arXiv (All)
open access
First-Order Optimization under Uniform Nondegeneracy: Geometry, Computation, and Information
Strongly convex minimization and its natural indefinite extension to strongly convex--strongly concave minimax problems combine quantitative control of curvature with a prescribed curvature orientation. We disentangle these two roles by retaining uniform nondegeneracy alone: curvature remains unifor
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
- Preconditioned Three-Term Conjugate Gradient Framework for Nonconvex Finite Minimax Problems#1083257
- On Minimax Optimality and Uniqueness of Fixed-Step First-Order Methods for Smooth Convex Optimization#984578
- On the complexity of proximal gradient and proximal gradient-Newton-CG methods for $\ell_1$-regularized Optimization#987427
- Object-centric diffusion policies for real-world robotic-arm imitation learning.#415380
- Sharp Fresh-Gradient Complexity of Nonconvex-Strongly-Concave Minimax Optimization#1154852
Record · ID 975022
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