Sökning: "Axel Ruhe"

Hittade 3 avhandlingar innehållade orden Axel Ruhe.

  1. 1. Model Order Reduction with Rational Krylov Methods

    Författare :K. Henrik A. Olsson; Axel Ruhe; Volker Mehrmann; KTH; []
    Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Model order reduction; dual rational Arnoldi; rational Krylov; moment matching; eigenvalue computation; stability analysis; heat exchanger model; Numerical analysis; Numerisk analys;

    Sammanfattning : Rational Krylov methods for model order reduction are studied. A dual rational Arnoldi method for model order reduction and a rational Krylov method for model order reduction and eigenvalue computation have been implemented. It is shown how to deflate redundant or unwanted vectors and how to obtain moment matching. LÄS MER

  3. 2. Sparse Matrices in Self-Consistent Field Methods

    Författare :Emanuel Rubensson; Pawel Salek; Hans Ågren; Anders Niklasson; Axel Ruhe; KTH; []
    Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; sparse matrix; self-consistent field; Hartree-Fock; Density Functional Theory; Density Matrix Purification; Theoretical chemistry; Teoretisk kemi;

    Sammanfattning : This thesis is part of an effort to enable large-scale Hartree-Fock/Kohn-Sham (HF/KS) calculations. The objective is to model molecules and materials containing thousands of atoms at the quantum mechanical level. HF/KS calculations are usually performed with the Self-Consistent Field (SCF) method. LÄS MER

  4. 3. Subspace Computations via Matrix Decompositions and Geometric Optimization

    Författare :Lennart Simonsson; Axel Ruhe; Linköpings universitet; []
    Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Mathematics; Numerical Analysis; Rank-revealing UTV; Jacobi-Davidson algorithm; Decomposition; Grassmann type algorithms; Numerical analysis; Numerisk analys;

    Sammanfattning : This thesis is concerned with the computation of certain subspaces connected to a given matrix, where the closely related problem of approximating the matrix with one of lower rank is given special attention. To determine the rank and obtain bases for fundamental subspaces such as the range and null space of a matrix, computing the singular value decomposition (SVD) is the standard method. LÄS MER