The GMRES algorithm of Saad and Schultz [SIAM J. Sci. Stat. Comput., 7 (1986), pp. 856–869] is an iterative method for approximately solving linear systems , with initial guess residual . employs the Arnoldi process to generate Krylov basis vectors (the columns ). It well known that this can be viewed as a factorization matrix at each iteration. Despite loss orthogonality, unit roundoff conditi...

In the past few years new methods have been proposed that can be seen as combinations of standard Krylov subspave methods, such as Bi{ CG and GMRES. One of the rst hybrid schemes of this type is CGS, actually the Bi{CG squared method. Other such hybrid schemes include BiCGSTAB (a combination of Bi{CG and GMRES(1)), QMRS, TFQMR, Hybrid GMRES (polynomial preconditioned GMRES) and the nested GMRES...

Much e ort has gone into the development of implicit methods for electromagnetic kinetic plasma simulation. One such implicit method is the implicit moment method [1]. The maximum allowable time step in this method has often been found to be governed by the iterative elliptic eld solver, and not the implicit moment method itself [2]. In ref. [2] it was shown, in one dimension, that by using a d...

This thesis presents a high-order Implicit Large-Eddy Simulation (ILES) approach for simulating transitional aerodynamic flows. The approach consists of a hybridized Discontinuous Galerkin (DG) method for the discretization of the Navier-Stokes (NS) equations and a parallel preconditioned Newton-GMRES solver for the resulting nonlinear system of equations. The combination of hybridized DG metho...

In the convergence analysis of the GMRES method for a given matrix A, one quantity of interest is the largest possible residual norm that can be attained, at a given iteration step k, over all unit norm initial vectors. This quantity is called the worst-case GMRES residual norm for A and k. We show that the worst case behavior of GMRES for the matrices A and A is the same, and we analyze proper...

We develop a parallel Jacobi-Davidson approach for finding a partial set of eigenpairs of large sparse polynomial eigenvalue problems with application in quantum dot simulation. A Jacobi-Davidson eigenvalue solver is implemented based on the Portable, Extensible Toolkit for Scientific Computation (PETSc). The eigensolver thus inherits PETSc’s efficient and various parallel operations, linear so...

Sparse linear systems are ubiquitous in various scientific computing applications. Inversion of sparse matrices with standard direct solve schemes are prohibitive for large systems due to their quadratic/cubic complexity. Iterative solvers, on the other hand, demonstrate better scalability. However, they suffer from poor convergence rates when used without a preconditioner. There are many preco...

Nonlinear least squares (NLS) problems arise in many applications. The common solvers require to compute and store the corresponding Jacobian matrix explicitly, which is too expensive for large problems. In this paper, we propose an effective Jacobian free method especially for large NLS problems because of the novel combination of using automatic differentiation for J(x)v and J (x)v along with...