Revisiting Tardos's Framework for Linear Programming: Faster Exact Solutions using Approximate Solvers

In breakthrough work, Tardos (Oper. Res. '86) gave a proximity based framework for solving linear programming (LP) in time depending only on the constraint matrix in the bit complexity model. In Tardos's framework, one reduces solving the LP $\min \langle c,{x}\rangle$, $Ax=b$, $x \geq 0$, $A \in \mathbb{Z}^{m \times n}$, to solving $O(nm)$ LPs in $A$ having small integer coefficient objectives and right-hand sides using any exact LP algorithm. This gives rise to an LP algorithm in time poly$(n,m\log\DeltaA)$, where $\DeltaA$ is the largest subdeterminant of $A$. A significant extension to the real model of computation was given by Vavasis and Ye (Math. Prog. '96), giving a specialized interior point method that runs in time poly$(n,m,\log\bar\chiA)$, depending on Stewart's $\bar{\chi}A$, a well-studied condition number. In this work, we extend Tardos's original framework to obtain such a running time dependence. In particular, we replace the exact LP solves with approximate ones, enabling us to directly leverage the tremendous recent algorithmic progress for approximate linear programming. More precisely, we show that the fundamental "accuracy" needed to exactly solve any LP in $A$ is inverse polynomial in $n$ and $\log\bar{\chi}A$. Plugging in the recent algorithm of van den Brand (SODA '20), our method computes an optimal primal and dual solution using ${O}(m n^{\omega+1} \log (n)\log(\bar{\chi}A+n))$ arithmetic operations, outperforming the specialized interior point method of Vavasis and Ye and its recent improvement by Dadush et al (STOC '20). At a technical level, our framework combines together approximate LP solutions to compute exact ones, making use of constructive proximity theorems -- which bound the distance between solutions of "nearby" LPs -- to keep the required accuracy low.