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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
Efficient Algorithms for Checking Fast Termination in VASS
Tomáš Brázdil, Krishnendu Chatterjee, Antonín Kučera, Petr Novot · 2017-08-29 · via cs.DS updates on arXiv.org

Vector Addition Systems with States (VASS) consists of a finite state space equipped with d counters, where in each transition every counter is incremented, decremented, or left unchanged. VASS provide a fundamental model for analysis of concurrent processes, parametrized systems, and they are also used as abstract models for programs for bounds analysis. While termination is the basic liveness property that asks the qualitative question of whether a given model always terminates or not, the more general quantitative question asks for bounds on the number of steps to termination. In the realm of quantitative bounds a fundamental problem is to obtain asymptotic bounds on termination time. Large asymptotic bounds such as exponential or higher already suggest that either there is some error in modeling, or the model is not useful in practice. Hence we focus on polynomial asymptotic bounds for VASS. While some well-known approaches (e.g., lexicographic ranking functions) are neither sound nor complete with respect to polynomial bounds, other approaches only present sound methods for upper bounds. In this work our main contributions are as follows: First, for linear asymptotic bounds we present a sound and complete method for VASS, and moreover, our algorithm runs in polynomial time. Second, we classify VASS according the normals of the vectors of the cycles. We show that singularities in the normal are the key reason for asymptotic bounds such as exponential and non-elementary for VASS. In absence of singularities, we show that the asymptotic complexity bound is always polynomial and of the form $Θ(n^k)$, for some k $\leq$ d. We present an algorithm, with time complexity polynomial in the size of the VASS and exponential in dimension d, to compute the optimal k.