  In this work, we present a new deterministic partition-based Global
Optimization (GO) algorithm that uses estimates of the local Lipschitz
constants associated with different sub-regions of the domain of the objective
function. The estimates of the local Lipschitz constants associated with each
partition are the result of adaptively balancing the global and local
information obtained so far from the algorithm, given in terms of absolute
slopes. We motivate a coupling strategy with local optimization algorithms to
accelerate the convergence speed of the proposed approach. In the end, we
compare our approach HALO (Hybrid Adaptive Lipschitzian Optimization) with
respect to popular GO algorithms using hundreds of test functions. From the
numerical results, the performance of HALO is very promising and can extend our
arsenal of efficient procedures for attacking challenging real-world GO
problems. The Python code of HALO is publicly available on GitHub.
\url{https://github.com/dannyzx/HALO}
