1. The Landscape of Ax=b Solvers Direct A = LU Iterative y’ = Ay Non- symmetric Symmetric positive definite More RobustLess Storage (if sparse) More Robust More General D

2. Conjugate gradient iteration One matrix-vector multiplication per iteration Two vector dot products per iteration Four n-vectors of working storage x 0 = 0, r 0 = b, p 0 = r 0 for k = 1, 2, 3,... α k = (r T k-1 r k-1 ) / (p T k-1 Ap k-1 ) step length x k = x k-1 + α k p k-1 approx solution r k = r k-1 – α k Ap k-1 residual β k = (r T k r k ) / (r T k-1 r k-1 ) improvement p k = r k + β k p k-1 search direction

3. Conjugate gradient: Krylov subspaces Eigenvalues: Au = λu { λ 1, λ 2,..., λ n } Cayley-Hamilton theorem: (A – λ 1 I)·(A – λ 2 I) · · · (A – λ n I) = 0 Therefore Σ c i A i = 0 for some c i so A -1 = Σ (–c i /c 0 ) A i–1 Krylov subspace: Therefore if Ax = b, then x = A -1 b and x  span (b, Ab, A 2 b,..., A n-1 b) = K n (A, b) 0  i  n 1  i  n

4. Conjugate gradient: Orthogonal sequences Krylov subspace: K i (A, b) = span (b, Ab, A 2 b,..., A i-1 b) Conjugate gradient algorithm: for i = 1, 2, 3,... find x i  K i (A, b) such that r i = (Ax i – b)  K i (A, b) Notice r i  K i+1 (A, b), so r i  r j for all j < i Similarly, the “directions” are A-orthogonal: (x i – x i-1 ) T ·A· (x j – x j-1 ) = 0 The magic: Short recurrences... A is symmetric => can get next residual and direction from the previous one, without saving them all.

5. Conjugate gradient: Convergence In exact arithmetic, CG converges in n steps (completely unrealistic!!) Accuracy after k steps of CG is related to: consider polynomials of degree k that are equal to 1 at 0. how small can such a polynomial be at all the eigenvalues of A? Thus, eigenvalues close together are good. Condition number: κ (A) = ||A|| 2 ||A -1 || 2 = λ max (A) / λ min (A) Residual is reduced by a constant factor by O(κ 1/2 (A)) iterations of CG.

6. (Matlab demo) CG on grid5(15) and bcsstk08 n steps of CG on bcsstk08

7. Lay out matrix and vectors by rows Hard part is matrix-vector product y = A*x Algorithm Each processor j: Broadcast x(j) Compute y(j) = A(j,:)*x May send more of x than needed Partition / reorder matrix to reduce communication x y P0 P1 P2 P3 P0 P1 P2 P3 Conjugate gradient: Parallel implementation

8. (Matlab demo) 2-way partition of eppstein mesh 8-way dice of eppstein mesh

9. Preconditioners Suppose you had a matrix B such that: 1.condition number κ (B -1 A) is small 2.By = z is easy to solve Then you could solve (B -1 A)x = B -1 b instead of Ax = b B = A is great for (1), not for (2) B = I is great for (2), not for (1) Domain-specific approximations sometimes work B = diagonal of A sometimes works Or, bring back the direct methods technology...

10. (Matlab demo) bcsstk08 with diagonal precond

11. Incomplete Cholesky factorization (IC, ILU) Compute factors of A by Gaussian elimination, but ignore fill Preconditioner B = R T R  A, not formed explicitly Compute B -1 z by triangular solves (in time nnz(A)) Total storage is O(nnz(A)), static data structure Either symmetric (IC) or nonsymmetric (ILU) x ARTRT R

12. (Matlab demo) bcsstk08 with ic precond

13. Incomplete Cholesky and ILU: Variants Allow one or more “levels of fill” unpredictable storage requirements Allow fill whose magnitude exceeds a “drop tolerance” may get better approximate factors than levels of fill unpredictable storage requirements choice of tolerance is ad hoc Partial pivoting (for nonsymmetric A) “Modified ILU” (MIC): Add dropped fill to diagonal of U or R A and R T R have same row sums good in some PDE contexts 2 3 1 4 2 3 1 4

