The authors' survey paper is devoted to the present state of computational methods in linear algebra. Questions discussed are the means and methods of estimating the quality of numerical solution of computational problems, the generalized inverse of a matrix, the solution of systems with rectangular and poorly conditioned matrices, the inverse eigenvalue problem, and more traditional questions such as algebraic eigenvalue problems and the solution of systems with a square matrix (by direct and iterative methods).

Computational methods of linear algebra

As the scope of quantum chemistry broadened from the consideration of stable molecules near equilibrium to encompass potential curves and surfaces, transition states, radicals, ions, and excited states, the shortcomings of the Hartree— Fock (HF) approximation for the description of the electronic structure of molecular systems became increasingly evident. The energy error of the restricted HF wave function, i.e., the difference between the HF limit energy (which is the limit approached by restricted self-consistent field calculations as the basis set approaches completeness) and the exact solution of the nonrelativistic Schrodinger equation, has been termed the correlation energy.(1) It reflects the fact that the HF Hamiltonian contains the average, rather than instantaneous, interelectron potential, and thus neglects the correlation between the motions of the electrons.

The Method of Configuration Interaction

This paper surveys the state of the art in sparse matrix research in January 1976. Much of the survey deals with the solution of sparse simultaneous linear equations, including the storage of such matrices and the effect of paging on sparse matrix algorithms. In the symmetric case, relevant terms from graph theory are defined. Band systems and matrices arising from the discretization of partial differential equations are treated as separate cases. Preordering techniques are surveyed with particular emphasis on partitioning (to block triangular form) and tearing (to bordered block triangular form). Methods for solving the least squares problem and for sparse linear programming are also reviewed. The sparse eigenproblem is discussed with particular reference to some fairly recent iterative methods. There is a short discussion of general iterative techniques, and reference is made to good standard texts in this field. Design considerations when implementing sparse matrix algorithms are examined and finally comments are made concerning the availability of codes in this area.

A survey of sparse matrix research

Super-matrix methods

http://web.mit.edu/kjb/www/Publications_Prior_to_1998/An_Accelerated_Subspace_Iteration_Method.pdf

H.R. Schwarz

Papers

The eigenvalue problem (A − λB)x = 0 for symmetric matrices of high order

Two algorithms for treating ax = λbx