neal young / Chrobak08Online

  • Png/Chrobak05Online.png Following Mettu and Plaxton, we study incremental algorithms for the $k$-medians problem. Such an algorithm must produce a nested sequence $F_1 \subseteq F_2 \subseteq \cdots \subseteq F_n$ of sets of facilities. Mettu and Plaxton show that incremental metric medians has a (roughly) $40$-competitive deterministic polynomial-time algorithm. We give improved algorithms, including a $(24+ε)$-competitive deterministic polynomial-time algorithm and a $5.44$-competitive, randomized, non-polynomial-time algorithm.

    We also consider the competitive ratio with respect to size. An algorithm is $s$-size-competitive if, for each $k$, the cost of $F_k$ is at most the minimum cost of any set of $k$ facilities, while the size of $F_k$ is at most $s k$. We present optimally competitive algorithms for this problem.

    Our proofs reduce incremental medians to the following online bidding problem: faced with some unknown threshold $T>0$, an algorithm must submit ``bids'' $b>0$ until it submits a bid as large as $T$. The algorithm pays the sum of its bids. We describe optimally competitive algorithms for online bidding.

    Our results on cost-competitive incremental medians extend to approximately metric distance functions, incremental fractional medians, and incremental bicriteria approximation.
    Journal version of [2006].

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