arXiv (All)
open access
ABrA-GD: Adaptive Bregman Accelerated Gradient Descent for Relatively Smooth and (Strongly-)Convex Optimization
We propose ABrA-GD (Adaptive Bregman Accelerated Gradient Descent), an adaptive algorithm for relatively smooth convex optimization. The algorithm is derived from a computable primal--dual certificate that guarantees progress by comparing the objective value with a lower bound on the minimum of a re
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
- An Adaptive Augmented Lagrangian Method for Deterministic and Stochastic Nonconvex Optimization#780975
- An Adaptive Augmented Lagrangian Method for Deterministic and Stochastic Nonconvex Optimization#779600
- Accelerated Bregman Proximal Gradient Methods from Dual Geometric Perspectives#1129394
- On the Complexity of BFGS Method for Smooth Convex Optimization#617683
- On the Complexity of BFGS Method for Smooth Convex Optimization#617183
Record · ID 1143342
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