Nearly Linear-Time Packing and Covering LP Solvers

Abstract

Packing and covering linear programs (PC-LPs) form an important class of linear programs (LPs) across computer science, operations research, and optimization. In 1993, Luby and Nisan constructed an iterative algorithm for approximately solving PC-LPs in nearly linear time, where the time complexity scales nearly linearly in $N$, the number of nonzero entries of the matrix, and polynomially in $\varepsilon$, the (multiplicative) approximation error. Unfortunately, all existing nearly linear-time algorithms for solving PC-LPs require time at least proportional to $\varepsilon^{-2}$. In this paper, we break this longstanding barrier by designing a packing solver that runs in time $\tilde{O}(N \varepsilon^{-1})$ and covering LP solver that runs in time $\tilde{O}(N \varepsilon^{-1.5})$. Our packing solver can be extended to run in time $\tilde{O}(N \varepsilon^{-1})$ for a class of well-behaved covering programs. In a follow-up work, Wang et al. showed that all covering LPs can be converted into well-behaved ones by a reduction that blows up the problem size only logarithmically. At high level, these two algorithms can be described as linear couplings of several first-order descent steps. This is an application of our linear coupling technique to problems that are not amenable to blackbox applications known iterative algorithms in convex optimization.