Two-Commodity Flow is Equivalent to Linear Programming under Nearly-Linear Time Reductions
Ming Ding, Rasmus Kyng, Peng Zhang

TL;DR
This paper presents a nearly-linear time reduction from linear programming to the 2-commodity flow problem, establishing their near-equivalence and enabling efficient solutions for both using similar algorithms.
Contribution
It improves upon Itai's polynomial-time reduction by nearly preserving problem size and bounding errors, showing that 2-commodity flow and linear programming are nearly equivalent in computational complexity.
Findings
Reduction runs in nearly-linear time with small size blow-up
Approximate solutions to 2-commodity flow yield solutions to linear programs efficiently
Establishes near-equivalence between 2-commodity flow and linear programming
Abstract
We give a nearly-linear time reduction that encodes any linear program as a 2-commodity flow problem with only a small blow-up in size. Under mild assumptions similar to those employed by modern fast solvers for linear programs, our reduction causes only a polylogarithmic multiplicative increase in the size of the program and runs in nearly-linear time. Our reduction applies to high-accuracy approximation algorithms and exact algorithms. Given an approximate solution to the 2-commodity flow problem, we can extract a solution to the linear program in linear time with only a polynomial factor increase in the error. This implies that any algorithm that solves the 2-commodity flow problem can solve linear programs in essentially the same time. Given a directed graph with edge capacities and two source-sink pairs, the goal of the 2-commodity flow problem is to maximize the sum of the flows…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
