arXiv (All)
open access
Sharp First-Order Lower Bounds for Higher-Order Smooth Nonconvex Optimization
We study the deterministic first-order oracle complexity of finding \(ε\)-stationary points in smooth nonconvex optimization when the objective satisfies higher-order smoothness assumptions. While the classical \(ε^{-2}\) rate is optimal under only Lipschitz gradients, higher-order smoothness leads
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
- Near-Optimal Exact-Value Zeroth-Order Complexity for Smooth Strongly Convex Optimization#1012895
- Dense Weak Hiding: Closing Complexity Gaps in Nonconvex and PL Finite-Sum Optimization under Individual Smoothness#1083184
- Matching Upper and Lower Bounds for Higher-Order Nonconvex Finite-Sum Optimization#1064746
- Tight Stochastic Condition-Number Dependence in Nonconvex-Strongly-Concave Minimax Optimization#1105436
- On the Convergence of a Noisy Gradient Method for Non-convex Distributed Resource Allocation: Saddle Point Escape#670365
Record · ID 1051471
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