arXiv subjects
Publications and source records attributed to Dimitrios Pappas.
In this article we provide a fast computational method in order to calculate the Moore-Penrose inverse of singular square matrices and of rectangular matrices. The proposed method proves to be much faster and has significantly better accuracy than the already proposed methods, while works for full and sparse matrices.
In this work a linearly constrained minimization of a positive semidefinite quadratic functional is examined. Our results are concerning infinite dimensional real Hilbert spaces, with a singular positive operator related to the functional, and considering as constraint a singular operator. The difference between the proposed minimization and previous work on this problem, is that it is considered for all vectors perpendicular to the kernel of the related operator or matrix.
A common optimization problem is the minimization of a symmetric positive definite quadratic form $< x,Tx >$ under linear constrains. The solution to this problem may be given using the Moore-Penrose inverse matrix. In this work we extend this result to infinite dimensional complex Hilbert spaces, making use of the generalized inverse of an operator. A generalization is given for positive diagonizable and arbitrary positive operators, not necessarily invertible, considering as constraint a singular operator. In particular, when $T$ is positive semidefinite, the minimization is considered for all vectors belonging to $\mathcal{N}(T)^\perp$.
In this paper we characterize EP operators through the existence of different types of factorizations. Our results extend to EP operators existing characterizations for EP matrices and give new characterizations both for EP matrices and EP operators.