Efficient parallel implementation of Kolmogorov superpositions
Roman Neruda · 2003
We analyze serial and parallel implementation of the learning algorithm based on Kolmogorov superposition theorem. Theoretical time complexity estimates are compared and parallel speedup is determined. Practical experiments show that the speedup in the order of 2n, where n is the input dimension, is achievable for real parallel environments (such as clusters of workstations).