Interval Linear Constraint
نویسنده
چکیده
We propose the use of the preconditioned interval Gauss-Seidel method as the backbone of an eecient linear equality solver in a CLP(Interval) language. The method, as originally designed, works only on linear systems with square coeecient matrices. Even imposing such a restriction, a naive incorporation of the traditional precon-ditioning algorithm in a CLP language incurs a high worst-case time complexity of O(n 4), where n is the number of variables in the linear system. In this paper, we generalize the algorithm for general linear systems with m constraints and n variables , and give a novel incremental adaptation of preconditioning of O(n 2 (n + m)) complexity. The eeciency of the incremental preconditioned interval Gauss-Seidel method is demonstrated using large-scale linear systems.
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