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Jan Vondrák

Publications and source records attributed to Jan Vondrák.

20 records · Page 2Linked to original sources

On Variants of the Matroid Secretary Problem

We present a number of positive and negative results for variants of the matroid secretary problem. Most notably, we design a constant-factor competitive algorithm for the "random assignment" model where the weights are assigned randomly to the elements of a matroid, and then the elements arrive on-line in an adversarial order (extending a result of Soto \cite{Soto11}). This is under the assumption that the matroid is known in advance. If the matroid is unknown in advance, we present an $O(\log r \log n)$-approximation, and prove that a better than $O(\log n / \log \log n)$ approximation is impossible. This resolves an open question posed by Babaioff et al. \cite{BIK07}. As a natural special case, we also consider the classical secretary problem where the number of candidates $n$ is unknown in advance. If $n$ is chosen by an adversary from $\{1,...,N\}$, we provide a nearly tight answer, by providing an algorithm that chooses the best candidate with probability at least $1/(H_{N-1}+1)$ and prove that a probability better than $1/H_N$ cannot be achieved (where $H_N$ is the $N$-th harmonic number).

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Submodular Maximization by Simulated Annealing

We consider the problem of maximizing a nonnegative (possibly non-monotone) submodular set function with or without constraints. Feige et al. [FOCS'07] showed a 2/5-approximation for the unconstrained problem and also proved that no approximation better than 1/2 is possible in the value oracle model. Constant-factor approximation was also given for submodular maximization subject to a matroid independence constraint (a factor of 0.309 Vondrak [FOCS'09]) and for submodular maximization subject to a matroid base constraint, provided that the fractional base packing number is at least 2 (a 1/4-approximation, Vondrak [FOCS'09]). In this paper, we propose a new algorithm for submodular maximization which is based on the idea of {\em simulated annealing}. We prove that this algorithm achieves improved approximation for two problems: a 0.41-approximation for unconstrained submodular maximization, and a 0.325-approximation for submodular maximization subject to a matroid independence constraint. On the hardness side, we show that in the value oracle model it is impossible to achieve a 0.478-approximation for submodular maximization subject to a matroid independence constraint, or a 0.394-approximation subject to a matroid base constraint in matroids with two disjoint bases. Even for the special case of cardinality constraint, we prove it is impossible to achieve a 0.491-approximation. (Previously it was conceivable that a 1/2-approximation exists for these problems.) It is still an open question whether a 1/2-approximation is possible for unconstrained submodular maximization.

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