arXiv (All)
open access
Multilevel Regularized Newton Methods with Fast Convergence Rates
We introduce new multilevel methods for solving large-scale unconstrained optimization problems. Specifically, the philosophy of multilevel methods is applied to Newton-type methods that regularize the Newton sub-problem using second order information from a coarse (low dimensional) sub-problem. The
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
- Global Linear Convergence of the Proximal Bundle Method under Unknown Piecewise Smoothness and Quadratic Growth#999009
- Polyak-Type Extragradient Methods for Monotone Root-Finding Problems#1062470
- Polyak-Type Extragradient Methods for Monotone Root-Finding Problems#1043098
- A Globally Convergent Third-Order Newton Method via Unified Semidefinite Programming Subproblems#662410
- A Globally Convergent Third-Order Newton Method via Unified Semidefinite Programming Subproblems#661360
Record · ID 1217243
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