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Submodular Secretary Problems: Cardinality, Matching, and Linear Constraints

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons134206

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

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Volltexte (frei zugänglich)

arXiv:1607.08805.pdf
(Preprint), 289KB

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Zitation

Kesselheim, T., & Tönnis, A. (2016). Submodular Secretary Problems: Cardinality, Matching, and Linear Constraints. Retrieved from http://arxiv.org/abs/1607.08805.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-002C-4E71-3
Zusammenfassung
We study various generalizations of the secretary problem with submodular objective functions. Generally, a set of requests is revealed step-by-step to an algorithm in random order. For each request, one option has to be selected so as to maximize a monotone submodular function while ensuring feasibility. For our results, we assume that we are given an offline algorithm computing an $\alpha$-approximation for the respective problem. This way, we separate computational limitations from the ones due to the online nature. When only focusing on the online aspect, we can assume $\alpha = 1$. In the submodular secretary problem, feasibility constraints are cardinality constraints. That is, out of a randomly ordered stream of entities, one has to select a subset size $k$. For this problem, we present a $0.31\alpha$-competitive algorithm for all $k$, which asymptotically reaches competitive ratio $\frac{\alpha}{e}$ for large $k$. In submodular secretary matching, one side of a bipartite graph is revealed online. Upon arrival, each node has to be matched permanently to an offline node or discarded irrevocably. We give an $\frac{\alpha}{4}$-competitive algorithm. In both cases, we improve over previously best known competitive ratios, using a generalization of the algorithm for the classic secretary problem. Furthermore, we give an $O(\alpha d^{-\frac{2}{B-1}})$-competitive algorithm for submodular function maximization subject to linear packing constraints. Here, $d$ is the column sparsity, that is the maximal number of none-zero entries in a column of the constraint matrix, and $B$ is the minimal capacity of the constraints. Notably, this bound is independent of the total number of constraints. We improve the algorithm to be $O(\alpha d^{-\frac{1}{B-1}})$-competitive if both $d$ and $B$ are known to the algorithm beforehand.