The Parallel Modification to the Levenberg-Marquardt Algorithm

The paper presents a parallel approach to the Levenberg-Marquardt algorithm (also called LM or LMA). The first section contains the mathematical basics of the classic LMA. Then the parallel modification to LMA is introduced. The classic Levenberg-Marquardt algorithm is sufficient for a training of small neural networks. For bigger networks the algorithm complexity becomes too big for the effective teaching. The main scope of this paper is to propose more complexity efficient approach to LMA by parallel computation. The proposed modification to LMA has been tested on a few function approximation problems and has been compared to the classic LMA. The paper concludes with the resolution that the parallel modification to LMA could significantly improve algorithm performance for bigger networks. Summary also contains a several proposals for the possible future work directions in the considered area.