A note on computing the generalized inverse of a matrix .
A note on consecutive ones in a binary matrix.
A note on direct methods for approximations of sparse Hessian matrices
Necessity of computing large sparse Hessian matrices gave birth to many methods for their effective approximation by differences of gradients. We adopt the so-called direct methods for this problem that we faced when developing programs for nonlinear optimization. A new approach used in the frame of symmetric sequential coloring is described. Numerical results illustrate the differences between this method and the popular Powell-Toint method.
A note on estimates of diagonal elements of the inverse of diagonally dominant tridiagonal matrices.
A Note on G-Generating Families and Isolated Gerschgorin Disks.
A note on linear approximations of matrices
A note on Newbery's algorithm for discrete least-squares approximation by trigonometric polynomials.
A Note on Optimal Block-Scaling of Matrices.
A Note on Partial Pivoting and Gaussian Elimination.
A note on symplectic block reflectors.
A note on the accuracy of symmetric eigenreduction algorithms.
A note on the inversion of Sylvester matrices in control systems.
A note on the numerical solution of complex Hamiltonian and skew-Hamiltonian eigenvalue problems.
A note on the OD-QSSA and Bohl-Marek methods applied to a class of mathematical models
The complex (bio)chemical reaction systems, frequently possess fast/slow phenomena, represent both difficulties and challenges for numerical simulation. We develop and test an enhancement of the classical QSSA (quasi-steady-state approximation) model reduction method applied to a system of chemical reactions. The novel model reduction method, the so-called delayed quasi-steady-state approximation method, proposed by Vejchodský (2014) and further developed by Papáček (2021) and Matonoha (2022), is...
A Note on the SSOR and USSOR Iterative Methods Applied to p-Cyclic Matrices.
A novel interpretation of least squares solution.
A novel parallel algorithm based on the Gram-Schmidt method for tridiagonal linear systems of equations.
A numerical method for solving inverse eigenvalue problems
A numerical method for solving inverse eigenvalue problems
Based on QR-like decomposition with column pivoting, a new and efficient numerical method for solving symmetric matrix inverse eigenvalue problems is proposed, which is suitable for both the distinct and multiple eigenvalue cases. A locally quadratic convergence analysis is given. Some numerical experiments are presented to illustrate our results.
A Numerical Procedure for Computing the Moore-Penrose Inverse.