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#### Think Eternally: Improved Algorithms for the Temp Secretary Problem and Extensions

##### MPS-Authors
/persons/resource/persons134206

Kesselheim,  Thomas
Algorithms and Complexity, MPI for Informatics, Max Planck Society;

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##### Fulltext (public)

arXiv:1606.06926.pdf
(Preprint), 284KB

##### Supplementary Material (public)
There is no public supplementary material available
##### Citation

Kesselheim, T., & Tönnis, A. (2016). Think Eternally: Improved Algorithms for the Temp Secretary Problem and Extensions. Retrieved from http://arxiv.org/abs/1606.06926.

Cite as: http://hdl.handle.net/11858/00-001M-0000-002C-4E68-A
##### Abstract
The \emph{Temp Secretary Problem} was recently introduced by Fiat et al. It is a generalization of the Secretary Problem, in which commitments are temporary for a fixed duration. We present a simple online algorithm with improved performance guarantees for cases already considered by Fiat et al.\ and give competitive ratios for new generalizations of the problem. In the classical setting, where candidates have identical contract durations $\gamma \ll 1$ and we are allowed to hire up to $B$ candidates simultaneously, our algorithm is $(\frac{1}{2} - O(\sqrt{\gamma}))$-competitive. For large $B$, the bound improves to $1 - O\left(\frac{1}{\sqrt{B}}\right) - O(\sqrt{\gamma})$. Furthermore we generalize the problem from cardinality constraints towards general packing constraints. We achieve a competitive ratio of $1 - O\left(\sqrt{\frac{(1+\log d + \log B)}{B}}\right) -O(\sqrt{\gamma})$, where $d$ is the sparsity of the constraint matrix and $B$ is generalized to the capacity ratio of linear constraints. Additionally we extend the problem towards arbitrary hiring durations. Our algorithmic approach is a relaxation that aggregates all temporal constraints into a non-temporal constraint. Then we apply a linear scaling algorithm that, on every arrival, computes a tentative solution on the input that is known up to this point. This tentative solution uses the non-temporal, relaxed constraints scaled down linearly by the amount of time that has already passed.