Parallelism in Matrix Computations

List of Figures.- List of Tables.- List of Algorithms.- Notations used in the book.- Part I Basics.- Parallel Programming Paradigms.- Computational Models.- Principles of parallel programming.- Fundamental kernels.- Vector operations.- Higher level BLAS.- General organization for dense matrix factorizations.- Sparse matrix computations.- Part II Dense and special matrix computations.- Recurrences and triangular systems.- Definitions and examples.- Linear recurrences.- Implementations for a given number of processors.- Nonlinear recurrences.- General linear systems.- Gaussian elimination.- Pair wise pivoting.- Block LU factorization.- Remarks.- Banded linear systems.- LUbased schemes with partial pivoting.- The Spike family of algorithms.- The Spike balance scheme.- A tearing based banded solver.- Tridiagonal systems.- Special linear systems.- Vandermonde solvers.- Banded Toeplitz linear systems solvers.- Symmetric and Anti symmetric Decomposition (SAS).- Rapid elliptic solvers.- Orthogonal factorization and linear least squares problems.- Definitions.- QR factorization via Givens rotations.- QR factorization via Householder reductions.- Gram Schmidt orthogonalization.- Normal equations vs. orthogonal reductions.- Hybrid algorithms when m"n.- Orthogonal factorization of block angular matrices.- Rank deficient linear least squares problems.- The symmetric eigenvalue and singular value problems.- The Jacobi algorithms.- Tridiagonalization based schemes.- Bidiagonalization via Householder reduction.- Part III Sparse matrix computations.- Iterative schemes for large linear systems.- An example.- Classical splitting methods.- Polynomial methods.- Preconditioners.- A tearing based solver for generalized banded preconditioners.- Row projection methods for large non symmetric linear systems.- Multiplicative Schwarz preconditioner with GMRES.- Large symmetric eigenvalue problems.- Computing dominant eigenpairs and spectral transformations.- The Lanczos method.- A block Lanczos approach for solving symmetric perturbed standard eigenvalue problems.- The Davidson methods.- The trace minimization method for the symmetric generalized eigenvalue problem.- The sparse singular value problem.- Part IV Matrix functions and characteristics.- Matrix functions and the determinant.- Matrix functions.- Determinants.- Computing the matrix pseudospectrum.- Grid based methods.- Dimensionality reduction on the domain: Methods based on path following.- Dimensionality reduction on the matrix: Methods based on projection.- Notes.- References.