Report Number: CS-TR-99-1622
Institution: Stanford University, Department of Computer Science
Title: Multicommodity and Generalized Flow Algorithms: Theory and Practice
Author: Oldham, Jeffrey David
Date: August 1999
Abstract: We present several simple, practical, and fast algorithms for linear programs, concentrating on network flow problems. Since the late 1980s, researchers developed different combinatorial approximation algorithms for fractional packing problems, obtaining the fastest theoretical running times to solve multicommodity minimum-cost and concurrent flow problems. A direct implementation of these multicommodity flow algorithms was several orders of magnitude slower than solving these problems using a commercial linear programming solver. Through experimentation, we determined which theoretically equivalent constructs are experimentally efficient. Guided by theory, we designed and implemented practical improvements while maintaining the same worst-case complexity bounds. The resulting algorithms solve problems orders of magnitude faster than commercial linear programming solvers and problems an order of magnitude larger. We also present simple, combinatorial algorithms for generalized flow problems. These problems generalize ordinary network flow problems by specifying a flow multiplier \mu(a) for each arc a. Using multipliers permit a flow problem to model transforming one type into another, e.g., currency exchange, and modification of the amount of flow, e.g., water evaporation from canals or accrual of interest in bank accounts. First, we show the generalized shortest paths problem can be solved using existing network flow ideas, i.e., by combining the Bellman-Ford-Moore shortest path framework and Megiddo's parametric search. Second, we combine this algorithm with fractional packing frameworks to yield the first polynomial-time combinatorial approximation algorithms for the generalized versions of the nonnegative-cost minimum-cost flow, concurrent flow, multicommodity maximum flow, and multicommodity nonnegative-cost minimum-cost flow problems. These algorithms show that generalized concurrent flow and multicommodity maximum flow have strongly polynomial approximation algorithms.
http://i.stanford.edu/pub/cstr/reports/cs/tr/99/1622/CS-TR-99-1622.pdf