The course is based on the following books:

Matrix Computations (third edition)

Gene H. Golub and Charles F. Van Loan

Johns Hopkins University Press, Baltimore, 1996

and

Templates for the Solution of Linear Systems: Building Blocks for Iterative Methods

Richard Barrett, Michael Berry, Tony F. Chan, James Demmel,

June Donato, Jack Dongarra, Victor Eijkhout, Roldan Pozo, Charles Romine, and Henk Van der Vorst

SIAM, Philadelphia, 1994

The pdf-file of this book can be obtained in the following way: Click on the

In order to be able to measure distances with respect to vectors and matrices we have defined normed spaces. Most of the spaces used in this course are Banach spaces Stefan Banach (1892-1945) . For the vector norms different choices are made. An important class of norms are the so-called Hölder norms Otto Ludwig Hölder (1859-1937). For the special choice p=2 we get the Euclid norm Euclid ( 325 b.c.- 265 b.c. ). With respect to matrix norms we consider norms derived from vector norms. Furthermore we mention the Frobenius norm George Ferdinand Frobenius (1849-1917).

Furthemore we use spaces where an inner product is defined. Most of these spaces are Hilbert spaces David Hilbert (1862-1943). Using the Hölder inequality we show that the Euclid norm satisfies the Cauchy-Schwarz inequality Augustin-Louis Cauchy (1789-1857) and Karl Hermann Amandus Schwarz (1843-1921).

We start our description of direct methods for solving linear systems with the Gauss elimination method Carl Friedrich Gauss (1777-1855). Thereafter we discuss the Choleski method for symmetric positive definite matrices. Furthermore we show that much work and memory can be saved in the case of band matrices.

In general it is difficult to get a good analysis of rounding errors due to floating point arithmetic done by computers. To motivate the study of rounding errors we note that several disasters are originated by rounding errors. Recent examples are: Patriot Missile Failure and the Explosion of the Ariane 5.

The Newton_Raphson method is an iterative method to solve nonlinear equations. The method is defined by Isaac Newton (1643-1727) and Joseph Raphson (1648-1715).

The standard iterative methods, which are used are the Gauss-Jacobi and the Gauss-Seidel method. Carl Friedrich Gauss (1777-1855) is a very famous mathematician working on abstract and applied mathematics. Carl Gustav Jacob Jacobi (1804-1851) is well known for instance for the Jacobian the determinant of the matrix of partial derivatives. He has also done work on iterative methods leading to the Gauss-Jacobi method.

Another iterative method is the Chebyshev method. This method is based on orthogonal polynomials bearing the name of Pafnuty Lvovich Chebyshev (1821-1894). The Gauss-Jacobi and Gauss-Seidel method use a very simple polynomial to approximate the solution. In the Chebyshev method an optimal polynomial is used.

Nowadays the most popular iterative methods are the preconditioned
Krylov methods (
Aleksei Nikolaevich Krylov ( 1863- 1945)
and
a second link).
For symmetric matrices the resulting method is known as the conjugate
gradient method. For non symmetric matrices different generalizations
exists for instance Bi-CG, CGS, Bi-CGSTAB, GCR, GMRES, GMRESR etc.
In the GCR, GMRES and GMRESR method a orthogonal basis for the
Krylov subspace is used. This basis is constructed from an
arbitrary basis using the Gram-Schmidt method (
Jorgen Pedersen Gram (1850-1916) and
Erhard Schmidt (1876-1959))