Row equivalence

In linear algebra, two matrices are row equivalent if one can be changed to the other by a sequence of elementary row operations. Alternatively, two m × n matrices are row equivalent if and only if they have the same row space. The concept is most commonly applied to matrices that represent systems of linear equations, in which case two matrices of the same size are row equivalent if and only if the corresponding homogeneous systems have the same set of solutions, or equivalently the matrices have the same null space. Because elementary row operations are reversible, row equivalence is an equivalence relation. It is commonly denoted by a tilde (~). There is a similar notion of column equivalence, defined by elementary column operations; two matrices are column equivalent if and only if their transpose matrices are row equivalent. Two rectangular matrices that can be converted into one another allowing both elementary row and column operations are called simply equivalent. An elementary row operation is any one of the following moves: Swap: Swap two rows of a matrix. Scale: Multiply a row of a matrix by a nonzero constant. Pivot: Add a multiple of one row of a matrix to another row. Two matrices A and B are row equivalent if it is possible to transform A into B by a sequence of elementary row operations. Row space The row space of a matrix is the set of all possible linear combinations of its row vectors. If the rows of the matrix represent a system of linear equations, then the row space consists of all linear equations that can be deduced algebraically from those in the system. Two m × n matrices are row equivalent if and only if they have the same row space. For example, the matrices are row equivalent, the row space being all vectors of the form . The corresponding systems of homogeneous equations convey the same information: In particular, both of these systems imply every equation of the form The fact that two matrices are row equivalent if and only if they have the same row space is an important theorem in linear algebra.

Global eigenvalue distribution of matrices defined by the skew-shift

Marius Christopher Lemm

We consider large Hermitian matrices whose entries are defined by evaluating the exponential function along orbits of the skew-shift

\binom{j}{2} \omega+jy+x \mod 1

for irrational

\omega

. We prove that the eigenvalue distribution of these matrices conv ...

2019

Dynamical Reduced Basis Methods for Hamiltonian Systems

Global eigenvalue distribution of matrices defined by the skew-shift

On finitely generated modules over quasi-Euclidean rings

Dynamical Reduced Basis Methods for Hamiltonian Systems

On finitely generated modules over quasi-Euclidean rings

Global eigenvalue distribution of matrices defined by the skew-shift