arXiv (All)
open access
Effective curvature dimension in smooth DC optimization
We introduce the effective curvature dimension of a smooth DC decomposition, defined as the dimension of the span of the Hessian ranges of its first convex component. We prove that the smallest attainable effective dimension over all smooth DC decompositions equals the minimum rank of a positive sem
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
- Sharp Minimax Rates for Highly Smooth Strongly Convex Zeroth-Order Optimization#1154899
- Can Acceleration in Gradient-Norm Minimization Be Anytime? Sharp Last-Iterate Limits in Smooth Convex Optimization#972760
- Revisiting Proximal Bundle Methods: Improved Rates under H{ö}lder Smoothness#1109852
- Optimal Parameter-Free Second-order Acceleration with Inexact Hessians under Generalized Smoothness#1117689
- Polyak-Type Extragradient Methods for Monotone Root-Finding Problems#1062470
Record · ID 1064922
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