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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
Minimum length path decompositions
Dariusz Dereniowski, Wieslaw Kubiak, Yori Zwols · 2013-02-12 · via cs.DS updates on arXiv.org

We consider a bi-criteria generalization of the pathwidth problem, where, for given integers $k,l$ and a graph $G$, we ask whether there exists a path decomposition $\cP$ of $G$ such that the width of $\cP$ is at most $k$ and the number of bags in $\cP$, i.e., the \emph{length} of $\cP$, is at most $l$. We provide a complete complexity classification of the problem in terms of $k$ and $l$ for general graphs. Contrary to the original pathwidth problem, which is fixed-parameter tractable with respect to $k$, we prove that the generalized problem is NP-complete for any fixed $k\geq 4$, and is also NP-complete for any fixed $l\geq 2$. On the other hand, we give a polynomial-time algorithm that, for any (possibly disconnected) graph $G$ and integers $k\leq 3$ and $l>0$, constructs a path decomposition of width at most $k$ and length at most $l$, if any exists. As a by-product, we obtain an almost complete classification of the problem in terms of $k$ and $l$ for connected graphs. Namely, the problem is NP-complete for any fixed $k\geq 5$ and it is polynomial-time for any $k\leq 3$. This leaves open the case $k=4$ for connected graphs.