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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
On Preemptive Scheduling of Unrelated Machines Using Line...
Nodari Vakhania · 2022-05-05 · via cs.DS updates on arXiv.org

We consider a basic problem of preemptive scheduling of $n$ non-simultaneously released jobs on a group of $m$ unrelated parallel machines so as to minimize maximum job completion time, the makespan. In the scheduling literature, the problem is commonly considered to be solvable in polynomial time by linear programming (LP) techniques proposed in Lawler and Labetoulle \cite{ll78}. The authors in \cite{ll78} give a LP formulation of the version with simultaneously released jobs and show how an optimal solution to this LP can be used to construct an optimal schedule to the latter problem. As the current study shows, for non-simultaneously released jobs, unlikely, there exist a linear program such that a schedule with the minimum makespan can be constructed based on an optimal LP solution. We also prove that, in case no splitting of the same job on a machine is allowed (i.e., job part assigned to a machine is to be processed without an interruption on that machine), the problem is NP-hard. As a side result, we obtain that, whenever job splitting is not allowed, given an optimal LP solution, it is NP-hard to find an optimal schedule with the minimum makespan that agrees with that LP solution. We also extend the schedule construction procedure based on an optimal LP solution from Lawler and Labetoulle \cite{ll78} for non-simultaneously released jobs. The extended procedure, among all feasible schedules that agree with any feasible (not necessarily optimal) LP solution, generates one with the minimum makespan. Such procedure is helpful, in particular, because, as we show, there may exist no optimal schedule that agrees with an optimal LP solution if jobs are non-simultaneously released.