Gauss-Newton Optimization

Follow this topic Ask about this topic

Momentum

2 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Jul 13Week of Sep 28

Latest papers 12

All topics

Gauss-Newton Accuracy and Indefinite Hessians: Uniform Coexistence in Low-Cost Sets
Oct 7, 2026Kihun Rhee, Hanjoon ByunGauss-Newton Optimization Second-Order Optimization
Beyond PINNs: A Unified Gauss--Newton and Petrov--Galerkin Framework for Neural and Hybrid PDE Solvers
Sep 17, 2026Nilo Schwencke, Roland MaierGauss-Newton Optimization Neural PDE Solvers
DirtyMoCap: Robust Motion Capture from Unconstrained Markers
Sep 17, 2026Long Wang, Shuting Zhao, Shen Yan +5Gauss-Newton Optimization 3D Human Pose Estimation
Zhejiang University, Hangzhou, China · Westlake University, Hangzhou, China · Fudan University, Shanghai, China +3
Second-Order Muon Done Right: A Principled Marriage of Spectral Geometry and Curvature
Aug 10, 2026Tong CheGauss-Newton Optimization Information Geometry
NVIDIA Research
Higher-Order Geometric Updates for Levenberg-Marquardt Method via Riemann Normal Coordinates
Jul 8, 2026Jianing Liu, Dong H. ZhangGauss-Newton Optimization Riemannian Optimization
State Key Laboratory of Chemical Reaction Dynamics and Department of Chemical Physics, University of Science and Technology of China, Hefei, 230026, China · State Key Laboratory of Chemical Reaction Dynamics, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 116023, China. · University of Chinese Academy of Sciences, Beijing, 100049, China +1
An Optimisation Framework for the Well-Conditioned Training of Physics-Informed Neural Networks
Jul 2, 2026Joseph Webb, Sadok Jerad, Coralia CartisGauss-Newton Optimization PDE Solving
Mathematical Institute, University of Oxford, Oxford, United Kingdom
Geometry-Correct Diffusion Posterior Sampling with Denoiser-Pullback Curvature Guidance and Manifold-Aligned Damping
May 27, 2026Seunghyeok Shin, Minwoo Kim, Dabin Kim +1Gauss-Newton Optimization Bayesian Inverse Problems
Department of Electrical and Computer Engineering, Inha University, Incheon, 22212, South Korea.
Coupling-Robust Accuracy in Multiphysics Physics Informed Neural Networks via Kronecker-Preconditioned Optimization
May 22, 2026Youngjae Park, Jaemin Kim, Junghwa HongGauss-Newton Optimization Neural Network Optimization
Dept. of Control and Instrumentation Engineering, Korea University, Sejong, South Korea · BK21 FOUR Smart Mobility Education and Research Team, Korea University, Sejong, South Korea
SCAMP: Sparse-anchor Control is One Small Projection
May 14, 2026Pengcheng Fang, Tengjiao Sun, Xiaoyu Zhan +4Gauss-Newton Optimization Text-to-Motion Generation
University of Southampton · Mogo AI Ltd. · Nanjing University
Error whitening: Why Gauss-Newton outperforms Newton
May 11, 2026Maricela Best McKay, Nathan P. Lawrence, Brian Wetton +1Gauss-Newton Optimization Deep Learning Optimization
University of British Columbia · University of California, Berkeley
Fast Gauss-Newton for Multiclass Cross-Entropy
May 7, 2026Mikalai Korbit, Mario ZanonGauss-Newton Optimization Neural Network Optimization
IMT School for Advanced Studies Lucca
Multi-Objective Hyperparameter Search via Damped Gauss--Newton Optimization
Jan 7, 2024Qinwu Xu, Yifan JiangGauss-Newton Optimization Second-Order Optimization

Oct 7, 2026, cs.LGJ/K move · Enter open · S save

Gauss-Newton Accuracy and Indefinite Hessians: Uniform Coexistence in Low-Cost Sets

Kihun Rhee, Hanjoon Byun

Gauss-Newton Optimization Second-Order Optimization Nonconvex Optimization Ridge Regression

Open paper AskPDF arXiv

We study the accuracy of Gauss-Newton curvature in ridge-regularized nonlinear least squares. Under local regularity and persistence of level-set curvature magnitude along an exact-fit section, we prove uniform coexistence of two curvature regimes. Global minimizers exist, and every global minimizer has relative Hessian error below

(1+\sqrt2)/8

, while the same low-cost set contains a point with an indefinite Hessian and relative error at least

15/8

. One positive ridge cap works for all independent center and label perturbations in fixed neighborhoods and every positive ridge weight up to the cap. These neighborhoods do not shrink as the ridge weight tends to zero. A pointwise certificate based on the current prediction level set controls the normal, mixed, and tangent parts of the Hessian correction. We prove a sharp relative-error bound over the stated pointwise class when the prediction map and ridge vary. Analytic examples describe the roles of output alignment, curvature orientation, and persistence. A separate structural result gives full Jacobian row rank throughout low-cost sets and exact interpolation near a rank-deficient reference.

Momentum

Related topics

Latest papers 12