IEEE

Leveraging Simplex Gradient Variance and Bias Reduction for Black-Box Optimization of Noisy and Costly Functions

Gradient variance errors in gradient-based search methods are largely mitigated using momentum, however the bias gradient errors may fail the numerical search methods in reaching the true optimum. We ...
IEEE

A Fixed-Time Proximal Gradient Neurodynamic Network With Time-Varying Coefficients for Composite Optimization Problems and Sparse Optimization Problems With Log-Sum Function

Abstract: This article presents a novel proximal gradient neurodynamic network (PGNN) for solving composite optimization problems (COPs). The proposed PGNN with time-varying coefficients can be ...
GitHub

Every Call is Precious: Global Optimization of Black-Box Functions with Unknown Lipschitz Constants

This repository implements ECP algorithm for solving non-convex black-box global optimization problems, as introduced in Every Call is Precious: Global Optimization of Black-Box Functions with Unknown ...
GitHub

Bayesian Optimization of Functions over Node Subsets in Graphs [NeurIPS2024]

This is the repo for GraphComBO, a Bayesian optimization tool for black-box and expensive functions defined over node subsets in graphs, where the goal is to find the optimal subset within a limited ...