In the ordinal Matroid Secretary Problem (MSP), elements from a weighted matroid are presented in random order to an algorithm that must incrementally select a large weight independent set. However, the algorithm can only compare pairs of revealed elements without using its numerical value. An algorithm is α probability-competitive if every element from the optimum appears with probability 1/α in the output. We present a technique to design algorithms with strong probability-competitive ratios, improving the guarantees for almost every matroid class considered in the literature: e.g., we get ratios of 4 for graphic matroids (improving on 2e by Korula and Pál [ICALP 2009]) and of 5.19 for laminar matroids (improving on 9.6 by Ma et al. [THEOR COMPUT SYST 2016]). We also obtain new results for superclasses of k column sparse matroids, for hypergraphic matroids, certain gammoids and graph packing matroids, and a 1 + O( log ρ/ρ) probability-competitive algorithm for uniform matroids of rank ρ based on Kleinberg's 1 + O( 1/ρ) utility-competitive algorithm [SODA 2005] for that class. Our second contribution are algorithms for the ordinal MSP on arbitrary matroids of rank ρ. We devise an O(log ρ) probability-competitive algorithm and an O(log log ρ) ordinal-competitive algorithm, a weaker notion of competitiveness but stronger than the utility variant. These are based on the O(log log ρ) utility-competitive algorithm by Feldman et al. [SODA 2015].
In the ordinal matroid secretary problem (MSP), candidates do not reveal numerical weights, but the decision maker can still discern if a candidate is better than another. An algorithm [Formula: see text] is probability-competitive if every element from the optimum appears with probability [Formula: see text] in the output. This measure is stronger than the standard utility competitiveness. Our main result is the introduction of a technique based on forbidden sets to design algorithms with strong probability-competitive ratios on many matroid classes. We improve upon the guarantees for almost every matroid class considered in the MSP literature. In particular, we achieve probability-competitive ratios of 4 for graphic matroids and of [Formula: see text] for laminar matroids. Additionally, we modify Kleinberg’s utility-competitive algorithm for uniform matroids in order to obtain an asymptotically optimal probability-competitive algorithm. We also contribute algorithms for the ordinal MSP on arbitrary matroids.
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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.
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