arXiv (All)
open access
Acceleration of Generalized Conditional Gradient Methods
We develop an adaptive acceleration of generalized conditional gradient methods for convex objectives with a strongly convex regularizer. A computable primal--dual residual certifies Lyapunov decrease without evaluating the regularizer or any Hessian. Each iteration accepts a balanced accelerated tr
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
- On the Nonoccurrence of Gradient Restart in Nesterov Accelerated Gradient Method#1401709
- Nonconvex Composite Functional Constraints via First-Order Augmented Lagrangian Methods under Local Regularity#1356116
- Gradient-Only Online Convex Optimization with a Single Quadratic Leader#1154813
- Accelerated Safe Gradient Flow#1186747
- Efficient Gradient Methods for Distributed Saddle Problems#200445
Record · ID 1400301
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