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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
An exact characterization of tractable demand patterns fo...
Dániel Marx, Paul Wollan · 2014-11-04 · via cs.DS updates on arXiv.org

We study the following general disjoint paths problem: given a supply graph $G$, a set $T\subseteq V(G)$ of terminals, a demand graph $H$ on the vertices $T$, and an integer $k$, the task is to find a set of $k$ pairwise vertex-disjoint valid paths, where we say that a path of the supply graph $G$ is valid if its endpoints are in $T$ and adjacent in the demand graph $H$. For a class $\mathcal{H}$ of graphs, we denote by $\mathcal{H}$-Maximum Disjoint Paths the restriction of this problem when the demand graph $H$ is assumed to be a member of $\mathcal{H}$. We study the fixed-parameter tractability of this family of problems, parameterized by $k$. Our main result is a complete characterization of the fixed-parameter tractable cases of $\mathcal{H}$-Maximum Disjoint Paths for every hereditary class $\mathcal{H}$ of graphs: it turns out that complexity depends on the existence of large induced matchings and large induced skew bicliques in the demand graph $H$ (a skew biclique is a bipartite graph on vertices $a_1$, $\dots$, $a_n$, $b_1$, $\dots$, $b_n$ with $a_i$ and $b_j$ being adjacent if and only if $i\le j$). Specifically, we prove the following classification for every hereditary class $\mathcal{H}$. 1. If $\mathcal{H}$ does not contain every matching and does not contain every skew biclique, then $\mathcal{H}$-Maximum Disjoint Paths is FPT. 2. If $\mathcal{H}$ does not contain every matching, but contains every skew biclique, then $\mathcal{H}$-Maximum Disjoint Paths is W[1]-hard, admits an FPT approximation, and the valid paths satisfy an analog of the Erdős-Pósa property. 3. If $\mathcal{H}$ contains every matching, then $\mathcal{H}$-Maximum Disjoint Paths is W[1]-hard and the valid paths do not satisfy the analog of the Erdős-Pósa property.