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global-optimization

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Showing 126–150 of 658 papers

TitleStatusHype
An Overview of the Burer-Monteiro Method for Certifiable Robot Perception—0
A Global Optimization Algorithm for K-Center Clustering of One Billion Samples—0
A Collaborative Visual SLAM Framework for Service Robots—0
A Non-parametric View of FedAvg and FedProx: Beyond Stationary Points—0
Certifiably Optimal Anisotropic Rotation Averaging—0
A novel mutation operator based on the union of fitness and design spaces information for Differential Evolution—0
A Novel Hybrid Grey Wolf Differential Evolution Algorithm—0
A Global Information Based Adaptive Threshold for Grouping Large Scale Global Optimization Problems—0
A Novel Design Method for Digital FIR/IIR Filters Based on the Shuffle Frog-Leaping Algorithm—0
Annealing for Distributed Global Optimization—0
Adaptive Gaussian process surrogates for Bayesian inference—0
Fractional order graph neural network—0
An Inter-molecular Adaptive Collision Scheme for Chemical Reaction Optimization—0
An Improved Structured Mesh Generation Method Based on Physics-informed Neural Networks—0
A new Taxonomy of Continuous Global Optimization Algorithms—0
A Generic Performance Model for Deep Learning in a Distributed Environment—0
Derivative-Free Global Optimization Algorithms: Population based Methods and Random Search Approaches—0
CBOL-Tuner: Classifier-pruned Bayesian optimization to explore temporally structured latent spaces for particle accelerator tuning—0
Chunk-based Chinese Spelling Check with Global Optimization—0
A New Scope and Domain Measure Comparison Method for Global Convergence Analysis in Evolutionary Computation—0
A New Optimization Approach Based on Rotational Mutation and Crossover Operator—0
A Generative Neural Annealer for Black-Box Combinatorial Optimization—0
A new approach for solving global optimization and engineering problems based on modified Sea Horse Optimizer—0
A New Approach for Finding the Global Optimal Point Using Subdividing Labeling Method (SLM)—0
A General Stochastic Algorithmic Framework for Minimizing Expensive Black Box Objective Functions Based on Surrogate Models and Sensitivity Analysis—0
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