Abstract
This paper analyzes and evaluates an efficient n-dimensional global optimization algorithm. It is a natural n-dimensional extension of the algorithm of Casado et al. [1]. This algorithm takes advantage of all available information to estimate better bounds of the function. Numerical comparison made on a wide set of multiextremal test functions has shown that on average the new algorithm works faster than a traditional interval analysis global optimization method.
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Martínez, J., Casado, L., García, I. et al. On an Efficient Use of Gradient Information for Accelerating Interval Global Optimization Algorithms. Numerical Algorithms 37, 61–69 (2004). https://doi.org/10.1023/B:NUMA.0000049456.81410.fc
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DOI: https://doi.org/10.1023/B:NUMA.0000049456.81410.fc