arXiv (All)
open access
Efficient Preconditioned Optimization Methods with Frequent Directions
Adaptive optimization methods have received considerable attention in recent years due to their strong empirical performance. However, they generally require maintaining a preconditioning matrix, which incurs prohibitive computational overhead in large-scale applications. To address these limitation
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
- How the Dirty Set Moves: Characterizing Schedule Reuse in Incremental Sparse Matrix Multiplication#1345312
- ABrA-GD: Adaptive Bregman Accelerated Gradient Descent for Relatively Smooth and (Strongly-)Convex Optimization#1143342
- Completing correlation matrices of arbitrary order by differential evolution method of global optimization: A Fortran program#1274770
Record · ID 1341676
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