  Mixed integer convex and nonlinear programs, MICP and MINLP, are expressive
but require long solving times. Recent work that combines learning methods on
solver heuristics has shown potential to overcome this issue allowing for
applications on larger scale practical problems. Gathering sufficient training
data to employ these methods still present a challenge since getting data from
traditional solvers are slow and newer learning approaches still require large
amounts of data. In order to scale up and make these hybrid learning approaches
more manageable we propose ReDUCE, a method that exploits structure within
small to medium size datasets. We also introduce the bookshelf organization
problem as an MINLP as a way to measure performance of solvers with ReDUCE.
Results show that existing algorithms with ReDUCE can solve this problem within
a few seconds, a significant improvement over the original formulation. ReDUCE
is demonstrated as a high level planner for a robotic arm for the bookshelf
problem.
