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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
Valued Workflow Satisfiability Problem
Jason Crampton, Gregory Z. Gutin, Daniel Karapetyan · 2015-01-30 · via cs.DS updates on arXiv.org

A workflow is a collection of steps that must be executed in some specific order to achieve an objective. A computerised workflow management system may enforce authorisation policies and constraints, thereby restricting which users can perform particular steps in a workflow. The existence of policies and constraints may mean that a workflow is unsatisfiable, in the sense that it is impossible to find an authorised user for each step in the workflow and satisfy all constraints. In this paper, we consider the problem of finding the "least bad" assignment of users to workflow steps by assigning a weight to each policy and constraint violation. To this end, we introduce a framework for associating costs with the violation of workflow policies and constraints and define the \emph{valued workflow satisfiability problem} (Valued WSP), whose solution is an assignment of steps to users of minimum cost. We establish the computational complexity of Valued WSP with user-independent constraints and show that it is fixed-parameter tractable. We then describe an algorithm for solving Valued WSP with user-independent constraints and evaluate its performance, comparing it to that of an off-the-shelf mixed integer programming package.