2 of QEVP corresponding to a simple eigenvalue µ C can be written as z = αu 1 u 2 for some vectors u 1, u 2 C n and some α C. Moreover, if µ = 1 then u 1 = ū 2 = u and there is an ω R such that DEVP holds. We have thus transformed the problem of finding solutions of the DEVP to the problem of finding eigenvalues of modulus one of a QEVP. This can be done by the structure preserving method similar to the method presented in Fassbender,Mackey,Mackey,Schröder (2008) TUHH Heinrich Voss Delay JD Beijing, May / 41

24 small critical delay problems Solving small critical delay problems Once an eigenpair (µ, z) of QEVP is known, u can be recovered from z = αu ū. TUHH Heinrich Voss Delay JD Beijing, May / 41

25 small critical delay problems Solving small critical delay problems Once an eigenpair (µ, z) of QEVP is known, u can be recovered from z = αu ū. One possibility is to compute the dominant singular vector of the n n matrix Z with z = vec(z ). Alternatively, one could chose u as a column of Z scaled such that u has norm one. TUHH Heinrich Voss Delay JD Beijing, May / 41

26 small critical delay problems Solving small critical delay problems Once an eigenpair (µ, z) of QEVP is known, u can be recovered from z = αu ū. One possibility is to compute the dominant singular vector of the n n matrix Z with z = vec(z ). Alternatively, one could chose u as a column of Z scaled such that u has norm one. Subsequently ω can be obtained by projection, i.e. ω = Im(Mu)H (Au + µbu) Mu 2. 2 TUHH Heinrich Voss Delay JD Beijing, May / 41

27 small critical delay problems Solving small critical delay problems Once an eigenpair (µ, z) of QEVP is known, u can be recovered from z = αu ū. One possibility is to compute the dominant singular vector of the n n matrix Z with z = vec(z ). Alternatively, one could chose u as a column of Z scaled such that u has norm one. Subsequently ω can be obtained by projection, i.e. ω = Im(Mu)H (Au + µbu) Mu 2. 2 Finally, τ may be computed from µ and ω as τ = Im( ln(µ))/ω. TUHH Heinrich Voss Delay JD Beijing, May / 41

28 small critical delay problems Structure preserving method for QEVP µ 2 (B M)z + µ(a M + M Ā)z + (M B)z = 0. TUHH Heinrich Voss Delay JD Beijing, May / 41

29 small critical delay problems Structure preserving method for QEVP µ 2 (B M)z + µ(a M + M Ā)z + (M B)z = 0. Recall that the Kronecker product does, in general, not commute, i.e. A B B A. However there is a symmetric permutation matrix P such that A B = P(B A)P. TUHH Heinrich Voss Delay JD Beijing, May / 41

30 small critical delay problems Structure preserving method for QEVP µ 2 (B M)z + µ(a M + M Ā)z + (M B)z = 0. Recall that the Kronecker product does, in general, not commute, i.e. A B B A. However there is a symmetric permutation matrix P such that A B = P(B A)P. Hence, QEVP can be rewritten as where (µ 2 A 2 + µa 1 + A 0 )z = 0 A 2 = PĀ0P, A 1 = PĀ1P, whith P = P 1 = P T R n2 n 2. TUHH Heinrich Voss Delay JD Beijing, May / 41

31 small critical delay problems Structure preserving method for QEVP µ 2 (B M)z + µ(a M + M Ā)z + (M B)z = 0. Recall that the Kronecker product does, in general, not commute, i.e. A B B A. However there is a symmetric permutation matrix P such that A B = P(B A)P. Hence, QEVP can be rewritten as where (µ 2 A 2 + µa 1 + A 0 )z = 0 A 2 = PĀ0P, A 1 = PĀ1P, whith P = P 1 = P T R n2 n 2. Such problems are called PCP-palindromic eigenvalue problems. TUHH Heinrich Voss Delay JD Beijing, May / 41

32 small critical delay problems Structure preserving method for QEVP The method can be divided into three steps. TUHH Heinrich Voss Delay JD Beijing, May / 41

33 small critical delay problems Structure preserving method for QEVP The method can be divided into three steps. First, the quadratic eigenvalue problem is reformulated as ( [ ] ] [ ] [ ]) [ ] 0 P [Ā1 µ Ā2 Ā 0 0 P A1 A + 2 A 0 µz = 0, P 0 Ā 0 Ā 0 P 0 A 0 A 0 z which is a linear PCP-palindromic problem, because with P also [ ] P P is a real symmetric permutation. TUHH Heinrich Voss Delay JD Beijing, May / 41

34 small critical delay problems Structure preserving method for QEVP The method can be divided into three steps. First, the quadratic eigenvalue problem is reformulated as ( [ ] ] [ ] [ ]) [ ] 0 P [Ā1 µ Ā2 Ā 0 0 P A1 A + 2 A 0 µz = 0, P 0 Ā 0 Ā 0 P 0 A 0 A 0 z which is a linear PCP-palindromic problem, because with P also [ ] P P is a real symmetric permutation. Second, using the factorization [ ] 0 P = UU T, where U = 1 [ I P 0 2 P ] ip ii we define [ ] C := U H A1 A 2 A 0 Ū. A 0 A 0 TUHH Heinrich Voss Delay JD Beijing, May / 41

35 small critical delay problems Structure preserving method for QEVP Since U is unitary, i.e. U H T U = I = U Ū, this pencil is equivalent to U H (µ [ ] 0 P [Ā1 Ā2 Ā 0 P 0 Ā 0 Ā 0 ] Ā 0 [Ā1 =µu T Ā2 Ā 0 Ā 0 =µ C + C. ] [ 0 P P 0 [ ] U + U H A1 A 2 A 0 Ū A 0 A 0 ] [ ]) A1 A + 2 A 0 Ū A 0 A 0 TUHH Heinrich Voss Delay JD Beijing, May / 41

36 small critical delay problems Structure preserving method for QEVP Since U is unitary, i.e. U H T U = I = U Ū, this pencil is equivalent to U H (µ [ ] 0 P [Ā1 Ā2 Ā 0 P 0 Ā 0 Ā 0 ] Ā 0 [Ā1 =µu T Ā2 Ā 0 Ā 0 =µ C + C. ] [ 0 P P 0 [ ] U + U H A1 A 2 A 0 Ū A 0 A 0 ] [ ]) A1 A + 2 A 0 Ū A 0 A 0 Third, consider an eigenpair (θ, x) of the real generalized eigenvalue problem or, equivalently, Re(C)x = θim(c)x Cx + i + θ i θ Cx = 0. TUHH Heinrich Voss Delay JD Beijing, May / 41

37 small critical delay problems Structure preserving method for QEVP Since U is unitary, i.e. U H T U = I = U Ū, this pencil is equivalent to U H (µ [ ] 0 P [Ā1 Ā2 Ā 0 P 0 Ā 0 Ā 0 ] Ā 0 [Ā1 =µu T Ā2 Ā 0 Ā 0 =µ C + C. ] [ 0 P P 0 [ ] U + U H A1 A 2 A 0 Ū A 0 A 0 ] [ ]) A1 A + 2 A 0 Ū A 0 A 0 Third, consider an eigenpair (θ, x) of the real generalized eigenvalue problem or, equivalently, Re(C)x = θim(c)x Cx + i + θ i θ Cx = 0. Note that µ := (i + θ)/(i θ) is on the unit circle if and only if θ is real or θ = (the latter resulting in µ = 1). Note that real simple eigenvalues of real pencils can be stably computed by e.g. the real QZ algorithm. TUHH Heinrich Voss Delay JD Beijing, May / 41

38 small critical delay problems Structure preserving method for QEVP Require: M, A, B C n n Ensure: solutions (ω j, τ j, u j ) j=1,... of (iωm + A + e iωτ B)u = 0 1: A 0 = M B, A 1 = A M + M Ā 2: Construct the permutation P [ ] H [ ] [ ] I ip 3: C = 1 A1 PA 0 P A 0 I ip 2 P ii A 0 A 0 P ii 4: Compute all eigenpairs (θ j, x j ) of Re(C)x = θim(c)x where θ j is real 5: for j=1,... do 6: µ j = i+θ j i θ j 7: z j = [I ip]x j 8: Compute u j as dominant singular vector of mat(z j ) 9: ω j = Im((Mu j ) H (Au j +µ j Bu j )) Mu j : τ j = Im( ln(µ j )) ω j 11: end for TUHH Heinrich Voss Delay JD Beijing, May / 41

39 small critical delay problems Structure preserving method for QEVP Require: M, A, B C n n Ensure: solutions (ω j, τ j, u j ) j=1,... of (iωm + A + e iωτ B)u = 0 1: A 0 = M B, A 1 = A M + M Ā 2: Construct the permutation P [ ] H [ ] [ ] I ip 3: C = 1 A1 PA 0 P A 0 I ip 2 P ii A 0 A 0 P ii 4: Compute all eigenpairs (θ j, x j ) of Re(C)x = θim(c)x where θ j is real 5: for j=1,... do 6: µ j = i+θ j i θ j 7: z j = [I ip]x j 8: Compute u j as dominant singular vector of mat(z j ) 9: ω j = Im((Mu j ) H (Au j +µ j Bu j )) Mu j : τ j = Im( ln(µ j )) ω j 11: end for The dominant computational part is step 4 with a complexity of O(n 6 ). TUHH Heinrich Voss Delay JD Beijing, May / 41

40 Iterative projection methods for nonlinear eigenproblems Outline 1 Problem definition 2 small critical delay problems 3 Iterative projection methods for nonlinear eigenproblems 4 A Jacobi-Davidson-type method for two-real-param. EVP 5 Numerical Experience TUHH Heinrich Voss Delay JD Beijing, May / 41

41 Iterative projection methods for nonlinear eigenproblems Iterative projection methods For linear sparse eigenproblems T (λ) = λb A very efficient methods are iterative projection methods (Lanczos, Arnoldi, Jacobi Davidson method, e.g.), where approximations to the wanted eigenvalues and eigenvectors are obtained from projections of the eigenproblem to subspaces of small dimension which are expanded in the course of the algorithm. TUHH Heinrich Voss Delay JD Beijing, May / 41

42 Iterative projection methods for nonlinear eigenproblems Iterative projection methods For linear sparse eigenproblems T (λ) = λb A very efficient methods are iterative projection methods (Lanczos, Arnoldi, Jacobi Davidson method, e.g.), where approximations to the wanted eigenvalues and eigenvectors are obtained from projections of the eigenproblem to subspaces of small dimension which are expanded in the course of the algorithm. Generalizations to nonlinear sparse eigenproblems nonlinear rational Krylov: Ruhe(2000,2005), Jarlebring, V. (2005)) TUHH Heinrich Voss Delay JD Beijing, May / 41

43 Iterative projection methods for nonlinear eigenproblems Iterative projection methods For linear sparse eigenproblems T (λ) = λb A very efficient methods are iterative projection methods (Lanczos, Arnoldi, Jacobi Davidson method, e.g.), where approximations to the wanted eigenvalues and eigenvectors are obtained from projections of the eigenproblem to subspaces of small dimension which are expanded in the course of the algorithm. Generalizations to nonlinear sparse eigenproblems nonlinear rational Krylov: Ruhe(2000,2005), Jarlebring, V. (2005)) Arnoldi method: quadratic probl.: Meerbergen (2001) general problems: V. (2003,2004); Liao, Bai, Lee, Ko (2006), Liao (2007) TUHH Heinrich Voss Delay JD Beijing, May / 41

44 Iterative projection methods for nonlinear eigenproblems Iterative projection methods For linear sparse eigenproblems T (λ) = λb A very efficient methods are iterative projection methods (Lanczos, Arnoldi, Jacobi Davidson method, e.g.), where approximations to the wanted eigenvalues and eigenvectors are obtained from projections of the eigenproblem to subspaces of small dimension which are expanded in the course of the algorithm. Generalizations to nonlinear sparse eigenproblems nonlinear rational Krylov: Ruhe(2000,2005), Jarlebring, V. (2005)) Arnoldi method: quadratic probl.: Meerbergen (2001) general problems: V. (2003,2004); Liao, Bai, Lee, Ko (2006), Liao (2007) Jacobi-Davidson: polynomial problems: Sleijpen, Boten, Fokkema, van der Vorst (1996) Hwang, Lin, Wang, Wang (2004,2005) general problems:t. Betcke, V. (2004), V. (2004,2007), Schwetlick, Schreiber (2006), Schreiber (2008) TUHH Heinrich Voss Delay JD Beijing, May / 41

45 Iterative projection methods for nonlinear eigenproblems Iterative projection method Require: Initial basis V with V H V = I; set m = 1 1: while m number of wanted eigenvalues do 2: compute eigenpair (µ, y) of projected problem V T T (λ)vy = 0. 3: determine Ritz vector u = Vy, u = 1,and residual r = T (µ)u 4: if r < ε then 5: accept approximate eigenpair λ m = µ, x m = u; increase m m + 1 6: reduce search space V if necessary 7: choose approximation (λ m, u) to next eigenpair, and compute r = T (λ m )u 8: end if 9: expand search space V = [V, vnew] 10: update projected problem 11: end while TUHH Heinrich Voss Delay JD Beijing, May / 41

46 Iterative projection methods for nonlinear eigenproblems Iterative projection method Require: Initial basis V with V H V = I; set m = 1 1: while m number of wanted eigenvalues do 2: compute eigenpair (µ, y) of projected problem V T T (λ)vy = 0. 3: determine Ritz vector u = Vy, u = 1,and residual r = T (µ)u 4: if r < ε then 5: accept approximate eigenpair λ m = µ, x m = u; increase m m + 1 6: reduce search space V if necessary 7: choose approximation (λ m, u) to next eigenpair, and compute r = T (λ m )u 8: end if 9: expand search space V = [V, vnew] 10: update projected problem 11: end while Main tasks expand search space choose eigenpair of projected problem (locking, purging) TUHH Heinrich Voss Delay JD Beijing, May / 41

47 Iterative projection methods for nonlinear eigenproblems Expanding the subspace Given subspace V C n. Expand V by a direction with high approximation potential for the next wanted eigenvector. TUHH Heinrich Voss Delay JD Beijing, May / 41

48 Iterative projection methods for nonlinear eigenproblems Expanding the subspace Given subspace V C n. Expand V by a direction with high approximation potential for the next wanted eigenvector. Let θ be an eigenvalue of the projected problem V H T (λ)vy = 0 and x = Vy corresponding Ritz vector, then inverse iteration yields suitable candidate v := T (θ) 1 T (θ)x TUHH Heinrich Voss Delay JD Beijing, May / 41

49 Iterative projection methods for nonlinear eigenproblems Expanding the subspace Given subspace V C n. Expand V by a direction with high approximation potential for the next wanted eigenvector. Let θ be an eigenvalue of the projected problem V H T (λ)vy = 0 and x = Vy corresponding Ritz vector, then inverse iteration yields suitable candidate v := T (θ) 1 T (θ)x BUT: In each step have to solve large linear system with varying matrix TUHH Heinrich Voss Delay JD Beijing, May / 41

50 Iterative projection methods for nonlinear eigenproblems Expanding the subspace Given subspace V C n. Expand V by a direction with high approximation potential for the next wanted eigenvector. Let θ be an eigenvalue of the projected problem V H T (λ)vy = 0 and x = Vy corresponding Ritz vector, then inverse iteration yields suitable candidate v := T (θ) 1 T (θ)x BUT: In each step have to solve large linear system with varying matrix and in a truly large problem the vector v will not be accessible but only an inexact solution ṽ := v + e of T (θ)v = T (θ)x, and the next iterate will be a solution of the projection of T (λ)x = 0 upon the expanded space Ṽ := span{v, ṽ}. TUHH Heinrich Voss Delay JD Beijing, May / 41

51 Iterative projection methods for nonlinear eigenproblems Expansion of search space ct. We assume that x is already a good approximation to an eigenvector of T ( ). Then v will be an even better approximation, and therefore the eigenvector we are looking for will be very close to the plane E := span{x, v}. TUHH Heinrich Voss Delay JD Beijing, May / 41

52 Iterative projection methods for nonlinear eigenproblems Expansion of search space ct. We assume that x is already a good approximation to an eigenvector of T ( ). Then v will be an even better approximation, and therefore the eigenvector we are looking for will be very close to the plane E := span{x, v}. We therefore neglect the influence of the orthogonal complement of x in V on the next iterate and discuss the nearness of the planes E and Ẽ := span{x, ṽ}. TUHH Heinrich Voss Delay JD Beijing, May / 41

53 Iterative projection methods for nonlinear eigenproblems Expansion of search space ct. We assume that x is already a good approximation to an eigenvector of T ( ). Then v will be an even better approximation, and therefore the eigenvector we are looking for will be very close to the plane E := span{x, v}. We therefore neglect the influence of the orthogonal complement of x in V on the next iterate and discuss the nearness of the planes E and Ẽ := span{x, ṽ}. If the angle between these two planes is small, then the projection of T (λ) upon Ṽ should be similar to the one upon span{v, v}, and the approximation properties of inverse iteration should be maintained. TUHH Heinrich Voss Delay JD Beijing, May / 41

54 Iterative projection methods for nonlinear eigenproblems Expansion of search space ct. We assume that x is already a good approximation to an eigenvector of T ( ). Then v will be an even better approximation, and therefore the eigenvector we are looking for will be very close to the plane E := span{x, v}. We therefore neglect the influence of the orthogonal complement of x in V on the next iterate and discuss the nearness of the planes E and Ẽ := span{x, ṽ}. If the angle between these two planes is small, then the projection of T (λ) upon Ṽ should be similar to the one upon span{v, v}, and the approximation properties of inverse iteration should be maintained. If this angle can become large, then it is not surprising that the convergence properties of inverse iteration are not reflected by the projection method. TUHH Heinrich Voss Delay JD Beijing, May / 41

55 Iterative projection methods for nonlinear eigenproblems Theorem Let φ 0 = arccos(x T v) denote the angle between x and v, and the relative error of ṽ by ε := e. TUHH Heinrich Voss Delay JD Beijing, May / 41

56 Iterative projection methods for nonlinear eigenproblems Theorem Let φ 0 = arccos(x T v) denote the angle between x and v, and the relative error of ṽ by ε := e. Then the maximal possible acute angle between the planes E and Ẽ is { β(ε) = arccos 1 ε 2 / sin 2 φ 0 if ε sin φ 0 π 2 if ε sin φ 0 TUHH Heinrich Voss Delay JD Beijing, May / 41

57 Iterative projection methods for nonlinear eigenproblems Proof TUHH Heinrich Voss Delay JD Beijing, May / 41

58 Iterative projection methods for nonlinear eigenproblems Expansion by inexact inverse iteration Obviously for every α R, α 0 the plane E is also spanned by x and x + αv. TUHH Heinrich Voss Delay JD Beijing, May / 41

59 Iterative projection methods for nonlinear eigenproblems Expansion by inexact inverse iteration Obviously for every α R, α 0 the plane E is also spanned by x and x + αv. If Ẽ(α) is the plane which is spanned by x and a perturbed realization x + αv + e of x + αv then by the same arguments as in the proof of the Theorem the maximum angle between E and Ẽ(α) is { γ(α, ε) = arccos 1 ε 2 / sin 2 φ(α) if ε sin φ(α) π 2 if ε sin φ(α) where φ(α) denotes the angle between x and x + αv. TUHH Heinrich Voss Delay JD Beijing, May / 41

60 Iterative projection methods for nonlinear eigenproblems Expansion by inexact inverse iteration Obviously for every α R, α 0 the plane E is also spanned by x and x + αv. If Ẽ(α) is the plane which is spanned by x and a perturbed realization x + αv + e of x + αv then by the same arguments as in the proof of the Theorem the maximum angle between E and Ẽ(α) is { γ(α, ε) = arccos 1 ε 2 / sin 2 φ(α) if ε sin φ(α) π 2 if ε sin φ(α) where φ(α) denotes the angle between x and x + αv. Since the mapping φ arccos 1 ε 2 / sin 2 φ decreases monotonically the expansion of the search space by an inexact realization of t := x + αv is most robust with respect to small perturbations, if α is chosen such that x and x + αv are orthogonal TUHH Heinrich Voss Delay JD Beijing, May / 41

61 Iterative projection methods for nonlinear eigenproblems Expansion by inexact inverse iteration ct. t := x + αv is orthogonal to x iff v = x x H x x H T (θ) 1 T (θ)x T (θ) 1 T (θ)x.. ( ) TUHH Heinrich Voss Delay JD Beijing, May / 41

62 Iterative projection methods for nonlinear eigenproblems Expansion by inexact inverse iteration ct. t := x + αv is orthogonal to x iff v = x x H x x H T (θ) 1 T (θ)x T (θ) 1 T (θ)x.. ( ) which yields a maximum acute angle between E and Ẽ(α) { arccos 1 ε 2 if ε 1 γ(α, ε) = π 2 if ε 1. TUHH Heinrich Voss Delay JD Beijing, May / 41

63 Iterative projection methods for nonlinear eigenproblems Expansion by inexact inverse iteration TUHH Heinrich Voss Delay JD Beijing, May / 41

64 Iterative projection methods for nonlinear eigenproblems Expansion by inexact inverse iteration ct. TUHH Heinrich Voss Delay JD Beijing, May / 41

65 Iterative projection methods for nonlinear eigenproblems Expansion by inexact inverse iteration ct. TUHH Heinrich Voss Delay JD Beijing, May / 41

66 Iterative projection methods for nonlinear eigenproblems Jacobi Davidson method The expansion v = x x H x x H T (θ) 1 T (θ)x T (θ) 1 T (θ)x.. ( ) of the current search space V is the solution of the equation (I T (θ)xx H x H T (θ)x )T (θ)(i xx H )t = r, t x TUHH Heinrich Voss Delay JD Beijing, May / 41

67 Iterative projection methods for nonlinear eigenproblems Jacobi Davidson method The expansion v = x x H x x H T (θ) 1 T (θ)x T (θ) 1 T (θ)x.. ( ) of the current search space V is the solution of the equation (I T (θ)xx H x H T (θ)x )T (θ)(i xx H )t = r, t x This is the so called correction equation of the Jacobi Davidson method which was derived in T. Betcke & Voss (2004) generalizing the approach of Sleijpen and van der Vorst (1996) for linear and polynomial eigenvalue problems. TUHH Heinrich Voss Delay JD Beijing, May / 41

68 Iterative projection methods for nonlinear eigenproblems Jacobi Davidson method The expansion v = x x H x x H T (θ) 1 T (θ)x T (θ) 1 T (θ)x.. ( ) of the current search space V is the solution of the equation (I T (θ)xx H x H T (θ)x )T (θ)(i xx H )t = r, t x This is the so called correction equation of the Jacobi Davidson method which was derived in T. Betcke & Voss (2004) generalizing the approach of Sleijpen and van der Vorst (1996) for linear and polynomial eigenvalue problems. Hence, the Jacobi Davidson method is the most robust realization of an expansion of a search space such that the direction of inverse iteration is contained in the expanded space in the sense that it is least sensitive to inexact solves of linear systems T (θ)v = T (θ)x. TUHH Heinrich Voss Delay JD Beijing, May / 41

69 Iterative projection methods for nonlinear eigenproblems Nonlinear Jacobi-Davidson 1: Start with orthonormal basis V ; set m = 1 2: determine preconditioner M T (σ) 1 ; σ close to first wanted eigenvalue 3: while m number of wanted eigenvalues do 4: compute eigenpair (µ, y) of projected problem V T T (λ)vy = 0. 5: determine Ritz vector u = Vy, u = 1,and residual r = T (µ)u 6: if r < ε then 7: accept approximate eigenpair λ m = µ, x m = u; increase m m + 1 8: reduce search space V if necessary 9: choose new preconditioner M T (µ) if indicated 10: choose approximation (λ m, u) to next eigenpair, and compute r = T (λ m)u 11: end if 12: solve approximately correction equation (I T (µ)uu H u H T (µ)u ) T (µ) (I uuh u H u ) t = r, 13: t = t VV T t,ṽ = t/ t, reorthogonalize if necessary 14: expand search space V = [V, ṽ] 15: update projected problem 16: end while t u TUHH Heinrich Voss Delay JD Beijing, May / 41

70 Iterative projection methods for nonlinear eigenproblems Correction equation The correction equation is solved approximately by a few steps of an iterative solver (GMRES or BiCGStab). TUHH Heinrich Voss Delay JD Beijing, May / 41

71 Iterative projection methods for nonlinear eigenproblems Correction equation The correction equation is solved approximately by a few steps of an iterative solver (GMRES or BiCGStab). The operator T (σ) is restricted to map the subspace x into itself. Hence, if M T (σ) is a preconditioner of T (σ), σ µ, then a preconditioner for an iterativ solver of the correction equation should be modified correspondingly to M := (I T (µ)xx H H xx x H )M(I T (µ)x x H x ). TUHH Heinrich Voss Delay JD Beijing, May / 41

72 Iterative projection methods for nonlinear eigenproblems Correction equation The correction equation is solved approximately by a few steps of an iterative solver (GMRES or BiCGStab). The operator T (σ) is restricted to map the subspace x into itself. Hence, if M T (σ) is a preconditioner of T (σ), σ µ, then a preconditioner for an iterativ solver of the correction equation should be modified correspondingly to M := (I T (µ)xx H H xx x H )M(I T (µ)x x H x ). Taking into account the projectors in the preconditioner, i.e. using M instead of M, raises the cost of the preconditioned Krylov solver only slightly (cf. Sleijpen, van der Vorst). Only one additional linear solve with system matrix M is required. TUHH Heinrich Voss Delay JD Beijing, May / 41

73 Iterative projection methods for nonlinear eigenproblems Common approach to Jacobi Davidson Expand the search space V by the direction defined by one step of Newton s method applied to ( ) ( T (θ)x 0 x T =, x 1 0) TUHH Heinrich Voss Delay JD Beijing, May / 41

74 Iterative projection methods for nonlinear eigenproblems Common approach to Jacobi Davidson Expand the search space V by the direction defined by one step of Newton s method applied to ( ) ( T (θ)x 0 x T =, x 1 0) i.e. ( ) ( T (θ) T (θ)x v 2x T = 0 α) ( ) T (θ)x 0 TUHH Heinrich Voss Delay JD Beijing, May / 41

75 Iterative projection methods for nonlinear eigenproblems Common approach to Jacobi Davidson Expand the search space V by the direction defined by one step of Newton s method applied to ( ) ( T (θ)x 0 x T =, x 1 0) i.e. ( ) ( T (θ) T (θ)x v 2x T = 0 α) ( ) T (θ)x 0 T (θ)v + αt (θ)x = T (θ)x, 2x T v = 0 TUHH Heinrich Voss Delay JD Beijing, May / 41

76 Iterative projection methods for nonlinear eigenproblems Common approach to Jacobi Davidson Expand the search space V by the direction defined by one step of Newton s method applied to ( ) ( T (θ)x 0 x T =, x 1 0) i.e. ( ) ( T (θ) T (θ)x v 2x T = 0 α) ( ) T (θ)x 0 T (θ)v + αt (θ)x = T (θ)x, 2x T v = 0 v = x αt (θ) 1 T (θ)x, x T v = 0 TUHH Heinrich Voss Delay JD Beijing, May / 41

77 Iterative projection methods for nonlinear eigenproblems Common approach to Jacobi Davidson Expand the search space V by the direction defined by one step of Newton s method applied to ( ) ( T (θ)x 0 x T =, x 1 0) i.e. ( ) ( T (θ) T (θ)x v 2x T = 0 α) ( ) T (θ)x 0 T (θ)v + αt (θ)x = T (θ)x, 2x T v = 0 v = x αt (θ) 1 T (θ)x, x T v = 0 x T x v = x x T T (θ) 1 T (θ)x T (θ) 1 T (θ)x, and the quadratic convergence of Newton s method explains the fast convergence of the Jacobi Davidson method. TUHH Heinrich Voss Delay JD Beijing, May / 41

78 Iterative projection methods for nonlinear eigenproblems Common approach to Jacobi Davidson BUT the resulting correction equation is solved only very inexactly which spoils the good approximation properties of Newton s method. TUHH Heinrich Voss Delay JD Beijing, May / 41 Expand the search space V by the direction defined by one step of Newton s method applied to ( ) ( T (θ)x 0 x T =, x 1 0) i.e. ( ) ( T (θ) T (θ)x v 2x T = 0 α) ( ) T (θ)x 0 T (θ)v + αt (θ)x = T (θ)x, 2x T v = 0 v = x αt (θ) 1 T (θ)x, x T v = 0 x T x v = x x T T (θ) 1 T (θ)x T (θ) 1 T (θ)x, and the quadratic convergence of Newton s method explains the fast convergence of the Jacobi Davidson method.

79 Iterative projection methods for nonlinear eigenproblems More general approach Assume that we are given a base method x k+1 = S(x k, θ k ), θ k+1 = p(x k+1, θ k ) which converges locally ˆx x k+1 = O( ˆx x k q 1 ), ˆλ θ k+1 = O( ˆλ θ k q 2 ). TUHH Heinrich Voss Delay JD Beijing, May / 41

80 Iterative projection methods for nonlinear eigenproblems More general approach Assume that we are given a base method which converges locally Robustification: x k+1 = S(x k, θ k ), θ k+1 = p(x k+1, θ k ) ˆx x k+1 = O( ˆx x k q 1 ), ˆλ θ k+1 = O( ˆλ θ k q 2 ). (i) Find v k = x k + αs(x k, θ k )x k, (t k ) H Bx k = 0 (ii) Choose x k+1 span{x k, v k }, for instance solving the projected problem P H k T (λ)p k z k = 0, x k+1 = P k z k where P k is the orthogonal projector onto span{x k, v k } (iii) θ k+1 = p(x k+1 ) where B is a Hermitian positive definite matrix (for instance if it is known in advance that the eigenvectors are B orthogonal). TUHH Heinrich Voss Delay JD Beijing, May / 41

81 A Jacobi-Davidson-type method for two-real-param. EVP Outline 1 Problem definition 2 small critical delay problems 3 Iterative projection methods for nonlinear eigenproblems 4 A Jacobi-Davidson-type method for two-real-param. EVP 5 Numerical Experience TUHH Heinrich Voss Delay JD Beijing, May / 41

82 A Jacobi-Davidson-type method for two-real-param. EVP A JD-type method for two-real-param. EVP Solve the NEP in two real parameters T (ω, τ)u := (iωm + A + e iωτ B)u = 0 by a straight forward adaption of (nonlinear) JD method: TUHH Heinrich Voss Delay JD Beijing, May / 41

83 A Jacobi-Davidson-type method for two-real-param. EVP A JD-type method for two-real-param. EVP Solve the NEP in two real parameters T (ω, τ)u := (iωm + A + e iωτ B)u = 0 by a straight forward adaption of (nonlinear) JD method: 1 given an ansatz space span(v ) of eigenvector approximations known approximations of eigenvectors or random vector TUHH Heinrich Voss Delay JD Beijing, May / 41

84 A Jacobi-Davidson-type method for two-real-param. EVP A JD-type method for two-real-param. EVP Solve the NEP in two real parameters T (ω, τ)u := (iωm + A + e iωτ B)u = 0 by a straight forward adaption of (nonlinear) JD method: 1 given an ansatz space span(v ) of eigenvector approximations 2 solve projected problem V H T (ω, τ)vz = 0 use presented method for small problems TUHH Heinrich Voss Delay JD Beijing, May / 41

85 A Jacobi-Davidson-type method for two-real-param. EVP A JD-type method for two-real-param. EVP Solve the NEP in two real parameters T (ω, τ)u := (iωm + A + e iωτ B)u = 0 by a straight forward adaption of (nonlinear) JD method: 1 given an ansatz space span(v ) of eigenvector approximations 2 solve projected problem V H T (ω, τ)vz = 0 3 compute approximate eigenvectors u i = Vz i and residuals r i = T (ω i, τ i )u i TUHH Heinrich Voss Delay JD Beijing, May / 41

86 A Jacobi-Davidson-type method for two-real-param. EVP A JD-type method for two-real-param. EVP Solve the NEP in two real parameters T (ω, τ)u := (iωm + A + e iωτ B)u = 0 by a straight forward adaption of (nonlinear) JD method: 1 given an ansatz space span(v ) of eigenvector approximations 2 solve projected problem V H T (ω, τ)vz = 0 3 compute approximate eigenvectors u i = Vz i and residuals r i = T (ω i, τ i )u i 4 stop, if enough eigentriples have converged we use r i 2 tol TUHH Heinrich Voss Delay JD Beijing, May / 41

87 A Jacobi-Davidson-type method for two-real-param. EVP A JD-type method for two-real-param. EVP Solve the NEP in two real parameters T (ω, τ)u := (iωm + A + e iωτ B)u = 0 by a straight forward adaption of (nonlinear) JD method: 1 given an ansatz space span(v ) of eigenvector approximations 2 solve projected problem V H T (ω, τ)vz = 0 3 compute approximate eigenvectors u i = Vz i and residuals r i = T (ω i, τ i )u i 4 stop, if enough eigentriples have converged 5 compute a correction c of an approx. eigenvector û i.e., û + c is a better approximation that s the ineresting part TUHH Heinrich Voss Delay JD Beijing, May / 41

88 A Jacobi-Davidson-type method for two-real-param. EVP A JD-type method for two-real-param. EVP Solve the NEP in two real parameters T (ω, τ)u := (iωm + A + e iωτ B)u = 0 by a straight forward adaption of (nonlinear) JD method: 1 given an ansatz space span(v ) of eigenvector approximations 2 solve projected problem V H T (ω, τ)vz = 0 3 compute approximate eigenvectors u i = Vz i and residuals r i = T (ω i, τ i )u i 4 stop, if enough eigentriples have converged 5 compute a correction c of an approx. eigenvector û i.e., û + c is a better approximation 6 expand V orth[v, c] and GOTO 2 repeat as long as neccessary TUHH Heinrich Voss Delay JD Beijing, May / 41

89 A Jacobi-Davidson-type method for two-real-param. EVP Correction equation - medium sized problems Given: approx. eigentriple (ˆω, ˆτ, û), Wanted: correction (δ, ɛ, c) TUHH Heinrich Voss Delay JD Beijing, May / 41

90 A Jacobi-Davidson-type method for two-real-param. EVP Correction equation - medium sized problems Given: approx. eigentriple (ˆω, ˆτ, û), Wanted: correction (δ, ɛ, c) [ ] T (ˆω + δ, ˆτ + ε)(û + c) one step of Newtons method applied to û H = 0 c TUHH Heinrich Voss Delay JD Beijing, May / 41

91 A Jacobi-Davidson-type method for two-real-param. EVP Correction equation - medium sized problems Given: approx. eigentriple (ˆω, ˆτ, û), Wanted: correction (δ, ɛ, c) [ ] T (ˆω + δ, ˆτ + ε)(û + c) one step of Newtons method applied to û H = 0 c [ ] T (ˆω, ˆτ) Tω (ˆω, ˆτ)û T τ (ˆω, ˆτ)û c [ ] û H δ ˆr = ε linear system, n + 1 complex equations, n complex + 2 real unknowns no standard software available TUHH Heinrich Voss Delay JD Beijing, May / 41

92 A Jacobi-Davidson-type method for two-real-param. EVP Correction equation - medium sized problems Given: approx. eigentriple (ˆω, ˆτ, û), Wanted: correction (δ, ɛ, c) [ ] T (ˆω + δ, ˆτ + ε)(û + c) one step of Newtons method applied to û H = 0 c [ ] T (ˆω, ˆτ) Tω (ˆω, ˆτ)û T τ (ˆω, ˆτ)û c [ ] û H δ ˆr = ε linear system, n + 1 complex equations, n complex + 2 real unknowns no standard software available ˆT 0 ˆTω û ˆTτ û c ˆr 0 ˆT ˆTω û ˆTτ û û H d δ = ˆr 0. 0 û H 0 0 ε 0 TUHH Heinrich Voss Delay JD Beijing, May / 41

