A two-pronged progress in structured dense matrix vector multiplication

Matrix-vector multiplication is one of the most fundamental computing primitives. Given a matrix A ϵ F^N×N and a vector b ϵ F^N, it is known that in the worst case(N2) operations over F are needed to compute Ab. Many types of structured matrices do admit faster multiplication. However, even given a matrix A that is known to have this property, it is hard in general to recover a representation of A exposing the actual fast multiplication algorithm. Additionally, it is not known in general whether the inverses of such structured matrices can be computed or multiplied quickly. A broad question is thus to identify classes of structured dense matrices that can be represented with O(N) parameters, and for which matrix-vector multiplication (and ideally other operations such as solvers) can be performed in a sub-quadratic number of operations. One such class of structured matrices that admit nearlinear matrix-vector multiplication are the orthogonal polynomial transforms whose rows correspond to a family of orthogonal polynomials. Other well known classes include the Toeplitz, Hankel, Vandermonde, Cauchy matrices and their extensions (e.g. conuent Cauchy-like matrices) that are all special cases of a low displacement rank property. In this paper, we make progress on two fronts: 1. We introduce the notion of recurrence width of matrices. For matrices A with constant recurrence width, we design algorithms to compute both Ab and ATb with a near-linear number of operations. This notion of width is finer than all the above classes of structured matrices and thus we can compute near-linear matrixvector multiplication for all of them using the same core algorithm. Furthermore, we show that it is possible to solve the harder problems of recovering the structured parameterization of a matrix with low recurrence width, and computing matrix-vector product with its inverse in near-linear time. 2. We additionally adapt our algorithm to a matrix-vector multiplication algorithm for a much more general class of matrices with displacement structure: those with low displacement rank with respect to quasiseparable matrices. This result is a novel connection between matrices with displacement structure and those with rank structure, two large but previously separate classes of structured matrices. This class includes Toeplitzplus-Hankel-like matrices, the Discrete Trigonometric Transforms, and more, and captures all previously known matrices with displacement structure under a unified parameterization and algorithm. Our work unifies, generalizes, and simplifies existing stateof-the-art results in structured matrix-vector multiplication. Finally, we show how applications in areas such as multipoint evaluations of multivariate polynomials can be reduced to problems involving low recurrence width matrices.