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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
Fast Fibonacci heaps with worst case extensions
Vladan Majerech · 2019-11-25 · via cs.DS updates on arXiv.org

We are concentrating on reducing overhead of heaps based on comparisons with optimal worstcase behaviour. The paper is inspired by Strict Fibonacci Heaps [1], where G. S. Brodal, G. Lagogiannis, and R. E. Tarjan implemented the heap with DecreaseKey and Meld interface in assymptotically optimal worst case times (based on key comparisons). In the paper [2], the ideas were elaborated and it was shown that the same asymptotical times could be achieved with a strategy loosing much less information from previous comparisons. There is big overhead with maintainance of violation lists in these heaps. We propose simple alternative reducing this overhead. It allows us to implement fast amortized Fibonacci heaps, where user could call some methods in variants guaranting worst case time. If he does so, the heaps are not guaranted to be Fibonacci until an amortized version of a method is called. Of course we could call worst case versions all the time, but as there is an overhead with the guarantee, calling amortized versions is prefered choice if we are not concentrated on complexity of the separate operation. We have shown, we could implement full DecreaseKey-Meld interface, but Meld interface is not natural for these heaps, so if Meld is not needed, much simpler implementation suffices. As I don't know application requiring Meld, we would concentrate on noMeld variant, but we will show the changes could be applied on Meld including variant as well. The papers [1], [2] shown the heaps could be implemented on pointer machine model. For fast practical implementations we would rather use arrays. Our goal is to reduce number of pointer manipulations. Maintainance of ranks by pointers to rank lists would be unnecessary overhead.