14. Incomplete Cholesky and ILU: Issues Choice of parameters good: smooth transition from iterative to direct methods bad: very ad hoc, problem-dependent tradeoff: time per iteration (more fill => more time) vs # of iterations (more fill => fewer iters) Effectiveness condition number usually improves (only) by constant factor (except MIC for some problems from PDEs) still, often good when tuned for a particular class of problems Parallelism Triangular solves are not very parallel Reordering for parallel triangular solve by graph coloring

15. (Matlab demo) 2-coloring of grid5(15)

16. Sparse approximate inverses Compute B -1  A explicitly Minimize || B -1 A – I || F (in parallel, by columns) Variants: factored form of B -1, more fill,.. Good: very parallel Bad: effectiveness varies widely AB -1

17. Support graph preconditioners: example Support graph preconditioners: example [Vaidya] A is symmetric positive definite with negative off-diagonal nzs B is a maximum-weight spanning tree for A (with diagonal modified to preserve row sums) factor B in O(n) space and O(n) time applying the preconditioner costs O(n) time per iteration G(A) G(B)

18. support each edge of A by a path in B dilation(A edge) = length of supporting path in B congestion(B edge) = # of supported A edges p = max congestion, q = max dilation condition number κ (B -1 A) bounded by p·q (at most O(n 2 )) G(A) G(B) Support graph preconditioners: example

19. can improve congestion and dilation by adding a few strategically chosen edges to B cost of factor+solve is O(n 1.75 ), or O(n 1.2 ) if A is planar in recent experiments [Chen & Toledo], often better than drop-tolerance MIC for 2D problems, but not for 3D. G(A) G(B) Support graph preconditioners: example

20. Domain decomposition (introduction) Partition the problem (e.g. the mesh) into subdomains Use solvers for the subdomains B -1 and C -1 to precondition an iterative solver on the interface Interface system is the Schur complement: S = G – E T B -1 E – F T C -1 F Parallelizes naturally by subdomains B0E 0CF ETET FTFT G A =

21. (Matlab demo) grid and matrix structure for overlapping 2-way partition of eppstein

22. Multigrid (introduction) For a PDE on a fine mesh, precondition using a solution on a coarser mesh Use idea recursively on hierarchy of meshes Solves the model problem (Poisson’s eqn) in linear time! Often useful when hierarchy of meshes can be built Hard to parallelize coarse meshes well This is just the intuition – lots of theory and technology

23. Other Krylov subspace methods Nonsymmetric linear systems: GMRES: for i = 1, 2, 3,... find x i  K i (A, b) such that r i = (Ax i – b)  K i (A, b) But, no short recurrence => save old vectors => lots more space BiCGStab, QMR, etc.: Two spaces K i (A, b) and K i (A T, b) w/ mutually orthogonal bases Short recurrences => O(n) space, but less robust Convergence and preconditioning more delicate than CG Active area of current research Eigenvalues: Lanczos (symmetric), Arnoldi (nonsymmetric)

24. The Landscape of Sparse Ax=b Solvers Direct A = LU Iterative y’ = Ay Non- symmetric Symmetric positive definite More RobustLess Storage More Robust More General

25. Complexity of direct methods n 1/2 n 1/3 2D3D Space (fill): O(n log n)O(n 4/3 ) Time (flops): O(n 3/2 )O(n 2 ) Time and space to solve any problem on any well- shaped finite element mesh

26. Complexity of linear solvers 2D3D Sparse Cholesky:O(n 1.5 )O(n 2 ) CG, exact arithmetic: O(n 2 ) CG, no precond: O(n 1.5 )O(n 1.33 ) CG, modified IC: O(n 1.25 )O(n 1.17 ) CG, support trees: O(n 1.20 )O(n 1.75 ) Multigrid:O(n) n 1/2 n 1/3 Time to solve model problem (Poisson’s equation) on regular mesh