Abstract: Tiedemann, et al. [Proc. of WALCOM, LNCS 8973, 2015, pp.210-221] defined multiobjective online problems and the competitive analysis for multi-objective online problems, and showed best possible online algorithms with respect to several measures of the competitive analysis. In this paper, we first point out that the definitions and frameworks of the competitive analysis due to Tiedemann, et al. do not necessarily capture the efficiency of online algorithms for multi-objective online problems and provide modified definitions of the competitive analysis for multi-objective online problems. Under the modified framework, we present a simple online algorithm Balanced Price Policy (bpp k ) for the multi-objective (k-objective) time series search problem, and show that the algorithm bpp k is best possible with respect to any measure of the competitive analysis (defined by a monotone function f ). For the modified framework, we also derive best possible values of the competitive ratio for the multi-objective time series search problem with respect to several representative measures of the competitive analysis.
Hasegawa and Itoh REF considered multi-objective time series search problem (M OT SS) and put forth a modified version of competitive analysis for M OT SS.
Optimal Online Algorithms for the Multi-Objective Time Series Search Problem
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Mathematics Computer Science