A Parallelizable Acceleration Framework for Packing Linear Programs
This framework addresses efficiency challenges in linear programming for scenarios with many variables but few constraints, offering broad applicability to exact, approximate, and distributed solvers.
The paper tackles packing linear programming problems with limited data by introducing a parallelizable acceleration framework that speeds up solvers by two orders of magnitude, providing worst-case guarantees for solution quality and speedup.
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.