Theory Seminar: Constant-Factor Approximation Algorithms for Parity-Constrained Facility Location Problems

Constant-Factor Approximation Algorithms for Parity-Constrained Facility Location Problems

Abstract: Facility location is a prominent optimization problem that has inspired a large quantity of both theoretical and practical studies in combinatorial optimization. Although the problem has been investigated under various settings reflecting typical structures within the optimization problems of practical interest, little is known on how the problem behaves in conjunction with parity constraints. This shortfall of understanding was rather disturbing when we consider the central role of parity in the field of combinatorics.

In this paper, we present the first constant-factor approximation algorithm for the facility location problem with parity constraints. We are given as the input a metric on a set of facilities and clients, the opening cost of each facility, and the parity requirement--odd, even, or unconstrained--of every facility in this problem. The objective is to open a subset of facilities and assign every client to an open facility so as to minimize the sum of the total opening costs and the assignment distances, but subject to the condition that the number of clients assigned to each open facility must have the same parity as its requirement.

Although the unconstrained facility location problem as a relaxation for this parity-constrained generalization has unbounded gap, we demonstrate that it yields a structured solution whose parity violation can be corrected at small cost. This correction is prescribed by a T-join on an auxiliary graph constructed by the algorithm. This auxiliary graph does not satisfy the triangle inequality, but we show that a carefully chosen set of shortcutting operations leads to a cheap and sparse T-join. Finally, we bound the correction cost by exhibiting a combinatorial multi-step construction of an upper bound.

This is joint work with Kangsan Kim and Yongho Shin.

Bio: Hyung-Chan An is an Assistant Professor at Yonsei University in the Department of Computer Science. His primary research interests include approximation algorithms and combinatorial optimization. Before joining Yonsei University, he was a postdoctoral researcher at Ecole Polytechnique Federale de Lausanne from 2012 to 2016, under the supervision of Ola Svensson and Aleksander Madry. He received his Ph.D. in Computer Science from Cornell University in 2012. His Ph.D. advisors are David Shmoys and Robert Kleinberg. He received his B.S. in Computer Science and Engineering from Seoul National University in 2006.