Experimental Improvements of Global Optimization Algorithms for Lipschitz Functions
Perceval Beja-Battais, Gaëtan Serré, Sophia Chirrane
⚠ This is a preprint. It may change before it is accepted for publication.


In this paper, we define an experimental context in which we tested the performances of LIPO and AdaLIPO, two global optimization algorithms for Lipschitz functions, introduced in [C. Malherbe and N. Vayatis, Global optimization of lipschitz functions, 2017]. We provide experimental proofs of the efficiency of those algorithms, led numerical statistical analysis of our results, and suggested two intuitive improvements from the vanilla version of the algorithms, referred as LIPO-E and AdaLIPO-E. Within our test bench, these improvements allow the algorithms to converge significantly faster and whenever they struggle to find a better maximizer. Finally, we defined the scope of application of LIPO and AdaLIPO. We show that they are very prone to the curse of dimensionality and tend quickly to Pure Random Search when the dimension increases. We provide source code for LIPO, AdaLIPO, and our enhanced versions