Neural Network Algorithm Based on Levenberg-Marquardt (LM) Algorithm

The neural network algorithm based on the Levenberg-Marquardt (LM) method represents a highly efficient training approach characterized by rapid convergence and exceptional performance. This algorithm combines gradient descent and Gauss-Newton methods, utilizing Jacobian matrix calculations for parameter updates. The implementation typically involves computing derivatives through backpropagation and solving linear systems using matrix inversion techniques. However, due to its computational intensity and the need to store large matrices (particularly the Hessian approximation), the algorithm demands substantial memory resources. When implementing this algorithm in code, developers should ensure sufficient system memory allocation and consider using optimized linear algebra libraries for efficient matrix operations. Proper memory management and system resource verification are essential prerequisites for successful deployment of LM-based neural network training.