SOURCE-LINKED INTELLIGENCE

Subspace Levenberg Marquardt Algorithms in Training Neural Networks

arXiv · AI, language, vision and robotics · article · Sep 1, 2026 · UTC

The Levenberg-Marquardt (LM) algorithm is a well-known second-order method for rapid convergence and strong robustness when training small- to medium-sized neural networks (NNs). However, its computational and memory costs increase significantly as the number of parameters in an NN grows. To address this limitation, subspace methods have been proposed, such as the Krylov subspace LM (KSLM) and the hybrid subspace LM (HSLM), making second-order algorithms more efficient. In this work, we evaluate the subspace Levenberg-Marquardt algorithms for regression and classification tasks in neural netwo

Read original source ↗ Open in workspace

recordType

paper

region

Global