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In numerical linear algebra, the method of successive over-relaxation (SOR) is a variant of the Gauss–Seidel method for solving a linear system of equations, resulting in faster convergence. A similar method can be used for any slowly converging iterative process .
See, in particular, the successive over-relaxation (SOR) and symmetric successive over-relaxation (SSOR) methods. [2] When David Young first began his research on iterative methods in the late 1940s, there was some skepticism with the idea of using iterative methods on the new computing machines to solve industrial-size problems. Ever since ...
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In some cases, Newton's method can be stabilized by using successive over-relaxation, or the speed of convergence can be increased by using the same method. In a robust implementation of Newton's method, it is common to place limits on the number of iterations, bound the solution to an interval known to contain the root, and combine the method ...
The modeling strategy of relaxation should not be confused with iterative methods of relaxation, such as successive over-relaxation (SOR); iterative methods of relaxation are used in solving problems in differential equations, linear least-squares, and linear programming.
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In applied mathematics, symmetric successive over-relaxation (SSOR), [1] is a preconditioner. If the original matrix can be split into diagonal, lower and upper triangular as = + + then the SSOR preconditioner matrix is defined as = (+) (+)