1 citations · 1 across the 3 of their papers we have counts for
3 papers
math.OC2026
Exploring an Alternative Line-Search Method for Lagrange-Newton Optimization
Ralf Möller
In the Lagrange-Newton method, where Newton's method is applied to a Lagrangian function that includes equality constraints, all stationary points are saddle points. It is therefor…
cs.NE2022
Derivation of Learning Rules for Coupled Principal Component Analysis in a Lagrange-Newton Framework
Ralf Möller
We describe a Lagrange-Newton framework for the derivation of learning rules with desirable convergence properties and apply it to the case of principal component analysis (PCA). I…
math.OC2020★ 1 cited
Derivation of Symmetric PCA Learning Rules from a Novel Objective Function
Ralf Möller
Neural learning rules for principal component / subspace analysis (PCA / PSA) can be derived by maximizing an objective function (summed variance of the projection on the subspace…