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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
Achieving Optimal Backlog in the Vanilla Multi-Processor ...
William Kuszmaul · 2019-10-30 · via cs.DS updates on arXiv.org

In each step of the $p$-processor cup game on $n$ cups, a filler distributes up to $p$ units of water among the cups, subject only to the constraint that no cup receives more than $1$ unit of water; an emptier then removes up to $1$ unit of water from each of $p$ cups. Designing strategies for the emptier that minimize backlog (i.e., the height of the fullest cup) is important for applications in processor scheduling, buffer management in networks, quality of service guarantees, and deamortization. We prove that the greedy algorithm (i.e., the empty-from-fullest-cups algorithm) achieves backlog $O(\log n)$ for any $p \ge 1$. This resolves a long-standing open problem for $p > 1$, and is asymptotically optimal as long as $n \ge 2p$. If the filler is an oblivious adversary, then we prove that there is a randomized emptying algorithm that achieve backlog $O(\log p + \log \log n)$ with probability $1 - 2^{-\operatorname{polylog}(n)}$ for $2^{\operatorname{polylog}(n)}$ steps. This is known to be asymptotically optimal when $n$ is sufficiently large relative to $p$. The analysis of the randomized algorithm can also be reinterpreted as a smoothed analysis of the deterministic greedy algorithm. Previously, the only known bound on backlog for $p > 1$, and the only known randomized guarantees for any $p$ (including when $p = 1$), required the use of resource augmentation, meaning that the filler can only distribute at most $p(1 - ε)$ units of water in each step, and that the emptier is then permitted to remove $1 + δ$ units of water from each of $p$ cups, for some $ε, δ> 0$.