# The Online Min-Sum Set Cover Problem

@article{Fotakis2020TheOM, title={The Online Min-Sum Set Cover Problem}, author={Dimitris Fotakis and Loukas Kavouras and Grigorios Koumoutsos and Stratis Skoulakis and Manolis Vardas}, journal={ArXiv}, year={2020}, volume={abs/2003.02161} }

We consider the online Min-Sum Set Cover (MSSC), a natural and intriguing generalization of the classical list update problem. In Online MSSC, the algorithm maintains a permutation on $n$ elements based on subsets $S_1, S_2, \ldots$ arriving online. The algorithm serves each set $S_t$ upon arrival, using its current permutation $\pi_{t}$, incurring an access cost equal to the position of the first element of $S_t$ in $\pi_{t}$. Then, the algorithm may update its permutation to $\pi_{t+1…

## 4 Citations

### Efficient Online Learning of Optimal Rankings: Dimensionality Reduction via Gradient Descent

- Computer ScienceNeurIPS
- 2020

This work shows how to achieve low regret for GMSSC in polynomial-time by employing dimensionality reduction from rankings to the space of doubly stochastic matrices, where it is shown how subgradients can be computed efficiently, by solving the dual of a configuration LP.

### On the Approximability of Multistage Min-Sum Set Cover

- Computer Science, MathematicsICALP
- 2021

The polynomial-time approximability of the multistage version of Min-Sum Set Cover (Mult-MSSC), a natural and intriguing generalization of the classical List Update problem, can be approximated within a factor of O(log2 n) in general instances, by randomized rounding, and within a Factor O(r2), if all requests have cardinality at most r.

### An Improved Algorithm For Online Reranking

- Computer Science
- 2022

A computationally efﬁcient randomized algorithm whose competitive ratio (A lg -to-O pt cost ratio) is O ( r 2) and the existence of a deterministic O (r 4 ) -competitive algorithm is proved and r is the maximum cardinality of sets R t.

### SIGACT News Online Algorithms Column 36

- Computer ScienceSIGACT News
- 2020

This column will discuss some papers in online algorithms that appeared in 2020, and has instead made a selection.

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