AI RESEARCH
An Efficient Global Optimization Algorithm with Adaptive Estimates of the Local Lipschitz Constants
arXiv CS.LG
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ArXi:2211.04129v4 Announce Type: cross In this work, we present a new deterministic partition-based global optimization algorithm, HALO (Hybrid Adaptive Lipschitzian Optimization), which uses estimates of the local Lipschitz constants associated with different sub-regions of the objective function's domain to compute lower bounds and guide the search toward global minimizers. These estimates are obtained by adaptively balancing the global and local information collected from the algorithm, based on absolute slopes.