arXiv:2605.18364v2 Announce Type: replace Abstract: Global optimization is a challenging problem, with plenty of algorithms displaying empirical success, but scarce theoretical backing. In this work, we propose a new theoretical framework called Proximal Basin Hopping (PBH), carefully tailored to combine proximal optimization and local minimization. We use it to construct a practical algorithm that converges to the global minimizer with high probability, when using a finite amount of samples. Proximal Basin Hopping outperforms well known algorithms with theoretical backing on standard syntheti
This research provides a new theoretical framework for global optimization, a foundational problem across many computational fields, demonstrating provable convergence guarantees previously scarce in practical algorithms.
Improved global optimization algorithms with theoretical backing can accelerate advancements in AI, machine learning, and scientific computing by making complex model training and design processes more robust and efficient.
The introduction of Proximal Basin Hopping (PBH) as a theoretically-sound and empirically effective global optimization method offers a more reliable path to solving complex optimization problems in various domains.
- · AI/ML researchers
- · Computational engineers
- · Drug discovery
- · Materials science
- · Algorithms lacking theoretical guarantees
- · Optimization methods with high failure rates
More efficient and reliable training of complex AI models becomes possible.
Accelerated discovery of new materials or drug candidates due to better optimization of design parameters.
Reduced computational costs and time for scientific simulations and engineering design processes across multiple industries.
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Read at arXiv cs.LG