A Parallelizable Acceleration Framework for Packing Linear Programs

TL;DR

This paper introduces a parallelizable acceleration framework for packing linear programs that significantly speeds up solving small-constraint problems while maintaining solution quality, applicable to various solver types.

Contribution

The paper proposes a black-box acceleration framework for packing linear programs that enhances existing solvers' speed and guarantees solution quality, applicable to linear and integer programs.

Findings

Achieves up to 100x speedup in experiments

Provides worst-case guarantees on solution quality

Applicable to exact, approximate, and distributed solvers

Abstract

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.