A Fast Iterative Method for Solving Stationary Heat Conduction Problems on Regions Partitioned into Rectangles.
A fast solver for the first biharmonic boundary value problem.
A FETI-DP preconditioner for mortar methods in three dimensions.
A Finite Element - Capacitane Method for Elliptic Problems on Regions Partitioned into Subregions.
A footnote on quaternion block-tridiagonal systems.
A full-Newton approach to separable nonlinear least squares problems and its application to discrete least squares rational approximation.
A fully parallel method for tridiagonal eigenvalue problem.
A Fully Stable Rational Version of the QR Algorithm for Tridiagonal Matrices.
A Generalisation of Systematic Relaxation Methods for Consistently Ordered Matrices.
A Generalization of the Schwarz Alternating Method to an Arbitrary Number of Subdomains.
A generalization of the steepest descent method for matrix functions.
A Generalization of the Stein-Rosenberg Theorem to Banach Spaces.
A generalized ADI iterative method.
A Generalized Conjugate Gradient, Least Square Method.
A Gradient Method for the Matrix Eigenvalue Problem A x = ... B x.
A hierarchical preconditioner for the mortar finite element method.
A homotopy approach to rational covariance extension with degree constraint
The solutions to the Rational Covariance Extension Problem (RCEP) are parameterized by the spectral zeros. The rational filter with a specified numerator solving the RCEP can be determined from a known convex optimization problem. However, this optimization problem may become ill-conditioned for some parameter values. A modification of the optimization problem to avoid the ill-conditioning is proposed and the modified problem is solved efficiently by a continuation method.
A hybrid Arnoldi-Faber iterative method for nonsymmetric systems of linear equations.
A hybrid method for nonlinear least squares that uses quasi-Newton updates applied to an approximation of the Jacobian matrix
In this contribution, we propose a new hybrid method for minimization of nonlinear least squares. This method is based on quasi-Newton updates, applied to an approximation of the Jacobian matrix , such that . This property allows us to solve a linear least squares problem, minimizing instead of solving the normal equation , where is the required direction vector. Computational experiments confirm the efficiency of the new method.
A Jacobi eigenreduction algorithm for definite matrix pairs.