Tensors, or multi-linear forms, are important objects in a variety of areas from analytics, to combinatorics, to computational complexity theory. Notions of tensor rank aim to quantify the “complexity” of these forms, and are thus also important. While there is one single definition of rank that completely captures the complexity of matrices (and hence linear transformations), there is no definitive analog for tensors. Rather, many notions of tensor rank have been defined over the years, each with their own set of uses.

In this talk I will survey the popular notions of tensor rank. I will give a brief history of their introduction, motivating their existence, and discuss some of their applications in computer science. I will also give proof sketches of recent results by Lovett, and Cohen and Moshkovitz, which prove asymptotic equivalence between three key notions of tensor rank over finite fields with at least three elements.