Square matrix

About: Square matrix is a research topic. Over the lifetime, 5000 publications have been published within this topic receiving 92428 citations.

Realizing Suleimanova-type Spectra via Permutative Matrices

Abstract: A permutative matrix is a square matrix such that every row is a permutation of the first row. A constructive version of a result attributed to SuleE˜A±manova is given via permutative matrices. A well-known result is strenghthened by showing that all realizable spectra containing at most four elements can be realized by a permutative matrix or by a direct sum of permutative matrices. The paper concludes by posing a problem.

Derivative Moments for Characteristic Polynomials from the CUE

Abstract: We calculate joint moments of the characteristic polynomial of a random unitary matrix from the circular unitary ensemble and its derivative in the case that the power in the moments is an odd positive integer. The calculations are carried out for finite matrix size and in the limit as the size of the matrices goes to infinity. The latter asymptotic calculation allows us to prove a long-standing conjecture from random matrix theory.

Generalized Pascal matrix of first order S-Z transforms

Abstract: The generalized Pascal matrix is introduced in this contribution. Using this matrix, the coefficients of transfer functions of the continuous-time and discrete-time linear circuits can be recalculated on the assumption that both circuits are connected by a general first-order S-Z transformation.

Domain-Decomposition-Type Methods for Computing the Diagonal of a Matrix Inverse

Abstract: This paper presents two methods based on domain decomposition concepts for determining the diagonal of the inverse of specific matrices. The first uses a divide-and-conquer principle and the Sherman-Morrison-Woodbury formula and assumes that the matrix can be decomposed into a $2 \times 2$ block-diagonal matrix and a low-rank matrix. The second method is a standard domain decomposition approach in which local solves are combined with a global correction. Both methods can be successfully combined with iterative solvers and sparse approximation techniques. The efficiency of the methods usually depends on the specific implementation, which should be fine-tuned for different test problems. Preliminary results for some two-dimensional (2D) problems are reported to illustrate the proposed methods.

Supersymmetric Generalizations of Matrix Models

Abstract: In this thesis generalizations of matrix and eigenvalue models involving supersymmetry are discussed. Following a brief review of the Hermitian one matrix model, the c=-2 matrix model is considered. Built from a matrix valued superfield this model displays supersymmetry on the matrix level. We stress the emergence of a Nicolai-map of this model to a free Hermitian matrix model and study its diagrammatic expansion in detail. Correlation functions for quartic potentials on arbitrary genus are computed, reproducing the string susceptibility of c=-2 Liouville theory in the scaling limit. The results may be used to perform a counting of supersymmetric graphs. We then turn to the supereigenvalue model, today's only successful discrete approach to 2d quantum supergravity. The model is constructed in a superconformal field theory formulation by imposing the super-Virasoro constraints. The complete solution of the model is given in the moment description, allowing the calculation of the free energy and the multi-loop correlators on arbitrary genus and for general potentials. The solution is presented in the discrete case and in the double scaling limit. Explicit results up to genus two are stated. Finally the supersymmetric generalization of the external field problem is addressed. We state the discrete super-Miwa transformations of the supereigenvalue model on the eigenvalue and matrix level. Properties of external supereigenvalue models are discussed, although the model corresponding to the ordinary supereigenvalue model could not be identified so far.