A Parallelizable Acceleration Framework for Packing Linear Programs
  This paper presents an acceleration framework for packing linear programming
problems where the amount of data available is limited, i.e., where the number
of constraints m is small compared to the variable dimension n. The framework
can be used as a black box to speed up linear programming solvers dramatically,
by two orders of magnitude in our experiments. We present worst-case guarantees
on the quality of the solution and the speedup provided by the algorithm,
showing that the framework provides an approximately optimal solution while
running the original solver on a much smaller problem. The framework can be
used to accelerate exact solvers, approximate solvers, and parallel/distributed
solvers. Further, it can be used for both linear programs and integer linear
programs.
