Iterative Relaxation Algorithm

See: Approximation Theory, Simultaneous Equations, Linear System, Elliptic Partial Differential Equation.

References

(Wikipedia, 2016) ⇒ http://wikipedia.org/wiki/Relaxation_(iterative_method) Retrieved:2016-2-18.
- In numerical mathematics, relaxation methods are iterative methods for solving systems of equations, including nonlinear systems.
  Relaxation methods were developed for solving large sparse linear systems, which arose as finite-difference discretizations of differential equations. They are also used for the solution of linear equations for linear least-squares problems and also for systems of linear inequalities, such as those arising in linear programming. They have also been developed for solving nonlinear systems of equations. Relaxation methods are important especially in the solution of linear systems used to model elliptic partial differential equations, such as Laplace's equation and its generalization, Poisson's equation. These equations describe boundary-value problems, in which the solution-function's values are specified on boundary of a domain; the problem is to compute a solution also on its interior. Relaxation methods are used to solve the linear equations resulting from a discretization of the differential equation, for example by finite differences.^[1] ^[2] ^[3]
  These iterative methods of relaxation should not be confused with "relaxations" in mathematical optimization, which approximate a difficult problem by a simpler problem, whose "relaxed" solution provides information about the solution of the original problem.

↑ Abraham Berman, Robert J. Plemmons, Nonnegative Matrices in the Mathematical Sciences, 1994, SIAM. ISBN 0-89871-321-8.
↑ David M. Young, Jr. Iterative Solution of Large Linear Systems, Academic Press, 1971. (reprinted by Dover, 2003)
↑ Richard S. Varga 2002 Matrix Iterative Analysis, Second ed. (of 1962 Prentice Hall edition), Springer-Verlag.