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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
On the Petri Nets with a Single Shared Place and Beyond
Thomas Hujsa, Bernard Berthomieu, Silvano Dal Zilio, Didier Le B · 2020-05-11 · via cs.DS updates on arXiv.org

Petri nets proved useful to describe various real-world systems, but many of their properties are very hard to check. To alleviate this difficulty, subclasses are often considered. The class of weighted marked graphs with relaxed place constraint (WMG=< for short), in which each place has at most one input and one output, and the larger class of choice-free (CF) nets, in which each place has at most one output, have been extensively studied to this end, with various applications. In this work, we develop new properties related to the fundamental and intractable problems of reachability, liveness and reversibility in weighted Petri nets. We focus mainly on the homogeneous Petri nets with a single shared place (H1S nets for short), which extend the expressiveness of CF nets by allowing one shared place (i.e. a place with at least two outputs and possibly several inputs) under the homogeneity constraint (i.e. all the output weights of the shared place are equal). Indeed, this simple generalization already yields new challenging problems and is expressive enough for modeling existing use-cases, justifying a dedicated study. One of our central results is the first characterization of liveness in a subclass of H1S nets more expressive than WMG=< that is expressed by the infeasibility of an integer linear program (ILP) of polynomial size. This trims down the complexity to co-NP, contrasting with the known EXPSPACE-hardness of liveness in the more general case of weighted Petri nets. In the same subclass, we obtain a new reachability property related to the live markings, which is a variant of the well-known Keller's theorem. Another central result is a new reversibility characterization for the live H1S class, simplifying its checking. Finally, we apply our results to use-cases, highlight their scalability and discuss their extensibility to more expressive classes.