  In multiparametric programming an optimization problem which is dependent on
a parameter vector is solved parametrically. In control, multiparametric
quadratic programming (mp-QP) problems have become increasingly important since
the optimization problem arising in Model Predictive Control (MPC) can be cast
as an mp-QP problem, which is referred to as explicit MPC. One of the main
limitations with mp-QP and explicit MPC is the amount of memory required to
store the parametric solution and the critical regions. In this paper, a method
for exploiting low rank structure in the parametric solution of an mp-QP
problem in order to reduce the required memory is introduced. The method is
based on ideas similar to what is done to exploit low rank modifications in
generic QP solvers, but is here applied to mp-QP problems to save memory. The
proposed method has been evaluated experimentally, and for some examples of
relevant problems the relative memory reduction is an order of magnitude
compared to storing the full parametric solution and critical regions.
