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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
The Maximum Colorful Arborescence problem parameterized b...
Guillaume Fertin, Julien Fradin, Christian Komusiewicz · 2017-10-20 · via cs.DS updates on arXiv.org

Let G=(V,A) be a vertex-colored arc-weighted directed acyclic graph (DAG) rooted in some vertex r, and let H be its color hierarchy graph, defined as follows: V(H) is the color set C of G, and an arc from color c to color c' exists in H if there is an arc in G from a vertex of color c to a vertex of color c'. In this paper, we study the MAXIMUM COLORFUL ARBORESCENCE problem (or MCA), which takes as input a DAG G with the additional constraint that H is also a DAG, and aims at finding in G an arborescence rooted in r, of maximum weight, and in which no color appears more than once. The MCA problem is motivated by the inference of unknown metabolites from mass spectrometry experiments. However, whereas the problem has been studied for roughly ten years, the crucial property that H is necessarily a DAG has only been pointed out and exploited very recently. In this paper, we further investigate MCA under this new light, by providing algorithmic results for the problem, with a specific focus on fixed-parameterized tractability (FPT) issues, and relatively to different structural parameters of H. In particular, we provide an O*(3^{nhs}) time algorithm for solving MCA, where nhs is the number of vertices of indegree at least two in H, thereby improving the O*(3^{|C|}) algorithm from [Böcker et al. 2008]. We also prove that MCA is W[2]-hard relatively to the treewidth Ht of H, and further show that it is FPT relatively to Ht+lc, where lc = |V| - |C|.