Maximizing Profit with Convex Costs in the Random-order Model

TL;DR

This paper develops competitive algorithms for online resource allocation with convex costs in the random-order model, extending previous work to supermodular costs and matroid constraints using convex duality.

Contribution

It introduces new algorithms with improved competitive ratios for convex cost problems under supermodular and matroid constraints, utilizing convex duality techniques.

Findings

Achieves $O(d)$-competitiveness for supermodular convex costs.

Provides $O(d^3 eta)$-competitiveness with matroid constraints.

Improves to $O(d^2 eta)$ when costs are separable.

Abstract

Suppose a set of requests arrives online: each request gives some value $v_i$ if accepted, but requires using some amount of each of $d$ resources. Our cost is a convex function of the vector of total utilization of these $d$ resources. Which requests should be accept to maximize our profit, i.e., the sum of values of the accepted demands, minus the convex cost? We consider this problem in the random-order a.k.a. secretary model, and show an $O(d)$-competitive algorithm for the case where the convex cost function is also supermodular. If the set of accepted demands must also be independent in a given matroid, we give an $O(d^3 \alpha)$-competitive algorithm for the supermodular case, and an improved $O(d^2\alpha)$ if the convex cost function is also separable. Here $\alpha$ is the competitive ratio of the best algorithm for the submodular secretary problem. These extend and improve previous results known for this problem. Our techniques are simple but use powerful ideas from convex duality, which give clean interpretations of existing work, and allow us to give the extensions and improvements.