Parallel black-box optimization of expensive high-dimensional multimodal functions via magnitude

Steve Huntsman

Parallel black-box optimization of expensive high-dimensional multimodal functions via magnitude

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arxiv 2201.11677 v1 pith:HXORE6O7 submitted 2022-01-27 math.OC cs.CE

Parallel black-box optimization of expensive high-dimensional multimodal functions via magnitude

Steve Huntsman This is my paper

classification math.OC cs.CE

keywords optimizationexpensiveexplo2functionshigh-dimensionalmagnitudemultimodaladvances

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Building on the recently developed theory of magnitude, we introduce the optimization algorithm EXPLO2 and carefully benchmark it. EXPLO2 advances the state of the art for optimizing high-dimensional ($D \gtrapprox 40$) multimodal functions that are expensive to compute and for which derivatives are not available, such as arise in hyperparameter optimization or via simulations.

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Scalably computing metric magnitude
math.NA 2026-07 conditional novelty 5.0

Hierarchical low-rank solvers beat dense and sparsified approaches for metric magnitude solves in experiments up to n=30,000, with a projected path to n≈10^5 via a containerized STRUMPACK/MPI pipeline.