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New matrix iterative methods for constraint solutions of the matrix equation AXB=C

机译:矩阵方程AXB = C的约束解的新矩阵迭代方法

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摘要

In this paper, two new matrix iterative methods are presented to solve the matrix equation AXB=C, the minimum residual problem min_(X∈S)||AXB- C|| and the matrix nearness problem min_(X∈SE)||X-X*||, where S is the set of constraint matrices, such as symmetric, symmetric R-symmetric and (R,S)-symmetric, and SE is the solution set of above matrix equation or minimum residual problem. These matrix iterative methods have faster convergence rate and higher accuracy than the matrix iterative methods proposed in Deng et al. (2006) [13], Huang et al. (2008) [15], Peng (2005) [16] and Lei and Liao (2007) [17]. Paige's algorithms are used as the frame method for deriving these matrix iterative methods. Numerical examples are used to illustrate the efficiency of these new methods.
机译:本文提出了两种新的矩阵迭代方法来求解矩阵方程AXB = C,最小残差问题min_(X∈S)|| AXB-C ||。以及矩阵邻近问题min_(X∈SE)|| XX * ||,其中S是约束矩阵的集合,例如对称,对称R对称和(R,S)对称,而SE是解集以上矩阵方程或最小残差问题。这些矩阵迭代方法比Deng等人提出的矩阵迭代方法具有更快的收敛速度和更高的精度。 (2006)[13],Huang等。 (2008)[15],Peng(2005)[16]和Lei and Liao(2007)[17]。 Paige算法被用作推导这些矩阵迭代方法的框架方法。数值例子用来说明这些新方法的效率。

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