Successive matrix squaring algorithm for parallel computing the weighted generalized inverseA+MN
Applied Mathematics and Computation
The representation and approximation for the weighted Moore—Penrose inverse
Applied Mathematics and Computation
Iterative Methods for Sparse Linear Systems
Iterative Methods for Sparse Linear Systems
A Robust Preconditioner with Low Memory Requirements for Large Sparse Least Squares Problems
SIAM Journal on Scientific Computing
A rank reduced matrix method in extreme learning machine
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part I
A third order iterative method for A†
International Journal of Computing Science and Mathematics
A novel method for training an echo state network with feedback-error learning
Advances in Artificial Intelligence
A novel iterative method for computing generalized inverse
Neural Computation
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The Moore-Penrose inverse of an arbitrary matrix (including singular and rectangular) has many applications in statistics, prediction theory, control system analysis, curve fitting and numerical analysis. In this paper, an algorithm based on the conjugate Gram-Schmidt process and the Moore-Penrose inverse of partitioned matrices is proposed for computing the pseudoinverse of an mxn real matrix A with m=n and rank r@?n. Numerical experiments show that the resulting pseudoinverse matrix is reasonably accurate and its computation time is significantly less than that of pseudoinverses obtained by the other methods for large sparse matrices.