Recurrent Neural Networks for Computing Pseudoinverses of Rank-Deficient Matrices
SIAM Journal on Scientific Computing
Recurrent neural networks for computing weighted Moore-Penrose inverse
Applied Mathematics and Computation
Modelling and Control of Robot Manipulators
Modelling and Control of Robot Manipulators
Neural Networks for Optimization and Signal Processing
Neural Networks for Optimization and Signal Processing
Convex Optimization
Computing generalized inverses using LU factorization of matrix product
International Journal of Computer Mathematics
A new method for computing Moore-Penrose inverse matrices
Journal of Computational and Applied Mathematics
Iterative method for computing the Moore-Penrose inverse based on Penrose equations
Journal of Computational and Applied Mathematics
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In this letter, we propose a novel iterative method for computing generalized inverse, based on a novel KKT formulation. The proposed iterative algorithm requires making four matrix and vector multiplications at each iteration and thus has low computational complexity. The proposed method is proved to be globally convergent without any condition. Furthermore, for fast computing generalized inverse, we present an acceleration scheme based on the proposed iterative method. The global convergence of the proposed acceleration algorithm is also proved. Finally, the effectiveness of the proposed iterative algorithm is evaluated numerically.