Approximating Sparse Covering Integer Programs Online

TL;DR

This paper develops online algorithms for approximating covering integer programs and their linear relaxations, achieving near-optimal competitive ratios in the online setting.

Contribution

It introduces the first online algorithms with provable guarantees for solving covering integer programs and their linear relaxations.

Findings

O(log k)-competitive algorithm for covering linear programs

O(log k log L)-competitive algorithm for covering integer programs

Results are optimal for polynomial-time online algorithms in this setting

Abstract

A covering integer program (CIP) is a mathematical program of the form: min {c^T x : Ax >= 1, 0 <= x <= u, x integer}, where A is an m x n matrix, and c and u are n-dimensional vectors, all having non-negative entries. In the online setting, the constraints (i.e., the rows of the constraint matrix A) arrive over time, and the algorithm can only increase the coordinates of vector x to maintain feasibility. As an intermediate step, we consider solving the covering linear program (CLP) online, where the integrality requirement on x is dropped. Our main results are (a) an O(log k)-competitive online algorithm for solving the CLP, and (b) an O(log k log L)-competitive randomized online algorithm for solving the CIP. Here k<=n and L<=m respectively denote the maximum number of non-zero entries in any row and column of the constraint matrix A. By a result of Feige and Korman, this is the best possible for polynomial-time online algorithms, even in the special case of set cover.