  Low-rank approximation is a technique to approximate a tensor or a matrix
with a reduced rank to reduce the memory required and computational cost for
simulation. Its broad applications include dimension reduction, signal
processing, compression, and regression. In this work, a dynamical low-rank
approximation method is developed for the time-dependent radiation transport
equation in slab geometry. Using a finite volume discretization in space and
Legendre polynomials in angle we construct a system that evolves on a low-rank
manifold via an operator splitting approach. We demonstrate that the lowrank
solution gives better accuracy than solving the full rank equations given the
same amount of memory.
