M. M. Tung, L. Soler, E. Defez et al.
We discuss the direct use of cubic-matrix splines to obtain continuous approximations to the unique solution of matrix models of the type $Y''(x) = f(x,Y(x))$. For numerical illustration, an estimation of the approximation error, an algorithm for its implementation, and an example are given.
J. M. Alonso, J. Sastre, J. Ibáñez et al.
A method for evaluating matrix polynomials have recently been developed that require one fewer matrix product ($1M$) than the Paterson--Stockmeyer (PS) method. Since the computational cost for large-scale matrices is asymptotically determined by the number of matrix products, this reduction directly affects the total execution time. However, the coefficients in these optimized formulas emerge as solutions to systems of nonlinear polynomial equations, resulting in multiple potential solution sets. An inappropriate selection of these coefficients can lead to numerical instability in floating-point arithmetic. This paper presents a systematic framework and a MATLAB implementation, MatrixPolEval1, used to obtain and validate stable coefficient sets for matrix polynomials of degrees $m \in \{8, 10, 12\}$ and above. The framework introduces structural variants to maintain stability even when the original configuration fails to yield a robust solution. The provided tool identifies stable coefficient sets using variable precision arithmetic (VPA) and provides a reliability indicator for expected accuracy. Numerical experiments on polynomials arising in applications, including the matrix exponential and geometric series, show that the framework achieves the $1M$ saving while maintaining numerical accuracy comparable to the PS method.
E. Defez, A. Hervas, L. Soler et al.
This paper presents the non-linear generalization of a previous work on matrix differential models. It focusses on the construction of approximate solutions of first-order matrix differential equations Y'(x)=f(x,Y(x)) using matrix-cubic splines. An estimation of the approximation error, an algorithm for its implementation and illustrative examples for Sylvester and Riccati matrix differential equations are given.