A quadratically convergent QR-like method without shifts for the Hermitian eigenvalue problem
نویسندگان
چکیده
منابع مشابه
A Quadratically Convergent Interior-Point Algorithm for the P*(κ)-Matrix Horizontal Linear Complementarity Problem
In this paper, we present a new path-following interior-point algorithm for -horizontal linear complementarity problems (HLCPs). The algorithm uses only full-Newton steps which has the advantage that no line searchs are needed. Moreover, we obtain the currently best known iteration bound for the algorithm with small-update method, namely, , which is as good as the linear analogue.
متن کاملA quadratically convergent VBSCF method.
A quadratically convergent valence bond self-consistent field method is described where the simultaneous optimisation of orbitals and the coefficients of the configurations (VB structures) is based on a Newton-Raphson scheme. The applicability of the method is demonstrated in actual calculations. The convergence and efficiency are compared with the Super-CI method. A necessary condition to achi...
متن کاملQR-like Algorithms for Eigenvalue Problems
In the year 2000 the dominant method for solving matrix eigen-value problems is still the QR algorithm. This paper discusses the family of GR algorithms, with emphasis on the QR algorithm. Included are historical remarks, an outline of what GR algorithms are and why they work, and descriptions of the latest, highly parallelizable, versions of the QR algorithm. Now that we know how to paralleliz...
متن کاملa quadratically convergent interior-point algorithm for the p*(κ)-matrix horizontal linear complementarity problem
in this paper, we present a new path-following interior-point algorithm for -horizontal linear complementarity problems (hlcps). the algorithm uses only full-newton steps which has the advantage that no line searchs are needed. moreover, we obtain the currently best known iteration bound for the algorithm with small-update method, namely, , which is as good as the linear analogue.
متن کاملA blocked QR-decomposition for the parallel symmetric eigenvalue problem
In this paper we present a new stable algorithm for the parallel QR-decomposition of ”tall and skinny” matrices. The algorithm has been developed for the dense symmetric eigensolver ELPA, whereat the QR-decomposition of tall and skinny matrices represents an important substep. Our new approach is based on the fast but unstable CholeskyQR algorithm [1]. We show the stability of our new algorithm...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Linear Algebra and its Applications
سال: 2006
ISSN: 0024-3795
DOI: 10.1016/j.laa.2005.01.016