On Equivalence of Matrices

TL;DR

This paper introduces a new matrix product called the semi-tensor product (STP), extending classical matrix operations to arbitrary dimensions, and explores related equivalences, structures, and their implications for matrix theory and applications.

Contribution

It proposes the semi-tensor product and related matrix equivalences, establishing new algebraic, geometric, and analytic structures that overcome traditional dimensional limitations.

Findings

Defined the semi-tensor product (STP) extending classical matrix multiplication.

Established lattice and vector space structures under matrix and vector equivalences.

Developed generalized concepts of eigenvalues, eigenvectors, and operators for matrices of arbitrary dimensions.

Abstract

A new matrix product, called the semi-tensor product (STP), is briefly reviewed. The STP extends the classical matrix product to two arbitrary matrices. Under STP the set of matrices becomes a monoid (semi-group with identity). Some related structures and properties are investigated. Then the generalized matrix addition is also introduced, which extends the classical matrix addition to a class of two matrices with different dimensions. Motivated by STP of matrices, two kinds of equivalences of matrices (including vectors) are introduced, which are called matrix equivalence (M-equivalence) and vector equivalence (V-equivalence) respectively. The lattice structure has been established for each equivalence. Under each equivalence, the corresponding quotient space becomes a vector space. Under M-equivalence, many algebraic, geometric, and analytic structures have been posed to the quotient space, which include (i) lattice structure; (ii) inner product and norm (distance); (iii) topology; (iv) a fiber bundle structure, called the discrete bundle; (v) bundled differential manifold; (vi) bundled Lie group and Lie algebra. Under V-equivalence, vectors of different dimensions form a vector space ${\cal V}$, and a matrix $A$ of arbitrary dimension is considered as an operator (linear mapping) on ${\cal V}$. When $A$ is a bounded operator (not necessarily square but includes square matrices as a special case), the generalized characteristic function, eigenvalue and eigenvector etc. are defined. In one word, this new matrix theory overcomes the dimensional barrier in certain sense. It provides much more freedom for using matrix approach to practical problems.