As a type of recurrent neural networks (RNNs) modeled as dynamic systems, the gradient neural network (GNN) is recognized as an effective method for static matrix inversion with exponential ...

This paper presents matrix inversion algorithms based on LU decomposition and QR decomposition and LDLT decomposition (i.e. improved Cholesky decomposition) and the time complexity of the three ...

Abstract. Our goal is to investigate and exploit an analogy between the scaled hyperpower family (SHPI family) of iterative methods for computing the matrix inverse and the discretization of Zhang ...

Section 6 contains algorithms for constructing pseudo-inverse matrices. We develop our methods in a detailed example in Section 7. Section 8 outlines the hybrid method for partial strip reconstruction ...

It gives the architecture of an optimized complex matrix inversion using Gauss-Jordan (GJ) elimination in Verilog with single precision floating-point representation.

Two methods are illustrated for the inversion of matrices, with special attention to the arrangement of the work on the computing sheet, and the application of frequent and powerful checks. The ...