arXiv (All)
open access
Higher-Order Geometric Updates for Levenberg-Marquardt Method via Riemann Normal Coordinates
chemical-physicscomputational-physicsdata-analysismachine-learningnumerical-analysisstatistics-and-probability
Nonlinear least-squares objectives form the foundation of scientific machine-learning tasks, yet even curvature-aware optimizers remain geometrically inconsistent at finite step sizes: Levenberg-Marquardt (LM) derives its direction from local Riemannian metrics but realizes it as a straight paramete
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Polyak-Type Extragradient Methods for Monotone Root-Finding Problems#1062470
- Polyak-Type Extragradient Methods for Monotone Root-Finding Problems#1043098
- A unified analysis on the speedup of accelerated gradient methods: An inertial dynamics approach#999641
- Global Linear Convergence of the Proximal Bundle Method under Unknown Piecewise Smoothness and Quadratic Growth#999009
- Residual-Controlled Douglas--Rachford Splitting for Differentiable Solver Layers#609633
Record · ID 1118639
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive