A Cholesky LR algorithm for the positive definite symmetric diagonal-plus-semiseparable eigenproblem

Transcription

1 A Cholesky LR algorithm for the positive definite symmetric diagonal-plus-semiseparable eigenproblem Bor Plestenjak Department of Mathematics University of Ljubljana Slovenia Ellen Van Camp and Marc Van Barel Department of Computer Science Katholieke Universiteit Leuven Belgium International Conference on Matrix Methods and Operator Equations, Moscow, June 20-25,

2 Outline Introduction Cholesky LR algorithm Laguerre s shift Implementation Numerical examples Conclusions International Conference on Matrix Methods and Operator Equations, Moscow, June 20-25,

3 Introduction semiseparable: every submatrix from the lower or upper triangular part has rank at most 1. diagonal-plus-semiseparable (DPSS): the sum D + S of a diagonal D and a semiseparable S. Givens-vector representation of a DPSS matrix is based on a vector f = [f 1,..., f n ] T, Givens [ ] ci s rotations G i = i, i = 1,..., n 1, and a diagonal d = [d 1,..., d n ] T as s i c i c 1 f 1 + d 1 c 2 s 1 f 1 c n 1 s n 2:1 f 1 s n 1:1 f 1 c 2 s 1 f 1 c 2 f 2 + d 2 c n 1 s n 2:1 f 2 s n 1:2 f 2 D + S =....., c n 1 s n 2:1 f 1 c n 1 s n 2:2 f 2 c n 1 f n 1 + d n 1 s n 1 f n 1 s n 1:1 f 1 s n 1:2 f 2 s n 1 f n 1 f n + d n where s a:b = s a s a 1 s b. When they appear, we assume that c n = 1 and s n = 0. We denote D + S = diag(d) + Giv(c, s, f). Our goal is to compute the smallest (or all) eigenvalues of a s.p.d. DPSS matrix. International Conference on Matrix Methods and Operator Equations, Moscow, June 20-25,

4 Motivation Let A be an n n symmetric matrix. One can find an orthogonal matrix Q such that B = Q T AQ is tridiagonal in O(n 3 ) flops. Next, the eigendecomposition of B is computed in O(n 2 ) flops. Instead of the tridiagonal structure one can use DPSS matrices. Vandebril, Van Camp, Van Barel, Mastronardi (2004): For an arbitrary diagonal matrix D there exists an orthogonal matrix Q such that Q T AQ = D+S is a DPSS matrix. The reduction is O(n 2 ) more expensive than in the tridiagonal case. The reduction algorithm has a Lanczos-Ritz convergence behaviour and performs a kind of nested subspace iteration at each step. A good choice of the diagonal can compensate slower reduction. Algorithms for the eigendecomposition of a symmetric DPSS matrix: Chandrasekaran, Gu (2004), in Numer. Math.: divide and conquer, Mastronardi, Van Camp, Van Barel (2003), tech. report: divide and conquer, Bini, Gemignani, Pan (2003), tech. report: QR algorithm, Fasino (2004), tech. report: QR algorithm, Van Camp, Delvaux, Van Barel, Vandebril, Mastronardi (2005), tech. report: implicit QR algorithm, Mastronardi, Van Camp, Van Barel, Vandebril (2004), tech. report: computation of eigenvectors International Conference on Matrix Methods and Operator Equations, Moscow, June 20-25,

5 Cholesky LR algorithm Let A be a s.p.d. matrix. A 0 = A k = 0, 1, 2,... choose shift σ k A k σ k I = V k V T k A k+1 = V T k V k + σ k I (Cholesky decomposition, V k is lower-triangular) Two steps of the zero shift Cholesky LR are equivalent to one step of the zero shift QR. The shift σ k should be such that A k σ k I is positive definite. When applied to a s.p.d. DPSS matrix D + S, the shift can be included into the diagonal part. Grad, Zakrajšek (1972): Cholesky LR with Laguerre s shifts for the symmetric tridiagonal matrices. International Conference on Matrix Methods and Operator Equations, Moscow, June 20-25,

6 DPSS is invariant to Cholesky LR Theorem: Let A = Giv(c, s, f) + diag(d) be a s.p.d DPSS matrix. 1. If A = V V T is the Cholesky decomposition of A, then V = tril(giv(c, s, f)) + diag( d), 2. If B = V T V, then B is a s.p.d. DPSS matrix B = Giv(ĉ, ŝ, f) + diag(d). A 4 = c 1 f 1 + d 1 c 2 s 1 f 1 c 2 f 2 + d 2 c 3 s 2 s 1 f 1 c 3 s 2 f 2 c 3 f 3 + d 3. V 4 = B 4 = s 3 s 2 s 1 f 1 s 3 s 2 f 2 s 3 f 3 f 4 + d 4 c 1 f1 + d 1 c 2 s 1 f1 c 2 f2 + d 2 c 3 s 2 s 1 f1 c 3 s 2 f2 c 3 f3 + d 3 s 3 s 2 s 1 f1 s 3 s 2 f2 s 3 f3 f4 + d 4 ĉ 1 f1 + d 1 ĉ 2 ŝ 1 f1 ĉ 2 f2 + d 2 ĉ 3 ŝ 2 ŝ 1 f1 ĉ 3 ŝ 2 f2 ĉ 3 f3 + d 3.. ŝ 3 ŝ 2 ŝ 1 f1 ŝ 3 ŝ 2 f2 ŝ 3 f3 f4 + d 4 International Conference on Matrix Methods and Operator Equations, Moscow, June 20-25,

7 Cholesky decomposition A 4 = c 1 f 1 + d 1 c 2 s 1 f 1 c 2 f 2 + d 2 c 3 s 2 s 1 f 1 c 3 s 2 f 2 c 3 f 3 + d 3 s 3 s 2 s 1 f 1 s 3 s 2 f 2 s 3 f 3 f 4 + d 4, V 4 = c 1 f1 + d 1 c 2 s 1 f1 c 2 f2 + d 2 c 3 s 2 s 1 f1 c 3 s 2 f2 c 3 f3 + d 3 s 3 s 2 s 1 f1 s 3 s 2 f2 s 3 f3 f4 + d 4. We compare the diagonal and the main subdiagonal of A and V V T : c k f k + d k = c k+1 s k f k = k 1 (c k s k 1 s j fj ) 2 + (c k fk + d k ) 2 = c 2 k q k + (c k fk + d k ) 2, j=1 k 1 c k c k+1 s k (s k 1 s j fj ) 2 c k+1 s k fk (c k fk + d k ) = c k c k+1 s k q k + c k+1 s k fk (c k fk + d k ), j=1 where The solution is q k := k 1 (s k 1 s k 2 s j fj ) 2. j=1 f k = f k c k q k dk + c k (f k c k q k ), dk = d k, k = 1,..., n. dk + c k (f k c k q k ) International Conference on Matrix Methods and Operator Equations, Moscow, June 20-25,

8 Algorithm for the Cholesky decomposition If A = Giv(c, s, f) + diag(d) is s.p.d., then A = V V T for V = tril(giv(c, s, f)) + diag( d). function [ f, d] = Cholesky(c, s, f, d) c n = 1 q 1 = 0 for k = 1,..., n : z k = f k c k q k y k = d k + c k z k f k = z k /y k d k = d k /y k q k+1 = s 2 k (q k + f 2 k ) Flops: 11n + O(1). International Conference on Matrix Methods and Operator Equations, Moscow, June 20-25,

9 Algorithm for the Cholesky decomposition If A = Giv(c, s, f) + diag(d) is s.p.d., then A = V V T for V = tril(giv(c, s, f)) + diag( d). function [ f, d] = Cholesky(c, s, f, d) c n = 1 q 1 = 0 for k = 1,..., n : z k = f k c k q k y k = d k + c k z k f k = z k /y k d k = d k /y k q k+1 = s 2 k (q k + f 2 k ) Flops: 11n + O(1). It follows from c k f k + d k = c 2 k q k + (c k fk + d k ) 2 that y k is in fact the diagonal element of V : c k fk + d k = d k + c k (f k c k q k ) = d k + c k z k = y k. A negative or zero value under the square root appears if A is not positive definite. International Conference on Matrix Methods and Operator Equations, Moscow, June 20-25,

10 Equations for V T V The product B = V T V is a s.p.d. DPSS matrix. The lower triangular elements of B are where k = 1,..., n and j > k. b kk = (c k fk + d k ) 2 + (s k fk ) 2, b jk = s k s k+1 s j 1 fk ( f j + c j dj ), B = Giv(ĉ, ŝ, f) + diag(d). For k = 1,..., n 1 we have ŝ 2 f n 2 k k = b 2 jk = f n 2 k p k,, where p k = (s k s k+1 s j 1 ) 2 ( f j + c j dj ) 2. j=k+1 For p k we can apply the recursion p n = 0 and p k = s 2 k j=k+1 ( p k+1 + ( f k+1 + c k+1 dk+1 ) 2). From ĉ k fk + d k = (c k fk + d k ) 2 + (s k fk ) 2 and the Cholesky decomposition we get ĉ k fk = c k z k + (s k fk ) 2. International Conference on Matrix Methods and Operator Equations, Moscow, June 20-25,

11 Equations for V T V The product B = V T V is a s.p.d. DPSS matrix. The lower triangular elements of B are where k = 1,..., n and j > k. b kk = (c k fk + d k ) 2 + (s k fk ) 2, b jk = s k s k+1 s j 1 fk ( f j + c j dj ), B = Giv(ĉ, ŝ, f) + diag(d). For k = 1,..., n 1 we have ŝ 2 f n 2 k k = b 2 jk = f n 2 k p k,, where p k = (s k s k+1 s j 1 ) 2 ( f j + c j dj ) 2. j=k+1 For p k we can apply the recursion p n = 0 and p k = s 2 k j=k+1 ( p k+1 + ( f k+1 + c k+1 dk+1 ) 2). From ĉ k fk + d k = (c k fk + d k ) 2 + (s k fk ) 2 and the Cholesky decomposition we get ĉ k fk = c k z k + (s k fk ) 2. Now, ĉ k, ŝ k, and f k can be computed from ĉ k fk = c k z k + (s k fk ) 2, ŝ k fk = f k pk. International Conference on Matrix Methods and Operator Equations, Moscow, June 20-25,

12 Algorithm for the V T V product Let A = Giv(c, s, f) + diag(d) be s.p.d. = A = V V T, where V = tril(giv(c, s, f)) + diag( d), = B = V T V = Giv(ĉ, ŝ, f) + diag(d). function [ĉ, ŝ, f] = VTV(c, s, f, z) c n = 1 f n = ( f n + d n ) 2 d n p n = 0 for k = n 1,..., 2, 1 p k = s 2 k (p k+1 + ( f k+1 + c k+1 dk+1 ) 2) [ĉ k, ŝ k, f k ] = Givens(c k z k + s 2 k f 2 k, f k pk ) [c, s, f] = Givens(x, y) returns the Givens transformation such that [ c s s c ] [ ] x y = [ ] f. 0 Flops: 16n + O(1) = one step of the zero shift Cholesky LR algorithm: 27n + O(1) flops. As the eigenvalues are invariant to the sign of ŝ k, k = 1,..., n 1, we do not care about it. International Conference on Matrix Methods and Operator Equations, Moscow, June 20-25,

13 Laguerre s shift Let A be a s.p.d. n n matrix with eigenvalues 0 < λ n λ n 1... λ 1. We apply Laguerre s method on the characteristic polynomial f(λ) = det(a λi). If x is an approximation for an eigenvalue of A and S 1 (x) = S 2 (x) = n i=1 n i=1 1 λ i x = f (x) f(x) 1 = f 2 (x) f(x)f (x) (λ i x) 2 f 2 (x) then the next approximation x by Laguerre s method is given by the equation n x = x +. S 1 (x) + (n 1)(nS 2 (x) S 21 (x)) Two important properties of Laguerre s method: if λ n is a simple eigenvalue and if x < λ n then x < x < λ n, the convergence towards λ n is cubic (linear for a multiple eigenvalue). International Conference on Matrix Methods and Operator Equations, Moscow, June 20-25,

14 Laguerre s shift Let A be a s.p.d. n n matrix with eigenvalues 0 < λ n λ n 1... λ 1. We apply Laguerre s method on the characteristic polynomial f(λ) = det(a λi). If x is an approximation for an eigenvalue of A and S 1 (x) = S 2 (x) = n i=1 n i=1 1 λ i x = f (x) f(x) 1 = f 2 (x) f(x)f (x) (λ i x) 2 f 2 (x) = trace((a xi) 1 ), = trace((a xi) 2 ), then the next approximation x by Laguerre s method is given by the equation n x = x +. S 1 (x) + (n 1)(nS 2 (x) S 21 (x)) Two important properties of Laguerre s method: if λ n is a simple eigenvalue and if x < λ n then x < x < λ n, the convergence towards λ n is cubic (linear for a multiple eigenvalue). International Conference on Matrix Methods and Operator Equations, Moscow, June 20-25,

15 Computation of Laguerre s shift We need S 1 (σ) = trace((a σi) 1 ) and S 2 (σ) = trace((a σi) 2 ). If A σi = V V T is the Cholesky decomposition and W = V 1, then S 1 (σ) = trace(w T W ) = W 2 F, S 2 (σ) = trace(w T W W T W ) = trace(w W T W W T ) = W W T 2 F. International Conference on Matrix Methods and Operator Equations, Moscow, June 20-25,

16 Computation of Laguerre s shift We need S 1 (σ) = trace((a σi) 1 ) and S 2 (σ) = trace((a σi) 2 ). If A σi = V V T is the Cholesky decomposition and W = V 1, then S 1 (σ) = trace(w T W ) = W 2 F, S 2 (σ) = trace(w T W W T W ) = trace(w W T W W T ) = W W T 2 F. Delvaux, Van Barel (2004): Let V = tril(giv(c, s, f)) + diag( d) be nonsingular and d i 0 for i = 1,..., n. Then W = V 1 = tril(giv( c, s, f)) + diag( d), where d 1 i = d i for i = 1,..., n. Let us assume that W = tril(giv( c, s, f)) + diag( d). The final algorithm will also be correct when W is not DPSS, which happens when d i = 0 for some i = 2,..., n 1. International Conference on Matrix Methods and Operator Equations, Moscow, June 20-25,

17 Computation of Laguerre s shift A 4 = c 1 f 1 + d 1 c 2 s 1 f 1 c 2 f 2 + d 2 c 3 s 2 s 1 f 1 c 3 s 2 f 2 c 3 f 3 + d 3 s 3 s 2 s 1 f 1 s 3 s 2 f 2 s 3 f 3 f 4 + d 4. Lemma: If A = Giv(c, s, f) + diag(d) is a symmetric n n DPSS matrix then A 2 F = n (c k f k + d k ) k=1 n 1 k=1 s 2 k f 2 k. Lemma: If W = tril(giv( c, s, f)) + diag( d) and c k 0 for k = 2,..., n 1, then W W T 2 F = W 2 F = ( ) n n 1 (W W T ) 2 kk + 2 (W W T 2 ) k+1,k, k=1 n (W W T ) kk. k=1 k=1 c k+1 International Conference on Matrix Methods and Operator Equations, Moscow, June 20-25,

18 Algorithm for the Laguerre s shift The following algorithm computes S 1 = W 2 F and S 2 = W W T 2 F. function [S 1, S 2 ] = invtrace(c, s, f, d, y) c n = c n = 1 for k = n 1,..., 2, 1 : r 1 = 0 [ c k, s k ] = Givens(c k c k+1 y k+1, c k+1 s k dk ) for k = 1,..., n 1 β k = c k+1 s k fk /( c k+1 y k y k+1 ) = w k+1,k / c k+1 ω k = c 2 k r k + y 2 k = (W W T ) kk ξ k = c k s k r k + β k /y k = (W W T ) k+1,k / c k+1 r k+1 = s 2 k r k + β 2 k ω n = r n + y 2 n S 1 = n k=1 ω k S 2 = n k=1 ω2 k + 2 n 1 k=1 ξ2 k Flops: 31n + O(1) flops. The algorithm is also correct if d k = 0 for some k = 2,..., n 1. International Conference on Matrix Methods and Operator Equations, Moscow, June 20-25,

19 A 4 = Implementation c 1 f 1 + d 1 c 2 s 1 f 1 c 2 f 2 + d 2 c 3 s 2 s 1 f 1 c 3 s 2 f 2 c 3 f 3 + d 3 s 3 s 2 s 1 f 1 s 3 s 2 f 2 s 3 f 3 f 4 + d 4. If s k is small enough for some k = 1,..., n 1, then we decouple the problem into two smaller problems with matrices A(1 : k, 1 : k) and A(k + 1 : n, k + 1 : n). If s n 1 is small enough, we take f n + d n as an eigenvalue of A and continue with vectors c(1 : n 2), s(1 : n 2), f(1 : n 1), and d(1 : n 1). New initial shift is f n + d n. Even if σ k < λ n, the Cholesky factorization can fail if the difference is too small. This is a problem since Laguerre s shifts converge faster to the smallest eigenvalue than (A k ) nn. One strategy is to relax the shift with a factor τ close to 1, for instance, τ = The computation of Laguerre s shift requires more than half of the operations for one step of the Cholesky LR. We can save work by fixing the shift once the shift improvement is small enough. The algorithm for Laguerre s shift fails if c k = 0 for some k = 2,..., n 1. A simple solution is to perturb c k into some small δ (for instance, ) whenever c k = 0. International Conference on Matrix Methods and Operator Equations, Moscow, June 20-25,

20 Numerical examples Numerical results were obtained with Matlab 7.0 running on a Pentium4 2.6 GHz Windows XP. We compared a Matlab implementation of the Cholesky LR algorithm, a Matlab implementation of the implicit QR algorithm for DPSS matrices [Van Camp, Delvaux, Van Barel, Vandebril, Mastronardi (2005)], the Matlab function eig. Exact eigenvalues were computed in Mathematica 5 using variable precision. The cutoff criterion for both Cholesky LR and implicit QR is With the maximum relative error we denote max 1 i n λ i λ i λ i. When the initial matrix is not DPSS, the reduction into a similar DPSS matrix is done by the algorithm of Vandebril, Van Camp, Van Barel, and Mastronardi (2004). There is a connection between the Lanczos method and the reduction into a similar DPSS matrix which causes that the largest eigenvalues of A are approximated by the lower right diagonal elements of the DPSS matrix. As this is not good for the Cholesky LR method, we reverse the direction of the columns and rows of the DPSS matrix. This can be done in linear time, see, e.g., Vandebril (2004). International Conference on Matrix Methods and Operator Equations, Moscow, June 20-25,

21 Numerical example 1 We take random s.p.d. DPSS matrices of the form A = diag(1,..., n) + triu(uv T, 1) + triu(uv T, 1) T + αi, where u and v are vectors of uniformly distributed random entries on [0, 1], obtained by the Matlab function rand, and the shift α is such that the smallest eigenvalue of A is 1. The condition numbers of these matrices are approximately n. Cholesky LR Implicit QR eig n t steps error t steps error t error International Conference on Matrix Methods and Operator Equations, Moscow, June 20-25,

22 Numerical example 2 We take symmetric positive definite matrices A = Q diag(1 : n)q T, where Q is a random orthogonal matrix. Now we have to transform the matrix into a similar DPSS matrix before we can apply Cholesky LR or implicit QR. Cholesky LR Implicit QR eig n t steps error t steps error t error : times for Cholesky LR and Implicit QR do not include the reduction to a DPSS matrix. International Conference on Matrix Methods and Operator Equations, Moscow, June 20-25,

23 Numerical example 3 We take symmetric positive definite matrices A = Q diag(λ 1,..., λ n )Q T, where Q is a random orthogonal matrix and 8+7 k 1 λ k = 10 n 1. Cholesky LR Implicit QR eig n t steps error t steps error t error : times for Cholesky LR and Implicit QR do not include the reduction to a DPSS matrix. International Conference on Matrix Methods and Operator Equations, Moscow, June 20-25,

24 Conclusions Cholesky LR algorithm exploits the structure of s.p.d. DPSS matrices. The method can be combined with Laguerre s shifts. It seems natural to compare the method to the implicit QR for DPSS matrices. In Cholesky LR the eigenvalues are computed from the smallest to the largest eigenvalue, therefore the method is very appropriate for applications where one is interested in few of the smallest eigenvalues. If the complete spectrum is computed, Cholesky LR is more expensive than implicit QR, but, as it tends to be slightly more accurate, it presents an alternative. The proposed method combined with the reduction to DPSS matrices can also be applied to a general s.p.d. matrix. International Conference on Matrix Methods and Operator Equations, Moscow, June 20-25,