  This paper is a survey of dictionary screening for the lasso problem. The
lasso problem seeks a sparse linear combination of the columns of a dictionary
to best match a given target vector. This sparse representation has proven
useful in a variety of subsequent processing and decision tasks. For a given
target vector, dictionary screening quickly identifies a subset of dictionary
columns that will receive zero weight in a solution of the corresponding lasso
problem. These columns can be removed from the dictionary prior to solving the
lasso problem without impacting the optimality of the solution obtained. This
has two potential advantages: it reduces the size of the dictionary, allowing
the lasso problem to be solved with less resources, and it may speed up
obtaining a solution. Using a geometrically intuitive framework, we provide
basic insights for understanding useful lasso screening tests and their
limitations. We also provide illustrative numerical studies on several
datasets.
