# Online Covering with Sum of $\ell_q$-Norm Objectives

## TL;DR

This paper develops an online primal-dual algorithm for fractional covering problems with complex ll_q-norm objectives, expanding the class of convex functions for which good online algorithms are known, with applications in network design and throughput maximization.

## Contribution

It introduces a nearly tight online algorithm for covering problems with ll_q-norm objectives, removing the monotonicity restriction of prior work.

## Key findings

- Achieves an O(ig(\, ext{log}\,d+ ext{log}\,
hoig))-competitive ratio.
- Extends online algorithms to non-monotone convex objectives.
- Provides new online algorithms for network design and throughput maximization.

## Abstract

We consider fractional online covering problems with $\ell_q$-norm objectives. The problem of interest is of the form $\min\{ f(x) \,:\, Ax\ge 1, x\ge 0\}$ where $f(x)=\sum_{e} c_e \|x(S_e)\|_{q_e} $ is the weighted sum of $\ell_q$-norms and $A$ is a non-negative matrix. The rows of $A$ (i.e. covering constraints) arrive online over time. We provide an online $O(\log d+\log \rho)$-competitive algorithm where $\rho = \frac{\max a_{ij}}{\min a_{ij}}$ and $d$ is the maximum of the row sparsity of $A$ and $\max |S_e|$. This is based on the online primal-dual framework where we use the dual of the above convex program. Our result expands the class of convex objectives that admit good online algorithms: prior results required a monotonicity condition on the objective $f$ which is not satisfied here. This result is nearly tight even for the linear special case. As direct applications we obtain (i) improved online algorithms for non-uniform buy-at-bulk network design and (ii) the first online algorithm for throughput maximization under $\ell_p$-norm edge capacities.