A Water-Filling Primal-Dual Algorithm for Approximating Non-Linear Covering Problems

TL;DR

This paper introduces a novel primal-dual algorithm with a water-filling approach for approximating non-linear covering problems, extending knapsack-cover inequalities to fractional settings and achieving near-optimal approximation ratios.

Contribution

It generalizes knapsack-cover inequalities to non-linear fractional covering problems and develops a water-filling primal-dual algorithm with proven approximation guarantees.

Findings

Achieves a (2+ε)-approximation for the generalized covering problem.

Provides a 2-approximation rounding algorithm.

Extends the approach to the Unsplittable Flow-Cover problem with a (4+ε)-approximation.

Abstract

Obtaining strong linear relaxations of capacitated covering problems constitute a major technical challenge even for simple settings. For one of the most basic cases, the Knapsack-Cover (Min-Knapsack) problem, the relaxation based on knapsack-cover inequalities achieves an integrality gap of 2. These inequalities have been exploited in more general environments, many of which admit primal-dual approximation algorithms. Inspired by problems from power and transport systems, we introduce a new general setting in which items can be taken fractionally to cover a given demand. The cost incurred by an item is given by an arbitrary non-decreasing function of the chosen fraction. We generalize the knapsack-cover inequalities to this setting an use them to obtain a $(2+\varepsilon)$-approximate primal-dual algorithm. Our procedure has a natural interpretation as a bucket-filling algorithm, which effectively balances the difficulties given by having different slopes in the cost functions: when some superior portion of an item presents a low slope, it helps to increase the priority with which the inferior portions may be taken. We also present a rounding algorithm with an approximation guarantee of 2. We generalize our algorithm to the Unsplittable Flow-Cover problem on a line, also for the setting where items can be taken fractionally. For this problem we obtain a $(4+\varepsilon)$-approximation algorithm in polynomial time, almost matching the $4$-approximation known for the classical setting.