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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
Lazy Local Search Meets Machine Scheduling
Chidambaram Annamalai · 2016-11-22 · via cs.DS updates on arXiv.org

We study the restricted case of Scheduling on Unrelated Parallel Machines. In this problem, we are given a set of jobs $J$ with processing times $p_j$ and each job may be scheduled only on some subset of machines $S_j \subseteq M$. The goal is to find an assignment of jobs to machines to minimize the time by which all jobs can be processed. In a seminal paper, Lenstra, Shmoys, and Tardos designed an elegant $2$-approximation for the problem in 1987. The question of whether approximation algorithms with better guarantees exist for this classic scheduling problem has since remained a source of mystery. In recent years, with the improvement of our understanding of Configuration LPs, it now appears an attainable goal to design such an algorithm. Our main contribution is to make progress towards this goal. When the processing times of jobs are either $1$ or $ε\in (0,1)$, we design an approximation algorithm whose guarantee tends to $1 + \sqrt{3}/2 \approx 1.8660254,$ for the interesting cases when $ε\to 0$. This improves on the $2-ε_0$ guarantee recently obtained by Chakrabarty, Khanna, and Li for some constant $ε_0 > 0$.