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Sequence Selection by Pareto Optimization

2019-11-05 | |  67 |   48 |   0
Abstract The problem of selecting a sequence of items from a universe that maximizes some given objective function arises in many real-world applications. In this paper, we propose an anytime randomized iterative approach POS EQ S EL, which maximizes the given objective function and minimizes the sequence length simultaneously. We prove that for any previously studied objective function, POS E Q S EL using a reasonable time can always reach or improve the best known approximation guarantee. Empirical results exhibit the superior performance of POS EQ S EL.

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