Gaëtan Serré, Perceval Beja-Battais, Sophia Chirrane, Argyris Kalogeratos, Nicolas Vayatis
Published 2024-06-28Version 1
In this paper, we propose simple yet effective empirical improvements to the algorithms of the LIPO family, introduced in [Malherbe2017], that we call LIPO+ and AdaLIPO+. We compare our methods to the vanilla versions of the algorithms over standard benchmark functions and show that they converge significantly faster. Finally, we show that the LIPO family is very prone to the curse of dimensionality and tends quickly to Pure Random Search when the dimension increases. We give a proof for this, which is also formalized in Lean mathematical language. Source codes and a demo are provided online.
$γ$-Competitiveness: An Approach to Multi-Objective Optimization with High Computation Costs in Lipschitz Functions
Acceleration of univariate global optimization algorithms working with Lipschitz functions and Lipschitz first derivatives
On the Finite-Time Complexity and Practical Computation of Approximate Stationarity Concepts of Lipschitz Functions
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