On-Line Difference Maximization
<|reference_start|>On-Line Difference Maximization: In this paper we examine problems motivated by on-line financial problems and stochastic games. In particular, we consider a sequence of entirely arbitrary distinct values arriving in random order, and must devise strategies for selecting low values followed by high values in such a way as to maximize the expected gain in rank from low values to high values.   First, we consider a scenario in which only one low value and one high value may be selected. We give an optimal on-line algorithm for this scenario, and analyze it to show that, surprisingly, the expected gain is n-O(1), and so differs from the best possible off-line gain by only a constant additive term (which is, in fact, fairly small -- at most 15).   In a second scenario, we allow multiple nonoverlapping low/high selections, where the total gain for our algorithm is the sum of the individual pair gains. We also give an optimal on-line algorithm for this problem, where the expected gain is n^2/8-\Theta(n\log n). An analysis shows that the optimal expected off-line gain is n^2/6+\Theta(1), so the performance of our on-line algorithm is within a factor of 3/4 of the best off-line strategy.<|reference_end|>
@article{kao2001on-line,
    title={On-Line Difference Maximization},
    author={Ming-Yang Kao, Stephen R. Tate},
    journal={SIAM Journal on Discrete Mathematics, 12(1):78-90, 1999},
    year={2001},
    archivePrefix={arXiv},
    eprint={cs/0101024},
    primaryClass={cs.DS cs.DM}
}