Papers › Faster First-Order Primal-Dual Methods for Linear Programming using Restarts and Sharpness
Faster First-Order Primal-Dual Methods for Linear Programming using Restarts and Sharpness
David Applegate, Oliver Hinder, Haihao Lu, Miles Lubin
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First-order primal-dual methods are appealing for their low memory overhead, fast iterations, and effective parallelization. However, they are often slow at finding high accuracy solutions, which creates a barrier to their use in traditional linear programming (LP) applications. This paper exploits the sharpness of primal-dual formulations of LP instances to achieve linear convergence using restarts in a general setting that applies to ADMM (alternating direction method of multipliers), PDHG (primal-dual hybrid gradient method) and EGM (extragradient method). In the special case of PDHG, without restarts we show an iteration count lower bound of Ω(κ² log(1/ϵ)), while with restarts we show an iteration count upper bound of O(κlog(1/ϵ)), where κ is a condition number and ϵ is the desired accuracy. Moreover, the upper bound is optimal for a wide class of primal-dual methods, and applies to the strictly more general class of sharp primal-dual problems. We develop an adaptive restart scheme and verify that restarts significantly improve the ability of PDHG, EGM, and ADMM to find high accuracy solutions to LP problems.
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