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This work evaluates the subspace Levenberg-Marquardt algorithms for regression and classification tasks in neural networks and compares the performance of subspace LM variants with the classical LM method, as well as other popular first-order algorithms, such as stochastic gradient descent and Adam.
The Levenberg-Marquardt (LM) algorithm is the most widely used method for solving nonlinear least-squares problems, as it combines the robustness of steepest descent with the fast local convergence of the Gauss-Newton method. However, its computational cost can become prohibitive for large-scale problems because each i...
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