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cs.DS updates on arXiv.org

PAC Learning with Bandit Feedback: Sharp Sample Complexity in the Realizable Setting Algorithms with Polynomially-Improved Approximation Factors for the $2 \rightarrow q$ Norm, and Applications A computational phase transition for learning-to-sample from Ising models Covering vertices by sequential stars Fermi-Dirac machines as quantizations of neurons A Comprehensive Evaluation of Vertex Elimination Algorithms for Algorithmic Differentiation A Tight Bound on Localization of Electrical Flows Optimal Dimension-Free Sampling for Regularized Classification Reducing the Randomness in Partition Oracles for Bounded Degree Minor-Free Graphs Beyond the Half-Approximation: Fair and Efficient Online Class Matching Efficient Uniform Sampling of Surjections via their Profiles Tractable Maximization of Budgeted Phylogenetic Diversity on Networks Utilizing Node Scanwidth Fairness in Aggregation: Optimal Top-$k$ and Improved Full Ranking Learning-Augmented Online Scheduling with Parsimonious Preemption Entropy Equivalence Testing Lumberjack: Better Differentially Private Random Forests through Heavy Hitter Detection in Trees The Secretary Problem with a Stochastic Precursor Polynomial-Time Robust Multiclass Linear Classification under Gaussian Marginals Efficient Banzhaf-Based Data Valuation for $k$-Nearest Neighbors Classification Block-Sphere Vector Quantization An Approximation Algorithm for Graph Label Selection Iterative Chow Filtering for Learning with Distribution Shift Complexity of Non-Log-Concave Sampling in Fisher Information Stochastic Matching via Local Sparsification Finite Sample Bounds for Learning with Score Matching What is Learnable in Valiant's Theory of the Learnable? Provable Quantization with Randomized Hadamard Transform Min-Max Optimization Requires Exponentially Many Queries Fast and Compact Graph Cuts for the Boykov-Kolmogorov Algorithm A proximal gradient algorithm for composite log-concave sampling
O(log log rank) Competitive-Ratio for the Matroid Secreta...
Oded Lachish · 2014-03-27 · via cs.DS updates on arXiv.org

In the \textit{Matroid Secretary Problem} (MSP), the elements of the ground set of a Matroid are revealed on-line one by one, each together with its value. An algorithm for the MSP is \textit{Matroid-Unknown} if, at every stage of its execution: (i) it only knows the elements that have been revealed so far and their values, and (ii) it has access to an oracle for testing whether or not any subset of the elements that have been revealed so far is an independent set. An algorithm is \textit{Known-Cardinality} if, in addition to (i) and (ii), it also initially knows the cardinality of the ground set of the Matroid. We present here a Known-Cardinality and \textit{Order-Oblivious} algorithm that, with constant probability, selects an independent set of elements, whose value is at least the optimal value divided by $O(\log{\logρ})$, where $ρ$ is the rank of the Matroid; that is, the algorithm has a \textit{competitive-ratio} of $O(\log{\logρ})$. The best previous results for a Known-Cardinality algorithm are a competitive-ratio of $O(\logρ)$, by Babaioff \textit{et al.} (2007), and a competitive-ratio of $O(\sqrt{\logρ})$, by Chakraborty and Lachish (2012). In many non-trivial cases the algorithm we present has a competitive-ratio that is better than the $O(\log{\logρ})$. The cases in which it fails to do so are easily characterized. Understanding these cases may lead to improved algorithms for the problem or, conversely, to non-trivial lower bounds.