Place : LIX, room Grace Hopper
Title : 3-Tensors: a natural way of representing rank-metric codes
Abstract : In coding theory, two important issues concern the encoding and the storage of a
code. In the most general case, given a finite set A and a positive integer n, a block
code C is a subset of A^n , endowed with a distance function (usually the Hamming one).
The space of messages M is then embedded in A^n , via an injective encoding map E,
such that E(M) = C. The need to have fast encoding and efficient representation of
codes led to look at algebraic structures on all the defining objects (the alphabet, the
space of messages and the code), and on the encoding map E. For this reason, one
usually only considers the alphabet as a field of q elements, the space of messages as
the vector space F_q^k , and the encoding map as a linear function into F_q^n , which leads
to the study of linear codes. In this framework, we can locate the generator matrix
of an [n, k] code C, which serves as a representation in order to store C, and also
as an encoding map. However, one can also read the parameters of the defining code
from its algebraic structure. This generator matrix can also be constructed for a vector
rank-metric code, and in analogous ways, one can extrapolate information on the code
from it.
In this talk we explain an analogous concept for rank-metric codes, which are con-
sidered as spaces of m × n matrices over a finite field F_q . This is the case of the
generator tensor, which is a similar object that one can use for storing, encoding and
reading the parameters of a rank-metric code. Moreover, the tensor representation
leads to the investigation of a new parameter, namely the tensor rank of an [n × m, k]
code C, that gives a measure on the storage and encoding complexity of C. This also
produces an interesting relation between rank-metric codes and algebraic complexity
theory.
It is a joint work with Eimear Byrne, Alberto Ravagnani and John Sheekey.