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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
Pattern matching algorithms in Blockchain for network fee...
Robert Susik, Robert Nowotniak · 2022-07-29 · via cs.DS updates on arXiv.org

Blockchain received a vast amount of attention in recent years and is still growing. The second generation of blockchain, such as Ethereum, allows execution of almost any program in Ethereum Virtual Machine (EVM), making it a global protocol for distributed applications. The code deployment and each operation performed in EVM cost the network fee called gas, which price varies and can be significant. That is why code optimization and well-chosen algorithms are crucial in programming on the blockchain. This paper evaluates the gas usage of several exact pattern matching algorithms on the Ethereum Virtual Machine. We also propose an efficient implementation of the algorithms in the Solidity/YUL language. We evaluate the gas fees of all the algorithms for different parameters (such as pattern length, alphabet size, and text size). We show a significant gas fee and execution time reduction with up to 22-fold lower gas usage and 55-fold speed-up comparing to StringUtils (a popular Solidity string library).