1 Department of Mathematics, Faculty of Science, Menoufia University, Shebeen El-Kom, Egypt. 2 Department of Mathematics, Faculty of Science, Zagazig University, Ash Sharqiyah, Egypt. In many ...

Abstract: This paper deals with the following generalized inverse eigenvalue problem for generalized seven-diagonal matrix: give three characteristic pairs and a matrix, get a generalized Jacobi ...

Computing the inverse of a matrix is one of the most important operations in machine learning. If some matrix A has shape n-by-n, then its inverse matrix Ai is n-by-n and the matrix product of Ai * A ...

Let $B \in M_{n}(C)$ be a row diagonally dominant matrix, i.e., $\sigma_i \left\vert b_{ii}\right\vert = \sum\limits_{{j=i} \atop {j\not=i}}^n} \left\vert b_{ij ...

The inverse of a matrix is a matrix that when multiplied with the original matrix will result in an identity matrix. Inverse Matrix operation can be divided into three parts: Swapping the rows to make ...

Inverting a matrix is one of the most common tasks in data science and machine learning. In this article I explain why inverting a matrix is very difficult and present code that you can use as-is, or ...

"Matrix inverses are matrices which when multiplied by their oringinal matrices equate to the identity matrix. Only square matrices are invertible, but rectangular matrices may have left or right ...

This paper proposes a novel shrinkage estimator for high-dimensional covariance matrices by extending the Oracle Approximating Shrinkage (OAS) of Chen et al. (2009) to target the diagonal elements of ...