93 A Jacobi-Davidson-type method for two-real-param. EVP Correction equation - medium sized problems Given: approx. eigentriple (ˆω, ˆτ, û), Wanted: correction (δ, ɛ, c) [ ] T (ˆω + δ, ˆτ + ε)(û + c) one step of Newtons method applied to û H = 0 c [ ] T (ˆω, ˆτ) Tω (ˆω, ˆτ)û T τ (ˆω, ˆτ)û c [ ] û H δ ˆr = ε linear system, n + 1 complex equations, n complex + 2 real unknowns no standard software available ˆT 0 ˆTω û ˆTτ û c ˆr 0 ˆT ˆTω û ˆTτ û û H d δ = ˆr 0. 0 û H 0 0 ε 0 Lemma: d = c, δ, ɛ R TUHH Heinrich Voss Delay JD Beijing, May / 41

94 A Jacobi-Davidson-type method for two-real-param. EVP Correction equation - medium sized problems Given: approx. eigentriple (ˆω, ˆτ, û), Wanted: correction (δ, ɛ, c) [ ] T (ˆω + δ, ˆτ + ε)(û + c) one step of Newtons method applied to û H = 0 c [ ] T (ˆω, ˆτ) Tω (ˆω, ˆτ)û T τ (ˆω, ˆτ)û c [ ] û H δ ˆr = ε linear system, n + 1 complex equations, n complex + 2 real unknowns no standard software available ˆT 0 ˆTω û ˆTτ û c ˆr 0 ˆT ˆTω û ˆTτ û û H d δ = ˆr 0. 0 û H 0 0 ε 0 Lemma: d = c, δ, ɛ R What if n is large and a preconditioner is available for ˆT only? TUHH Heinrich Voss Delay JD Beijing, May / 41

95 A Jacobi-Davidson-type method for two-real-param. EVP Correction equation - large size With system reads [ ] [ ] ˆT 0 ˆTω û ˆTτ û T =, K =, U = 0 ˆT ˆT ω û ˆTτ û [ ] c r T K c U H 0 δ = r 0. ε 0 [û ] 0 0 û TUHH Heinrich Voss Delay JD Beijing, May / 41

96 A Jacobi-Davidson-type method for two-real-param. EVP Correction equation - large size With system reads [ ] [ ] ˆT 0 ˆTω û ˆTτ û T =, K =, U = 0 ˆT ˆT ω û ˆTτ û [ ] c r T K c U H 0 δ = r 0. ε 0 [û ] 0 0 û Eliminating (δ, ε) yields [ c c] (I K (U H K ) 1 U H )T(I UU H ) [ r r] = (I K (U H K ) 1 U H ) TUHH Heinrich Voss Delay JD Beijing, May / 41

97 A Jacobi-Davidson-type method for two-real-param. EVP Correction equation - large size With system reads [ ] [ ] ˆT 0 ˆTω û ˆTτ û T =, K =, U = 0 ˆT ˆT ω û ˆTτ û [ ] c r T K c U H 0 δ = r 0. ε 0 [û ] 0 0 û Eliminating (δ, ε) yields [ c c] (I K (U H K ) 1 U H )T(I UU H ) [ r r] = (I K (U H K ) 1 U H ) [ [ c c] r r] looks like other JD correction equations: T = is complemented by projectors TUHH Heinrich Voss Delay JD Beijing, May / 41

98 A Jacobi-Davidson-type method for two-real-param. EVP Correction equation - large size With system reads [ ] [ ] ˆT 0 ˆTω û ˆTτ û T =, K =, U = 0 ˆT ˆT ω û ˆTτ û [ ] c r T K c U H 0 δ = r 0. ε 0 [û ] 0 0 û Eliminating (δ, ε) yields [ c c] (I K (U H K ) 1 U H )T(I UU H ) [ r r] = (I K (U H K ) 1 U H ) [ [ c c] r r] looks like other JD correction equations: T = is complemented by projectors How does this help with the preconditioner? TUHH Heinrich Voss Delay JD Beijing, May / 41

99 A Jacobi-Davidson-type method for two-real-param. EVP Preconditioning the correction equation Correction equation [ c c] (I K (U H K ) 1 U H )T(I UU H ) [ r r] = (I K (U H K ) 1 U H ) TUHH Heinrich Voss Delay JD Beijing, May / 41

100 A Jacobi-Davidson-type method for two-real-param. EVP Preconditioning the correction equation Correction equation [ c c] (I K (U H K ) 1 U H )T(I UU H ) [ r r] = (I K (U H K ) 1 U H ) preconditioner: (I K (U H K ) 1 U H )P(I UU H ) with P = [ ] P 0 0 P TUHH Heinrich Voss Delay JD Beijing, May / 41

101 A Jacobi-Davidson-type method for two-real-param. EVP Preconditioning the correction equation Correction equation [ c c] (I K (U H K ) 1 U H )T(I UU H ) [ r r] = (I K (U H K ) 1 U H ) preconditioner: (I K (U H K ) 1 U H )P(I UU H ) with P = [ ] P 0 0 P need to apply in a left preconditioned Krylov solver: (I UU H )P 1 (I K (U H P 1 K ) 1 U H P 1 )(I K (U H K ) 1 U H )T(I UU H ) TUHH Heinrich Voss Delay JD Beijing, May / 41

102 A Jacobi-Davidson-type method for two-real-param. EVP Preconditioning the correction equation Correction equation [ c c] (I K (U H K ) 1 U H )T(I UU H ) [ r r] = (I K (U H K ) 1 U H ) preconditioner: (I K (U H K ) 1 U H )P(I UU H ) with P = [ ] P 0 0 P need to apply in a left preconditioned Krylov solver: (I UU H )P 1 (I K (U H P 1 K ) 1 U H P 1 )(I K (U H K ) 1 U H )T(I UU H ) Efficient implementation needs one application of P 1 ˆT per iteration and additionally 2 T products and 3 P solves as in other JD variants TUHH Heinrich Voss Delay JD Beijing, May / 41

