Sparse Matrix Methods Day 1 Overview Matlab and

Sparse Matrix Methods • Day 1: Overview • • • Matlab and examples Data structures Ax=b Sparse matrices and graphs Fill-reducing matrix permutations Matching and block triangular form • Day 2: Direct methods • Day 3: Iterative methods D

(Demo in Matlab) • • • Matlab sprand spy sparse, full matrix arithmetic and indexing examples of sparse matrices from different applications (from UF site)

Matlab sparse matrices: Design principles • All operations should give the same results for sparse and full matrices (almost all) • Sparse matrices are never created automatically, but once created they propagate • Performance is important -- but usability, simplicity, completeness, and robustness are more important • Storage for a sparse matrix should be O(nonzeros) • Time for a sparse operation should be O(flops) (as nearly as possible)

Data structures 31 0 53 0 59 0 41 26 0 • Full: • 2 -dimensional array of real or complex numbers • (nrows*ncols) memory 31 41 59 26 53 1 3 2 3 1 • Sparse: • compressed column storage • about (1. 5*nzs +. 5*ncols) memory D

(Demos in Matlab) • Ab • Cholesky factorization and orderings

Solving linear equations x = A b; • Is A square? no => use QR to solve least squares problem • Is A triangular or permuted triangular? yes => sparse triangular solve • Is A symmetric with positive diagonal elements? yes => attempt Cholesky after symmetric minimum degree • Otherwise => use LU on A(: , colamd(A))

Matrix factorizations in Matlab • Cholesky: • R = chol(A); • simple left-looking column algorithm • Nonsymmetric LU: • [L, U, P] = lu(A); • left-looking “GPMOD”, depth-first search, symmetric pruning • Orthogonal: • [Q, R] = qr(A); • George-Heath algorithm: row-wise Givens rotations

Graphs and Sparse Matrices: Cholesky factorization Fill: new nonzeros in factor 3 1 6 8 4 9 7 G(A) 2 4 9 7 6 8 10 5 3 1 10 5 G+(A) [chordal] 2 Symmetric Gaussian elimination: for j = 1 to n add edges between j’s higher-numbered neighbors

Elimination Tree 3 1 10 7 9 6 8 4 5 4 10 8 7 3 6 9 Cholesky factor 5 G+(A) 2 2 1 T(A) : parent(j) = min { i > j : (i, j) in G+(A) } • T describes dependencies among columns of factor • Can compute T from G(A) in almost linear time • Can compute G+(A) easily from T D

(Demos in Matlab) • matrix and graph • elimination tree • orderings in detail

Fill-reducing matrix permutations • Minimum degree: • Eliminate row/col with fewest nzs, add fill, repeat • Theory: can be suboptimal even on 2 D model problem • Practice: often wins for medium-sized problems • Nested dissection: • Find a separator, number it last, proceed recursively • Theory: approx optimal separators => approx optimal fill and flop count • Practice: often wins for very large problems • Banded orderings (Reverse Cuthill-Mc. Kee, Sloan, . . . ): • Try to keep all nonzeros close to the diagonal • Theory, practice: often wins for “long, thin” problems • Best modern general-purpose orderings are ND/MD hybrids.

Fill-reducing permutations in Matlab • Nonsymmetric approximate minimum degree: • p = colamd(A); • column permutation: lu(A(: , p)) often sparser than lu(A) • also for QR factorization • Symmetric approximate minimum degree: • p = symamd(A); • symmetric permutation: chol(A(p, p)) often sparser than chol(A) • Reverse Cuthill-Mc. Kee • p = symrcm(A); • A(p, p) often has smaller bandwidth than A • similar to Sparspak RCM D

(Demos in Matlab) • nonsymmetric LU • dmperm, dmspy, components

Matching and block triangular form • Dulmage-Mendelsohn decomposition: • Bipartite matching followed by strongly connected components • Square, full rank A: • [p, q, r] = dmperm(A); • A(p, q) has nonzero diagonal and is in block upper triangular form • also, strongly connected components of a directed graph • also, connected components of an undirected graph • Arbitrary A: • [p, q, r, s] = dmperm(A); • maximum-size matching in a bipartite graph • minimum-size vertex cover in a bipartite graph • decomposition into strong Hall blocks