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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
Scheduling on uniform and unrelated machines with biparti...
2021-06-28 · via cs.DS updates on arXiv.org

In this paper the problem of scheduling of jobs on parallel machines under incompatibility relation is considered. In this model a binary relation between jobs is given and no two jobs that are in the relation can be scheduled on the same machine. In particular, we consider job scheduling under incompatibility relation forming bipartite graphs, under makespan optimality criterion, on uniform and unrelated machines. We show that no algorithm can achieve a good approximation ratio for uniform machines, even for a case of unit time jobs, under $P \neq NP$. We also provide an approximation algorithm that achieves the best possible approximation ratio, even for the case of jobs of arbitrary lengths $p_j$, under the same assumption. Precisely, we present an $O(n^{1/2-ε})$ inapproximability bound, for any $ε> 0$; and $\sqrt{p_{sum}}$-approximation algorithm, respectively. To enrich the analysis, bipartite graphs generated randomly according to Gilbert's model $\mathcal{G}_{n,n,p(n)}$ are considered. For a broad class of $p(n)$ functions we show that there exists an algorithm producing a schedule with makespan almost surely at most twice the optimum. Due to our knowledge, this is the first study of randomly generated graphs in the context of scheduling in the considered model. For unrelated machines, an FPTAS for $R2|G = bipartite|C_{\max}$ is provided. We also show that there is no algorithm of approximation ratio $O(n^bp_{\max}^{1-ε})$, even for $Rm|G = bipartite|C_{max}$ for $m \ge 3$ and any $ε> 0$, $b > 0$, unless $P = NP$.