103 A Jacobi-Davidson-type method for two-real-param. EVP Some details Alternative expansion sometimes the projected problem has no solution (Re(C)x = θim(c)x has no real eigenvalues) TUHH Heinrich Voss Delay JD Beijing, May / 41

104 A Jacobi-Davidson-type method for two-real-param. EVP Some details Alternative expansion sometimes the projected problem has no solution (Re(C)x = θim(c)x has no real eigenvalues) then what to use as approximate Ritz triple for the correction equation? TUHH Heinrich Voss Delay JD Beijing, May / 41

105 A Jacobi-Davidson-type method for two-real-param. EVP Some details Alternative expansion sometimes the projected problem has no solution (Re(C)x = θim(c)x has no real eigenvalues) then what to use as approximate Ritz triple for the correction equation? we use ˆω = 0, e i ˆωˆτ = σ (given) and û span(v ) such that T (ˆω, ˆτ)û 2 = min TUHH Heinrich Voss Delay JD Beijing, May / 41

106 A Jacobi-Davidson-type method for two-real-param. EVP Some details Alternative expansion Restart sometimes the projected problem has no solution (Re(C)x = θim(c)x has no real eigenvalues) then what to use as approximate Ritz triple for the correction equation? we use ˆω = 0, e i ˆωˆτ = σ (given) and û span(v ) such that T (ˆω, ˆτ)û 2 = min cost grows like O(k 6 ), so becomes prohibitve for larger k TUHH Heinrich Voss Delay JD Beijing, May / 41

107 A Jacobi-Davidson-type method for two-real-param. EVP Some details Alternative expansion Restart sometimes the projected problem has no solution (Re(C)x = θim(c)x has no real eigenvalues) then what to use as approximate Ritz triple for the correction equation? we use ˆω = 0, e i ˆωˆτ = σ (given) and û span(v ) such that T (ˆω, ˆτ)û 2 = min cost grows like O(k 6 ), so becomes prohibitve for larger k restart with space spanned by converged eigenvectors and a few unconverged ones with the best residuals TUHH Heinrich Voss Delay JD Beijing, May / 41

108 A Jacobi-Davidson-type method for two-real-param. EVP Some details Alternative expansion Restart sometimes the projected problem has no solution (Re(C)x = θim(c)x has no real eigenvalues) then what to use as approximate Ritz triple for the correction equation? we use ˆω = 0, e i ˆωˆτ = σ (given) and û span(v ) such that T (ˆω, ˆτ)û 2 = min cost grows like O(k 6 ), so becomes prohibitve for larger k restart with space spanned by converged eigenvectors and a few unconverged ones with the best residuals Real problems if M, A, B are real, then eigentriples come in pairs (ω, τ, u), ( ω, τ, u) TUHH Heinrich Voss Delay JD Beijing, May / 41

109 A Jacobi-Davidson-type method for two-real-param. EVP Some details Alternative expansion Restart sometimes the projected problem has no solution (Re(C)x = θim(c)x has no real eigenvalues) then what to use as approximate Ritz triple for the correction equation? we use ˆω = 0, e i ˆωˆτ = σ (given) and û span(v ) such that T (ˆω, ˆτ)û 2 = min cost grows like O(k 6 ), so becomes prohibitve for larger k restart with space spanned by converged eigenvectors and a few unconverged ones with the best residuals Real problems if M, A, B are real, then eigentriples come in pairs (ω, τ, u), ( ω, τ, u) when an eigenvector u converges, add u to searchspace TUHH Heinrich Voss Delay JD Beijing, May / 41

110 Outline Numerical Experience 1 Problem definition 2 small critical delay problems 3 Iterative projection methods for nonlinear eigenproblems 4 A Jacobi-Davidson-type method for two-real-param. EVP 5 Numerical Experience TUHH Heinrich Voss Delay JD Beijing, May / 41

111 Numerical Experience Numerical example Consider the parabolic problem u t ((1 + x 2 + y 2 + z 2 ) u) + [1, 0, 1] u + u α(1 + x 2 )u(t τ) = 0 (1) with spatial variables x, y and z on Ω = (0, 1) (0, 1) (0, 1) with Dirichlet boundary condition u = 0 on Ω. TUHH Heinrich Voss Delay JD Beijing, May / 41

112 Numerical Experience Numerical example Consider the parabolic problem u t ((1 + x 2 + y 2 + z 2 ) u) + [1, 0, 1] u + u α(1 + x 2 )u(t τ) = 0 (1) with spatial variables x, y and z on Ω = (0, 1) (0, 1) (0, 1) with Dirichlet boundary condition u = 0 on Ω. A discretization with piecewise quadratic ansatz functions on a tetrahedral grid using COMSOL yielded an eigenvalue problem of dimension n = Mẋ(t) + Ax(t) + Bx(t τ) = 0 TUHH Heinrich Voss Delay JD Beijing, May / 41

113 Numerical Experience Numerical example Consider the parabolic problem u t ((1 + x 2 + y 2 + z 2 ) u) + [1, 0, 1] u + u α(1 + x 2 )u(t τ) = 0 (1) with spatial variables x, y and z on Ω = (0, 1) (0, 1) (0, 1) with Dirichlet boundary condition u = 0 on Ω. A discretization with piecewise quadratic ansatz functions on a tetrahedral grid using COMSOL yielded an eigenvalue problem of dimension n = Mẋ(t) + Ax(t) + Bx(t τ) = 0 For α = 100 the problem has four pairs of eigentriples. TUHH Heinrich Voss Delay JD Beijing, May / 41

114 Numerical Experience Numerical Example 10 0 Convergence History Example residual norm iteration TUHH Heinrich Voss Delay JD Beijing, May / 41

115 Numerical Experience Numerical Example 10 0 Convergence History Example residual norm iteration x axis: number of iteration: tic=5; together 31 TUHH Heinrich Voss Delay JD Beijing, May / 41

116 Numerical Experience Numerical Example 10 0 Convergence History Example residual norm iteration y axis: norm of residual: tic=10 2 ; tolerance= TUHH Heinrich Voss Delay JD Beijing, May / 41