TL;DR
It is shown that predictions enable an algorithm that is simultaneously O (1)-consistent and O (log n )-robust for online sorting with predictions, and this result is extended to the setting of multiple predictions.
This work builds on the variable-metric framework of Sakaue et al., adding a self-normalized rank-one update, and replaces the $\log\det$ potential by the trace power $\tr(H^{-1/2})$, which is bounded outright and removes the $\ln T$.
This paper studies fractional matching on general graphs in the fully online model of Huang et al. (JACM 2020), in which all vertices arrive online and remain available for only a limited time, and extends the classic Water-Filling algorithm to the fully online setting, establishing that Water-Filling is not optimal in...
Competitive analysis is central to the study of online algorithms, but upper bounds are often highly problem-specific. We develop a more unifying methodology via the minimax viewpoint. Guided by Yao's principle, we reduce worst-case competitive analysis to Bayesian online design under an arbitrary correlated prior over...
The optimal query complexity up to constant factors is determined: while the noiseless complexity matches the classical complexity of sorting, each untruthful counterexample incurs an additional cost of order $n$.
The connection between packing and online sorting can be used not only for lower bounds, but also for algorithms, and it is proved that the same competitive ratio is achievable for other translational packing problems, including online packing of d-dimensional unit hyperdisks in R^{d+1}.
A hindsight dual construction is built upon: a hindsight dual construction, which resolves the infeasibility issues in traditional online primal-dual methods, and a time-dependent dual packing that maintains feasibility over dynamic request sets.
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