Linear computation coding is concerned with the compression of multidimensional linear functions, i.e. with reducing the computational effort of multiplying an arbitrary vector to an arbitrary, but known, constant matrix.
This paper advances over the state-of-the art, that is based on a discrete matching pursuit (DMP) algorithm, by a step-wise optimal search.
Offering significant performance gains over DMP, it is however computationally infeasible for large matrices and high accuracy.
Therefore, a reduced-state algorithm is introduced that offers performance superior to DMP, while still being computationally feasible even for large matrices.
Depending on the matrix size, the performance gain over DMP is on the order of at least 10%.