Microsoft Research | Oblivious Online Contention Resolution Schemes @MicrosoftResearch | Uploaded December 2022 | Updated October 2024, 1 week ago.
2022 Data-driven Optimization Workshop: Oblivious Online Contention Resolution Schemes
Speaker: Hu Fu, Shanghai University of Finance and Economics
Contention resolution schemes (CRSs) are powerful tools for obtaining “ex post feasible” solutions from candidates that are drawn from “ex ante feasible” distributions. Online contention resolution schemes (OCRSs), the online version, have found myriad applications in Bayesian and stochastic problems, such as prophet inequalities and stochastic probing. When the ex ante distribution is unknown, it was unknown whether good CRSs/OCRSs exist with no sample (in which case the scheme is oblivious) or few samples from the distribution.
In this work, we give a simple 1/e-selectable oblivious single item OCRS by mixing two simple schemes evenly, and show, via a Ramsey theory argument, that it is optimal. On the negative side, we show that no CRS or OCRS with O(1) samples can be Ω(1)-balanced/selectable (i.e., preserve every active candidate with a constant probability) for graphic or transversal matroids.
2022 Data-driven Optimization Workshop: Oblivious Online Contention Resolution Schemes
Speaker: Hu Fu, Shanghai University of Finance and Economics
Contention resolution schemes (CRSs) are powerful tools for obtaining “ex post feasible” solutions from candidates that are drawn from “ex ante feasible” distributions. Online contention resolution schemes (OCRSs), the online version, have found myriad applications in Bayesian and stochastic problems, such as prophet inequalities and stochastic probing. When the ex ante distribution is unknown, it was unknown whether good CRSs/OCRSs exist with no sample (in which case the scheme is oblivious) or few samples from the distribution.
In this work, we give a simple 1/e-selectable oblivious single item OCRS by mixing two simple schemes evenly, and show, via a Ramsey theory argument, that it is optimal. On the negative side, we show that no CRS or OCRS with O(1) samples can be Ω(1)-balanced/selectable (i.e., preserve every active candidate with a constant probability) for graphic or transversal matroids.
