Conditions for Existence, Representations, and Computation of Matrix Generalized Inverses

Conditions for the existence and representations of -, -, and -inverses which satisfy certain conditions on ranges and/or null spaces are introduced. These representations are applicable to complex matrices and involve solutions of certain matrix equations. Algorithms arising from the introduced representations are developed. Particularly, these algorithms can be used to compute the Moore-Penrose inverse, the Drazin inverse, and the usual matrix inverse. The implementation of introduced algorithms is defined on the set of real matrices and it is based on the Simulink implementation of GNN models for solving the involved matrix equations. In this way, we develop computational procedures which generate various classes of inner and outer generalized inverses on the basis of resolving certain matrix equations. As a consequence, some new relationships between the problem of solving matrix equations and the problem of numerical computation of generalized inverses are established. Theoretical results are applicable to complex matrices and the developed algorithms are applicable to both the time-varying and time-invariant real matrices.

[1]  Hebing Wu,et al.  The representation and approximation for the generalized inverse AT, S(2) , 2003, Appl. Math. Comput..

[2]  Dharmendra Kumar Gupta,et al.  A new representation for aT,S(2,3) , 2014, Appl. Math. Comput..

[3]  Xiaoji Liu,et al.  Successive Matrix Squaring Algorithm for Computing the Generalized Inverse AT,S(2) , 2012, J. Appl. Math..

[4]  Predrag S. Stanimirović,et al.  Adjoint Mappings and Inverses of Matrices , 2006 .

[5]  Yunong Zhang,et al.  Comparison on Zhang neural network and gradient neural network for time-varying linear matrix equation AXB = C solving , 2008, 2008 IEEE International Conference on Industrial Technology.

[6]  Vasilios N. Katsikis,et al.  Application of the Least Squares Solutions in Image Deblurring , 2015 .

[7]  Yimin Wei,et al.  A characterization and representation of the generalized inverse A(2)T,S and its applications , 1998 .

[8]  Xiaoji Liu,et al.  Higher-order convergent iterative method for computing the generalized inverse and its application to Toeplitz matrices , 2013 .

[9]  Luo Fa-long,et al.  Neural network approach to computing matrix inversion , 1992 .

[10]  T. Kaczorek,et al.  Computation of the Drazin inverse of a singular matrix making use of neural networks , 1992 .

[11]  Predrag S. Stanimirovic,et al.  Recurrent Neural Network for Computing the Drazin Inverse , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[12]  Yong-Lin Chen,et al.  Representation and approximation of the outer inverse AT,S(2) of a matrix A , 2000 .

[13]  The generalized inverseAT*/(2) and its applications , 2003 .

[14]  D. Pappas,et al.  Image deblurring process based on separable restoration methods , 2014 .

[15]  Predrag S. Stanimirovic,et al.  Successive matrix squaring algorithm for computing outer inverses , 2008, Appl. Math. Comput..

[16]  M. Drazin A class of outer generalized inverses , 2012 .

[17]  A. Cichocki,et al.  Neural networks for solving systems of linear equations and related problems , 1992 .

[18]  Shwetabh Srivastava,et al.  A new representation forA (2,3) T ,S , Applied mathematics and Computation (Elsevier, SCI ) , 2014 .

[19]  Deqiang Liu,et al.  The representation of generalized inverse AT,S(2,3) and its applications , 2009 .

[20]  Predrag S. Stanimirovic,et al.  A class of numerical algorithms for computing outer inverses , 2014, J. Comput. Appl. Math..

[21]  Soo-Young Lee,et al.  An Optimization Network for Matrix Inversion , 1987, NIPS.

[22]  Yimin Wei,et al.  Neural network approach to computing outer inverses based on the full rank representation , 2016 .

[23]  Jun Wang,et al.  Recurrent Neural Networks for Computing Pseudoinverses of Rank-Deficient Matrices , 1997, SIAM J. Sci. Comput..

[24]  Predrag S. Stanimirovic,et al.  Recurrent Neural Network for Computing Outer Inverse , 2016, Neural Computation.

[25]  Jun Wang,et al.  A recurrent neural network for real-time matrix inversion , 1993 .

[26]  Ke Chen,et al.  MATLAB Simulation and Comparison of Zhang Neural Network and Gradient Neural Network for Online Solution of Linear Time-Varying Matrix Equation AXB-C=0 , 2008, ICIC.

[27]  S. Qiao,et al.  Generalized Inverses: Theory and Computations , 2018 .

[28]  Predrag S. Stanimirovic,et al.  Full-rank representations of {2, 4}, {2, 3}-inverses and successive matrix squaring algorithm , 2011, Appl. Math. Comput..

[29]  Gerhard Zielke,et al.  Report on test matrices for generalized inverses , 1986, Computing.

[30]  Tianping Chen,et al.  A Novel Iterative Method for Computing Generalized Inverse , 2014, Neural Computation.

[31]  Guoliang Chen,et al.  Full-rank representation of generalized inverse AT, S(2) and its application , 2007, Comput. Math. Appl..

[32]  Igor Stojanovic,et al.  Removal of blur in images based on least squares solutions , 2013 .

[33]  Hebing Wu,et al.  (T,S) splitting methods for computing the generalized inverse and rectangular systems , 2001, Int. J. Comput. Math..

[34]  Yimin Wei Recurrent neural networks for computing weighted Moore-Penrose inverse , 2000, Appl. Math. Comput..

[35]  N. S. Urquhart Computation of Generalized Inverse Matrices which Satisfy Specified Conditions , 1968 .